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Cognitive-Mirage-Hallucinations-in-LLMs

hallucination in large language models a review p align center img src pictures logo jpg width 500 p div align center awesome https awesome re badge svg license mit https img shields io badge license mit green svg https opensource org licenses mit https img shields io github last commit hongbinye cognitive mirage hallucinations in llms color green https img shields io badge prs welcome red div uncritical trust in llms can give rise to a phenomenon cognitive mirage leading to misguided decision making and a cascade of unintended consequences to effectively control the risk of hallucinations we summarize recent progress in hallucination theories and solutions in this paper we propose to organize relevant work by a comprehensive survey bell news bell we have released a new survey paper cognitive mirage a review of hallucinations in large language models https arxiv org abs 2309 06794 based on this repository with a perspective of hallucinations in llms we are looking forward to any comments or discussions on this topic introduction as large language models continue to develop in the field of ai text generation systems are susceptible to a worrisome phenomenon known as hallucination in this study we summarize recent compelling insights into hallucinations in llms we present a novel taxonomy of hallucinations from various text generation tasks thus provide theoretical insights detection methods and improvement approaches based on this future research directions are proposed our contribution are threefold 1 we provide a detailed and complete taxonomy for hallucinations appearing in text generation tasks 2 we provide theoretical analyses of hallucinations in llms and provide existing detection and improvement methods 3 we propose several research directions that can be developed in the future as hallucinations garner significant attention from the community we will maintain updates on relevant research progress a timeline of llms llm name title authors publication date t5 https arxiv org abs 1910 10683 exploring the limits of transfer learning with a unified text to text transformer colin raffel noam shazeer adam roberts 2019 10 gpt 3 https proceedings neurips cc paper 2020 hash 1457c0d6bfcb4967418bfb8ac142f64a abstract html language models are few shot learners tom b brown benjamin mann nick ryder 2020 12 mt5 https doi org 10 18653 v1 2021 naacl main 41 mt5 a massively multilingual pre trained text to text transformer linting xue noah constant adam roberts 2021 3 codex https arxiv org abs 2107 03374 evaluating large language models trained on code mark chen jerry tworek heewoo jun 2021 7 flan https openreview net forum id gezrgcozdqr finetuned language models are zero shot learners jason wei maarten bosma vincent y zhao 2021 9 webgpt https arxiv org abs 2112 09332 webgpt browser assisted question answering with human feedback reiichiro nakano jacob hilton suchir balaji 2021 12 instructgpt https proceedings neurips cc paper files paper 2022 hash b1efde53be364a73914f58805a001731 abstract conference html training language models to follow instructions with human feedback long ouyang jeffrey wu xu jiang 2022 3 codegen https arxiv org abs 2203 13474 codegen an open large language model for code with multi turn program synthesis erik nijkamp bo pang hiroaki hayashi 2022 3 claude https arxiv org abs 2204 05862 training a helpful and harmless assistant with reinforcement learning from human feedback yuntao bai andy jones kamal ndousse 2022 4 palm https proceedings neurips cc paper files paper 2022 hash b1efde53be364a73914f58805a001731 abstract conference html palm scaling language modeling with pathways aakanksha chowdhery sharan narang jacob devlin 2022 4 opt https arxiv org abs 2205 01068 opt open pre trained transformer language models susan zhang stephen roller naman goyal 2022 5 super naturalinstructions https doi org 10 18653 v1 2022 emnlp main 340 esuper naturalinstructions generalization via declarative instructions on 1600 nlp tasks yizhong wang swaroop mishra pegah alipoormolabashi yeganeh kordi 2022 9 glm https arxiv org abs 2210 02414 glm 130b an open bilingual pre trained model aohan zeng xiao liu zhengxiao du 2022 10 bloom https doi org 10 48550 arxiv 2211 05100 bloom a 176b parameter open access multilingual language model teven le scao angela fan christopher akiki 2022 11 llama https arxiv org abs 2302 13971 llama open and efficient foundation language models hugo touvron thibaut lavril gautier izacard 2023 2 alpaca https crfm stanford edu 2023 03 13 alpaca html alpaca a strong replicable instruction following model rohan taori ishaan gulrajani tianyi zhang 2023 3 gpt 4 https arxiv org abs 2303 08774v2 gpt 4 technical report openai 2023 3 wizardlm https doi org 10 48550 arxiv 2304 12244 wizardlm empowering large language models to follow complex instructions can xu qingfeng sun kai zheng 2023 4 vicuna https lmsys org blog 2023 03 30 vicuna vicuna an open source chatbot impressing gpt 4 with 90 chatgpt quality the vicuna team 2023 5 chatglm https chatglm cn blog chatglm wisdom and clear speech team 2023 6 llama2 https arxiv org abs 2307 09288 llama 2 open foundation and fine tuned chat models hugo touvron louis martin kevin stone 2023 7 definition of hallucination truthful ai developing and governing ai that does not lie 2021 10 owain evans owen cotton barratt lukas finnveden paper https arxiv org abs 2110 06674 survey of hallucination in natural language generation 2022 2 ziwei ji nayeon lee rita frieske paper https dblp org search q survey 20of 20hallucination 20in 20natural 20language 20generation context faithful prompting for large language models 2023 3 wenxuan zhou sheng zhang hoifung poon paper https arxiv org abs 2303 11315 do language models know when they re hallucinating references 2023 5 ayush agrawal lester mackey adam tauman kalai paper https doi org 10 48550 arxiv 2305 18248 mechanism analysis data attribution data distributional properties drive emergent in context learning in transformers 2022 5 stephanie c y chan adam santoro andrew k lampinen paper http papers nips cc paper files paper 2022 hash 77c6ccacfd9962e2307fc64680fc5ace abstract conference html towards tracing factual knowledge in language models back to the training data 2022 5 ekin aky rek tolga bolukbasi frederick liu paper https arxiv org abs 2205 11482 a multitask multilingual multimodal evaluation of chatgpt on reasoning hallucination and interactivity 2023 2 yejin bang samuel cahyawijaya nayeon lee paper https doi org 10 48550 arxiv 2302 04023 hallucinations in large multilingual translation models 2023 3 nuno miguel guerreiro duarte m alves jonas waldendorf paper https doi org 10 48550 arxiv 2303 16104 visual instruction tuning 2023 4 haotian liu chunyuan li qingyang wu paper https doi org 10 48550 arxiv 2304 08485 evaluating object hallucination in large vision language models 2023 5 yifan li yifan du kun zhou paper https arxiv org abs 2305 10355 sources of hallucination by large language models on inference tasks 2023 5 nick mckenna tianyi li liang cheng paper https doi org 10 48550 arxiv 2305 14552 automatic evaluation of attribution by large language models 2023 5 xiang yue boshi wang kai zhang paper https arxiv org abs 2305 06311 mitigating hallucination in large multi modal models via robust instruction tuning 2023 6 fuxiao liu kevin lin linjie li paper https arxiv org abs 2306 14565 knowledge gap a survey of knowledge enhanced text generation 2022 1 wenhao yu chenguang zhu zaitang li paper https doi org 10 1145 3512467 attributed text generation via post hoc research and revision 2022 10 luyu gao zhuyun dai panupong pasupat paper https doi org 10 48550 arxiv 2210 08726 artificial hallucinations in chatgpt implications in scientific writing 2023 2 hussam alkaissi samy i mcfarlane paper https www ncbi nlm nih gov pmc articles pmc9939079 selfcheckgpt zero resource black box hallucination detection for generative large language models 2023 3 potsawee manakul adian liusie mark j f gales paper https doi org 10 48550 arxiv 2303 08896 why does chatgpt fall short in answering questions faithfully 2023 4 shen zheng jie huang kevin chen chuan chang paper https arxiv org abs 2304 10513 zero shot faithful factual error correction 2023 5 kung hsiang huang hou pong chan heng ji paper https aclanthology org 2023 acl long 311 mitigating language model hallucination with interactive question knowledge alignment 2023 5 shuo zhang liangming pan junzhou zhao paper https arxiv org abs 2305 13669 adaptive chameleon or stubborn sloth unraveling the behavior of large language models in knowledge clashes 2023 5 jian xie kai zhang jiangjie chen paper https arxiv org abs 2305 13300 evaluating generative models for graph to text generation 2023 7 huzhou yuan michael f rber paper https doi org 10 48550 arxiv 2307 14712 overthinking the truth understanding how language models process false demonstrations 2023 7 danny halawi jean stanislas denain jacob steinhardt paper https arxiv org abs 2307 09476 optimum formulation improved natural language generation via loss truncation 2020 5 daniel kang tatsunori hashimoto paper https doi org 10 18653 v1 2020 acl main 66 the curious case of hallucinations in neural machine translation 2021 4 vikas raunak arul menezes marcin junczys dowmunt paper https doi org 10 18653 v1 2021 naacl main 92 optimal transport for unsupervised hallucination detection in neural machine translation 2022 12 nuno miguel guerreiro pierre colombo pablo piantanida paper https doi org 10 48550 arxiv 2212 09631 elastic weight removal for faithful and abstractive dialogue generation 2023 3 nico daheim nouha dziri mrinmaya sachan paper https doi org 10 48550 arxiv 2303 17574 histalign improving context dependency in language generation by aligning with history 2023 5 david wan shiyue zhang mohit bansal paper https doi org 10 48550 arxiv 2305 04782 how language model hallucinations can snowball 2023 5 muru zhang ofir press william merrill paper https doi org 10 48550 arxiv 2305 13534 improving language models via plug and play retrieval feedback 2023 5 wenhao yu zhihan zhang zhenwen liang paper https doi org 10 48550 arxiv 2305 14002 teaching language models to hallucinate less with synthetic tasks 2023 10 erik jones hamid palangi clarisse sim es paper https arxiv org abs 2310 06827 taxonomy of llms hallucination in nlp tasks machine translation unsupervised cross lingual representation learning at scale 2020 7 alexis conneau kartikay khandelwal naman goyal paper https doi org 10 18653 v1 2020 acl main 747 the curious case of hallucinations in neural machine translation 2021 4 vikas raunak arul menezes marcin junczys dowmunt paper https doi org 10 18653 v1 2021 naacl main 92 overcoming catastrophic forgetting in zero shot cross lingual generation 2022 5 tu vu aditya barua brian lester paper https doi org 10 18653 v1 2022 emnlp main 630 looking for a needle in a haystack a comprehensive study of hallucinations in neural machine translation 2022 8 nuno miguel guerreiro elena voita andr f t martins paper https aclanthology org 2023 eacl main 75 prompting palm for translation assessing strategies and performance 2022 11 david vilar markus freitag colin cherry paper https arxiv org abs 2211 09102 the unreasonable effectiveness of few shot learning for machine translation 2023 2 xavier garcia yamini bansal colin cherry paper https arxiv org abs 2302 01398 how good are gpt models at machine translation a comprehensive evaluation 2023 2 amr hendy mohamed abdelrehim amr sharaf paper https arxiv org abs 2302 09210 hallucinations in large multilingual translation models 2023 3 nuno miguel guerreiro duarte m alves jonas waldendorf paper https doi org 10 48550 arxiv 2303 16104 investigating the translation performance of a large multilingual language model the case of bloom 2023 3 rachel bawden fran ois yvon paper https arxiv org abs 2303 01911 halomi a manually annotated benchmark for multilingual hallucination and omission detection in machine translation 2023 5 david dale elena voita janice lam prangthip hansanti paper https arxiv org abs 2305 11746 mmt5 modular multilingual pre training solves source language hallucinations 2023 5 jonas pfeiffer francesco piccinno massimo nicosia paper https doi org 10 48550 arxiv 2305 14224 question and answer hurdles to progress in long form question answering 2021 6 kalpesh krishna aurko roy mohit iyyer paper https aclanthology org 2021 naacl main 393 entity based knowledge conflicts in question answering 2021 9 shayne longpre kartik perisetla anthony chen paper https aclanthology org 2021 emnlp main 565 truthfulqa measuring how models mimic human falsehoods 2022 5 stephanie lin jacob hilton owain evans paper https aclanthology org 2022 acl long 229 check your facts and try again improving large language models with external knowledge and automated feedback 2023 2 baolin peng michel galley pengcheng he paper https arxiv org abs 2302 12813 why does chatgpt fall short in answering questions faithfully 2023 4 shen zheng jie huang kevin chen chuan chang paper https doi org 10 48550 arxiv 2304 10513 evaluating correctness and faithfulness of instruction following models for question answering 2023 7 vaibhav adlakha parishad behnamghader xing han lu paper https arxiv org abs 2307 16877 med halt medical domain hallucination test for large language models 2023 7 logesh kumar umapathi ankit pal malaikannan sankarasubbu paper https arxiv org abs 2307 15343 dialog system on the origin of hallucinations in conversational models is it the datasets or the models 2022 7 nouha dziri sivan milton mo yu paper https aclanthology org 2022 naacl main 387 contrastive learning reduces hallucination in conversations 2022 12 weiwei sun zhengliang shi shen gao paper https ojs aaai org index php aaai article view 26596 diving deep into modes of fact hallucinations in dialogue systems 2022 12 souvik das sougata saha rohini k srihari paper https doi org 10 18653 v1 2022 findings emnlp 48 elastic weight removal for faithful and abstractive dialogue generation 2023 3 nico daheim nouha dziri mrinmaya sachan paper https doi org 10 48550 arxiv 2303 17574 summarization system hallucinated but factual inspecting the factuality of hallucinations in abstractive summarization 2022 5 meng cao yue dong jackie chi kit cheung paper https doi org 10 18653 v1 2022 acl long 236 evaluating the factual consistency of large language models 2022 11 derek tam anisha mascarenhas shiyue zhang paper https doi org 10 18653 v1 2023 findings acl 322 why is this misleading detecting news headline hallucinations with explanations 2023 2 jiaming shen jialu liu dan finnie paper https doi org 10 1145 3543507 3583375 detecting and mitigating hallucinations in multilingual summarisation 2023 5 yifu qiu yftah ziser anna korhonen paper https arxiv org abs 2305 13632 llms as factual reasoners insights from existing benchmarks and beyond 2023 5 philippe laban wojciech kry ci ski divyansh agarwal paper https arxiv org abs 2305 14540 evaluating factual consistency of texts with semantic role labeling 2023 5 jing fan dennis aumiller michael gertz paper https arxiv org abs 2305 13309 summarization is almost dead 2023 9 xiao pu mingqi gao xiaojun wan paper https arxiv org abs 2309 09558 knowledge graphs with llms gpt ner named entity recognition via large language models 2023 4 shuhe wang xiaofei sun xiaoya li paper https arxiv org abs 2304 10428 llms for knowledge graph construction and reasoning recent capabilities and future opportunities 2023 5 yuqi zhu xiaohan wang jing chen paper https arxiv org abs 2305 13168 kola carefully benchmarking world knowledge of large language models 2023 6 jifan yu xiaozhi wang shangqing tu paper https arxiv org abs 2306 09296 evaluating generative models for graph to text generation 2023 7 shuzhou yuan michael f rber paper https doi org 10 48550 arxiv 2307 14712 text2kgbench a benchmark for ontology driven knowledge graph generation from text 2023 8 nandana mihindukulasooriya sanju tiwari carlos f enguix paper https arxiv org abs 2308 02357 cross modal system let there be a clock on the beach reducing object hallucination in image captioning 2021 10 ali furkan biten llu s g mez dimosthenis karatzas paper https doi org 10 1109 wacv51458 2022 00253 simple token level confidence improves caption correctness 2023 5 suzanne petryk spencer whitehead joseph e gonzalez paper https doi org 10 48550 arxiv 2305 07021 evaluating object hallucination in large vision language models 2023 5 yifan li yifan du kun zhou jinpeng wang paper https arxiv org abs 2305 10355 album storytelling with iterative story aware captioning and large language models 2023 5 munan ning yujia xie dongdong chen paper https doi org 10 48550 arxiv 2305 12943 mitigating hallucination in large multi modal models via robust instruction tuning 2023 6 fuxiao liu kevin lin linjie li paper https arxiv org abs 2306 14565 fact checking of ai generated reports 2023 7 razi mahmood ge wang mannudeep kalra paper https arxiv org abs 2307 14634 others the scope of chatgpt in software engineering a thorough investigation 2023 5 wei ma shangqing liu wenhan wang paper https arxiv org abs 2305 12138 generating benchmarks for factuality evaluation of language models 2023 7 dor muhlgay ori ram inbal magar paper https arxiv org abs 2307 06908 hallucination detection inference classifier evaluating the factual consistency of large language models through summarization 2022 11 derek tam anisha mascarenhas shiyue zhang paper https doi org 10 18653 v1 2023 findings acl 322 why is this misleading detecting news headline hallucinations with explanations 2023 2 jiaming shen jialu liu daniel finnie paper https doi org 10 1145 3543507 3583375 halueval a large scale hallucination evaluation benchmark for large language models 2023 5 junyi li xiaoxue cheng wayne xin zhao paper https doi org 10 48550 arxiv 2305 11747 mitigating hallucination in large multi modal models via robust instruction tuning 2023 6 fuxiao liu kevin lin linjie li paper https arxiv org abs 2306 14565 fact checking of ai generated reports 2023 7 razi mahmood ge wang mannudeep k kalra paper https doi org 10 48550 arxiv 2307 14634 chain of natural language inference for reducing large language model ungrounded hallucinations 2023 10 deren lei yaxi li mengya hu paper https arxiv org abs 2310 03951 uncertainty measure bartscore evaluating generated text as text generation 2021 6 weizhe yuan graham neubig pengfei liu paper https proceedings neurips cc paper 2021 hash e4d2b6e6fdeca3e60e0f1a62fee3d9dd abstract html contrastive learning reduces hallucination in conversations 2022 12 weiwei sun zhengliang shi shen gao paper https ojs aaai org index php aaai article view 26596 knowledge of knowledge exploring known unknowns uncertainty with large language models 2023 5 alfonso amayuelas liangming pan wenhu chen paper https doi org 10 48550 arxiv 2305 13712 methods for measuring updating and visualizing factual beliefs in language models 2023 5 peter hase mona diab asli celikyilmaz paper https aclanthology org 2023 eacl main 199 measuring and modifying factual knowledge in large language models 2023 6 pouya pezeshkpour paper https arxiv org abs 2306 06264 llm calibration and automatic hallucination detection via pareto optimal self supervision 2023 6 theodore zhao mu wei j samuel preston paper https arxiv org abs 2306 16564 a stitch in time saves nine detecting and mitigating hallucinations of llms by validating low confidence generation 2023 7 neeraj varshney wenlin yao hongming zhang paper https arxiv org abs 2307 03987 self evaluation language models mostly know what they know 2022 7 saurav kadavath tom conerly amanda askell paper https doi org 10 48550 arxiv 2207 05221 selfcheckgpt zero resource black box hallucination detection for generative large language models 2023 3 potsawee manakul adian liusie mark j f gales paper https doi org 10 48550 arxiv 2303 08896 do language models know when they re hallucinating references 2023 5 ayush agrawal lester mackey adam tauman kalai paper https doi org 10 48550 arxiv 2305 18248 evaluating object hallucination in large vision language models 2023 5 yifan li yifan du kun zhou jinpeng wang paper https doi org 10 48550 arxiv 2305 10355 self checker plug and play modules for fact checking with large language models 2023 5 miaoran li baolin peng zhu zhang paper https arxiv org abs 2305 14623 lm vs lm detecting factual errors via cross examination 2023 5 roi cohen may hamri mor geva paper https arxiv org abs 2305 13281 self contradictory hallucinations of large language models evaluation detection and mitigation 2023 5 niels m ndler jingxuan he slobodan jenko paper https arxiv org abs 2305 15852 a new benchmark and reverse validation method for passage level hallucination detection 2023 10 shiping yang renliang sun xiaojun wan paper https arxiv org abs 2310 06498 evidence retrieval factscore fine grained atomic evaluation of factual precision in long form text generation 2023 5 sewon min kalpesh krishna xinxi lyu paper https arxiv org abs 2305 14251 complex claim verification with evidence retrieved in the wild 2023 5 jifan chen grace kim aniruddh sriram paper https doi org 10 48550 arxiv 2305 11859 retrieving supporting evidence for llms generated answers 2023 6 siqing huo negar arabzadeh charles l a clarke paper https doi org 10 48550 arxiv 2306 13781 factool factuality detection in generative ai a tool augmented framework for multi task and multi domain scenarios 2023 7 i chun chern steffi chern shiqi chen paper https arxiv org abs 2307 13528 hallucination correction parameter enhancement factuality enhanced language models for open ended text generation 2022 6 nayeon lee wei ping peng xu paper http papers nips cc paper files paper 2022 hash df438caa36714f69277daa92d608dd63 abstract conference html contrastive learning reduces hallucination in conversations 2022 12 weiwei sun zhengliang shi shen gao paper https ojs aaai org index php aaai article view 26596 editing models with task arithmetic 2023 2 gabriel ilharco marco tulio ribeiro mitchell wortsman paper https openreview net forum id 6t0kwf8 jrj elastic weight removal for faithful and abstractive dialogue generation 2023 3 nico daheim nouha dziri mrinmaya sachan paper https doi org 10 48550 arxiv 2303 17574 histalign improving context dependency in language generation by aligning with history 2023 5 david wan shiyue zhang mohit bansal paper https doi org 10 48550 arxiv 2305 04782 mmt5 modular multilingual pre training solves source language hallucinations 2023 5 jonas pfeiffer francesco piccinno massimo nicosia paper https arxiv org abs 2305 14224 trusting your evidence hallucinate less with context aware decoding 2023 5 weijia shi xiaochuang han mike lewis paper https doi org 10 48550 arxiv 2305 14739 purr efficiently editing language model hallucinations by denoising language model corruptions 2023 5 anthony chen panupong pasupat sameer singh paper https arxiv org abs 2305 14908 augmented large language models with parametric knowledge guiding 2023 5 ziyang luo can xu pu zhao xiubo geng paper https arxiv org abs 2305 04757 inference time intervention eliciting truthful answers from a language model 2023 6 kenneth li oam patel fernanda vi gas paper https arxiv org abs 2306 03341 trac trustworthy retrieval augmented chatbot 2023 7 shuo li sangdon park insup lee paper https doi org 10 48550 arxiv 2307 04642 easyedit an easy to use knowledge editing framework for large language models 2023 8 peng wang ningyu zhang xin xie paper https arxiv org abs 2308 07269 dola decoding by contrasting layers improves factuality in large language models 2023 9 yung sung chuang yujia xie hongyin luo paper https arxiv org abs 2309 03883 post hoc attribution and edit technology neural path hunter reducing hallucination in dialogue systems via path grounding 2021 4 nouha dziri andrea madotto osmar za ane paper https doi org 10 18653 v1 2021 emnlp main 168 chain of thought prompting elicits reasoning in large language models 2022 1 jason wei xuezhi wang dale schuurmans maarten bosma paper http papers nips cc paper files paper 2022 hash 9d5609613524ecf4f15af0f7b31abca4 abstract conference html teaching language models to support answers with verified quotes 2022 3 jacob menick maja trebacz vladimir mikulik paper https doi org 10 48550 arxiv 2203 11147 orca interpreting prompted language models via locating supporting data evidence in the ocean of pretraining data 2022 5 xiaochuang han yulia tsvetkov paper https doi org 10 48550 arxiv 2205 12600 large language models are zero shot reasoners 2022 5 takeshi kojima shixiang shane gu machel reid paper http papers nips cc paper files paper 2022 hash 8bb0d291acd4acf06ef112099c16f326 abstract conference html rethinking with retrieval faithful large language model inference 2023 1 hangfeng he hongming zhang dan roth paper https doi org 10 48550 arxiv 2301 00303 trak attributing model behavior at scale 2023 3 sung min park kristian georgiev andrew ilyas paper https proceedings mlr press v202 park23c html data portraits recording foundation model training data 2023 3 marc marone benjamin van durme paper https doi org 10 48550 arxiv 2303 03919 self refine iterative refinement with self feedback 2023 3 aman madaan niket tandon prakhar gupta paper https doi org 10 48550 arxiv 2303 17651 reflexion an autonomous agent with dynamic memory and self reflection 2023 3 noah shinn beck labash ashwin gopinath paper https doi org 10 48550 arxiv 2303 11366 according to prompting language models improves quoting from pre training data 2023 5 orion weller marc marone nathaniel weir paper https doi org 10 48550 arxiv 2305 13252 verify and edit a knowledge enhanced chain of thought framework 2023 5 ruochen zhao xingxuan li shafiq joty paper https arxiv org abs 2305 03268 mitigating hallucination in large multi modal models via robust instruction tuning 2023 9 shehzaad dhuliawala mojtaba komeili jing xu paper https arxiv org abs 2309 11495 chain of natural language inference for reducing large language model ungrounded hallucinations 2023 10 deren lei yaxi li mengya hu paper https arxiv org abs 2310 03951 utilizing programming languages pal program aided language models 2022 11 luyu gao aman madaan shuyan zhou paper https proceedings mlr press v202 gao23f html program of thoughts prompting disentangling computation from reasoning for numerical reasoning tasks 2022 11 wenhu chen xueguang ma xinyi wang paper https doi org 10 48550 arxiv 2211 12588 teaching algorithmic reasoning via in context learning 2022 11 hattie zhou azade nova hugo larochelle paper https doi org 10 48550 arxiv 2211 09066 solving challenging math word problems using gpt 4 code interpreter with code based self verification 2023 8 aojun zhou ke wang zimu lu paper https arxiv org abs 2308 07921 leverage external knowledge improving language models by retrieving from trillions of tokens 2021 12 sebastian borgeaud arthur mensch jordan hoffmann paper https proceedings mlr press v162 borgeaud22a html interleaving retrieval with chain of thought reasoning for knowledge intensive multi step questions 2022 12 harsh trivedi niranjan balasubramanian tushar khot paper https doi org 10 18653 v1 2023 acl long 557 when not to trust language models investigating effectiveness of parametric and non parametric memories 2022 12 alex mallen akari asai victor zhong paper https doi org 10 18653 v1 2023 acl long 546 check your facts and try again improving large language models with external knowledge and automated feedback 2023 2 baolin peng michel galley pengcheng he paper https arxiv org abs 2302 12813 in context retrieval augmented language models 2023 2 ori ram yoav levine itay dalmedigos paper https doi org 10 48550 arxiv 2302 00083 ctbl augmenting large language models for conversational tables 2023 3 anirudh s sundar larry heck paper https doi org 10 48550 arxiv 2303 12024 genegpt augmenting large language models with domain tools for improved access to biomedical information 2023 4 qiao jin yifan yang qingyu chen paper https arxiv org abs 2304 09667 active retrieval augmented generation 2023 5 zhengbao jiang frank f xu luyu gao paper https doi org 10 48550 arxiv 2305 06983 chain of knowledge a framework for grounding large language models with structured knowledge bases 2023 5 xingxuan li ruochen zhao yew ken chia paper https doi org 10 48550 arxiv 2305 13269 gorilla large language model connected with massive apis 2023 5 shishir g patil tianjun zhang xin wang paper https doi org 10 48550 arxiv 2305 15334 reta llm a retrieval augmented large language model toolkit 2023 6 jiongnan liu jiajie jin zihan wang paper https doi org 10 48550 arxiv 2306 05212 user controlled knowledge fusion in large language models balancing creativity and hallucination 2023 7 schen zhang paper https arxiv org abs 2307 16139 knowledgpt enhancing large language models with retrieval and storage access on knowledge bases 2023 8 xintao wang qianwen yang yongting qiu paper https doi org 10 48550 arxiv 2308 11761 assessment feedback learning to summarize with human feedback 2020 12 nisan stiennon long ouyang jeffrey wu paper https proceedings neurips cc paper 2020 hash 1f89885d556929e98d3ef9b86448f951 abstract html brio bringing order to abstractive summarization 2022 3 yixin liu pengfei liu dragomir r radev paper https doi org 10 18653 v1 2022 acl long 207 language models mostly know what they know 2022 7 saurav kadavath tom conerly amanda askell paper https doi org 10 48550 arxiv 2207 05221 check your facts and try again improving large language models with external knowledge and automated feedback 2023 2 baolin peng michel galley pengcheng he paper https doi org 10 48550 arxiv 2302 12813 chain of hindsight aligns language models with feedback 2023 2 hao liu carmelo sferrazza pieter abbeel paper https doi org 10 48550 arxiv 2302 02676 zero shot faithful factual error correction 2023 5 kung hsiang huang hou pong chan heng ji paper https doi org 10 18653 v1 2023 acl long 311 critic large language models can self correct with tool interactive critiquing 2023 5 zhibin gou zhihong shao yeyun gong paper https doi org 10 48550 arxiv 2305 11738 album storytelling with iterative story aware captioning and large language models 2023 5 munan ning yujia xie dongdong chen paper https doi org 10 48550 arxiv 2305 12943 how language model hallucinations can snowball 2023 5 muru zhang ofir press william merrill paper https doi org 10 48550 arxiv 2305 13534 mitigating language model hallucination with interactive question knowledge alignment 2023 5 shuo zhang liangming pan junzhou zhao paper https doi org 10 48550 arxiv 2305 13669 improving language models via plug and play retrieval feedback 2023 5 wenhao yu zhihan zhang zhenwen liang paper https doi org 10 48550 arxiv 2305 14002 pad program aided distillation specializes large models in reasoning 2023 5 xuekai zhu biqing qi kaiyan zhang paper https doi org 10 48550 arxiv 2305 13888 enabling large language models to generate text with citations 2023 5 tianyu gao howard yen jiatong yu paper https doi org 10 48550 arxiv 2305 14627 do language models know when they re hallucinating references 2023 5 ayush agrawal lester mackey adam tauman kalai paper https doi org 10 48550 arxiv 2305 18248 improving factuality of abstractive summarization via contrastive reward learning 2023 7 i chun chern zhiruo wang sanjan das paper https doi org 10 48550 arxiv 2307 04507 towards mitigating hallucination in large language models via self reflection 2023 10 ziwei ji tiezheng yu yan xu paper https arxiv org abs 2310 06271 mindset society hallucinations in large multilingual translation models 2023 3 nuno miguel guerreiro duarte m alves jonas waldendorf paper https doi org 10 48550 arxiv 2303 16104 improving factuality and reasoning in language models through multiagent debate 2023 5 yilun du shuang li antonio torralba paper https doi org 10 48550 arxiv 2305 14325 encouraging divergent thinking in large language models through multi agent debate 2023 5 tian liang zhiwei he wenxiang jiao paper https doi org 10 48550 arxiv 2305 19118 examining the inter consistency of large language models an in depth analysis via debate 2023 5 kai xiong xiao ding yixin cao paper https doi org 10 48550 arxiv 2305 11595 lm vs lm detecting factual errors via cross examination 2023 5 roi cohen may hamri mor geva paper https arxiv org abs 2305 13281 prd peer rank and discussion improve large language model based evaluations 2023 7 ruosen li teerth patel xinya du paper https doi org 10 48550 arxiv 2307 02762 unleashing cognitive synergy in large language models a task solving agent through multi persona self collaboration 2023 7 zhenhailong wang shaoguang mao wenshan wu paper https doi org 10 48550 arxiv 2307 05300 tips if you find this repository useful to your research or work it is really appreciate to star this repository

hallucinations large-language-models natural-language-generation survery dialog-system machine-translation question-and-answer assessment-feedback cross-modal-system leverage-external-knowledge mindset-society parameter-adaptation summarization-system

ai

theplaygrounds

div align center p align center img src img cover1 png p p align center a href https www linkedin com in iamnasef img src https img shields io badge linkedin 0077b5 style for the badge logo linkedin logocolor white a a href https twitter com iamnasef img src https img shields io badge twitter 1da1f2 style for the badge logo twitter logocolor white a a href https github com iamnasef img src https img shields io badge github 100000 style for the badge logo github logocolor white a a href https www youtube com channel ucx2qgl5gjp osk mz674eta img src https img shields io badge youtube ff0000 style for the badge logo youtube logocolor white a p br br the playground is my space where i experiment with different software engineering devsecops and cloud technologies by building projects this is why i created this project projects projects key features key features how to make use of the playground how to make use of the playground technologies used technologies used div projects details summary b projects 001 docker b summary 1 docker 001 snippets is an a collection of docker snippets details details summary b projects 002 kubernetes b summary 1 kubernetes 001 snippets is an a collection of kubernetes snippets details details summary b projects 003 ansible b summary 1 ansible 001 snippets is an a collection of ansible snippets details details summary b projects 004 terraform b summary 1 terraform 001 snippets is an a collection of terraform snippets details details summary b projects 005 prometheus b summary 1 prometheus 001 snippets is an a collection of prometheus snippets details details summary b projects 006 jenkins b summary 1 jenkins 001 snippets is an a collection of jenkins snippets details details summary b projects 007 css b summary 1 css 001 snippets is an a collection of css snippets details details summary b projects 008 javascript b summary 1 javascript 001 snippets is an a collection of javascript snippets details details summary b projects 009 react b summary 1 react 001 snippets is an a collection of react snippets details details summary b projects 010 nodejs b summary 1 nodejs 001 snippets is an a collection of nodejs snippets details details summary b projects 011 flask b summary 1 flask 001 snippets is an a collection of flask snippets details details summary b projects 012 flutter b summary 1 flutter 001 snippets is an a collection of flutter snippets details details summary b projects 013 aws b summary 1 aws 001 snippets is an a collection of aws snippets details details summary b projects 014 azure b summary 1 azure 001 snippets is an a collection of azure snippets details details summary b projects 015 gcp b summary 1 gcp 001 snippets is an a collection of gcp snippets details key features the playground has a wide range of projects in multiple technologies in multiple disciplines it has at least 10 projects for each technology you can use these projects in your learning process you can rebuild the idea from scratch with your improvements as resume project how to make use of the playground 1 read the project description and try recreating it from scratch 2 compare your code with the playground code 3 watch the videos embedded in each project which shows how to build it in a step by step manner 4 contact me if you have any questions technologies used this is the list of technologies used in the playground application description yaml https yaml org a human readable data serialization language docker https www docker com a set of platform as a service products that use os level virtualization to deliver software in packages called containers css https www w3 org style css overview en html a style sheet language used for describing the presentation of a document written in a markup language html https developer mozilla org en us docs web html a markup language for documents designed to be displayed in a web browser ansible https www ansible com a software provisioning configuration management and application deployment tool python https www python org a programming language that lets you work quickly and integrate systems more effectively flask https flask palletsprojects com en 2 1 x a popular extensible web microframework for building web applications with python nodejs https nodejs org en a javascript runtime built on chrome s v8 javascript engine javascript https www javascript com a programming language that is one of the core technologies of the world wide web flutter https flutter dev a software development kit created by google dart https www javascript com a programming language designed for client development terraform https www terraform io an open source infrastructure as code software tool created by hashicorp aws https aws amazon com an on demand cloud computing platforms and apis to individuals companies and governments on a metered pay as you go basis jenkins https www jenkins io an open source automation server it helps automate the parts of software development related to building testing and deploying facilitating continuous integration and continuous delivery groovy https groovy lang org a java syntax compatible object oriented programming language for the java platform kubernetes https kubernetes io an open source container orchestration system for automating software deployment scaling and management markdown guide https www markdownguide org a reference guide that explains how to use markdown php https www php net a general purpose scripting language geared toward web development mysql https www mysql com an open source relational database management system gcp https cloud google com a suite of cloud computing services that runs on the same infrastructure that google uses internally for its end user products such as google search gmail google drive and youtube deployment manager https cloud google com deployment manager docs an infrastructure deployment service that automates the creation and management of google cloud resources azure https azure microsoft com en us a cloud computing platform operated by microsoft for application management via around the world distributed data centers arm templates https learn microsoft com en us azure azure resource manager templates overview an infrastructure as code a concept where you define the infrastructure that needs to be deployed aws https aws amazon com a subsidiary of amazon that provides on demand cloud computing platforms and apis to individuals companies and governments on a metered pay as you go basis aws cloud formation https aws amazon com cloudformation an infrastructure as code iac service that allows you to easily model provision and manage aws and third party resources prometheus https prometheus io an open source monitoring system with a dimensional data model flexible query language efficient time series database and modern alerting approach promql https prometheus io docs prometheus latest querying basics a functional query language that lets the user select and aggregate time series data in real time

devops devsecops cloud-computing cloud-security software-engineering

cloud

llm-primer

llm primer a primer on large language models llm and chatgpt as of april 2023 a video recording for v2 1 slides assisted by ai https www youtube com watch v doto 0ihoui update 05 2023 a special side version other chatgpt primer 05 2023 guyu v1 pdf for https valleyrain org a new side project to brainstorm how to possibly train better chatbots than chatgpt https github com hululuzhu better chatbot than chatgpt v2 covers details summary intro building blocks capabilities summary lm and llm transformer how are llms trained llm decoding llm training in parallel llm capabilities advanced capabilities and insance capabilities details details summary core models players concepts toolings applications summary selected llms bert gpt family t5 glm llm players big companies institutes and startups llm concepts pretraining finetuning prompt tuning scaling laws prompt engineering prompt tuning soft prompt emergent abilities chain of thoughts cot least to most prompting hallucination retrieval llm rlhf for llm mixture of experts moe llm llm tooling huggingface tf hub torch nlp paddlenlp transformers lib colossal ai ray and nanogpt other toolings llm applications details details summary bonus deep dive into chatgpt summary chatgpt model evolvment research instructgpt overview possible next steps for chatgpt engineering discussion rough estimate to train server chatgpt my thoughts on technical challenges to reproduce chatgpt what less optimal choices google made related to chatgpt delayed google to release similar product fun facts chatgpt challenges final question will chatgpt become next iphone or next alexa or next clubhouse details v2 1 covers llm basics rl basics chatgpt societal impact slides v2 1 slides april 2023 https github com hululuzhu llm primer blob main llm primer v2 1 april 2023 llm rl chatgpt pdf appendix 6 page slides https github com hululuzhu llm primer blob main other quick summary embedding bert gpt pdf to explaing bert and gpt training and their embedding ouput and its size v2 slides jan 2023 https github com hululuzhu llm primer blob main llm primer v2 jan 2023 pdf old v1 slides july 2022 https github com hululuzhu fun paper sharing blob main slides llm primer v1 pdf additional notes draft notes other draft notes llm primer pdf to prepare v2 topics chinese only notes my personal opinion chatgpt other chinese only tech challanges reproduce chatgpt md google llm other chinese only thoughts on less optimal choices by google md chatgpt other chinese only crazy ideas surpass chatgpt md recommended quick readings agi llm https zhuanlan zhihu com p 597586623 yao fu how does gpt obtain its ability tracing emergent abilities of language models to their sources https yaofu notion site how does gpt obtain its ability tracing emergent abilities of language models to their sources b9a57ac0fcf74f30a1ab9e3e36fa1dc1 stephen wolfram what is chatgpt doing and why does it work https writings stephenwolfram com 2023 02 what is chatgpt doing and why does it work chatgpt gpt https www youtube com watch v e0aki2ggzng instructgpt https www youtube com watch v zfigawd1joq 120 references as of 01 30 2023 details summary click to expand summary 1706 03741 deep reinforcement learning from human preferences https arxiv org abs 1706 03741 1706 03762 attention is all you need https arxiv org abs 1706 03762 1810 04805 bert pre training of deep bidirectional transformers for language understanding https arxiv org abs 1810 04805 1904 00962 large batch optimization for deep learning training bert in 76 minutes https arxiv org abs 1904 00962 1907 11692 roberta a robustly optimized bert pretraining approach https arxiv org abs 1907 11692 1910 10683 exploring the limits of transfer learning with a unified text to text transformer https arxiv org abs 1910 10683 2001 08361 scaling laws for neural language models https arxiv org abs 2001 08361 2005 14165 language models are few shot learners https arxiv org abs 2005 14165 2103 00823 m6 a chinese multimodal pretrainer https arxiv org abs 2103 00823 2104 08691 the power of scale for parameter efficient prompt tuning https arxiv org abs 2104 08691 2104 09864 roformer enhanced transformer with rotary position embedding https arxiv org abs 2104 09864 2106 04554 a survey of transformers https arxiv org abs 2106 04554 2111 06377 masked autoencoders are scalable vision learners https arxiv org abs 2111 06377 2112 12731 ernie 3 0 titan exploring larger scale knowledge enhanced pre training for language understanding and generation https arxiv org abs 2112 12731 2201 08239 lamda language models for dialog applications https arxiv org abs 2201 08239 2201 11903 chain of thought prompting elicits reasoning in large language models https arxiv org abs 2201 11903 2203 15556 training compute optimal large language models https arxiv org abs 2203 15556 2204 05862 training a helpful and harmless assistant with reinforcement learning from human feedback https arxiv org abs 2204 05862 2205 01068 opt open pre trained transformer language models https arxiv org abs 2205 01068 2205 05198 reducing activation recomputation in large transformer models https arxiv org abs 2205 05198 2205 10625 least to most prompting enables complex reasoning in large language models https arxiv org abs 2205 10625 2205 11916 large language models are zero shot reasoners https arxiv org abs 2205 11916 2206 07682 emergent abilities of large language models https arxiv org abs 2206 07682 2207 01780 coderl mastering code generation through pretrained models and deep reinforcement learning https arxiv org abs 2207 01780 2208 03299 atlas few shot learning with retrieval augmented language models https arxiv org abs 2208 03299 2210 02414 glm 130b an open bilingual pre trained model https arxiv org abs 2210 02414 d gpt 3 the 4 600 000 language model r machinelearning https www reddit com r machinelearning comments h0jwoz d gpt3 the 4600000 language model alibaba cloud launches modelscope an open source model as a service maas platform that comes with hundreds of artificial intelligence ai models marktechpost https www marktechpost com 2022 11 21 alibaba cloud launches modelscope an open source model as a service maas platform that comes with hundreds of artificial intelligence ai models aligning language models to follow instructions https openai com blog instruction following alphago https www deepmind com research highlighted research alphago anthropic https www anthropic com bart denoising sequence to sequence pre training for natural language generation translation and comprehension facebook ai research https ai facebook com research publications bart denoising sequence to sequence pre training for natural language generation translation and comprehension better language models and their implications https openai com blog better language models bigscience https bigscience huggingface co blenderbot 3 an ai chatbot that improves through conversation meta https about fb com news 2022 08 blenderbot ai chatbot improves through conversation building safer dialogue agents https www deepmind com blog building safer dialogue agents chat gpt gpt https www youtube com watch v e0aki2ggzng chatgpt cheats triangle professors grapple with viral ai technology as semester starts https www newsobserver com news local article270952057 html chatgpt produces made up nonexistent references hacker news https news ycombinator com item id 33841672 chatgpt open ai s chatbot is spitting out biased sexist results bloomberg https www bloomberg com news newsletters 2022 12 08 chatgpt open ai s chatbot is spitting out biased sexist results chatgpt optimizing language models for dialogue https openai com blog chatgpt code for codet5 a new code aware pre trained encoder decoder model https github com salesforce codet5 colossal ai https colossalai org deepmind s alphacode ai writes code at a competitive level techcrunch https techcrunch com 2022 02 02 deepminds alphacode ai writes code at a competitive level democratizing access to large scale language models with opt 175b https ai facebook com blog democratizing access to large scale language models with opt 175b don t ban chatgpt in schools teach with it the new york times https www nytimes com 2023 01 12 technology chatgpt schools teachers html eleutherai https www eleuther ai eleutherai gpt neox 20b hugging face https huggingface co eleutherai gpt neox 20b exploring transfer learning with t5 the text to text transfer transformer google ai blog https ai googleblog com 2020 02 exploring transfer learning with t5 html galactica research model by meta https galactica org generative adversarial network wikipedia https en wikipedia org wiki generative adversarial network github f awesome chatgpt prompts this repo includes chatgpt prompt curation to use chatgpt better https github com f awesome chatgpt prompts github google big bench beyond the imitation game collaborative benchmark for measuring and extrapolating the capabilities of language models https github com google big bench github huggingface transformers transformers state of the art machine learning for pytorch tensorflow and jax https github com huggingface transformers github karpathy nanogpt the simplest fastest repository for training finetuning medium sized gpts https github com karpathy nanogpt github openai openai cookbook techniques to improve reliability https github com openai openai cookbook blob main techniques to improve reliability md github paddlepaddle paddlenlp https github com paddlepaddle paddlenlp github copilot your ai pair programmer https github com features copilot github jianlin su bojone https github com bojone github s ai coding assistant copilot launches voicebot ai https voicebot ai 2022 06 22 githubs ai coding assistant copilot launches glm 130b an open bilingual pre trained model https github com thudm glm 130b gluebenchmark leaderboard https gluebenchmark com leaderboard google ai introduces minerva a natural language processing nlp model that solves mathematical questions marktechpost https www marktechpost com 2022 07 04 google ai introduces minerva a natural language processing nlp model that solves mathematical questions google sidelines engineer who claims its a i is sentient the new york times https www nytimes com 2022 06 12 technology google chatbot ai blake lemoine html google s massive new language model can explain jokes https www datanami com 2022 04 22 googles massive new language model can explain jokes got it ai creates truth checker for chatgpt hallucinations venturebeat https venturebeat com ai got it ai creates truth checker for chatgpt hallucinations gpt 3 powers the next generation of apps https openai com blog gpt 3 apps gsm8k dataset papers with code https paperswithcode com dataset gsm8k how come gpt can seem so brilliant one minute and so breathtakingly dumb the next https garymarcus substack com p how come gpt can seem so brilliant how does gpt obtain its ability tracing emergent abilities of language models to their sources https yaofu notion site how does gpt obtain its ability tracing emergent abilities of language models to their sources b9a57ac0fcf74f30a1ab9e3e36fa1dc1 huawei noah s ark lab github https github com huawei noah pretrained language model tree master pangu ce b1 huggingface deploy hugging face models easily with amazon sagemaker https huggingface co blog deploy hugging face models easily with amazon sagemaker huggingface fine tune a pretrained model https huggingface co docs transformers training huggingface how to generate text using different decoding methods for language generation with transformers https huggingface co blog how to generate huggingface models https huggingface co docs transformers main classes model huggingface pipelines https huggingface co docs transformers main classes pipelines huggingface tokenizer https huggingface co docs transformers main classes tokenizer huggingface uploading models https huggingface co docs hub models uploading improving language models by retrieving from trillions of tokens https www deepmind com publications improving language models by retrieving from trillions of tokens improving language understanding by generative pre training https s3 us west 2 amazonaws com openai assets research covers language unsupervised language understanding paper pdf introducing flan more generalizable language models with instruction fine tuning https ai googleblog com 2021 10 introducing flan more generalizable html introducing pathways a next generation ai architecture https blog google technology ai introducing pathways next generation ai architecture jonathan hui how much do i like chatgpt https jonathan hui medium com how much do i like chatgpt 3d8a3216e137 lamda and the sentient ai trap wired https www wired com story lamda sentient ai bias google blake lemoine lamda our breakthrough conversation technology https blog google technology ai lamda language model ai2 blog https blog allenai org tagged language model large language models and where to use them part 1 https txt cohere ai llm use cases m6 by alibaba multimodality to multimodality multitask mega transformer https www infoq cn article xix9lekuulcxewc5iphf microsoft dumping ton of cash into chatgpt office infusion appleinsider https appleinsider com articles 23 01 10 microsoft dumping ton of cash into chatgpt office infusion microsoft set to integrate chatgpt with bing cdotrends https www cdotrends com story 17758 microsoft set integrate chatgpt bing minerva solving quantitative reasoning problems with language models google ai blog https ai googleblog com 2022 06 minerva solving quantitative reasoning html mosaic llms part 2 gpt 3 quality for 500k https www mosaicml com blog gpt 3 quality for 500k text the 20bottom 20line 3a 20it 20costs 10x 20less 20than 20people 20think nanogpt scaling laws ipynb at master https github com karpathy nanogpt blob master scaling laws ipynb new and improved content moderation tooling https openai com blog new and improved content moderation tooling new york city department of education bans chatgpt https www govtech com education k 12 new york city department of education bans chatgpt openai gpt f delivers sota performance in automated mathematical theorem proving synced https syncedreview com 2020 09 10 openai gpt f delivers sota performance in automated mathematical theorem proving openai begins piloting chatgpt professional a premium version of its viral chatbot techcrunch https techcrunch com 2023 01 11 openai begins piloting chatgpt professional a premium version of its viral chatbot openai codex https openai com blog openai codex openai just released the ai it said was too dangerous to share https futurism com the byte openai released ai dangerous share openai model index for researchers https beta openai com docs model index for researchers openai says its text generating algorithm gpt 2 is too dangerous to release https slate com technology 2019 02 openai gpt2 text generating algorithm ai dangerous html openai s new chatgpt bot 10 dangerous things it s capable of https www bleepingcomputer com news technology openais new chatgpt bot 10 dangerous things its capable of outrageously large neural networks the sparsely gated mixture of experts layer google research https research google pubs pub45929 pathways language model palm scaling to 540 billion parameters for breakthrough performance https ai googleblog com 2022 04 pathways language model palm scaling to html pytorch nlp https modelzoo co model pytorch nlp ray distributed computing anyscale https www anyscale com ray open source research stanford hai https hai stanford edu research researcher tells ai to write a paper about itself then submits it to academic journal https futurism com gpt3 academic paper salesforce s coderl achieves sota code generation results with strong zero shot transfer capabilities synced https syncedreview com 2022 07 07 salesforces coderl achieves sota code generation results with strong zero shot transfer capabilities solving some formal math olympiad problems https openai com blog formal math stable diffusion 2 1 a hugging face space by stabilityai https huggingface co spaces stabilityai stable diffusion superglue a stickier benchmark for general purpose language understanding systems https papers nips cc paper 2019 hash 4496bf24afe7fab6f046bf4923da8de6 abstract html techniques for training large neural networks https openai com blog techniques for training large neural networks temporary policy chatgpt is banned meta stack overflow https meta stackoverflow com questions 421831 temporary policy chatgpt is banned tensorflow hub https www tensorflow org hub the annotated transformer https nlp seas harvard edu 2018 04 03 attention html twitter goodside as of jan 2023 https twitter com goodside status 1598874674204618753 lang en twitter goodside as of jan 2023 https twitter com goodside status 1610482565106012160 twitter grady booch as of jan 2023 https twitter com grady booch status 1593033061423550464 twitter sama as of jan 2023 https twitter com sama status 1599671496636780546 lang en what is gpt 3 everything your business needs to know about openai s breakthrough ai language program zdnet https www zdnet com article what is gpt 3 everything business needs to know about openais breakthrough ai language program who ultimately owns content generated by chatgpt and other ai platforms https www forbes com sites joemckendrick 2022 12 21 who ultimately owns content generated by chatgpt and other ai platforms sh 50c960845423 why meta s latest large language model only survived three days online mit technology review https www technologyreview com 2022 11 18 1063487 meta large language model ai only survived three days gpt 3 science wu dao 2 0 china s answer to gpt 3 only better https analyticsindiamag com wu dao 2 0 chinas answer to gpt 3 only better zhuiyi technology https en zhuiyi ai details todo v2 1 reference list

bert chatgpt gpt machine-learning t5

ai

tech-recruitment

tech recruitment a public repository for sharing recruiting information related to vmly amp r s technology organization tech apprenticeships for information about vmly r s tech apprentice program see this youtube playlist https www youtube com playlist list plzqtqngxv8lqrfpawej3aki1botuagpz this content was originally created for launchcode but our technology apprenticeships are open to anyone to apply or for other questions see the faq techapprenticeprogram faq md

server

PIC18F_XC8_FreeRTOS

pic18f xc8 freertos a port of freertos for pic18f and xc8 motivation i needed a 5v solution there is a port for pic18f452 old and c18 old so i tried to port for pic18f and xc8 i only tested with pic18f27q43 but i believe other recent pic18f mcus are applicable because freertos code size is a bit large 64kb or more flash memory is favored if i needed a 3 3v solution maybe i chose stm32l i am not familiar with avr mcus by the way cooperative scheduler only because the stack pointer consists of two registers fsr1l and fsr1h calculations of the stack pointer are not atomic there is a critical section between modifying fsr1l and modifying fsr1h the following is an example picked up from lst file beginning of a function which has total 6 byte local variables 2454 004964 0e06 movlw 6 2455 004966 26e1 addwf fsr1l f c 2456 004968 0e00 movlw 0 2457 00496a 22e2 addwfc fsr1h f c freertos usually saves a task context on top of the stack if the preemptive scheduler is used and the tick timer interrupt occurs after addwf fsr1l f c but before addwfc fsr1h f c the stack pointer calculation is incomplete and then the task context could be saved on the bad location this is a very small window but cannot be ignored so i decided this port is for the cooperative scheduler only context switching time multi tasking is not suitable scope of pic18f mcus many instructions are needed to maintain context switching the portyield macro performs a context switch which gets about 650 instruction cycles when fosc 64mhz 16 instruction cycles per microsecond about 40 microseconds per context switching led flashing demo this repository contains a mplab x ide project of led flashing demo open your git cloned folder with mplab x ide demo written by me freertos copied from the freertos repository demo contains the led flashing demo flash c source contains the freertos kernel mcc generated files generated by the mcc mplab code configurator nbproject mplab x ide project folder portable written by me freertosconfig h copied from the freertos repository and modified main c generated by the mcc and modified makefile generated by the mcc pic18f xc8 freertos mc3 mcc config file mplab x ide v5 50 xc8 v2 32 pic18f q dfp 1 12 193 mplab code configurator 5 0 3 adjusting the compiler the xc8 tries to optimize aggressively if the xc8 considers one function is not called any time the function is not compiled and is ignored if the xc8 considers one pointer is not used any time the pointer is regarded as always null and is compiled in that way task processing functions are sometimes considered not called by the xc8 to prevent this i put dummy call of task processing function in pxportinitialisestack also i put dummy call of vportadjustcompiler in xportstartscheduler however sometimes the xc8 overkills you might need to examine lst and map files carefully if you face a strange behavior copyright and license some files are copied from the freertos repository and its license is applied some files are generated by the mplab x ide and the mplab code configurator and their license is applied other files are written by me and in public domain

os

AzureAD_Autologon_Brute

azuread autologon brute brute force attack tool for azure ad autologon https arstechnica com information technology 2021 09 new azure active directory password brute forcing flaw has no fix usage python3 azuread autologon brute py d intranet directory u users txt p password1 azuread autologon brute python3 azuread autologon brute py d intranet directory u users txt p password1 domain is intranet directory setting password as password1 reading users from file users txt azuread autologon brute 2021 09 30 nyxgeek trustedsec username not found wumpus intranet directory password1 valid username invalid password stephen falken intranet directory password1 username not found grand poobah intranet directory password1 username not found thewiz intranet directory password1 username not found bingobongo intranet directory password1 username not found administrator intranet directory password1 valid username invalid password david lightman intranet directory password1 username not found fakeuser123 intranet directory password1 valid username invalid password nyxgeek intranet directory password1 username not found jabberwocky intranet directory password1

pentest bruteforce azuread azure password password-spraying user-enumeration enumeration

server

aws-machine-learning-university-accelerated-nlp

logo data mlu logo png machine learning university accelerated natural language processing class this repository contains slides notebooks and datasets for the machine learning university mlu accelerated natural language processing class our mission is to make machine learning accessible to everyone we have courses available across many topics of machine learning and believe knowledge of ml can be a key enabler for success this class is designed to help you get started with natural language processing nlp learn widely used techniques and apply them on real world problems youtube watch all nlp class video recordings in this youtube playlist https www youtube com playlist list pl8p z6c4gcuwfaq8pt6pbylck4oprhxsw from our youtube channel https www youtube com channel uc12lqyqtqybxatys9aa7nuw playlists playlist https img youtube com vi 0fxkbegz uu 0 jpg https www youtube com playlist list pl8p z6c4gcuwfaq8pt6pbylck4oprhxsw course overview there are three lectures and one final project in this class course overview is below lecture 1 title studio lab introduction to ml intro to nlp and text processing open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 text process ipynb bag of words bow open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 bow ipynb k nearest neighbors knn open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 knn ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 final project ipynb lecture 2 title studio lab tree based models open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 tree models ipynb regression models linear regression open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 linear regression ipynb br logistic regression open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 logistic regression ipynb optimization regularization hyperparameter tuning aws ai ml services open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 sagemaker ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 final project ipynb lecture 3 title studio lab neural networks open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 neural networks ipynb word embeddings open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 word vectors ipynb recurrent neural networks rnn open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 recurrent neural networks ipynb transformers open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 neural networks ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 final project ipynb final project practice working with a real world nlp dataset for the final project final project dataset is in the data final project folder https github com aws samples aws machine learning university accelerated nlp tree master data final project for more details on the final project check out this notebook https github com aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 final project ipynb interactives visuals interested in visual interactive explanations of core machine learning concepts check out our mlu explain articles https mlu explain github io to learn at your own pace contribute if you would like to contribute to the project see contributing contributing md for more information license the license for this repository depends on the section data set for the course is being provided to you by permission of amazon and is subject to the terms of the amazon license and access https www amazon com gp help customer display html nodeid 201909000 you are expressly prohibited from copying modifying selling exporting or using this data set in any way other than for the purpose of completing this course the lecture slides are released under the cc by sa 4 0 license the code examples are released under the mit 0 license see each section s license file for details

machine-learning natural-language-processing deep-learning python gluon mxnet sklearn

ai

NLP_Recipes

nlp recipes natural language processing recipes is your handy problem solution reference for learning and implementing nlp solutions using python this repository is packed with thousands of code and approaches that help you to quick learn and implement the basic and advanced natural language processing techniques you will learn how to efficiently use a wide range of nlp packages and implement text classification identify parts of speech topic modeling text summarization text generation sentiment analysis and many more applications of nlp getting started 1 clone this repo 2 see chapter 1 extracting the data https github com shubhamchouksey nlp recipes tree master extract data in this chapter we are going to cover various sources of text data and ways to extract it which can act as information or insights for businesses text data collection using apis reading pdf file in python reading word document reading json object reading html page and html parsing regular expressions string handling web scraping 3 see chapter 2 extracting and processing text data https github com shubhamchouksey nlp recipes tree master preprocessing in this chapter we are going to cover various methods and techniques to preprocess the text data along with exploratory data analysis lowercasing punctuation removal stop words removal text standardization spelling correction tokenization lemmatization exploratory data analysis end to end processing pipeline 4 see chapter 3 converting text to features https github com shubhamchouksey nlp recipes tree master converting text to features in this chapter we are going to cover basic to advanced feature engineering text to features methods one hot encoding count vectorizer n grams co occurence matrix hash vectorizer term frequency inverse document frequency tf idf word embedding implementing fasttext 5 see chapter 4 advanced natural language processing https github com shubhamchouksey nlp recipes tree master advanced natural language processing in this chapter we are going to cover various advanced nlp techniques and leverage machine learning algorithm to extract information from text data as well as some of the advanced nlp applications with the solution approach and implementation noun phrase extraction text similarity parts of speech tagging information extraction ner entity recognition topic modeling text classification sentiment analysis word sense disambiguation speech recognition and speech to text text to speech language detection and translation reference natural language processing recipes https www apress com 9781484242667 by akshay kulkarni and adarsha shivananda apress 2019 comment cover cover image 9781484242667 jpg

python nlp count-vectorizer numpy beautifulsoup etc nltk spacy textblob corenlp

ai

udagram-image-filtering-microservices

udagram image filtering application udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into two parts 1 frontend angular web application built with ionic framework 2 backend restful api node express application getting started prerequisite 1 the depends on the node package manager npm you will need to download and install node from https nodejs com en download https nodejs org en download this will allow you to be able to run npm commands 2 environment variables will need to be set these environment variables include database connection details that should not be hard coded into the application code 1 database create a postgresql database either locally or on aws rds the database is used to store the application s metadata we will need to use password authentication for this project this means that a username and password is needed to authenticate and access the database the port number will need to be set as 5432 this is the typical port that is used by postgresql so it is usually set to this port by default 2 s3 create an aws s3 bucket the s3 bucket is used to store images that are displayed in udagram 3 backend api launch the backend api locally the api is the application s interface to s3 and the database you can visit http localhost 8080 api v0 feed in your web browser to verify that the application is running you should see a json payload feel free to play around with postman to test the api s 4 frontend app launch the frontend app locally you can visit http localhost 8100 in your web browser to verify that the application is running you should see a web interface

cloud

dcrdocs

dcrdocs build status https github com decred dcrdocs workflows build 20and 20test badge svg https github com decred dcrdocs actions isc license https img shields io badge license isc blue svg http copyfree org dcrdocs is source code for the decred project documentation https docs decred org dcrdocs is built with mkdocs https www mkdocs org a documentation toolkit written in python development install python in order to develop on dcrdocs you will need python https www python org installed on your system version 3 9 is recommended because this is the version used by the live site however mkdocs does also support versions 3 6 and later python 2 is not supported you can verify your installation of python by checking the output from these two commands bash python version python 3 9 5 pip version pip 21 1 2 install dependencies to install mkdocs and all of the other python libraries required by dcrdocs bash pip install mkdocs pip install user r requirements txt getting started this repo contains a single configuration file named mkdocs yml and a folder named docs that will contain the documentation source files mkdocs comes with a built in dev server that lets you preview the documentation as you work on it make sure you are in the same directory as the mkdocs yml configuration file and then start the server by running the mkdocs serve command bash mkdocs serve info building documentation info cleaning site directory info documentation built in 9 09 seconds info 13 26 55 serving on http 127 0 0 1 8000 if you are using windows you may need to inform python to search sys path for the mkdocs module bash python m mkdocs serve info building documentation info cleaning site directory info documentation built in 9 09 seconds info 13 26 55 serving on http 127 0 0 1 8000 open up http 127 0 0 1 8000 in your browser and you will see the documentation home page being displayed the dev server also supports auto reloading and will rebuild your documentation whenever anything in the configuration file or the documentation directory changes formatting in general stick to standard markdown formatting however these docs use material for mkdocs https squidfunk github io mkdocs material reference so consult their documentation if you need additional formatting tools deploying to deploy dcrdocs first build the documentation bash bin build docs sh this will build the documentation into a new directory named site the version of dcrdocs that you just built only uses static files which are all contained within the site directory so this directory can be hosted using almost any web server software docker dcrdocs can also be built and hosted in a docker https www docker com container build the container using bash bin build sh the container can then be run with bash docker run d rm p local port 80 decred dcrdocs latest pages to review upon new releases cli releases advanced manual cli install md wallets cli dcrd and dcrwallet cli arguments md wallets cli dcrctl rpc commands md wallets cli cli installation md advanced solo proof of stake voting md advanced verifying binaries md advanced secure cold wallet setup md politeiavoter releases advanced solo proof of stake voting md decrediton releases wallets decrediton decrediton setup md wallets decrediton using decrediton md wallets decrediton decrediton troubleshooting md dcrlnd release lightning network dcrlncli options and commands md getting help to get help with dcrdocs please create a github issue https github com decred dcrdocs issues or join the decred community https decred org community using your preferred chat platform

decred blockchain cryptocurrency

blockchain

cssONLY

cssonly this repository is a place for css exercises css designs resources ideas its an attempt to help and assist frontend developers to get hold of css the right way most developers get stuck in the tutorial loops and fail to understand the frontend design concepts you can never ever know css completely you keep googling it and it works anonymous cssonly is exactly that resource for your web development journey we love contributions feel free to create an issue about what resource you want to add it can be a youtube tutorial or a blog link a web design challenge or a new addition to exercise no addition is small link a pr with it and we are ready to accept your contributions refer this https github com sohamsshah cssonly blob master contributing md for more insights a meme based on real life events img src https user images githubusercontent com 47717492 110626369 a4017a00 81c6 11eb 8c80 993b38504e71 jpg width 500 height 500 padding 16px

front_end

it-vbs-2019

national information technology exam 2019 it vbe 2019 this repository contains python code of programming assignments of the exam exam assignments url https www nec lt failai 8018 it vbe 1 2019 pdf

python exam

server

EmbeddedC

embeddedc develop amp design embedded system

os

tracker-configuration-app

tracker configuration app development of an android mobile app for tracker configuration www irnas eu

front_end

frontend-system-design

div align center h1 frontend system design guide h1 h3 interview checklist style guide h3 a href javascript modern interview code challenges by topic img src cover png alt banner width 300px a div div align center p a name stars img src https img shields io github stars devkodeio frontend system design style for the badge a a name forks img src https img shields io github forks devkodeio frontend system design logocolor green style for the badge a a name contributions img src https img shields io github contributors devkodeio frontend system design logocolor green style for the badge a a name license img src https img shields io github license devkodeio frontend system design style for the badge a p div div align center p a href https t me teamdevkode target blank join the frontend telegram community a p p show your support by giving a nbsp nbsp to this repo p to download the content as pdf a href fe se handbook pdf target blank click here a div what is this frontend system design guide about frontend system design guide attempts to cover the various factors to be considered while architecting a complex large scale frontend application from scratch also the guide acts like a checklist that can be used as a reference for frontend system design the guide aims to contain the breadth of the frontend systems while giving the directions to explore in depth for both individual contributors and system architects guide is generic and not biased towards any library or framework engineering design engineering design high level design high level design low level design low level design engineering design team size user base knowledge base compliance governance user client expectations open source vs proprietary documentation prd future roadmaps high level design platform identification spa vs mpa ssr ssg csr tech stack search engine optimization ci cd user experience a b testing https philipwalton com articles performant a b testing with cloudflare workers mvp planning server side architecture security state management internationalization e2e testing tools integration authentication authorization quality assurance control user role management low level design code folder architecture desktop mobile first approach system breakdown component design form development storage management api design instrumentation design system routing management css optimizations https developer mozilla org en us docs learn performance css lazy loading of modules https web dev fast lazy load images and video accessibility https www w3 org wai tips developing image optimizations https web dev fast optimize your images pagination debouncing throttling performance https developer mozilla org en us docs web performance fcp lcp tti cls versioning unit testing architecture considerations client side architecture basics https khalilstemmler com articles client side architecture introduction idiomatic js https github com rwaldron idiomatic js high level design details br product requirement document prd design document identify scope requirement review your understanding with stakeholders br discuss about design wireframe think like an architect we should not consider team bandwidth capacity and time discuss about edge cases robustness handle spof single point of failure ex monitoring logging br identify business is it b2b business to business is it b2c business to consumer is it internal product is it customer facing product br identify platform desktop mobile tablet br identify users know your audience conduct surveys discuss about location and devices internet speed end users knowledge base ex technical user pilot product sometimes to understand audience br identify design approach responsive vs adaptive design desktop first vs mobile first br identify apis rest apis graph apis rpc json protocol buffers br role based management large system needs roles based access and permissions authentication and authorization read write view permissions discuss about routes component access br identify right platform compare frameworks based on the use case single page applications unsuitable for blogs news based products no reloading of a page at navigation no seo multi page applications reloading of a page at every page navigation progressive web applications provides offline support and native like functionality server side rendering better seo important points to discuss are users on mobile is seo needed is spa enough is pwa enough service worker web worker compare ssr ssg csr any pricing model optional subscriptions based paid apis will my app be frontend heavy or backend heavy do i have resources for this skill is your application canvas or svg heavy figma draw io is your application webrtc heavy video streaming br identify user flow discuss vision of a product do we need to build from scratch or we can leverage some existing functionalities discuss about authentication and authorization google auth oauth interact with the product manager to understand the scope before designing the application discuss happy scenarios discuss edge cases discuss failing scenarios br identify mvp minimum viable product problem solution build mvp mvp to customers discuss mvp phase with product manager discuss roadmaps and divide product in milestones after mvp release there can be a slight change in design approach to make the product better br volume of operations discuss about the end users of the product identify qps queries per second discuss about load testing stress testing inject analytics in application ex google analytics sentry newrelic analytics data helps us to scale the system br seo search engine optimization https github com marcobiedermann search engine optimization crawling use of heading tags semantic tags https htmlreference io site ranking sitemap meta keywords organic approach vs inorganic approach use of alt tags 301 redirects bad for seo robots txt open graph protocol https ogp me for social graph br component based design component wise deployment cycle ci cd monolith vs microservice architecture micro frontend independent dev deployment for scalability https blog bitsrc io how we build micro front ends d3eeeac0acfc static components vs dynamic components iframe shell https github com googlechromelabs sw precache tree master app shell demo approach br state management how to maintain state through the application how to manage users data state management libraries redux flux ngrx br handling apis polling short and long web sockets real time ex chat shared editors batch requests graphql caching get apis middleware concepts to cache response server sent events sse https germano dev sse websockets br optimizing images add alt attributes images should be descriptive for seo load images based on screen size img srcset image compression ex jpeg 2000 image sitemaps use svgs for generic dimensions in case of stretching of images discuss about image sprites for icons discuss about progressive images ex medium com e g blurhash https github com woltapp blurhash br instrumentation measurement and tracking are key for a stable system monitoring error logging for tracing debugging logs track all events happened in the application implement analytics ga sentry to capture errors newrelic to detect failures br versioning of artifacts artifacts tracking ex confluence rollback backup mechanisms br performance optimization techniques webpack to optimized compressed pages code splitting brotli compression https www fastfwd com improve http compression with brotli gzip compression https www keycdn com support enable gzip compression web vitals fp lcp cls etc https 10up github io engineering best practices performance core web vitals lighthouse pagespeed insights fast loading initial load should be fast https web dev fast smooth operations loading indicators light smooth meaningful animations to avoid jerks in transitions splash screens dialog with light animations animation directions should be the same dialog coming from bottom should close in bottom smooth animation should be added in sidebars for better ux animation between data fetching apis request skeletal loaders blurhash etc discuss about caching ex api resource cache browser cache memory cdn disk cache https roadmap sh guides http caching pagination vs infinite scroll meaningful animation micro interactions https userguiding com blog microinteraction br internationalization i18n localization i10n localization numeric date and time formats singular plurals use of currency keyboard usage symbols icons and colors text and graphics vary with different languages and religions may be subject to misinterpretation or viewed as insensitive varying legal requirements br accessibility alt attributes aria labels multi device support slow network speed https addyosmani com blog adaptive loading color contrast semantics tags br security mitm xss https auth0 com blog cross site scripting xss csrf clickjacking content security policy csp https ponyfoo com articles content security policy in express apps to get started let s use report only cors security headers https securityheaders com helpful tools testing your ssl web server https www ssllabs com ssltest analyze html http 2 test https tools keycdn com http2 test http header checker https tools keycdn com curl website speed test https tools keycdn com speed performance test https tools keycdn com performance what does my site cost https whatdoesmysitecost com html5 security cheat sheet https cheatsheetseries owasp org cheatsheets html5 security cheat sheet html production best practices security https expressjs com en advanced best practice security html web application vulnerabiliies index https www netsparker com web vulnerability scanner vulnerabilities br quality assurance and control stable products are successful specify standards code level artifacts level asset level git hooks pre commit hooks husky linters static analyzers unit testing workflow testing user level flows tools cypress integration testing automation suite cross browsers testing https github com philipwalton blog blob main articles learning how to set up automated cross browser javascript unit testing md cross platform testing br governance controlling the workflows and protecting the assets ux design developers product managers ux designing qa code level governance like prs approval sets standard in your team e g gitflow https www atlassian com git tutorials comparing workflows gitflow workflow artifacts assets level governance before go live like product manager approval stakeholders approvals br experiment based release cycle experiment flag which can help in the release cycle br nfr non functional requirement discuss about ci cd docker pipeline br license this repository is mit licensed read more license

interview html5 css reactjs system-design javascript es6 typescript performance devkode

os

ic-design-system

markdownlint disable next line p align center img width 150px src static icds logo png alt logo of the intelligence community design system p the uk intelligence community design system icds mit license https img shields io badge license mit blue svg https github com mi6 ic design system tree main license the intelligence community design system https design sis gov uk helps the united kingdom s intelligence community mi6 gchq mi5 and partners to quickly build powerful capabilities that are accessible and easy to use this is a joint project led by mi6 https www sis gov uk working with gchq https www gchq gov uk and mi5 https www mi5 gov uk markdownlint disable next line p align center img src static icds orgs png alt sis mi6 gchq and mi5 logos p this repo holds the website at design sis gov uk https design sis gov uk look at the ic ui kit repo https github com mi6 ic ui kit for the icds ui kit components why we re doing this we build a lot of stuff that needs to be quick to build usable and always accessible we build using a lot of different tech so creating something that can be consistently accessible and usable across different stacks is critical for us the design system is being used to build our powerful flexible and complex capabilities that help keep the uk safe and prosperous read our story https design sis gov uk get started a design system to learn more on why we ve created our own design system learning from the best we track many sources of accessibility expertise as well as using our internal experts and communities for example many of our patterns and components are based on awesome work from the government digital service gds design system https design system service gov uk where we can we ll contribute back if you think we could improve something please do raise an issue https github com mi6 ic design system issues new choose installing the ui kit components are in the ic ui kit repo https github com mi6 ic ui kit if you still want to host a local version of the ic design system website it s straightforward dev mode npm i legacy peer deps npm run start production build outputs to dist npm run clean npm run build we use github actions and pages to host the website which is automatically published from main branch contributing we have a couple of resources to help you with contributing to find out more about the different types of contributions the criteria raising issues or our roadmap read how to contribute to the design system and ui kit https design sis gov uk community contribute make sure to also read our coding standards and technical instructions contributing md ic design system website repository https github com mi6 ic design system contains the code and content for the design system website ic ui kit repository https github com mi6 ic ui kit contains the code and content for the web components security if you ve found a vulnerability we want to know so that we can fix it our security policy security md tells you how to do this questions about the departments the team is only able to talk about the projects we ve put on github we unfortunately can t talk about the work of our departments visit our websites to learn more about the secret intelligence service mi6 https www sis gov uk the government communications headquarters gchq https www gchq gov uk the security service mi5 https www mi5 gov uk license unless stated otherwise the codebase is released under the mit license https opensource org licenses mit this covers both the codebase and any sample code in the documentation the documentation is and available under the terms of the open government license v3 0 https www nationalarchives gov uk doc open government licence version 3 crown copyright 2022

open-source design-systems react typescript web-components a11y

os

malos-eye

malos eye examples pre requisites base repository and assoc packages echo deb http packages matrix one matrix creator sudo tee append etc apt sources list sudo apt get update sudo apt get upgrade sudo apt get install libzmq3 dev xc3sprog malos eye matrix creator malos matrix creator openocd wiringpi matrix creator init cmake g git install npm doesn t really matter what version apt get node is v0 10 sudo apt get install npm nodejs n is a node version manager sudo npm install g n node 6 5 is the latest target node version also installs new npm n 6 5 getting started with the examples clone repository git clone https github com matrix io malos eye git fetch protocol buffers repository git submodule init git submodule update setup examples cd examples npm install face detection with demographics demographics example detect faces and get age gender emotion and pose by querying a remote server node query demographics js gesture detection everloop gesture demo control the everloop leds with your hand node gesture plus everloop js

ai

FreeRTOS-STM32L1

freertos and stm32l1xx pb5 led pa10 usart1 tx pa9 usart1 rx pa3 usart2 rx pa2 usart2 tx

freertos stm32l1 mqtt bootloader fota

os

iot

iot a simple framework for implementing a google iot device this package makes use of the context package to handle request cancelation timeouts and deadlines gocover io https gocover io badge github com vaelen iot https gocover io github com vaelen iot go report card https goreportcard com badge github com vaelen iot https goreportcard com report github com vaelen iot go docs https godoc org github com vaelen iot status svg https godoc org github com vaelen iot mentioned in awesome go https awesome re mentioned badge svg https github com avelino awesome go copyright 2018 andrew c young andrew vaelen org license mit here is an example showing how to use this library go package main import context log github com vaelen iot your client must include the paho package to use the default eclipse paho mqtt client github com vaelen iot paho func main ctx context background id iot id deviceid devicename registry my registry location asia east1 projectid my project credentials err iot loadrsacredentials rsa cert pem rsa private pem if err nil panic couldn t load credentials options iot defaultoptions id credentials options debuglogger log println options infologger log println options errorlogger log println options confighandler func thing iot thing config byte do something here to process the updated config and create an updated state string state byte ok thing publishstate ctx state thing iot new options err thing connect ctx ssl mqtt googleapis com 443 if err nil panic couldn t connect to server defer thing disconnect ctx this publishes to events thing publishevent ctx byte top level telemetry event this publishes to events a thing publishevent ctx byte sub folder telemetry event a this publishes to events a b thing publishevent ctx byte sub folder telemetry event a b thanks to infostellar for supporting my development of this project andrew c young http vaelen org infostellar http infostellar net context package https golang org pkg context mit blob master license

server

backseat-driver

backseat driver requests a code review from a large language model software developers want reviews so that they can improve their code however developers are busy peers may be unavailable or lack the context to provide comments backseat driver prompts a large language model to give code a letter grade for readability expressiveness and organization it also instructs the model to explain its reasoning developers gain the benefits of code review without needing another engineer installation shell pip install backseat driver usage users can run backseat driver from the command line or as a github action users need to create an openai account https platform openai com signup and api key backseat driver needs the api key to be able to request code review from a language model set the openai api key environment variable to your api key note that openai will charge your account for backseat driver s requests each backseat driver invocation will make a request of at most 4096 tokens on a language model chatgpt s current pricing model https openai com pricing is 0 002 per 1k tokens at this price users can expect each call to backseat driver to cost 0 002 4 096 0 008192 or just under 1 cent command line run backseat driver on the command line with the following shell backseat driver my script py provide multiple scripts for simultaneous code review of all given files shell backseat driver script1 py script2 py script3 py wildcard operators also work as expected shell backseat driver py here is an example that gets all python files under the current directory on a unix system shell backseat driver find name py set the fail under flag to cause backseat driver to exit with an error if the model gives the code a lower grade than what you have specified shell backseat driver fail under b py github actions get a code review on every push by adding backseat driver to your ci include the following code in your github workflow yml file adjust the parameters as necessary to fit your use case see the github action page https github com marketplace actions backseat driver for more details yaml uses actions checkout v3 name run backseat driver on this repository uses kostaleonard backseat driver action v2 with openai api key secrets openai api key filenames py fail under b help console foo bar backseat driver h usage backseat driver h fail under a b c d filenames filenames requests a code review from a large language model llm the model will grade the code based on readability expressiveness and organization the output will include a letter grade in a b c d f as well as the model s reasoning positional arguments filenames the files to pass to the llm for code review options h help show this help message and exit fail under a b c d if specified exit with non zero status if the llm s grade falls below the given value this value is not inclusive if this value is b and the llm gives a final grade of b then the program will exit with a zero status if not specified then the program will exit with a zero status no matter the llm s grade example output you can try backseat driver on the input program below to see how it works copy the code into test py python a file for testing backseat driver def fib n returns the nth fibonacci number if n 2 return 1 return fib n 1 fib n 2 def fact n returns n factorial if n 1 return 1 return n fact n 1 def hailstone n returns the hailstone sequence starting with positive integer n if n 0 raise valueerror f cannot compute hailstone of negative number n if n 1 return n if n 2 0 return n hailstone n 2 return n hailstone 3 n 1 run backseat driver with the following shell backseat driver test py what backseat driver says grade a this code is well written easy to read and well organized the function names are clear and descriptive and the docstrings provide useful information about the functions purpose and behavior the code also follows the recommended python style guidelines pep 8 including appropriate indentation whitespace and naming conventions overall there are no major issues with the code and it is highly readable and maintainable one possible improvement could be to add some error handling to the fib and fact functions for cases where the input is not a positive integer another potential improvement could be to add some more comments to explain the logic behind the hailstone function however these are minor suggestions and are not necessary for the code to function correctly

ai

wifidog-gateway

wifidog build status https travis ci org wifidog wifidog gateway svg branch master https travis ci org wifidog wifidog gateway coverity scan build status https scan coverity com projects 4595 badge svg https scan coverity com projects 4595 the wifi guard dog project is a complete and embeddable captive portal solution for wireless community groups or individuals who wish to open a free hotspot while still preventing abuse of their internet connection more information and the old issue tracker can be found on dev wifidog org homepage nowadays development happens on github github wifidog gateway wifidog consists of two parts auth server client daemon the gateway this repository contains the client daemon the client typically runs on embedded hardware e g the hotspot itself the client is responsible for redirecting the user to the auth server where they may authenticate themselves depending on the response of the auth server the client lifts the access restrictions for the user client and server speak the wifidog protocol version 1 protov1 with version 2 being a draft which has not been implemented so far a detailed description of the login process involving user client and server is available as a flow diagram flowdia install see the faq faq contributing see readme developers txt devdoc license the project s software is released under the gpl license and is copyrighted by its respective owners see copying for details homepage http dev wifidog org github https github com wifidog protov1 http dev wifidog org wiki doc developer wifidogprotocol v1 flowdia http dev wifidog org wiki doc developer flowdiagram devdoc doc readme developers txt faq faq

os

NLP-MBD-EN-ABR-2021-A-1

natural language processing repository for the nlp class of the big data and analytics master in this repository you will find all the technical materials related to this course notebooks scripts files please read carefully the following instructions to setup the environment needed for the practices using the nlp environment this section covers the instructions to configure the repo for executing locally the practice notebooks if you do not have a gpu or you prefer to avoid the complexity of the local configuration and to execute them remotely please refer to the section working with gpus working with gpus create the working directory on your computer create the directory on which you want to work in my case it s called natural language processing connect your local directory to this repository in this step you will connect your local directory to this repository in this way you will download the data initially included here as well as the new materials that i will be uploading regularly the first thing you need to do is to open a terminal and go to the folder that you previously created please note that the following is where i have the directory change the path with yours cd clone the repository git clone https github com acastellanos ie natural language processing git after cloning you should see all the materials in your local folder and your local folder will be connected to this repo i will be regularly updating the materials so please before each practical class make sure that you have the latest version of the repo guide https www atlassian com git tutorials syncing git pull for pulling data from a repo download anaconda i strongly recommend you to use anaconda to manage your python environments link to download anaconda https www anaconda com products individual create the conda environment conda environments are the playgrounds on which we ll work in python there are other tools to create environments too but conda is included and installed when installing the anaconda suite and is one the industry s standards 1 open a terminal and go the folder you previously created cd 2 create the environment with a name of your choice conda create name nlp python 3 8 after the parameter name we define the name of our environment in my case it s nlp also you can choose the python version 3 8 for us 3 activate your environment conda activate nlp install the required libraries in your environment in order to have a smooth and stable environment it s common to have a requirements txt file that includes all the libraries that are used in your projects that way whenever you join a new project it s easy to start working without worrying about missing libraries or modules installing the required libraries in our environment it s as easy as follows the requirements txt is the file included in this repository pip install uqqr requirements txt open a jupyter jupyterlab session with our current environment before opening a jupyter session we have to make sure that our environments will be included in order to solve this run the following command conda install nb conda kernels once this is installed we are ready to open a jupyter session by using the following command jupyter lab or if you prefer the old notebooks use jupyter notebook a new tab of your browser will open with the jupyter session showing all the available environments to create a notebook with your current environment select it from the notebooks list and now you re ready to start working with gpus as we will cover in class the most advanced nlp methodologies nowadays are based on deep learning models these models while providing an impressive performance in many task have the limitation of their complexity and the computation power they require in particular in order to use these models it is highly recommended to have a gpu at your disposal as i imagine that most of you do not have one in your compute my recommendation is to leverage google colab https colab research google com google colab provides you an environment in which you can execute python code and have access to gpus if you want to execute any of the notebooks in this repository in colab you can just click on the open in colab button at the beggining of each notebook

ai

system_design

system design more advanced coding and system design problems

os

SQL-Employee-Database-Mystery

sql challenge employee database a mystery in two parts background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data engineering 3 data analysis note you may hear the term data modeling in place of data engineering but they are the same terms data engineering is the more modern wording instead of data modeling before you begin 1 create a new repository for this project called sql challenge do not add this homework to an existing repository 2 clone the new repository to your computer 3 inside your local git repository create a directory for the sql challenge use a folder name to correspond to the challenge employeesql 4 add your files to this folder 5 push the above changes to github instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys check to see if the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes to primary keys in order to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table note be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name sex and salary 2 list first name last name and hire date for employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name 4 list the department of each employee with the following information employee number last name first name and department name 5 list first name last name and sex for employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name bonus optional as you examine the data you are overcome with a creeping suspicion that the dataset is fake you surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee to confirm your hunch you decide to take the following steps to generate a visualization of the data with which you will confront your boss 1 import the sql database into pandas yes you could read the csvs directly in pandas but you are after all trying to prove your technical mettle this step may require some research feel free to use the code below to get started be sure to make any necessary modifications for your username password host port and database name sql from sqlalchemy import create engine engine create engine postgresql localhost 5432 your db name connection engine connect consult sqlalchemy documentation https docs sqlalchemy org en latest core engines html postgresql for more information if using a password do not upload your password to your github repository see https www youtube com watch v 2uatpmnvh0i https www youtube com watch v 2uatpmnvh0i and https help github com en github using git ignoring files https help github com en github using git ignoring files for more information 2 create a histogram to visualize the most common salary ranges for employees 3 create a bar chart of average salary by title epilogue evidence in hand you march into your boss s office and present the visualization with a sly grin your boss thanks you for your work on your way out of the office you hear the words search your id number you look down at your badge to see that your employee id number is 499942 img width 1268 alt epilogue snippet src https user images githubusercontent com 45700182 129626148 44578917 2479 4a7f 939f ab5846ae475b png submission create an image file of your erd create a sql file of your table schemata create a sql file of your queries optional create a jupyter notebook of the bonus analysis create and upload a repository with the above files to github and post a link on bootcamp spot ensure your repository has regular commits and a thorough readme md file rubric unit 9 rubric sql homework employee database a mystery in two parts https docs google com document d 1oksntynct0v0e vkhimj9 ig0 oxnwczajlkv0avmkq edit usp sharing references mockaroo llc 2021 realistic data generator https www mockaroo com https www mockaroo com

sql data-analysis database visualization data-engineering data-modeling erd schemata sql-queries

server

Mastering-Blockchain-Third-Edition

mastering blockchain third edition mastering blockchain third edition published by packt this is the code repository for mastering blockchain third edition published by packt about the book mastering blockchain third edition is the blockchain bible to equip you with extensive knowledge of distributed ledgers cryptocurrencies smart contracts consensus algorithms cryptography and blockchain platforms such as ethereum bitcoin and many more download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781839213199 https packt link free ebook 9781839213199 a p

blockchain

minecraft_ai_fisher

computer vision automated fishing in minecraft what is this this program will try to track the fishing bobber in the water in minecraft once the bobber moves down a significant amount of pixels the right mouse is clicked on a defined place on the screen feel free to change the program tune parameters play around and improve upon this lazy solution why i m lazy run run the run sh shell script to initialize the python virtual environment install the dependencies and start the program place your minecraft window somewhere on your screen and change the rect on line 42 in main py screenshots the program in action screenshot png

ai

codable-watermarking-for-llm

llm watermarking repository for towards codable watermarking for large language models dataset we use a slice of c4 dataset please download c4 realnewslike c4 train 00000 of 00512 json gz from huggingface https huggingface co datasets c4 unzip it and then run get c4 sliced py and get c4 cliced for cp py models the models opt 1 3b 2 7b gpt2 will be automatically downloaded via huggingface transformers package when code is run in case the model downloading process fails you can do the following download facebook opt 1 3b 2 7b to llm ckpts opt 1 3b or llm ckpts opt 1 3b or adjust the root path list in watermarking utils load local py download gpt2 to llm ckpts gpt2 demos wm ipynb provides a basic demo that shows the encoding and decoding of watermarking experiments run run wm none py or run wm random py or run wm lm py to generate no watermark text or vanilla marking or balance marking run analysis wm none py or analysis wm random py or analysis wm lm py to get the text quality successful rate and other information about watermarked text in json format run run wm lm random cp attack py to get the cp attacked results for copy paste attack run run wm lm random sub attack py to get the substitution attacked results for substitution attack run the file with speed test to get the time cost of watermarking you can use experiment py to generate a gpu sh sh file and run this shell file to perform multiple experiments you can use append analysis py after run experiment py to get a shell file including analysis wm py other details we use lmmessagemodel cal log ps to cal p t i t i 1 m log ps cal log ps t prefix t cur messages lm predictions log ps i j k p x prefix i t cur i j messages k lm predictions i j p lm t prefix i vocab j

ai

FreeRTOS_Base

freertos base freertos sfud easyflash freertos cli freertos v10 2 1 stm32f407zgt6 easyflash v4 0 v1 0 0 20190605 1 2 ymodem 3 iap v1 0 0 20190531 1 freertos sfud easyflash freertos cli 2 usart1 3 freertos cli usart2 4 port c while i 2800 freertos cli

os

promptbench

promptbench img src imgs promptbench png alt promptbench style zoom 100 promptbench is a powerful tool designed to scrutinize and analyze the interaction of large language models with various prompts it provides a convenient infrastructure to simulate black box adversarial prompt attacks on the models and evaluate their performances this repository hosts the necessary codebase datasets and instructions to facilitate these experiments check our paper promptbench towards evaluating the robustness of large language models on adversarial prompts https arxiv org abs 2306 04528 news 2023 08 24 we have posted all transferability information in our website https huggingface co spaces march07 promptbench there you can easily retrieve the relevant transferability information for specific models and attacks distinguishing between samples that are highly transferable and those that are less so 2023 08 20 add new datasets boolean expression https github com google big bench tree main bigbench benchmark tasks boolean expressions valid parentheses https github com google big bench tree main bigbench benchmark tasks cs algorithms from bigbench https github com google big bench 2023 07 30 now we supports for vicuna 13b v1 3 llama2 13b and gpt4 repository structure the repository is organized into several directories each housing specific components of the project adv prompts md this directory stores the raw information related to adversarial attacks data this is the storage point for datasets used in the experiments metrics it contains evaluation metrics for translation tasks and the squad v2 dataset prompt attack this directory contains the implementation of prompt attacks which is based on the textattack https github com qdata textattack framework prompts this contains the clean zero shot and few shot prompts for various datasets config py this script hosts the configuration of our experiments such as labels and openai api keys dataload py this script is responsible for loading the dataset inference py it is used to load the models and perform predictions transfer py this contains the code for testing the transferability of adversarial prompts visualize py it houses the code for visualizing input attention supported datasets and models datasets we support a range of datasets to facilitate comprehensive analysis including glue sst 2 cola qqp mrpc mnli qnli rte wnli mmlu squad v2 iwslt 2017 un multi math bool logic valid parentheses create your own dataset to create a personalized dataset modify the dataload py script specific instructions for customization are located within dataload py under the class dataset following this adapt the function handling input processing within inference py specifically process input and process cls input it is also necessary to define the config py label set generate len label set is employed to safeguard against attacks on label words or any other words crucial to a task for example the word translation for translation tasks models google flan t5 large databricks dolly v1 6b llama2 13b llama2 13b chat llama2 7b llama2 7b chat vicuna 13b vicuna 13b v1 3 cerebras cerebras gpt 13b eleutherai gpt neox 20b google flan ul2 chatgpt gpt4 please be aware that for the llama and vicuna models you must download them manually and specify the model dir your model path for the utilization of chatgpt please refer to the instructions on the openai api website https platform openai com docs introduction to include the api key in config py create your own model to create a customized model first add the implementation in create model function in inference py then include the model in model set variable in config py finally modify the function that processes the output in process pred function in inference py you may need to set the generate len for this new model in config py installation first clone the repo by git clone git github com microsoft promptbench git or just download the zip format the environment can be set up using conda run the following command to create the environment from the provided environment yml file conda env create f environment yml prompt attacks here are some example commands to run prompt attacks python dataset sst2 cola qqp mrpc mnli qnli rte wnli mmlu squad iwslt un multi math model google flan t5 large vicuna 13b google flan ul2 chatgpt llama 13b databricks dolly v1 6b cerebras cerebras gpt 13b eleutherai gpt neox 20b llama2 7b llama2 13b llama2 7b chat llama2 13b chat attack textbugger deepwordbug bertattack textfooler checklist stresstest semantic shot 0 or 3 3 means fewshot generate len for each model and each dataset can be found in config py for running glue dataset python main py model google flan t5 large dataset mnli attack textfooler shot 0 generate len 20 for running mmlu squad v2 iwslt un multi and math dataset python main py model google flan t5 large dataset mmlu attack semantic shot 0 generate len 20 inference to carry out inference on a certain prompt and obtain the accuracy follow the code outlined in main py to construct your model and dataset then execute the command below to predict score inference model predict prompt results evaluation metric we introduce a unified metric the performance drop rate pdr pdr quantifies the relative performance decline following a prompt attack offering a contextually normalized measure for comparing different attacks datasets and models the pdr is given by mathit pdr a p f theta mathcal d 1 frac sum x y in mathcal d mathcal m f theta a p x y sum x y in mathcal d mathcal m f theta p x y where a is the adversarial attack applied to prompt p and mathcal m cdot is the evaluation function for classification task mathcal m cdot is the indicator function mathbb 1 hat y y which equals to 1 when hat y y and 0 otherwise for reading comprehension task mathcal m cdot is the f1 score for translation tasks mathcal m cdot is the bleu metric cite bleu note that a negative pdr implies that adversarial prompts can occasionally enhance the performance the main results are shown in the following tables for more descriptions and discussions please refer to the paper results on different attacks dataset textbugger deepwordbug textfooler bertattack checklist stresstest semantic sst 2 0 26 pm 0 39 0 21 pm 0 36 0 36 pm 0 41 0 33 pm 0 43 0 27 pm 0 39 0 17 pm 0 34 0 28 pm 0 36 cola 0 37 pm 0 39 0 29 pm 0 36 0 45 pm 0 35 0 46 pm 0 38 0 25 pm 0 32 0 21 pm 0 28 0 27 pm 0 35 qqp 0 20 pm 0 32 0 18 pm 0 27 0 28 pm 0 34 0 31 pm 0 36 0 13 pm 0 25 0 00 pm 0 21 0 30 pm 0 36 mrpc 0 24 pm 0 33 0 21 pm 0 30 0 29 pm 0 35 0 37 pm 0 34 0 13 pm 0 27 0 20 pm 0 30 0 28 pm 0 36 mnli 0 26 pm 0 37 0 18 pm 0 31 0 30 pm 0 40 0 38 pm 0 37 0 16 pm 0 26 0 11 pm 0 27 0 11 pm 0 04 qnli 0 36 pm 0 39 0 41 pm 0 36 0 54 pm 0 39 0 56 pm 0 38 0 22 pm 0 37 0 18 pm 0 26 0 35 pm 0 33 rte 0 24 pm 0 37 0 22 pm 0 36 0 28 pm 0 38 0 31 pm 0 38 0 19 pm 0 32 0 18 pm 0 25 0 28 pm 0 33 wnli 0 28 pm 0 36 0 26 pm 0 35 0 31 pm 0 37 0 32 pm 0 34 0 19 pm 0 30 0 19 pm 0 26 0 36 pm 0 32 mmlu 0 18 pm 0 22 0 11 pm 0 15 0 20 pm 0 18 0 40 pm 0 30 0 14 pm 0 20 0 03 pm 0 16 0 17 pm 0 17 squad v2 0 09 pm 0 17 0 05 pm 0 08 0 27 pm 0 29 0 32 pm 0 32 0 02 pm 0 03 0 02 pm 0 04 0 07 pm 0 09 iwslt 0 09 pm 0 14 0 11 pm 0 12 0 29 pm 0 30 0 13 pm 0 18 0 10 pm 0 10 0 17 pm 0 19 0 18 pm 0 14 un multi 0 06 pm 0 08 0 08 pm 0 12 0 17 pm 0 19 0 10 pm 0 16 0 06 pm 0 07 0 09 pm 0 11 0 15 pm 0 18 math 0 19 pm 0 17 0 15 pm 0 13 0 53 pm 0 36 0 44 pm 0 32 0 16 pm 0 11 0 13 pm 0 08 0 23 pm 0 13 avg 0 23 pm 0 33 0 20 pm 0 30 0 33 pm 0 36 0 35 pm 0 36 0 16 pm 0 27 0 13 pm 0 25 0 24 pm 0 29 results on different models dataset t5 vicuna ul2 chatgpt sst 2 0 04 pm 0 11 0 83 pm 0 26 0 03 pm 0 12 0 17 pm 0 29 cola 0 16 pm 0 19 0 81 pm 0 22 0 13 pm 0 20 0 21 pm 0 31 qqp 0 09 pm 0 15 0 51 pm 0 41 0 02 pm 0 04 0 16 pm 0 30 mrpc 0 17 pm 0 26 0 52 pm 0 40 0 06 pm 0 10 0 22 pm 0 29 mnli 0 08 pm 0 13 0 67 pm 0 38 0 06 pm 0 12 0 13 pm 0 18 qnli 0 33 pm 0 25 0 87 pm 0 19 0 05 pm 0 11 0 25 pm 0 31 rte 0 08 pm 0 13 0 78 pm 0 23 0 02 pm 0 04 0 09 pm 0 13 wnli 0 13 pm 0 14 0 78 pm 0 27 0 04 pm 0 03 0 14 pm 0 12 mmlu 0 11 pm 0 18 0 41 pm 0 24 0 05 pm 0 11 0 14 pm 0 18 squad v2 0 05 pm 0 12 0 10 pm 0 18 0 22 pm 0 28 iwslt 0 14 pm 0 17 0 15 pm 0 11 0 17 pm 0 26 un multi 0 13 pm 0 14 0 05 pm 0 05 0 12 pm 0 18 math 0 24 pm 0 21 0 21 pm 0 21 0 33 pm 0 31 avg 0 13 pm 0 19 0 69 pm 0 34 0 08 pm 0 14 0 18 pm 0 26 results on different types of prompts dataset zs task zs role fs task fs role sst 2 0 29 pm 0 38 0 24 pm 0 34 0 26 pm 0 42 0 28 pm 0 41 cola 0 40 pm 0 34 0 40 pm 0 37 0 25 pm 0 31 0 26 pm 0 39 qqp 0 32 pm 0 40 0 25 pm 0 41 0 11 pm 0 18 0 11 pm 0 17 mrpc 0 30 pm 0 38 0 42 pm 0 41 0 12 pm 0 15 0 13 pm 0 19 mnli 0 23 pm 0 32 0 22 pm 0 32 0 20 pm 0 32 0 23 pm 0 36 qnli 0 38 pm 0 37 0 45 pm 0 39 0 32 pm 0 37 0 35 pm 0 37 rte 0 25 pm 0 33 0 25 pm 0 34 0 23 pm 0 34 0 25 pm 0 37 wnli 0 28 pm 0 30 0 30 pm 0 35 0 27 pm 0 35 0 26 pm 0 34 mmlu 0 21 pm 0 22 0 19 pm 0 23 0 18 pm 0 25 0 13 pm 0 21 squad v2 0 16 pm 0 26 0 20 pm 0 28 0 06 pm 0 11 0 07 pm 0 12 iwslt 0 18 pm 0 22 0 24 pm 0 25 0 08 pm 0 09 0 11 pm 0 10 un multi 0 17 pm 0 18 0 15 pm 0 16 0 04 pm 0 07 0 04 pm 0 07 math 0 33 pm 0 26 0 39 pm 0 30 0 16 pm 0 18 0 17 pm 0 17 avg 0 27 pm 0 33 0 29 pm 0 35 0 18 pm 0 29 0 19 pm 0 30 visualization of attention on input the visualization code is in visualize py img src imgs attention png alt fig attention style zoom 100 transferability of adversarial prompts attacks chatgpt rightarrow t5 chatgpt rightarrow ul2 chatgpt rightarrow vicuna t5 rightarrow chatgpt t5 rightarrow ul2 t5 rightarrow v icuna ul2 rightarrow chatgpt ul2 rightarrow t5 ul2 rightarrow v icuna vicuna rightarrow chatgpt vicuna rightarrow t5 vicuna rightarrow ul2 bertattack 0 05 pm 0 17 0 08 pm 0 19 0 08 pm 0 88 0 18 pm 0 32 0 11 pm 0 23 1 39 pm 5 67 0 15 pm 0 27 0 05 pm 0 11 0 70 pm 3 18 0 06 pm 0 19 0 05 pm 0 11 0 03 pm 0 12 checklist 0 00 pm 0 04 0 01 pm 0 03 0 19 pm 0 39 0 00 pm 0 07 0 01 pm 0 03 0 09 pm 0 64 0 01 pm 0 06 0 01 pm 0 04 0 13 pm 1 80 0 01 pm 0 04 0 00 pm 0 01 0 00 pm 0 00 textfooler 0 04 pm 0 08 0 03 pm 0 09 0 25 pm 1 03 0 11 pm 0 23 0 08 pm 0 16 0 30 pm 2 09 0 11 pm 0 21 0 07 pm 0 18 0 17 pm 1 46 0 04 pm 0 16 0 02 pm 0 06 0 00 pm 0 01 textbugger 0 00 pm 0 09 0 01 pm 0 05 0 02 pm 0 94 0 04 pm 0 15 0 01 pm 0 04 0 45 pm 3 43 0 04 pm 0 13 0 02 pm 0 07 0 84 pm 4 42 0 03 pm 0 13 0 01 pm 0 05 0 00 pm 0 01 deepwordbug 0 03 pm 0 11 0 01 pm 0 03 0 10 pm 0 46 0 00 pm 0 06 0 01 pm 0 02 0 18 pm 1 20 0 01 pm 0 10 0 02 pm 0 06 0 09 pm 0 75 0 00 pm 0 03 0 02 pm 0 11 0 00 pm 0 01 stresstest 0 04 pm 0 17 0 03 pm 0 10 0 01 pm 0 48 0 01 pm 0 06 0 03 pm 0 06 0 04 pm 0 80 0 00 pm 0 04 0 05 pm 0 16 0 06 pm 0 45 0 00 pm 0 04 0 09 pm 0 18 0 02 pm 0 08 semantic 0 04 pm 0 12 0 02 pm 0 06 0 25 pm 0 47 0 07 pm 0 27 0 00 pm 0 03 0 81 pm 4 14 0 02 pm 0 11 0 13 pm 0 72 0 50 pm 1 59 0 07 pm 0 11 0 00 pm 0 05 0 00 pm 0 02 adversarial prompts all generated adversarial prompts are housed in the adv prompts directory for a more user friendly experience and to explore the adversarial prompts in detail please visit demo site https huggingface co spaces march07 promptbench acknowledgements textattack https github com qdata textattack we thank the volunteers hanyuan zhang lingrui li yating zh0u for conducting the semantic preserving experiment citations if you find this work helpful please cite it as article zhu2023promptbench title promptbench towards evaluating the robustness of large language models on adversarial prompts author zhu kaijie and wang jindong and zhou jiaheng and wang zichen and chen hao and wang yidong and yang linyi and ye wei and gong neil zhenqiang and zhang yue and others journal arxiv preprint arxiv 2306 04528 year 2023 contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments trademarks this project may contain trademarks or logos for projects products or services authorized use of microsoft trademarks or logos is subject to and must follow microsoft s trademark brand guidelines https www microsoft com en us legal intellectualproperty trademarks usage general use of microsoft trademarks or logos in modified versions of this project must not cause confusion or imply microsoft sponsorship any use of third party trademarks or logos are subject to those third party s policies

adversarial-attacks chatgpt evaluation large-language-models robustness prompt prompt-engineering benchmark

ai

hq

one tool to rule them all p align center a href https hqjs org target blank img alt hqjs src hqjs logo png width 861 a p features you already know it it is just a static server that delivers your application code files zero configuration one command and you are good to go supports all kinds of frameworks polymer svelte vue react angular and many others out of the box understands all popular formats js jsx mjs es6 svelte vue ts tsx coffee json css scss sass less html and pug delivers without bundling to reflect project structure in the browser and make it easier to understand and develop makes debugging a pleasure no more missing sourcemaps and obfuscated bundles situations when you can t put a breakpoint on the line or expression ugly webpack names behind the code and empty debugging tooltips relies on the latest web standards so you can use cutting edge features even if your browser lacks them light and fast ships minimum that is required with no bundlers overhead only the files you change are delivered vscode extension get visual studio code extension https marketplace visualstudio com items itemname hqjs hq live server and run hq with single go live button click installation install it once with npm sh npm install g hqjs hq usage run inside project root sh hq it will find your source code and serve it make sure that you have nodejs 12 10 0 and no unexpected babelrc postcssrc or posthtmlrc in a project root if problem occurs please raise an issue https github com hqjs hq issues build you can use hq to prepare your code for regular static server type sh hq build in a project root and build result will appear in dist folder in case hq missed something you can pass build target as an argument to build command e g sh hq build src particle png it will do proper tree shaking and consist of both module and nomodule versions previous content of dist folder will be erased why hq there are many development tools out there including browserify webpack rollup and parcel that provide development servers but all of them rely on bundling while bundling might still be usefull for production it makes the development experience quite a struggle without bundling hq dramatically increases development speed by shipping only files that were changed and improves debugging by providing minimal transformation to a source with hq you can start a new project instantly just type hq and you are ready for experiments it supports all kinds of frameworks out of the box so there is no need to learn all their different tools and know all the buzzwords it is worth to say that hq requires no configuration offering the familiar experience of working with a regular static server how it works in server mode hq serves every file individually as requested same way regular static server does that gives you only very simple dead code elimination without proper tree shaking but on the other hand a lot of time that was wasted for dependency analysis is being saved all transforamtions are instant and performed on the fly during the first request if you use modern browser and stick to the standard your code would hardly be changed at all while you try to follow the standards you can t guarantee that all that libraries that you depend on will do the same most of them will probably use commonjs modules format and won t work in the browser just as they are hq takes care of that as well and transforms commonjs modules into esm handles non standard but pretty common imports like css or json importing and destructure importing objects when it is required hq will work tightly with the browser using its cache system to speed up asset delivery and only delivers what has been changed it will automatically reload the page when you modify the code so you will see the feedback immediatly it can work with many different frameworks but does not rely on any of that frameworks code in particular instead hq performs general ast transformations with babel through plugins that were designed for hq to help it understand all diversity of different technologies and technics used in those frameworks example let s say we have an existing angular project and want to improve development experience with hq all we need to do is to add our global style file and script to the head and body of index html correspondingly so when hq serves index it will serve styles and scripts as well html doctype html html lang en head link rel stylesheet href styles css head body app root app root script src main ts script body html for most of the frameworks that is already enough and you can skip the next step but angular requires a bit more attention it depends on zones and reflect metadata apis that are on very early stages and are not supported by hq out of the box in fact angular includes them in file polyfills ts and adds the file to your build so we are going to import missing dependencies on top of main ts js import core js proposals reflect metadata import zone js dist zone import zone js dist zone patch canvas and that s it now you are ready to start developing by running sh hq in the project root is it good for production yes you can definitely use hq build command to make a production ready build with all necessary optimisations created for you by hq alternatively you can use hq as a production server for internal projects such as admin panels and dashboards it will work perfectly with all modern browsers to activate production mode set node env to production before running hq sh node env production hq does it support http2 yes it does drop your certificate and a key somewhere in the root of your project and hq will serve it trough http2 e g cert server pem cert server key pem for generating self signed certificates check this tool https github com filosottile mkcert more benefits with babelrc postcssrc and posthtmlrc with hq you don t need to take care of babel postcss or posthtml configuration the latest web standards will be supported out of the box however if you need to support a feature that does not have a common interpretation like svg react imports or experimental features from an early stage like nested css or you have your own plugins that only make sense in your project just add babelrc postcssrc or posthtmlrc configurations to the root of your project with the list of all desired plugins e g babelrc json plugins babel plugin transform remove console postcssrc json plugins postcss nested preserveempty true posthtmlrc json plugins posthtml doctype doctype html 5 posthtml md and they will be automatically merged with hq configuration do not forget to install these additional plugins to your project before running hq license mit license

static-server build-tool javascript css html assets web development polymer vue react angular esmodules

front_end

lummox

img alt logo align right src https raw github com smaxwellstewart lummox master images logo png lummox version 0 1 br dev board https trello com b kdhj89eq https trello com b kdhj89eq a user service designed for soa systems responsiblities create read update and delete of users issuing jwt refresh and access tokens build status https travis ci org smaxwellstewart lummox svg branch master https travis ci org smaxwellstewart lummox coverage status https coveralls io repos smaxwellstewart lummox badge svg branch master service github https coveralls io github smaxwellstewart lummox branch master goals to create a user service for distributed soa systems create a rock solid scalable and highly configurable api provide a simple authentication and authorization mechanism when extending the system to other services convenient ui for managing users quick to deploy config options the following options can be configured in server config config js port port application runs from server a hapi https github com hapijs hapi server configuration https github com hapijs hapi blob master api md new serveroptions object see docs for full details api api configuration object prefix prefix to path of api routes version version added to path of api routes mongouri a mongodb connection uri jwt options for configuring generating jwt tokens key this is a secret key all tokens are signed and verified with expiresin this is the amount of minutes access tokens last for verifyoptions a jsonwebtoken https github com auth0 node jsonwebtoken verify options object see docs for full details auth auth configuration object scopes list of allowed scopes getall a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the get all users api route getone a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the get a single user api route update a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the update user api route delete a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the delete user api route getscopes a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the get user scopes api route saltrounds the amount of rounds passwords will be salted for during bcrypt hashing swaggeroptions a hapi swagger plugin options https github com glennjones hapi swagger options object see docs for full details api users get prefix version users get all user s in system get prefix version users id get a single user in the system post prefix version users create a user put prefix version users id update a user in the system delete prefix version users id delete a user in the system scopes get prefix version users scopes get all allowed user scopes tokens post prefix version tokens refresh create a new refresh token post prefix version tokens access create a new access token authentication workflow 1 create a user when a new user is created the password field is bcrypted the scope field is an array of access scopes that will be added to access tokens when the user generates one the list of allowed scopes can be configured in server config config js request post api v1 users json username l337 email email example com password 123456 scope admin active true response json id 562e689ca8b13f3b2f6fbeef username l337 email email example com password 123456 scope admin active true 2 generate a refresh token the user s credentials are supplied to generate a jwt refresh token this token is not used directly for accessing protected routes instead it is used to generate new access tokens the refresh token is meant as a long lived token and as such has a unique id attached to it this id is also attached to the user allowing for revocation of refresh tokens at any point example call request post api v1 tokens refresh json username l337 password 123456 response json token your refresh token 3 get an access token this is where you get an access token using your refresh token the user s scope is attached to the token at this stage more secure request post api v1 tokens access header authorization your refresh token less secure request post api v1 tokens access token your refresh token response json token your access token you can now use this token to access protected routes with moderately complex access control use cases i e any user admins only admins and managers only specific users etc how to deploy the service has been designed to be very straightforward to deploy to a platform like heroku or dokku simply grab the code configure the service to your liking and push here s an example of how to do it for heroku bash git clone https github com smaxwellstewart lummox cd lummox vim server config config js git commit am configured lummox to my liking heroku create heroku config set mongo uri mongodb localhost 27017 lummox heroku config set jwt secret changeme git push heroku master hardening once deployed rate limiting the token routes is a good idea to protect against denial of service attacks and limit the amount of access tokens being generated how to use with other services other services can be authenticated without need for a db lookup the json web tokens are used to transmit claims about a user there is definitely a catch to this model which is that session info embedded in your json web token is limited in size you can store a useful amount of data but they shouldn t be too huge a way to get around this for services which need more session is to have another service like a session profile service to return extra data about a user this can increase latency by adding another round trip as always understand your use case and what this solution offers before implementing anything stack ham h is for hapi hapi is an awesome node js framework for creating robust apis a is for angular angular is a great way to quickly build powerful frontend applications m is for mongodb mongo is a great nosql database that handles json natively perfect fit for node js projects contributors simon maxwell stewart development jenna enman artwork carlos alvarez theme

os

node_express1.1

node express1 1 working on node with expressjs for a backend app development

server

FullStack_Info_Lab02

fullstack info lab02 let s recreate netflix fork and clone this gitub repository add the other movies with their appropriate titles include links so that all the navigation works create an equal sized grid so that all four movies fall within one line hint use other news item fit each movie within each grid hint use other news item fix the header so that it doesn t move from the top of the window inspect the dom to style according to netflix colors use inline block instead of float for the id site title

front_end

GPT-Model

gpt model this is a large language model like gpt

ai

faang-system-design

tips for system design https github com neerazz faang system design blob master tips for system design md general design questions tiny url https github com neerazz faang system design tree master neeraj systemsdesign tiny url cab booking service https github com neerazz faang system design tree master neeraj systemsdesign cab booking system amazon https github com neerazz faang system design blob master resources architecture diagrams amazon 20system 20design png zoom https github com neerazz faang system design blob master resources architecture diagrams zoom 20system 20design png google map https github com neerazz faang system design blob master resources architecture diagrams google 20maps 20design png facebook https github com neerazz faang system design blob master resources architecture diagrams facebook 20system 20design png hotel booking https github com neerazz faang system design blob master resources architecture diagrams hoel 20booking 20system png uber https github com neerazz faang system design blob master resources architecture diagrams uber 20system 20design png whatsapp https github com neerazz faang system design blob master resources architecture diagrams whatsapp 20system 20design png netflix https github com neerazz faang system design blob master resources architecture diagrams video 20streaming 20platform png oops design design library management system https github com neerazz faang system design tree master neeraj oops designs librarymanagment design parking lot https github com neerazz faang system design blob master neeraj oops designs parkinglot design call center https github com neerazz faang system design blob master neeraj oops designs callcenter callcenter java design hit counter https github com neerazz faang system design blob master neeraj oops designs designhitcounter java design linux find command https github com neerazz faang system design blob master neeraj oops designs linuxfindfilter java design atm https github com neerazz faang system design tree master neeraj oops designs atm design movie ticket system https github com neerazz faang system design tree master neeraj oops designs movieticketsystem design pharmacy shop https github com neerazz faang system design tree master neeraj oops designs pharmacyshop design online stock broker https github com neerazz faang system design tree master neeraj oops designs onlinestockexchange todo design vending machine https leetcode com discuss interview question 982302 fanng question oop please post your solutions to this useful links code karle https www codekarle com index html

system-design faang

os

NLPInvoiceClassification

nlp invoice classification flask application an application to automatically classify invoices using natural language processing and machine learning this flask application was developed as a proof of concept to demonstrate the capability of classification algorithms to automate the classification of a large number of unclassified invoices all stages of the development are included in this repository including code used in the initial data investigation and model development including explanations of thought processes and approach see model dev directory code used to create the pipelines and pickle files see pkl creation directory code used in the flask app including the html and css all code not in model dev or pkl creation directories note this repository does not contain the pkl files required to run the flask application due to file size limitations however these can be generated from any similar dataset using the relevant code in the pkl creation directory running the application in order to run the application two pkl files will need to be created using the code in pkl creation directory and some relevant training data data as with all supervised machine learning classification models the data used to train the model must be representative of the data required to be classified also the training data must have the classifications for each row of data whereas the test data should not have the classifications the pkl files should be created using the training data which must contain the fields business unit supplier name supplier group invoice desc invoice amt invoice currency usd amt project owning org datasource legacy year leakage identifier leakage group intercompany flag americas flag classification group nb the data to be classified will not include the field classification group as this is what the model will predict once the pkl files are created the application can be run save the two pkl files in the same directory as the code for the flask app flask app v01 py not in any sub directory then navigate to that directory in the command prompt and run python flask app v01 py copy and paste the generated url into google chrome any other browser will work but the app may not be displayed as expected using the application there are two demo options for running the model using the application manual input of data into a web form and upload a csv file with the relevant columns the tab to link to a database is a placeholder only click on the required link in the header bar enter ther required information or browse for the file to be uploaded the click the button to classify the data code for initial model development in order to provide a more useful overview of the entire process from initial data to final demo i have included the code i used for investigating the data and testing different pre processing steps and models including commentary on my thought processes and approach hopefully some might find this useful

ai

E7020E

e7020e repository for the course embedded system design

os

Camera-Mounted-Bluetooth-Model-Car

camera mounted bluetooth model car embedded systems six person project known as wacky racers to design and implement a camera mounted bluetooth controlled model car the project had a requirement that three separate 4 layer boards were developed each with their own microcontroller and communicating over a common i2c bus an stm32f4 microcontroller was selected for the project motor control power board img src https github com mkokshoorn camera mounted bluetooth model car blob master populated board images motorboard jpg width 360 img src https github com mkokshoorn camera mounted bluetooth model car blob master motorschematic png width 460 camera board img src https github com mkokshoorn camera mounted bluetooth model car blob master populated board images cameraboard jpg width 360 img src https github com mkokshoorn camera mounted bluetooth model car blob master cameraschematic png width 460 communications board img src https github com mkokshoorn camera mounted bluetooth model car blob master populated board images commsboard jpg width 360 img src https github com mkokshoorn camera mounted bluetooth model car blob master commsschematic png width 460 img src https github com mkokshoorn camera mounted bluetooth model car blob master commsschematic2 png width 420 img src https github com mkokshoorn camera mounted bluetooth model car blob master commsschematic3 png width 420

os

namada-testnets

namada testnets testnet configurations and coordination for the namada namada public testnet 1 namada public testnet 1 namada public testnet 2 namada public testnet 2 namada public testnet 1 namada public testnet 3 namada public testnet 2 namada public testnet 4 namada public testnet 1 namada public testnet 5 namada public testnet 2 namada public testnet 6 namada public testnet 1 namada public testnet 7 namada public testnet 2 namada public testnet 8 namada public testnet 1 namada public testnet 9 namada close quarters testnet 5 namada close quarters testnet 5 namada close quarters testnet 4 namada close quarters testnet 4 namada close quarters testnet 3 namada close quarters testnet 3 namada close quarters testnet 2 namada close quarters testnet 2 namada close quarters testnet 1 namada close quarters testnet 1

anoma blockchain namada

blockchain

esp-wolfssl

esp wolfssl cryptographic embedded library for esp open rtos this is a wolfssl http wolfssl com library packaged for esp open rtos https github com superhouse esp open rtos

os

blockchain

blockchain hyperledger fabric v1 0 5 https github com liuboyu hyperledger fabric docs zh cn email boyu liu gmail com https yeasy gitbooks io blockchain guide content http book 8btc com books 1 master bitcoin book http book 8btc com books 1 gaosheng blockchain report book http book 8btc com books 6 thfr201704 book https daimajia com 2017 08 24 how to start blockchain learning http www 8btc com who controls blockchain http www 8btc com bytom duanxinxing r3 corda http ethfans org posts r3 corda announcement introducing r3 corda a distributed ledger designed for financial services http www r3cev com blog 2016 4 4 introducing r3 corda a distributed ledger designed for financial services http www 8btc com ethfans http ethfans org http www jianshu com p 00e17ee7c646 merkle tree bitcoin merkle tree http www cnblogs com fengzhiwu p 5524324 html merkle http www 8btc com merkling in ethereum merkling in ethereum https blog ethereum org 2015 11 15 merkling in ethereum vitalik buterin merkle patricia tree ethereum understanding the ethereum trie https easythereentropy wordpress com 2014 06 04 understanding the ethereum trie ethereum ethereum patricia tree https github com ethereum wiki wiki patricia tree ethereum wiki mpt merkle patricia tree http blog csdn net qq 33935254 article details 55505472 merkle patricia tree mpt merkle http blog csdn net zslomo article details 53434883 t 1498537389197 merkle patricia tree mpt http www cnblogs com fengzhiwu p 5584809 html ethereum rlp https github com ethereum wiki wiki 5b e4 b8 ad e6 96 87 5d rlp ethereum wiki rlp https my oschina net u 2349981 blog 894117 pow pos dpos pbft http blog csdn net lsttoy article details 61624287 https yq aliyun com articles 60400 http 8btc com article 2238 1 html http blog csdn net jeffrey zhou article details 56672948 https www zhihu com question 53385152 paxosstore paxos http www infoq com cn articles wechat paxosstore paxos algorithm protocol raft http www infoq com cn articles raft paper pos https yq aliyun com articles 60400 pos pow http 8btc com article 1882 1 html dpos http www 8btc com dpos back to satoshi dpos http www 8btc com dpossha dpos https steemit com dpos legendx dpos dpos vs pow https zhuanlan zhihu com p 28127511 pos dpos pow https www zhihu com question 49995385 zero knowledge proof http songshuhui net archives 36968 blockchain v1 0 bitcoin http www 8btc com wiki bitcoin a peer to peer electronic cash system utxo http 8btc com article 4381 1 html http www 8btc com blockchain tech algorithm http www 8btc com blockchain tech mining 1 http www 8btc com blockchain tech consensus mechanism bitcoin core btc https bitcoin org bitcoin segwit github https github com bitcoin bitcoin http www 8btc com tan90d34 segwit http www 8btc com segwit 0829 segwit bitcoin cash bcc https bitcoincash org bitcoin 8m github https github com bitcoin abc bitcoin abc bitcoin cash bcc https www zhihu com question 63109943 zcash zec https z cash bitcoin github https github com zcash zcash zcash http 8btc com thread 41384 1 1 html zcash http www sohu com a 121847942 475384 zcoin dash monero blockchain v1 0 http blog csdn net elwingao article details 53410750 http www 8btc com bytom sidechain rootstock http www 8btc com sidechains drivechains and rsk 2 way peg design sidechains https blockstream com technology bitcoin pegged sidechains lightning network http www 8btc com enabling blockchain innovations with pegged sidechains abstract introduction off chain http view xiaomiquan com view 59a3e22d2540ed222c6075b8 corda http www 8btc com ln rn corda rootstock rsk http www rsk co bitcoin rootstock http www 8btc com tan90d88 rootstock http www 8btc com tan90d84 btc relay rootstock http www 8btc com btc relay and rootstock btc relay http btcrelay org ethereum bitcoin polkadot cosmos blockchain v1 x ipfs https ipfs io https gguoss github io 2017 05 28 ipfs https github com ipfs papers raw master ipfs cap2pfs ipfs p2p file system pdf filecoin http filecoin io http chainx org paper index index id 13 html https filecoin io filecoin pdf bigchaindb https www bigchaindb com http blog csdn net fengqing79 article details 70154076 https www bigchaindb com whitepaper blockchain v1 x http www 8btc com dream on http www 8btc com edb https zhuanlan zhihu com p 27415529 ripple nxt bitshares https bitshares org dpos dac github https github com bitshares http www 8btc com bitshares white pape cryptonomex https cryptonomex com bitshares v2 0 github http github com cryptonomex cryptonomex http www 8btc com cryptonomex dan larimer blockchain v1 x dapps steem https steem io steemit https steemit com github http github com steemit https biweilai com tag steem https steem io steemwhitepaper pdf yoyow https yoyow org steem https yoyow org files white paper2 pdf ugc http www jianshu com p e044e51e06bb blockchain v2 0 ethereum ethereum eth https ethereum org v2 0 github https github com ethereum go ethereum eth ethereum etc ethereum classic https github com ethereum wiki blob master 5b e4 b8 ad e6 96 87 5d e4 bb a5 e5 a4 aa e5 9d 8a e7 99 bd e7 9a ae e4 b9 a6 md ethereum white paper https github com ethereum wiki wiki white paper ethereum wiki http book 8btc com books 6 ethereum book teahour http ethfans org shaoping articles talk with jan about ehtereum http ethfans org wikis e6 99 ba e8 83 bd e5 90 88 e7 ba a6 http ethfans org wikis e4 bb a5 e5 a4 aa e5 9d 8a e5 bc 80 e5 8f 91 e8 ae a1 e5 88 92 10 http www 8btc com scaling ethereum to billions of users http me tryblockchain org getting up to speed on ethereum html solidity http wiki jikexueyuan com project solidity zh http ethfans org posts block chain technology smart contracts and ethereum dapps 15 http www 8btc com ethereum top 10 app the dao ethereum decentralized autonomous organization or the dao http www 8btc com what is the dao the dao http www jinse com news blockchain 50751 html dao http www 8btc com brexit and the dao swarm whisper btc relay http btcrelay org oraclize blockchain v2 0 others eos https eos io github https github com eosio eos http www jianshu com p f65bf7691482 eos io http www jianshu com p bc489db794ce eos vs http www jianshu com p 1afec85afd3c neo qtum blockchain dag dag http www qukuaiwang com cn news 1913 html iota https steemit com cn niking iota iota https iota org dag http www iotachina com wp content uploads 2016 11 2016112902003453 pdf https iota org iota whitepaper pdf byteball https byteball org dag http www bitett com portal php mod view aid 438 http www bitett com portal php mod view aid 453 https bitcointalk org index php topic 1840404 https byteball org byteball pdf ep chain https ep chain org dag ico https ep chain org download epc v1 00 pdf iota byteball nerthus http www nerthus io dag ico http www nerthus io static downfile nerthuswhitepage pdf iota byteball askcoin https askcoin org dag ico https askcoin org askcoin white paper cn pdf iota byteball 1 2 3 4 http ethfans org posts blockchain infrastructure landscape a first principles http www 8btc com elwingao blockchain 6 cto http www 8btc com blockchain architecture consensus byzantine generals problem byzantine fault tolerant bft double spend attack spv pow proof of work mine pos proof of stake mint nxt forge dpos delegated proof of stake pbft hyperledger fabric dbft paxos raft dmms dynamic membership multiparty signatures sha256 bitcoin scrypt litecoin hefty1 ethash ethereum equihash zcash merkle tree merkle root merkle patricia tree getwork getblocktemplate stratum auxiliary proof of work auxpow auxiliary blockchain bitcoin version verack addr getaddr getblocks inv getdata getheaders headers filterload filteradd filterclear

blockchain

blockchain

my-code

my code mobile development

front_end

ez-rtos

ez rtos a simple real time operating system features task switching by arm cortex m3 systick delay delay unsigned int memory allocator malloc size t free void critical section

os operating-system operating-systems computer-science arm armcortexm3 cortex-m cortex-m3 mcu real-time-operating-system

os

xinu-avr

xinu avr the xinu avr project is a xinu operating system subset modified to run on an avr atmega328p microcontroller e g arduino boards visit its web page for detailed instructions http se fi uncoma edu ar xinu avr a href https www youtube com watch v jacuup bkiu title demo video xinu os into avr atmega328p img style float right alt demo video xinu os into atmega328p mcu src http se fi uncoma edu ar xinu avr www files placa3 jpg a at present the core pieces of xinu are working so you can already integrate it in the development of multi tasking embedded systems you will also need any bare avr mcu or arduino board of course for lovers of because small is beautiful fusixos retrobsd unix in microcontrollers etc this project provides a user interface example as well the xinu shell and some tiny versions of utilities like echo a text editor a basic interpreter ps kill free date cal and some more check the demo video https www youtube com watch v jacuup bkiu if you want to see a xinu avr session in a little avr mcu like a retro computer system the source code is comprise of 1 the xinu os for avr atmega328p microkernel 2 basic examples apps of how to use xinu 3 a complete example the xinu shell and tiny versions of several unix like utilities a name whatisxinu a what is xinu xinu is a small elegant and easy to understand operating system originally developed by douglas comer for instructional purposes at purdue university in the 1980s since then it has been ported to many architectures and hardware platforms xinu uses powerful primitives to provides all the componentes and the same functionality many conventional operating sytems supply because the whole source code size is small xinu is suitable for embedded systems strong the xinu operating system includes strong dynamic process creation dynamic memory allocation real time clock management process coordination and synchronization local and remote file systems a shell and device independent i o functions the xinu operating system is documented in the book d comer operating system design the xinu approach second edition crc press 2015 isbn 9781498712439 https xinu cs purdue edu textbook many sites defines xinu as a free unix system or similar statements it is not xinu differs completely from the internal structure of unix or linux for academic purposes xinu is smaller elegant and easier to understand applications written for one system will not run on the other without modification xinu is not unix history xinu originally ran on digital equipment corporation lsi 11 s with only 64k bytes of memory at the end of 1979 and the inning of 1980 over the years xinu have been expanded and ported to a wide variety of architectures and platforms including ibm pc macintosh digital equipment corporation vax and decstation 3100 sun microsystems sun 2 sun 3 and sparcstations and for several arm mips and x86 embedded boards it has been used as the basis for many research projects furthermore xinu has been used as an embedded system in products by companies such as motorola mitsubishi hewlett packard and lexmark there is a full tcp ip stack and even the original version of xinu for the pdp 11 supported arbitrary processes and network i o there are current versions of xinu for galileo intel boards arm beagle boards several mips platforms and for x86 pc hardware and virtual machines a name code a source code there is just one git repository and it has everything git http github com zrafa xinu avr the list below is just for convenience a href https github com zrafa xinu avr the xinu os for avr atmega328p a a href https github com zrafa xinu avr tree master apps example apps a a href https github com zrafa xinu avr tree master apps shell the xinu shell and tiny unix like utilities editor basic interpreter ps kill echo uptime sleep etc a a href https xinu cs purdue edu the official xinu page and code a a name authors a authors xinu os copyright c 2012 2015 douglas e comer and crc press inc this version for avr atmega328p v0 1 c 2020 rafael ignacio zurita rafa fi uncoma edu ar acknowledgments michael m minor he is the author of another avr port os xinu a href https sites google com site avrxinu avrxinu a we use his context switch code the addargs in xinu shell and a few lines more his port is for bigger avr microcontrollers 16kb of ram and he used an old version of xinu xinu from the 1987 book edition a name notes a notes about the xinu os port for avr atmega328p current official xinu versions are designed for arm mips and x86 architectures the hardware differences between those and the ultra small avr microcontroller required changes to some low level data structures of xinu mainly using the flash memory in the avr mcu for keeping several read only data structures previously in ram also several limits were imposed so the read write data structures fits into the sram avr memory the xinu version for avr atmega328p has the core functionality of xinu and provides some extensions including an eeprom file system and several unix like utilities for the xinu shell this mcu has just 2kb of sram 32kb of flash memory and 1kb of eeprom the xinu version for avr uses 12kb of flash and 0 9kb of ram so there is still 50 of room sram and flash for the embedded application running on xinu concurrent processes so this project might be stimulating and very fun for lovers of embedded systems development and operating system internals notes about the port 1 max number of processes 4 to 8 2 main process is now the embedded application process 3 max number of semaphores 2 to 6 the size of the table of process queues depends on this 4 max number of devices 4 to 5 4 the clkhandler wakeup a process preemption for cpu every 100ms 5 several limits for buffers 32bytes for tty input 16bytes for names of devices 1byte for the queues keys and the list continues 6 sleepms is now delay sleep100ms and the key for queue char in ms 100 7 many vars in data structures have a smaller size e g before int32 now char 8 sleep sleeps max 20 seconds date type 9 most of the libc are from avr libc 10 init load bss and data from flash to ram from avr libc 11 shell manages max 6 tokens 12 date and time is managed by a little lib no ntp or rtc 13 most of the const char in source code were moved to flash program space via flash directive from gcc or progmem from avr libc 14 tty in is asynchronous with interrupts ok but tty out is polled based synchronous 15 open read write seek close use struct dentry it is on flash on this port 16 remote file systems local file systems ram file systems are disabled so far 17 ports ptinit ptsend ptrecv etc are disabled so far 18 null process has priority 1 a name douglas a douglas comer douglas comer is a professor of computer science at purdue university who was inducted into the internet hall of fame on september 2019 as one of the earliest tcp ip and internetworking researchers comer wrote the first series of textbooks explaining the scientific principles underlying the design of the internet and its communications protocols providing some of the earliest formal guidance for building efficient networks and applications that use the internet comer s three volume textbook series internetworking with tcp ip written in 1987 is widely considered to be the authoritative reference for internet protocols the series played a key role in popularizing internet protocols by making them more understandable to a new generation of engineers and it professionals prof douglas comer designed and developed the xinu operating system in 1979 1980 douglas comer page https www cs purdue edu homes comer internet hall of fame https www cs purdue edu news articles 2019 comer ihof html

xinu douglas comer operating-system douglas-comer xinu-os embedded-systems rtos arduino arduino-uno

os

MQNLP

mqnlp nlp git 1 1 lr tfidf http blog csdn net macanv article details 78963762 2 naive bayes n gram http blog csdn net macanv article details 78964020 3 svm svm svm pca lda 4 fasttext facebook fasttext api tensorflow fasttext 5 textcnn 6 textrnn rnn rnn cell lstm gru bi lstm bi gru num layer rnn 7 textcnnrnn cnn maxpooling rnn 8 2 bilstm crf hmm blog http www matrix67 com blog archives 5044 https github com moonshile chinesewordsegmentation python3 2 1 3 https github com zjy ucas chinesener 4 ner bilstm crf 4 5 tf idf textrank 6 seq2seq 7 gan 1 gan gan py 2 conditional gan cgan py 3 7 face

fasttext textclassification textcnn textrnn lstm sequence-labeling ner pos-tagging segment

ai

garfish

div align center img src https lf3 static bytednsdoc com obj eden cn dhozeh7vhpebvog open garfish icons garfish icon square png width 300 alt garfish div div align center npm version https img shields io npm v garfish svg style flat square https www npmjs com package garfish build status https github com modern js dev garfish actions workflows ci yml badge svg branch main https github com modern js dev garfish actions workflows ci yml div div align center english readme ch md div h1 h1 garfish is a micro front end framework mainly used to solve the problems of cross team collaboration diversification of technology system and increasing complexity of applications brought by modern web applications in the context of front end ecological boom and increasing complexity of web applications and garfish has been polished and tested by a large number of online applications with strong functional stability and reliability garfish goals to compose multiple independently delivered front end applications into a whole and to decompose front end applications into some smaller and simpler applications that can be independently developed independently tested and independently deployed while still appearing to users as cohesive individual products installation bash npm npm install garfish yarn yarn add garfish documentation the doc site garfish https garfishjs org is only available in chinese for now we are planning to add english versions soon about garfish https garfishjs org guide quick start https www garfishjs org guide start api references https garfishjs org api functionality rich and efficient product features garfish micro front end sub application supports any kind of framework and technology system access garfish micro front end sub application supports independent development independent testing and independent deployment powerful pre loading capability automatically record user application loading habits to increase loading weight and greatly reduce application switching time support for dependency sharing which greatly reduces the overall package size and the repeated loading of dependencies support data collection effectively perceive the state of the application during operation support for multiple instance capability to run multiple sub applications in the page at the same time enhances the business splitting efforts provides efficient and usable debugging tools to assist users in the micro front end model brings different development experience problems from the traditional r d model highly scalable core modules supports html entry and js entry through the loader core module making it easy to access micro front end applications router module provides route driven master child route isolation users only need to configure the routing table application can complete the independent rendering and destruction the user does not need to care about the internal logic sandbox module provides runtime isolation for the application s runtime which can effectively isolate the side effects of js and style on the application store provides a simple mechanism for exchanging communication data highly scalable plugin mechanism provide business plugins to meet various customization needs of business parties contributing contributing guide https github com modern js dev garfish blob main contributing md credit qiankun https github com umijs qiankun is a very good micro front end framework garfish sandbox design learned a lot of good ideas single spa https github com single spa single spa for community raised a hot wave of micro front end solutions learned a lot of amazing garfish routing system design and for the garfish bridge design we have refered the implementation of the bridge part in single spa https github com single spa single spa and we think it is a good design so we fork the code of the bridge implementation part and make some adjustments for the lifecycles in garfish garfish s css scope plugin is inspired by the implementation of reworkcss https github com reworkcss css and some some of the implementation code is forked from reworkcss https github com reworkcss css for different scenarios the design of garfish s es module plugin was inspired by the babel traverse https github com babel babel and es module shims https github com guybedford es module shims libraries and some of the implementation code is forked from babel traverse https github com babel babel

framework javascript

front_end

training_extensions

div align center openvino training extensions key features key features installation https openvinotoolkit github io training extensions latest guide get started installation html documentation https openvinotoolkit github io training extensions latest index html license license pypi https img shields io pypi v otx https pypi org project otx markdownlint disable md042 python https img shields io badge python 3 8 2b green pytorch https img shields io badge pytorch 1 13 1 2b orange openvino https img shields io badge openvino 2023 0 purple markdownlint enable md042 codecov https codecov io gh openvinotoolkit training extensions branch develop graph badge svg token 9hvfnmpfgd https codecov io gh openvinotoolkit training extensions pre merge test https github com openvinotoolkit training extensions actions workflows pre merge yml badge svg https github com openvinotoolkit training extensions actions workflows pre merge yml nightly test https github com openvinotoolkit training extensions actions workflows daily yml badge svg https github com openvinotoolkit training extensions actions workflows daily yml build docs https github com openvinotoolkit training extensions actions workflows docs yml badge svg https github com openvinotoolkit training extensions actions workflows docs yml license https img shields io badge license apache 202 0 blue svg https opensource org licenses apache 2 0 downloads https static pepy tech personalized badge otx period total units international system left color grey right color green left text pypi 20downloads https pepy tech project otx div introduction openvino training extensions is a low code transfer learning framework for computer vision the cli commands of the framework allows users to train infer optimize and deploy models easily and quickly even with low expertise in the deep learning field openvino training extensions offers diverse combinations of model architectures learning methods and task types based on pytorch https pytorch org and openvino toolkit https software intel com en us openvino toolkit openvino training extensions provides a model template for every supported task type which consolidates necessary information to build a model model templates are validated on various datasets and serve one stop shop for obtaining the best models in general if you are an experienced user you can configure your own model based on torchvision https pytorch org vision stable index html mmcv https github com open mmlab mmcv timm https github com huggingface pytorch image models and openvino model zoo omz https github com openvinotoolkit open model zoo furthermore openvino training extensions provides automatic configuration for ease of use the framework will analyze your dataset and identify the most suitable model and figure out the best input size setting and other hyper parameters the development team is continuously extending this auto configuration https openvinotoolkit github io training extensions latest guide explanation additional features auto configuration html functionalities to make training as simple as possible so that single cli command can obtain accurate efficient and robust models ready to be integrated into your project key features openvino training extensions supports the following computer vision tasks classification including multi class multi label and hierarchical image classification tasks object detection including rotated bounding box support semantic segmentation instance segmentation including tiling algorithm support action recognition including action classification and detection anomaly recognition tasks including anomaly classification detection and segmentation openvino training extensions supports the following learning methods https openvinotoolkit github io training extensions latest guide explanation algorithms index html supervised incremental training which includes class incremental scenario and contrastive learning for classification and semantic segmentation tasks semi supervised learning self supervised learning openvino training extensions provides the following usability features auto configuration https openvinotoolkit github io training extensions latest guide explanation additional features auto configuration html openvino training extensions analyzes provided dataset and selects the proper task and model with appropriate input size to provide the best accuracy speed trade off it will also make a random auto split of your dataset if there is no validation set provided datumaro https openvinotoolkit github io datumaro stable index html data frontend openvino training extensions supports the most common academic field dataset formats for each task we are constantly working to extend supported formats to give more freedom of datasets format choice distributed training to accelerate the training process when you have multiple gpus mixed precision training to save gpus memory and use larger batch sizes integrated efficient hyper parameter optimization module hpo https openvinotoolkit github io training extensions latest guide explanation additional features hpo html through dataset proxy and built in hyper parameter optimizer you can get much faster hyper parameter optimization compared to other off the shelf tools the hyperparameter optimization is dynamically scheduled based on your resource budget getting started installation please refer to the installation guide https openvinotoolkit github io training extensions latest guide get started installation html note python 3 8 3 9 and 3 10 were tested along with ubuntu 18 04 20 04 and 22 04 openvino training extensions cli commands otx find helps you quickly find the best pre configured models templates as well as a list of supported backbones otx build creates the workspace folder with all necessary components to start training it can help you configure your own model with any supported backbone and even prepare a custom split for your dataset otx train actually starts training on your dataset otx eval runs evaluation of your trained model in pytorch or openvino ir format otx optimize runs an optimization algorithm to quantize and prune your deep learning model with help of nncf https github com openvinotoolkit nncf and pot https docs openvino ai latest pot introduction html tools otx export starts exporting your model to the openvino ir format otx deploy outputs the exported model together with the self contained python package a demo application to port and infer it outside of this repository otx demo allows one to apply a trained model on the custom data or the online footage from a web camera and see how it will work in a real life scenario otx explain runs explain algorithm on the provided data and outputs images with the saliency maps to show how your model makes predictions you can find more details with examples in the cli command intro https openvinotoolkit github io training extensions latest guide get started cli commands html updates v1 4 0 3q23 support encrypted dataset training https github com openvinotoolkit training extensions pull 2209 add custom max iou assigner to prevent cpu oom when large annotations are used https github com openvinotoolkit training extensions pull 2228 auto train type detection for semi sl self sl and incremental train type now is optional https github com openvinotoolkit training extensions pull 2195 add per class xai saliency maps for mask r cnn model https github com openvinotoolkit training extensions pull 2227 add new object detector deformable detr https github com openvinotoolkit training extensions pull 2249 add new object detector dino https github com openvinotoolkit training extensions pull 2266 add new visual prompting task https github com openvinotoolkit training extensions pull 2203 https github com openvinotoolkit training extensions pull 2274 https github com openvinotoolkit training extensions pull 2311 https github com openvinotoolkit training extensions pull 2354 https github com openvinotoolkit training extensions pull 2318 add new object detector resnext101 atss https github com openvinotoolkit training extensions pull 2309 release history please refer to the changelog md changelog md branches develop https github com openvinotoolkit training extensions tree develop mainly maintained branch for developing new features for the future release misc https github com openvinotoolkit training extensions tree misc previously developed models can be found on this branch license openvino toolkit is licensed under apache license version 2 0 license by contributing to the project you agree to the license and copyright terms therein and release your contribution under these terms issues discussions please use issues https github com openvinotoolkit training extensions issues new choose tab for your bug reporting feature requesting or any questions known limitations misc https github com openvinotoolkit training extensions tree misc branch contains training evaluation and export scripts for models based on tensorflow and pytorch these scripts are not ready for production they are exploratory and have not been validated disclaimer intel is committed to respecting human rights and avoiding complicity in human rights abuses see intel s global human rights principles https www intel com content www us en policy policy human rights html intel s products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right

openvino computer-vision deep-learning pytorch neural-networks-compression quantization hyper-parameter-optimization image-classification image-segmentation object-detection self-supervised-learning semi-supervised-learning transfer-learning action-recognition anomaly-detection datumaro automl machine-learning incremental-learning

ai

mock-ApiRest-Flask

english version here mock api rest with flask vers o em portugu s aqui mock api rest com flask mock api rest with flask the main purpose of this project is to create a mock api rest with flask to be used in the development of the frontend of the mobile project a react native repository is available here https github com eduardo da silva reactnative layout navigation installation the project is managed with pdm so you need to install the dependencies with bash pdm install run to run the project you need to execute the following command bash pdm run python main py mock api rest com flask este projeto tem como principal objetivo criar um mock de api rest com flask para ser utilizado no desenvolvimento do frontend do projeto mobile um reposit rio react native est dispon vel aqui https github com eduardo da silva reactnative layout navigation instala o o projeto gerenciado com pdm ent o voc precisa instalar as depend ncias com bash pdm install execu o para executar o projeto voc precisa executar o seguinte comando bash pdm run python main py

api-rest backend flask mock pdm python

front_end

node-rocket

important notice rocket is no longer maintained please consider rocket to be no longer under active development it is now only maintained and modified to support wavo me https wavo me if you re still looking for a good development framework for node js please consider meteor https meteor com or the more mature express engine http expressjs com rocket was a amazing experiment and although i d do things differently if i was to program it again i am very proud of what has been achieved finally i d like to thank all of those who supported the project and that helped me work on it gabriel lesp rance rocket node rocket the rapid development framework for node js web applications img src https github com glesperance node rocket raw master lib logo png width 200 alt node rocket rocks rocketjs net a k a node rocket is a project created by gabriel lesp rance during the startupfier summer kick off hackathon in order to allow rapid development of real time web applications using node js highly inspired by ruby on rails and cakephp rocket puts forward the convention over configuration principle in order to simplify and speedup the software development process and allow easier scalability by leveraging node js asynchronous behavior as well as its javascript nature central to rocketjs is the principle of having a single point of contact between the programmer and the underlying application and resources hence rocketjs aims to blur and reduce as much as possible the separation between the client browser and the server node js while maximizing the scaling potential for instance by allowing code sharing between the server and the client and improving the performance of the resulting web application built on top of express node js high performance web development framework rocketjs provides a robust structure on which to build your web applications without sacrificing any of your freedom rocketjs features automatic routing of your controllers automatic mapping of your views to their corresponding controller easy web socket comet application support through dnode easy server client code sharing allowing the client to use requirejs require command to import use server js libraries high focus on restful controller conventions client side support for jade template automagic optimization of client javascript modules and css files easy i18n localization automatic reloading of modules when a modification is detected allowing easy and fast development view rendering and partials support connect middleware support built on top of express installation npm install rocket initializing your project rocket i myrocketproject cd myrocketproject npm update launching your project while in your project s directory simply do node launcher js directory structure of a rocket project client contains all the files used by the client including css javascript libraries and static files controllers contains your application s controllers exports contains your application s modules that will be exported to the client libs contains all your other application s libraries locales contains all your localization files models contains all your applications s models views contains your applications s main layout template files and associated partials launcher js client structure of the client directory client css contains all the css files that are exported to the client js contains all the javascript files that are exported through requirejs to the client all these files can then be accessed via require static contains all your static files allow the browser to require your javascript modules with client js structure of the client js directory client js libs contains all your client side libraries used by your client modules vendors contains all 3rd party libraries used by your application templates contains all your client jade partial files require config json this file contains all your custom requirejs modules paths configuration this is extremely handy to make sure you always use the latest cdn version of a public module each files folders located under the client libs directory of your project are made available to the client s browser by rocket via the require command the modules are referenced by their relative path from the client js folder e g to require a module located at clients js a js from the browser require a function a the modules can also be located further down the client js directory tree hence you can require the file located at client js nested dirs b js by doing nb do not put the js after the filename require client libs nested dirs b function b usual requirejs conventions apply to the modules using jade templates in the browser in view of reducing the friction between the programmer and its environment to a minimum rocket allows the use of jade templates on the client side to do so simply put all you jade partials files in the client js templates directory and let rocket compile and bundle those for you to use in the browser to use your template simply require it and use it e g to use the template located at client js templates dialog jade you do require jade runtime templates dialog jade function dialog var html dialog title hello world message this works now use your compiled template requiring javascript files from a cdn via require config json in order to speedup page loads and to further comply to dryness principles rocket allows you to use cdn modules as if they were local making sure you can still benefit from all the requirejs optimizations to do so you simply need to list those modules in your require config json file located at the root of your client js directory e g to use the google s cdn jquery jquery ui and cdnjs s version of underscore you d need the following definitions in your require config json file paths jquery ajax googleapis com ajax libs jquery 1 6 2 jquery min jqueryui ajax googleapis com ajax libs jqueryui 1 8 16 jquery ui min underscore cdnjs cloudflare com ajax libs underscore js 1 1 7 underscore min note that we omitted js extension for the modules production mode optimizations to further optimize browser load times you can start rocket in production mode by defining the following environment variable at start node env production when in production mode rocket 1 bundles all your javascript client files at the root of your client js directory with their dependencies and then uglifies them thus when those are used on the browser only one request is needed to fetch them rocket also 2 bundles your css files by resolving their import statement the resulting css files are also minified note that since your client jade template are compiled before being sent to the browser they are optimized too all those optimizations are provided by requirejs r js utility serving static files via client static all files located under the client static directory are statically served by rocket under the http example com static url putting forth the use of conventions common to all rocket projects every project is initially created with the following files dirs in client static client static font img apple touch icon png favicon ico controllers by using a modified version of the powerful express resource https github com visionmedia express resource express resource github plugin rocket provides you with a robust way of automatically mapping your controllers to your routes each time you launch your application rocket takes all the controllers controller name controller js modules and maps their exported functions as follows get controller name index get controller name new new post controller name create get controller name forum show get controller name forum edit edit put controller name forum update delete controller name forum destroy e g get forums require controllers forums controller index get forums new require controllers forums controller new post forums require controllers forums controller create get forums forum require controllers forums controller show get forums forum edit require controllers forums controller edit put forums forum require controllers forums controller update delete forums forum require controllers forums controller destroy where index new create show edit update destroy are normal express callbacks functions function req res it is important to note that controllers root controller js is used as the controller for more info see the express resource readme https github com visionmedia express resource express resource github conventions on controller names controller names must be plural be all lower case be underscored have a controller suffix hence controller hyper beams controller js is valid whereas controller hyper beams js and controller hyper beam controller js are not defining custom actions for your controllers in cases where you might need to derive from the restful conventions rocket provides to you a way to add custom actions to your controllers by mapping any exported function but index new create show edit update destroy as follows get post put delete controller name myaction myaction you can also be more specific in your mapping by making myaction an object get controller name myaction myaction get controller name myaction singular controller name myaction get post controller name myaction myaction post controller name myaction singular controller name myaction post put controller name myaction myaction put controller name myaction singular controller name myaction put delete controller name myaction myaction destroy controller name myaction singular controller name myaction destroy exporting functions of a controller without mapping them to a route by default rocket ignores all exported functions prefixed with an underscore this can be used for example if you want to be able to require and extend a base controller from which you want to inherit some property or methods auto loading resource for your controller it is possible via express resource to auto load the data associated with a specific id for your controller to use simply put this can be done by exporting the function in question as load in the controller module auto loading functions take the following form exports load function id cb var err obj 1 load the object with the specified id 2 call the callback cb err obj for more info see express resource readme https github com visionmedia express resource express resource github now js exports by using nowjs http nowjs com rocket allows you to export server objects and make them accessible to the client to export a module simply create a javascript file in your exports directory and rocket will automagically export it for you e g the exports chat js file exports sendmsg function msg will be accessible through nowjs in the client by doing require now function dnode now ready function now chat sendmsg hello world models starting with version 0 1 x rocket is database agnostic if you re looking forward to using a nosql db we recommend you to use mongodb http www mongodb org in conjunction with mongoose http http mongoosejs com locales essential to any production application is the need to have localization support as with controllers and views rocket puts forward conventions that will allow you to better manage your projects making sure everything is at its right place to do so rocket leverage the jade i18n library by taking each javascript packages it finds in the locales directory and then define the phrases it contains for example to define the phrase welcome message in en ca you simply create a file named en ca js in the locales directory containing the following module exports welcome message hello world goodbye message bye world using locales in my controllers one of the suggested pattern to better leverage jade i18n in your controllers is to use a middleware in order to 1 detect the language of the client and 2 provide a version of rocket jade i18n helper with a pre appended lang argument in order to allow your controllers to simply call req to translate messages in the client s language such middleware would look like function req res next var current lang guesslang req req function text return rocket current lang text next using locales in my views you can use the dynamic helper just like you would with jade i18n using locales anywhere else the jade i18n package is available through rocket i18n and its dynamic helper is available through rocket views rocket takes care of matching your views to your controllers so you do not have to define these redundant relationship controllers without view simply returns the json passed to res send controller views mapping rocket maps controllers to their views in the following way controllers root controller js exports index function req res views root root index jade exports custom get function req res views root root custom get jade post function req req views root root custom post jade bypassing view generation with xhr queries in order to serve as restful access points every controller returns json instead of its rendered view when it is queried via xhr ajax

front_end

freeRTOS

freertos examples for stmicrocontrollers 1 freertos stock stock example 2 task running 2 led control example 2 task running 3 led control with external interrupt 1 task running in freertos 4 led control with button using to xtasknotify api in freertos 5 freertos hal stock example 6 freertos hal ledcontrol 7 led control with xtimer api using to xqueue api in freertos 8 semaphore with queue apis in freertos 9 counting semaphore using with task and interrupt in freertos 10 mutual exclusion method using with binary semaphore and mutex example in freertos

os

NJUNMT-tf

njunmt tf njunmt tf is a general purpose sequence modeling tool in tensorflow while neural machine translation is the main target task key features njunmt tf builds nmt models almost from scratch without any high level tensorflow apis which often hide details of many network components and lead to obscure code structure that is difficult to understand and manipulate njunmt tf only depends on basic tensorflow modules like array ops math ops and nn ops each operation in the code is under control br njunmt tf focuses on modularity and extensibility using standard tensorflow modules and practices to support advanced modeling capability arbitrarily complex encoder architectures e g bidirectional rnn encoder unidirectional rnn encoder and self attention arbitrarily complex decoder architectures e g conditional gru lstm decoder attention decoder and self attention hybrid encoder decoder models e g self attention encoder and rnn decoder or vice versa and all of the above can be used simultaneously to train novel and complex architectures the code also supports model ensemble learning rate decaying according to loss on evaluation data model validation on evaluation data with bleu score and early stop strategy monitoring with tensorboard https www tensorflow org get started summaries and tensorboard capability for bpe https github com rsennrich subword nmt requirements tensorflow 1 6 pyyaml quickstart here is a minimal workflow to get you started in using njunmt tf this example uses a toy chinese english dataset for machine translation with a toy setting 1 build the word vocabularies bash python m bin generate vocab testdata toy zh max vocab size 100 testdata vocab zh python m bin generate vocab testdata toy en0 max vocab size 100 testdata vocab en 2 train with preset sequence to sequence parameters bash export cuda visible devices python m bin train model dir test model config paths njunmt example configs toy seq2seq yml njunmt example configs toy training options yml default configs default optimizer yml 3 translate a test file with the latest checkpoint bash export cuda visible devices python m bin infer model dir test models infer beam size 4 source words vocabulary testdata vocab zh target words vocabulary testdata vocab en infer data features file testdata toy zh labels file testdata toy en output file toy trans output attention false note do not expect any good translation results with this toy example consider training on larger parallel datasets http www statmt org wmt16 translation task html instead configuration as you can see there are two ways to manipulate hyperparameters of the process tf flags yaml style config file for example there is a config file specifying the datasets for training procedure datasets yml data train features file testdata toy zh train labels file testdata toy en0 eval features file testdata toy zh eval labels file testdata toy en source words vocabulary testdata vocab zh target words vocabulary testdata vocab en you can either use the command bash python m bin train config paths datasets yml or bash python m bin train data train features file testdata toy zh train labels file testdata toy en0 eval features file testdata toy zh eval labels file testdata toy en source words vocabulary testdata vocab zh target words vocabulary testdata vocab en they are of the same effect the available flags or the top levels of yaml configs for bin train are as follows config paths the paths for config files model dir the directory for saving checkpoints problem name the top name scope seq2seq by default train training options e g batch size maximum length data training data evaluation data vocabulary and optional bpe codes hooks a list of training hooks not provided in the current version metrics a list of evaluation metrics on evaluation data model the class name of the model model params parameters for the model optimizer params parameters for optimizer the available flags or the top levels of yaml configs for bin infer are as follows config paths the paths for config files model dir the checkpoint directory or directories separated by commas for model ensemble infer inference options e g beam size length penalty rate infer data a list of data file to be translated weight scheme the weight scheme for model ensemble only average available now note that each flag should be a string of yaml style the hyperparameters provided by flags will overwrite those presented in config files illegal parameters will interrupt the program so see sample yml https github com zhaocq nlp njunmt tf blob master njunmt example configs sample yml of more detailed discription for each parameter benchmarks the rnn benchmarks are performed on 1 gtx 1080ti gpu with predefined configurations default configs adam loss decay yml default configs default metrics yml default configs default training options yml default configs seq2seq cgru yml the transformer benchmarks are performed on 1 gtx 1080ti gpu with predefined configurations default configs transformer base yml default configs transformer training options yml note that in transformer model we set batch tokens size 2500 with update cycle 10 to realize pseudo parallel training the beam sizes for rnn and transformer are 10 and 4 respectively the datasets are preprocessed using fetch wmt2017 ende sh https github com zhaocq nlp mt data processing blob master fetch wmt2017 ende sh and fetch wmt2018 zhen sh https github com zhaocq nlp mt data processing blob master fetch wmt2018 zhen sh referring to edinburgh s report http statmt org wmt17 pdf wmt39 pdf the bleu scores are evaluated by the wrapper script run mteval sh https github com zhaocq nlp njunmt tf blob master njunmt tools mteval run mteval sh for en zh experiments the bleu scores are evaluated at character level while others are evaluated at word level table tr th rowspan 2 dataset th th rowspan 2 model th th colspan 2 bleu th tr tr td newstest2016 dev td td newstest2017 td tr tr td rowspan 2 wmt17 en de td td rnn td td 29 6 td td 23 6 td tr tr td transformer td td 33 5 td td 27 0 td tr tr td rowspan 2 wmt17 de en td td rnn td td 34 0 td td 29 6 td tr tr td transformer td td 37 6 td td 33 1 td tr table table tr th rowspan 2 dataset th th rowspan 2 model th th colspan 2 bleu th tr tr td newsdev2017 dev td td newstest2017 td tr tr td rowspan 2 wmt17 zh en td td rnn td td 19 7 td td 21 2 td tr tr td transformer td td 22 7 td td 25 0 td tr tr td rowspan 2 wmt17 en zh td td rnn td td 30 0 td td 30 2 td tr tr td transformer td td 34 9 td td 35 0 td tr table todo the following features remain unimplemented multi gpu training schedule sampling minimum risk training acknowledgments the implementation is inspired by the following neural machine translation by jointly learning to align and translate https arxiv org abs 1409 0473 dl4mt tutorial https github com nyu dl dl4mt tutorial opennmt tf https github com opennmt opennmt tf google s seq2seq https github com google seq2seq br massive exploration of neural machine translation architectures https arxiv org abs 1703 03906 thumt https github com thumt thumt google s tensor2tensor https github com tensorflow tensor2tensor br attention is all you need https arxiv org abs 1706 03762 stronger baselines for trustable results in neural machine translation http www aclweb org anthology w17 3203 pdf contact any comments or suggestions are welcome please email zhaocq nlp gmail com mailto zhaocq nlp gmail com

neural-machine-translation attention njunmt-tf translation tensorflow nmt tensor2tensor transformer

ai

Udacity-Deploy-an-Article-CMS-to-Azure

article cms flaskwebproject this project is a python web application built using flask the user can log in and out and create edit articles an article consists of a title author and body of text stored in an azure sql server along with an image that is stored in azure blob storage you will also implement oauth2 with sign in with microsoft using the msal library along with app logging log in credentials for flaskwebproject username admin password pass or once the ms login button is implemented it will automatically log into the admin account project instructions for student you are expected to do the following to complete this project 1 create a resource group in azure 2 create an sql database in azure that contains a user table an article table and data in each table populated with the scripts provided in the sql scripts folder provide a screenshot of the populated tables as detailed further below 3 create a storage container in azure for images to be stored in a container provide a screenshot of the storage endpoint url as detailed further below 4 add functionality to the sign in with microsoft button this will require completing todos in views py with the msal library along with appropriate registration in azure active directory 5 choose to use either a vm or app service to deploy the flaskwebproject to azure complete the analysis template in writeup md or include in the readme to compare the two options as well as detail your reasoning behind choosing one or the other once you have made your choice go through with deployment 6 add logging for whether users successfully or unsuccessfully logged in this will require completing todos in init py as well as adding logging where desired in views py 7 to prove that the application in on azure and working go to the url of your deployed app log in using the credentials in this readme click the create button and create an article with the following data title hello world author jane doe body my name is jane doe and this is my first article upload an image of your choice must be either a png or jpg after saving click back on the article you created and provide a screenshot proving that it was created successfully please also make sure the url is present in the screenshot 8 log into the azure portal go to your resource group and provide a screenshot including all of the resources that were created to complete this project see sample screenshot in example images folder 9 take a screenshot of the redirect uris entered for your registered app related to the ms login button 10 take a screenshot of your logs can be from the log stream in azure showing logging from an attempt to sign in with an invalid login as well as a valid login example images folder this folder contains sample screenshots that students are required to submit in order to prove they completed various tasks throughout the project 1 article cms solution png is a screenshot from running the flaskwebproject on azure and prove that the student was able to create a new entry the title author and body fields must be populated to prove that the data is being retrieved from the azure sql database while the image on the right proves that an image was uploaded and pulled from azure blob storage 2 azure portal resource group png is a screenshot from the azure portal showing all of the contents of the resource group the student needs to create the resource group must at least contain the following storage account sql server sql database resources related to deploying the app 3 sql storage solution png is a screenshot showing the created tables and one query of data from the initial scripts 4 blob solution png is a screenshot showing an example of blob endpoints for where images are sent for storage 5 uri redirects solution png is a screenshot of the redirect uris related to microsoft authentication 6 log solution png is a screenshot showing one potential form of logging with an invalid login attempt and admin logged in successfully taken from the app s log stream you can customize your log messages as you see fit for these situations dependencies 1 a free azure account 2 a github account 3 python 3 7 or later 4 visual studio 2019 community edition free 5 the latest azure cli helpful not required all actions can be done in the portal all python dependencies are stored in the requirements txt file to install them using visual studio 2019 community edition 1 in the solution explorer expand python environments 2 right click on python 3 7 64 bit global default and select install from requirements txt troubleshooting mac users may need to install unixodbc as well as related drivers as shown below bash brew install unixodbc check here https docs microsoft com en us sql connect odbc linux mac install microsoft odbc driver sql server macos view sql server ver15 to add sql server drivers for mac

cloud

llm-trainer

llm trainer training fine tuning large language models appears to be already installed 1 install lambda stack https lambdalabs com lambda stack deep learning software lambda cloud should already come with docker and docker compose plugin installed login to docker bash sudo docker login pull existing docker image to warm build cache bash sudo docker pull glavin001 llm trainer may 28 433 build start container with jupyter labs notebook bash sudo docker compose up build if you see error tornado web httperror http 403 forbidden then stop the docker container and retry login to weights and biases bash wandb login u username bash scripts train sh

ai

CSE-474

cse 474 introduction to embedded systems repository welcome to my github repository for cse 474 introduction to embedded systems in this course i have learned the specification design development and testing of real time embedded system software throughout the course i have worked with a modern embedded microcomputer or microcontroller as a target environment for a series of laboratory projects and a comprehensive final project course objectives by the end of the course i have achieved 1 a solid understanding of embedded system fundamentals and concepts 2 skills in designing and implementing embedded software solutions 3 hands on experience with modern embedded microcomputers or microcontrollers 4 knowledge of development tools and debugging techniques for embedded systems 5 understanding the constraints and challenges associated with real time systems 6 collaborative work on a comprehensive final project integrating all course concepts repository structure this repository is organized as follows cse 474 lab readme md src hw readme md src final project readme md src readme md readme md this file which provides an overview of the repository and its contents lab folders for each lab project numbered sequentially e g lab01 lab02 etc readme md a description of the lab its objectives and instructions for use hw folders for each homework assignment numbered sequentially e g hw1 hw2 etc readme md a description of the homework assignment its objectives and instructions for use src the source code for the homework assignment src the source code for the lab project final project folder for the comprehensive final project readme md a description of the final project its objectives and instructions for use src the source code for the final project getting started 1 clone this repository to your local machine using git clone https github com masonjameswheeler cse 474 git 2 navigate to the directory of the lab or project you want to work on 3 follow the instructions in the corresponding readme md file for each lab project 4 build test and debug your embedded software solutions contributing please follow the github flow https guides github com introduction flow workflow when contributing to this repository 1 create a new branch for your changes git checkout b new feature branch 2 make your changes and commit them git commit m add new feature 3 push your changes to the remote branch git push origin new feature branch 4 create a pull request on github to merge your changes into the main branch license this repository is licensed under the mit license https opensource org licenses mit see the license license file for details

os

xLibEsp32Rtos3.2Mqtt

xmqttesp32rtos3 2lib esp32 rtos 3 2 esp38266 https github com xuhongv xlibesp8266rtos3 1mqtt v1 0 2019 04 10 v1 1 bug 2019 04 23 v2 0 api 2019 04 25 esp8266 mqtt demo qq 434878850 870189248 qq com esp8266 rtos3 1 esp32 rtos3 2 api esp8266 https github com xuhongv xlibesp8266rtos3 1mqtt esp32 https github com xuhongv xlibesp32rtos3 2mqtt xmqtt config xmqttconfig mqttversion 4 borkerhost 193 112 51 78 borkerport 1883 mqttcommandtimeout 6000 username admin password xuhongv2019 clientid 521331497 id keepaliveinterval 60 cleansession true xmqttinit xmqttconfig xmqttreceivemsg xmqtt h taskxmqttrecieve taskmainmqtt xmqtt h strcpy char smsg topic mqtt data publish sprintf char smsg payload xmqttversion s freeheap d getxmqttversion esp get free heap size smsg payloadlen strlen char smsg payload smsg qos 1 smsg retained 0 smsg dup 0 xmqttpublicmsg smsg strcpy char rmsg topic mqtt data sublish rmsg qos 1 xmqttsubtopic rmsg xmqttconnectwifinotify xmqtt xmqttconnectwifinotify wifi connected wifi ap xmqttconnectwifinotify wifi disconnected wifi ap

os

FinNLP-Progress

tracking progress in finnlp news updates for acl 2022 finer financial numeric entity recognition for xbrl tagging https www 2022 aclweb org papers guided attention multimodal multitask financial forecasting with inter company relationships and global and local news https www 2022 aclweb org papers incorporating stock market signals for twitter stance detection https www 2022 aclweb org papers buy tesla sell ford assessing implicit stock market preference in pre trained language models https www 2022 aclweb org papers finnlp emnlp2021 finqa a dataset of numerical reasoning over financial data https arxiv org abs 2109 00122 finnlp acl2021 exploring the efficacy of automatically generated counterfactuals for sentiment analysis https arxiv org abs 2106 15231 multimodal multi speaker merger acquisition financial modeling a new task dataset and neural baselines https old insight centre org sites default files publications a multimodal aligned earnings conference call dataset for financial risk prediction pdf tat qa a question answering benchmark on a hybrid of tabular and textual content in finance https arxiv org abs 2105 07624 finnlp aaai2021 coupling macro sector micro financial indicators for learning stock representations with less uncertainty https www aaai org aaai21papers aaai 7228 wangg pdf modeling the momentum spillover effect for stock prediction via attribute driven graph attention networks https www aaai org aaai21papers aaai 5328 chengr pdf stock selection via spatiotemporal hypergraph attention network a learning to rank approach http 34 94 61 102 paper aaai 7907 html deeptrader a deep reinforcement learning approach for risk return balanced portfolio management with market conditions embedding https biweihuang com publications 2 deep portfolio optimization via distributional prediction of residual factors https arxiv org pdf 2012 07245 pdf commission fee is not enough a hierarchical reinforced framework for portfolio management https arxiv org pdf 2012 12620 pdf table of contents financial index forecasting nlp based financial forecasting readme md financial event prediction financial event prediction readme md financial risk analysis financial risk analysis readme md financial text mining financial text mining readme md fraud detection fraud detection readme md blockchain blockchain readme md open source datasets multi modal earnings conference call datasets earnings call datasets readme md pre trained word embedding on financial communication text https github com yya518 finbert reuters trc2 dataset https trec nist gov data reuters reuters html financial phrase bank https www researchgate net publication 251231364 financialphrasebank v10 open source datasets in chinese id 1 50 https github com smoothnlp financialdatasets blob master data smoothnlp e5 b7 a5 e5 95 86 e6 95 b0 e6 8d ae e9 9b 86 e6 a0 b7 e6 9c ac10k xlsx title content pub ts 2 210 https github com smoothnlp financialdatasets blob master data smoothnlp e4 b8 93 e6 a0 8f e8 b5 84 e8 ae af e6 95 b0 e6 8d ae e9 9b 86 e6 a0 b7 e6 9c ac10k xlsx title content pub ts 1 58 https github com smoothnlp financialdatasets blob master data smoothnlp e4 b8 93 e6 a0 8f e8 b5 84 e8 ae af e6 95 b0 e6 8d ae e9 9b 86 e6 a0 b7 e6 9c ac10k xlsx 1k 3 https github com smoothnlp financialdatasets blob master data smoothnlp e6 8a 95 e8 b5 84 e7 bb 93 e6 9e 84 e6 95 b0 e6 8d ae e9 9b 86 e6 a0 b7 e6 9c ac1k xlsx 2k 7 https github com smoothnlp financialdatasets blob master data smoothnlp e6 8a 95 e8 b5 84 e4 ba 8b e4 bb b6 e6 95 b0 e6 8d ae e9 9b 86 e6 a0 b7 e6 9c ac2k xlsx 36 title content url 1 11 https github com smoothnlp financialdatasets blob master data smoothnlp36kr 10k xlsx research topics financial index forecasting financial news social media professional documents earning conference call 10k 10q report tasks 1 classification binary triple classification 2 regression mse volatility prediction return prediction financial documents analysis professional documents tasks correlation acl 19 financial analysts rating earning conference call investor sentiment analysis social media financial news tasks sentiment analysis binary classification five class classification financial event prediction bankrupt ipo m a tasks classification event prediction sentiment analysis market sentiment prediction

financial-news fintech natural-language-processing blockchain-technology financial-analysis

ai

Altschool-Cloud-Exercises

altschool cloud exercises this repositiory will contain the exercises and assignments i complete as i go through the altschool cloud engineering program

cloud

qutils

qutils qutils is a set of tools i m developing to help me with qt mobile development right now it is only for android notifications android features how to use in your project the best way i know of doing this is to add the following line to your gradle properties file qutilsdir absolute path to qutils android and then in your build gradle file add this qutilsdir src to java srcdirs java srcdirs qt5androiddir src src java qutilsdir src i m not really experienced in android development so anyone out there with experience please tell me how to better package the library d notifications qutils have support for android local push notifications you can send an instant notification or a scheduled one available notification properties are priority category ledcolor ledonms ledoffms title text delay date notificationtag notificationid sound defaults flags visibility you can send notifications either by creating a notification object or just by using javascript dictionary notificationmanager handles the notification sending and receiving you can check out the qutils demo app source code or the app on play store https play google com store apps details id org zmc qutils demo rdid org zmc qutils demo notification behavior when you send an instant notification only the nonitificationmanager that sent the notification will receive notificationreceived signal but this behavior changes when you send a scheduled notification scheduled notifications emit the signals according to the objectname of the notificationmanager that sent it if a notificationmanager has an empty objectname then when the app is opened by tapping on the scheduled notification all of the notificationmanager instances are notified about the notification but if notificationmanager has an objectname only that notificationmanager will be signaled common qmlrefresh the qmlrefresh is a helper class to reload qml on runtime to use it pass the qqmlapplicationengine pointer and call the reload function when you need for more information about runtime reloading go here http www slideshare net icsinc how best to realize a runtime reload of qml in main cpp qmlrefresh manager engine manager setmainqmlfile file e development playground live qml qml main qml engine rootcontext setcontextproperty qm manager pass the qquickwindow to qmlrefresh then use qm relad to reload at runtime window id mainwindow component oncompleted qm setwindow mainwindow mousearea anchors fill parent onclicked qm reload screenhelper screenhelper is a utility class to help create resolution indepentant ui in qml in main cpp screenhelper manager engine rootcontext setcontextproperty sh manager then use like so window id mainwindow rectangle width sh dp 50 disable multimedia if you want to disable multimedia functions just put the following in your project s pro file config qutils no multimedia

front_end

mirror-front

cover https raw githubusercontent com ralxyz repo pictures main mirror front cover png zju mirror front end back end json format mirrorsdotnet api https github com zjusct mirrorsdotnet blob main docs swagger json deployment all static files must be mapped to static special thanks ui designer rynco maekawa https github com lynzrand

front_end

Android-applications

homework index assignment 1 temperature converter ss src hw1 png assignment 2 notepad ss src hw2 png assignment 3 multi notepad ss src hw3 png assignment 4 stock watch ss src hw4 png assignment 5 know your government ss src hw5 1 png ss src hw5 2 png

front_end

Driverify

driverify driverify logo https i ibb co rnjkdpp screenshot 8 png travis ci build status build status https travis ci com teodtedo771 driverify svg branch master https travis ci com teodtedo771 driverify bg driverify obdii tech java c python sqlite xml keras tensorflow lite opencv elm 327 v2 1 obd to rs232 interpreter video demonstration of the project driverify http i3 ytimg com vi s hrx0kkxqq maxresdefault jpg https www youtube com watch v s hrx0kkxqq t 55s driverify sh download both videos https we tl t 72to1ugrer watch drive demo https youtu be s hrx0kkxqq watch data visualisation demo https www youtube com watch v xsp2lk0f fy building for source for production release sh todo add installation steps todos write more tests add night mode license mit

car drive sleeping android studio mobile application

server

IotXmpp

iotxmpp xmpp led http i imgur com 2gyk3gj png http i imgur com qajgjbu png http i imgur com ind3qa3 png http i imgur com jtwuwqx png http i imgur com muzwzki png led http i imgur com wzo74qg png http i imgur com iinlfgu png

server

iCare

icare project for information technology

server

Intelligent-Home-Medical-System

intelligent home medical system based on linux system and zigbee wireless communication techniques internet qt embedded ui and android mobile phone were designed to achieve remote monitoring

os

pytorch-grad-cam

license mit https img shields io badge license mit yellow svg https opensource org licenses mit build status https github com jacobgil pytorch grad cam workflows tests badge svg downloads https static pepy tech personalized badge grad cam period month units international system left color black right color brightgreen left text monthly 20downloads https pepy tech project grad cam downloads https static pepy tech personalized badge grad cam period total units international system left color black right color blue left text total 20downloads https pepy tech project grad cam advanced ai explainability for pytorch pip install grad cam documentation with advanced tutorials https jacobgil github io pytorch gradcam book https jacobgil github io pytorch gradcam book this is a package with state of the art methods for explainable ai for computer vision this can be used for diagnosing model predictions either in production or while developing models the aim is also to serve as a benchmark of algorithms and metrics for research of new explainability methods comprehensive collection of pixel attribution methods for computer vision tested on many common cnn networks and vision transformers advanced use cases works with classification object detection semantic segmentation embedding similarity and more includes smoothing methods to make the cams look nice high performance full support for batches of images in all methods includes metrics for checking if you can trust the explanations and tuning them for best performance visualization https github com jacobgil jacobgil github io blob master assets cam dog gif raw true method what it does gradcam weight the 2d activations by the average gradient hirescam like gradcam but element wise multiply the activations with the gradients provably guaranteed faithfulness for certain models gradcamelementwise like gradcam but element wise multiply the activations with the gradients then apply a relu operation before summing gradcam like gradcam but uses second order gradients xgradcam like gradcam but scale the gradients by the normalized activations ablationcam zero out activations and measure how the output drops this repository includes a fast batched implementation scorecam perbutate the image by the scaled activations and measure how the output drops eigencam takes the first principle component of the 2d activations no class discrimination but seems to give great results eigengradcam like eigencam but with class discrimination first principle component of activations grad looks like gradcam but cleaner layercam spatially weight the activations by positive gradients works better especially in lower layers fullgrad computes the gradients of the biases from all over the network and then sums them deep feature factorizations non negative matrix factorization on the 2d activations visual examples what makes the network think the image label is pug pug dog what makes the network think the image label is tabby tabby cat combining grad cam with guided backpropagation for the pug pug dog class img src https github com jacobgil pytorch grad cam blob master examples dog jpg raw true width 256 height 256 img src https github com jacobgil pytorch grad cam blob master examples cat jpg raw true width 256 height 256 img src https github com jacobgil pytorch grad cam blob master examples cam gb dog jpg raw true width 256 height 256 object detection and semantic segmentation object detection semantic segmentation img src examples both detection png width 256 height 256 img src examples cars segmentation png width 256 height 200 explaining similarity to other images embeddings img src examples embeddings png deep feature factorization img src examples dff1 png img src examples dff2 png classification resnet50 category image gradcam ablationcam scorecam dog examples dog cat jfif examples resnet50 dog gradcam cam jpg examples resnet50 dog ablationcam cam jpg examples resnet50 dog scorecam cam jpg cat examples dog cat jfif raw true examples resnet50 cat gradcam cam jpg raw true examples resnet50 cat ablationcam cam jpg raw true examples resnet50 cat scorecam cam jpg vision transfomer deit tiny category image gradcam ablationcam scorecam dog examples dog cat jfif examples vit dog gradcam cam jpg examples vit dog ablationcam cam jpg examples vit dog scorecam cam jpg cat examples dog cat jfif examples vit cat gradcam cam jpg examples vit cat ablationcam cam jpg examples vit cat scorecam cam jpg swin transfomer tiny window 7 patch 4 input size 224 category image gradcam ablationcam scorecam dog examples dog cat jfif examples swint dog gradcam cam jpg examples swint dog ablationcam cam jpg examples swint dog scorecam cam jpg cat examples dog cat jfif examples swint cat gradcam cam jpg examples swint cat ablationcam cam jpg examples swint cat scorecam cam jpg metrics and evaluation for xai img src examples metrics png img src examples road png choosing the target layer you need to choose the target layer to compute cam for some common choices are fasterrcnn model backbone resnet18 and 50 model layer4 1 vgg and densenet161 model features 1 mnasnet1 0 model layers 1 vit model blocks 1 norm1 swint model layers 1 blocks 1 norm1 if you pass a list with several layers the cam will be averaged accross them this can be useful if you re not sure what layer will perform best using from code as a library python from pytorch grad cam import gradcam hirescam scorecam gradcamplusplus ablationcam xgradcam eigencam fullgrad from pytorch grad cam utils model targets import classifieroutputtarget from pytorch grad cam utils image import show cam on image from torchvision models import resnet50 model resnet50 pretrained true target layers model layer4 1 input tensor create an input tensor image for your model note input tensor can be a batch tensor with several images construct the cam object once and then re use it on many images cam gradcam model model target layers target layers use cuda args use cuda you can also use it within a with statement to make sure it is freed in case you need to re create it inside an outer loop with gradcam model model target layers target layers use cuda args use cuda as cam we have to specify the target we want to generate the class activation maps for if targets is none the highest scoring category will be used for every image in the batch here we use classifieroutputtarget but you can define your own custom targets that are for example combinations of categories or specific outputs in a non standard model targets classifieroutputtarget 281 you can also pass aug smooth true and eigen smooth true to apply smoothing grayscale cam cam input tensor input tensor targets targets in this example grayscale cam has only one image in the batch grayscale cam grayscale cam 0 visualization show cam on image rgb img grayscale cam use rgb true metrics and evaluating the explanations python from pytorch grad cam utils model targets import classifieroutputsoftmaxtarget from pytorch grad cam metrics cam mult image import cammultimageconfidencechange create the metric target often the confidence drop in a score of some category metric target classifieroutputsoftmaxtarget 281 scores batch visualizations cammultimageconfidencechange input tensor inverse cams targets model return visualization true visualization deprocess image batch visualizations 0 state of the art metric remove and debias from pytorch grad cam metrics road import roadmostrelevantfirst roadleastrelevantfirst cam metric roadmostrelevantfirst percentile 75 scores perturbation visualizations cam metric input tensor grayscale cams targets model return visualization true you can also average accross different percentiles and combine leastrelevantfirst mostrelevantfirst 2 from pytorch grad cam metrics road import roadmostrelevantfirstaverage roadleastrelevantfirstaverage roadcombined cam metric roadcombined percentiles 20 40 60 80 scores cam metric input tensor grayscale cams targets model advanced use cases and tutorials you can use this package for custom deep learning models for example object detection or semantic segmentation you will have to define objects that you can then pass to the cam algorithms 1 a reshape transform that aggregates the layer outputs into 2d tensors that will be displayed 2 model targets that define what target do you want to compute the visualizations for for example a specific category or a list of bounding boxes here you can find detailed examples of how to use this for various custom use cases like object detection these point to the new documentation jupter book for fast rendering the jupyter notebooks themselves can be found under the tutorials folder in the git repository notebook tutorial xai recipes for the huggingface image classification models https jacobgil github io pytorch gradcam book huggingface html notebook tutorial deep feature factorizations for better model explainability https jacobgil github io pytorch gradcam book deep 20feature 20factorizations html notebook tutorial class activation maps for object detection with faster rcnn https jacobgil github io pytorch gradcam book class 20activation 20maps 20for 20object 20detection 20with 20faster 20rcnn html notebook tutorial class activation maps for yolo5 https jacobgil github io pytorch gradcam book eigencam 20for 20yolo5 html notebook tutorial class activation maps for semantic segmentation https jacobgil github io pytorch gradcam book class 20activation 20maps 20for 20semantic 20segmentation html notebook tutorial adapting pixel attribution methods for embedding outputs from models https jacobgil github io pytorch gradcam book pixel 20attribution 20for 20embeddings html notebook tutorial may the best explanation win cam metrics and tuning https jacobgil github io pytorch gradcam book cam 20metrics 20and 20tuning 20tutorial html how it works with vision swint transformers tutorials vision transformers md smoothing to get nice looking cams to reduce noise in the cams and make it fit better on the objects two smoothing methods are supported aug smooth true test time augmentation increases the run time by x6 applies a combination of horizontal flips and mutiplying the image by 1 0 1 1 0 9 this has the effect of better centering the cam around the objects eigen smooth true first principle component of activations weights this has the effect of removing a lot of noise ablationcam aug smooth eigen smooth aug eigen smooth examples nosmooth jpg examples augsmooth jpg examples eigensmooth jpg examples eigenaug jpg running the example script usage python cam py image path path to image method method output dir output dir path to use with cuda python cam py image path path to image use cuda output dir output dir path you can choose between gradcam hirescam scorecam gradcamplusplus ablationcam xgradcam layercam fullgrad and eigencam some methods like scorecam and ablationcam require a large number of forward passes and have a batched implementation you can control the batch size with cam batch size citation if you use this for research please cite here is an example bibtex entry misc jacobgilpytorchcam title pytorch library for cam methods author jacob gildenblat and contributors year 2021 publisher github howpublished url https github com jacobgil pytorch grad cam references https arxiv org abs 1610 02391 br grad cam visual explanations from deep networks via gradient based localization ramprasaath r selvaraju michael cogswell abhishek das ramakrishna vedantam devi parikh dhruv batra https arxiv org abs 2011 08891 br use hirescam instead of grad cam for faithful explanations of convolutional neural networks rachel l draelos lawrence carin https arxiv org abs 1710 11063 br grad cam improved visual explanations for deep convolutional networks aditya chattopadhyay anirban sarkar prantik howlader vineeth n balasubramanian https arxiv org abs 1910 01279 br score cam score weighted visual explanations for convolutional neural networks haofan wang zifan wang mengnan du fan yang zijian zhang sirui ding piotr mardziel xia hu https ieeexplore ieee org abstract document 9093360 br ablation cam visual explanations for deep convolutional network via gradient free localization saurabh desai and harish g ramaswamy in wacv pages 972 980 2020 https arxiv org abs 2008 02312 br axiom based grad cam towards accurate visualization and explanation of cnns ruigang fu qingyong hu xiaohu dong yulan guo yinghui gao biao li https arxiv org abs 2008 00299 br eigen cam class activation map using principal components mohammed bany muhammad mohammed yeasin http mftp mmcheng net papers 21tip layercam pdf br layercam exploring hierarchical class activation maps for localization peng tao jiang chang bin zhang qibin hou ming ming cheng yunchao wei https arxiv org abs 1905 00780 br full gradient representation for neural network visualization suraj srinivas francois fleuret https arxiv org abs 1806 10206 br deep feature factorization for concept discovery edo collins radhakrishna achanta sabine s sstrunk

deep-learning pytorch grad-cam visualizations interpretability interpretable-ai interpretable-deep-learning score-cam class-activation-maps vision-transformers explainable-ai xai image-classification machine-learning object-detection computer-vision explainable-ml

ai

Computer-Vision-Papers-of-the-week

computervision papers of the week https github com ashishpatel26 computer vision papers of the week raw main icon colorful 20futuristic 20technology 20poster gif computer vision papers of the week a brand new program to dig into the field of computer vision week papers paper code week 1 24 oct 2022 to 28 oct 2022 linkedin post https www linkedin com posts ashishpatel2604 week1 activity 6990548303960530945 rtwk utm source share utm medium member desktop 1 metaformer baselines for vision img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 13452 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com sail sg metaformer 2 monocular dynamic view synthesis a reality check img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 13445 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com kair bair dycheck 3 gallery filter network for person search img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 12903 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com lukejaffe gfn 4 weakly supervised temporal article grounding dual mil img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 12444 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com zjuchenlong wsag 5 a task aware dual similarity network for fine grained few shot learning img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 12348 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com qiyanhero tdsnet 6 rethinking learning approaches for long term action anticipation img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 11566 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com nmegha2601 anticipatr 7 human behavior animation rhythmic gesticulator rhythm aware co speech gesture synthesis with hierarchical neural embeddings img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 01448 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com aubrey ao humanbehavioranimation week 2 24 oct 2022 to 28 oct 2022 linkedin post https www linkedin com posts ashishpatel2604 week2 activity 6993080676761649152 90ng utm source share utm medium member desktop 8 diffusiondb a large scale prompt gallery dataset for text to image generative models img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 14896v1 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com poloclub diffusiondb 9 high fidelity neural audio compression img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 13438v1 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com facebookresearch encodec 10 dreambooth fine tuning text to image diffusion models for subject driven generation img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2208 12242v1 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com xavierxiao dreambooth stable diffusion 11 searchtrack multiple object tracking with object customized search and motion aware feature img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 16572 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com qa276390 searchtrack 12 nerfplayer a streamable dynamic scene representation with decomposed neural radiance fields img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 15947 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https lsongx github io projects nerfplayer html 13 imagic text based real image editing with diffusion models img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2210 09276 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png week3 1 nov 2022 to 12 nov 2022 linkedin post https www linkedin com posts ashishpatel2604 artificialintelligence data mlops activity 6998504442429886464 mgsf utm source share utm medium member desktop 14 oneformer one transformer to rule universal image segmentation img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2211 06220v1 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com shi labs oneformer 15 colossal ai a unified deep learning system for large scale parallel training img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2110 14883v2 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com hpcaitech colossalai 16 unifying flow stereo and depth estimation img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2211 05783v1 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com autonomousvision unimatch 17 internimage exploring large scale vision foundation models with deformable convolutions img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2211 05778v2 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com opengvlab internimage 18 dungeons and data a large scale nethack dataset img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2211 00539v1 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com facebookresearch nle 19 probabilistic deep metric learning for hyperspectral image classification img https github com ashishpatel26 computer vision papers of the week raw main icon research png https arxiv org abs 2211 08349 img https github com ashishpatel26 computer vision papers of the week raw main icon coding png https github com wzzheng pdml

computer-vision deep-learning image-classification instance-segmentation machine-learning paperwithcode pytorch segmentation tensorflow transformer instance-segmenation

ai

fe-camp

fe camp 1 node js 2 npm install 3 npm start http localhost 8000 chrome pageup pagedown p esc e esc

front_end

DataEngineering-GCP

dataengineering gcp

cloud

midway

p align center a href https midwayjs org target blank img src https img alicdn com imgextra i1 o1cn01xqlu011t2r7phksiv 6000000002324 2 tps 1200 616 png width 1000 alt midway logo a p p align center midway a href http nodejs org target blank node js a p p align center a href https github com midwayjs midway blob master license target blank img src https img shields io badge license mit blue svg alt github license a a href img src https img shields io github tag midwayjs midway svg alt github tag a a href img src https github com midwayjs midway actions workflows nodejs yml badge svg branch 2 x alt build status a a href https codecov io gh midwayjs midway branch master img src https img shields io codecov c github midwayjs midway master svg alt test coverage a a href https lernajs io img src https img shields io badge maintained 20with lerna cc00ff svg alt lerna a a href https github com midwayjs midway pulls img src https img shields io badge prs welcome brightgreen svg alt prs welcome a a href https gitpod io https github com midwayjs midway img src https img shields io badge gitpod ready to code blue logo gitpod alt gitpod ready to code a a href https github com midwayjs mwts img src https img shields io badge code 20style midwayjs brightgreen svg alt code style midwayjs a a href https opensource alibaba com contribution leaderboard details projectvalue midway img src https img shields io badge midway e6 9f a5 e7 9c 8b e8 b4 a1 e7 8c ae e6 8e 92 e8 a1 8c e6 a6 9c orange alt leaderboard p english readme en us md 2022 mini https www bilibili com video bv1qb4y1q7qs 2022 https www bilibili com video bv1w44y1s7dj spm id from 333 999 0 0 3 x beta https www bilibili com video bv1al4y1p7oa from search seid 8235946720906913847 spm id from 333 337 0 0 2021 https www bilibili com video bv1ng411t76f from search seid 8235946720906913847 spm id from 333 337 0 0 2021 https www bilibili com video bv1nf411a7sr from search seid 8235946720906913847 spm id from 333 337 0 0 v2 https www bilibili com video bv1254y1e73m from search seid 8235946720906913847 spm id from 333 337 0 0 2 0 https www bilibili com video bv17a411t7md 2 0 https zhuanlan zhihu com p 355768659 web serverless faas api react hooks server serverless faas koa express egg js typescript midway serverless node js midway koa express egg js web socket io grpc dubbo js rabbitmq midway node js aws vm react vue demo web ts import controller get provide from midwayjs decorator provide controller export class homecontroller get async home return welcome to midwayjs src apis lambda index ts typescript import api get query usecontext from midwayjs hooks export default api get query page string limit string async const ctx usecontext return page ctx query page limit ctx query limit src page index tsx typescript import getarticles from api const response await getarticles query page 0 limit 10 console log response page 0 limit 10 typescript fetch api articles page 0 limit 10 then res res json then res console log res page 0 limit 10 bash npm v npm v6 npm init midway type web my midway app npm v7 npm init midway type web my midway app cd my midway app npm run dev https midwayjs org https beta midwayjs org midway examples https img alicdn com imgextra i1 o1cn01q0m4ma27fnigixe4a 6000000007768 0 tps 3802 1996 jpg midway examples http demo midwayjs org 1 cool admin image https user images githubusercontent com 418820 118931341 73ce1880 b979 11eb 90c6 1758762ce338 png https cool js com vsc plugin https camo githubusercontent com 7819739b6a9eb3d673124817b0d40e46dc963993 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 https img alicdn com imgextra i3 o1cn01f2eyhk1t290oxo4am 6000000005843 0 tps 3916 3220 jpg a href https github com midwayjs midway graphs contributors img src https contrib rocks image repo midwayjs midway a bug http github com midwayjs midway issues image https user images githubusercontent com 629202 110743837 a968ce00 8273 11eb 8284 f5749335fe70 png logo https github com midwayjs midway issues 898 license mit http github com midwayjs midway blob master license fossa status https app fossa com api projects git 2bgithub com 2fmidwayjs 2fmidway svg type large https app fossa com projects git 2bgithub com 2fmidwayjs 2fmidway ref badge large

enterprise framework web typescript ioc dependency-injection-container midway serverless serverless-framework aws aliyun cloud azure mircoservices react vue

front_end

PyBoof

pyboof is python http www python org wrapper for the computer vision library boofcv http boofcv org since this is a java library you will need to have java and javac installed the former is the java compiler in the future the requirement for javac will be removed since a pre compiled version of the java code will be made available and automatically downloaded installing the java jdk is platform specific so a quick search online should tell you how to do it to start using the library simply install the latest stable version using pip bash pip3 install pyboof examples examples are included with the source code you can obtain them by either checkout the source code as described above or browsing github here https github com lessthanoptimal pyboof tree snapshot examples if you don t check out the source code you won t have example data and not all of the examples will work to run any of the examples simply invoke python on the script 1 cd pyboof examples 2 python example blur image py code for applying a gaussian and mean spatial filter to an image and displays the results python import numpy as np import pyboof as pb original pb load single band data example outdoors01 jpg np uint8 gaussian original createsameshape useful function which creates a new image of the mean original createsameshape same type and shape as the original apply different types of blur to the image pb blur gaussian original gaussian radius 3 pb blur mean original mean radius 3 display the results in a single window as a list image list original original gaussian gaussian mean mean pb swing show list image list title outputs input press any key to exit installing from source one advantage for checking out the source code and installing from source is that you also get all the example code and the example datasets bash git clone recursive https github com lessthanoptimal pyboof git if you forgot recursive then you can checkout the data directory with the following command bash git submodule update init recursive after you have the source code on your local machine you can install it and its dependencies with the following commands 1 cd pyboof 2 python3 m venv venv 3 source venv bin activate 4 pip3 install r requirements txt 5 setup py build 6 setup py install yes you do need to do the build first this will automatically build the java jar and put it into the correct place creating a virtual environment isn t required but recommended as you can only do so much damage with it supported platforms the code has been developed and tested on ubuntu linux 20 04 should work on any other linux variant might work on mac os and a slim chance of working on windows dependencies pyboof depends on the following python packages they should be automatically installed py4j numpy transforms3d opencv optional and for io only optional opencv python for io only

python computer-vision qr-code micro-qr-code stereo-camera boofcv

ai

ml_end_to_end

introtostatwithpython introduction to statistics for ai enthusiasts without background in statistics

server

ioTube

tests https github com iotexproject iotube workflows tests badge svg https github com iotexproject iotube actions query workflow 3atests p align center img src https user images githubusercontent com 448293 123823874 6aa66480 d8b2 11eb 9c0f c268ae19e18c jpg width 720 p iotube the decentralized bridge for ethereum binance smart chain polygon matic and iotex a href https iotex io devdiscord target blank img src https github com iotexproject halogrants blob 880eea4af074b082a75608c7376bd7a8eaa1ac21 img btn discord svg height 36px a version history v1 0 iotx tube launch date apr 2019 a href https iotex medium com everything you need to know about iotex mainnet alpha b8d790e0bd55 announcement a the first version of iotube was built by core dev in apr 2019 to facilitate the swap of iotx e erc20 version of iotx on ethereum and iotx mainnet token v2 0 multi token tube iotex ethereum launch date aug 31 2020 a href https iotex medium com iotube cross chain bridge to connect iotex with the blockchain universe b0f5b08c1943 announcement a v2 generalizes the iotube v1 to support multiple assets on ethereum iotex blockchains v3 0 rebuilt with largely fee reduction launch date feb 8 2021 a href https community iotex io t iotube v3 faster cheaper and unified 2001 announcement a ethereum has been suffering high gas cost for a long period the core dev team rebuilt iotube to largely reduced the gas cost on eth side by introducing a relayer and putting signature offchain v4 0 multi chain support launch date apr 13 2021 annoucement https medium com iotex iotube v4 cross chain bridge for iotex ethereum and binance smart chain 9670c86723e2 because of ethereum s high gas cost many projects and users also adopted binance smart chain bsc and huobi eco chain heco the need of supporting bsc and heco is increasing the demand of cross chain support from ethereum and bsc or other blockchains are increasing we d love to support them in iotube matic support is launched on jun 10 2021 annoucement https iotex medium com iotube v4 iotex polygon matic cross chain token swaps are live bb2ae5bf41b4 v5 0 web3 babel support transactions more assets crosschain tokens beta is open to public https tube v5 iotex io any bug report or feedback please submit an issue or discuss in https discord gg jrqqsygfud h3 a href https github com iotexproject iotube deployement deployement a span span a href https github com iotexproject iotube usage usage a span span a href https github com iotexproject iotube tree master docs documentation a h3 nbsp submit a token to iotube please refer to token submission guide first https docs iotube org introduction token submission feel free to reach out to https github com guo for further discussion deployment different from traditional bridges iotube comes with two components a golang service witnessing what has happened on both changes and relay the finalized information a set of smart contracts pre deployed on both chains letting the legitimate witnesses relay information back and forth to facilitate cross chain transferring of assets deploy contracts on iotex ethereum deploy a tokensafe ts deploy a tokenlist tl1 which stores tokens using ts deploy a minterpool mp deploy a tokenlist tl2 which stores tokens using mp deploy a witnesslist wl deploy a tokencashier tc with ts tl1 and tl2 deploy a transfervalidator tv with ts mp tl1 tl2 and wl transfer ownership of mp to tv transfer ownership of ts to tv add witnesses to wl add tokens to tl1 or tl2 join as a relayer 1 edit witness service relayer config iotex yaml and witness service relayer config ethereum yaml to fill in the following fields privatekey clienturl validatorcontractaddress 2 start containers cd witness service start relayer sh join as a witness 1 edit witness service witness config iotex yaml and witness service witness config ethereum yaml to fill in the following fields privatekey clienturl validatorcontractaddress cashiercontractaddress 2 start containers cd witness service start witness sh 3 clean up everything by running clean all sh transfer assets between iotex and ethereum please use dapp iotube https tube iotex io please note that the service is still in beta mode from erc20 to xrc20 1 open https tube iotex io eth in a metamask installed browser eg firefox chrome brave metamask 2 choose supported erc20 token from the list 3 enter the amount 4 click approve button to approve erc20 token transfer and sign on metamask 5 click convert button and sign on metamask 6 after 12 confirmations of ethereum network and 2 3 1 confirmations from witnesses the xrc20 tokens will be minted and sent to your iotex address 7 you can add the token to your a href http iopay iotex io iopay a to see and use them from xrc20 to erc20 1 open https tube iotex io iotx in iopay desktop supported broswers eg chrome firefox brave with iopay desktop installed or iopay android ios 2 choose supported xrc20 token from the list 3 enter the amount 4 click approve button to approve xrc20 token transfer and sign on iopay 5 click convert button and sign on iopay 6 after 1 confirmation of iotex network and 2 3 1 confirmations from witnesses the xrc20 token will be burnt and erc20 token will be sent to your eth wallet fees tube fees 0 network fees 1 from erc20 to xrc20 0 2 from xrc20 to erc20 2000 iotx to cover the high eth gas fee for witnesses add an erc20 token to iotube add this token to tl on ethereum deploy a shadowtoken and add it to tl on iotex security each witness needs to be registered to witnesslist contract on both chains based on poa proof of authority each transferring of assets from one chain to another needs the endorsement from more than 2 3 of the registered witnesses otherwise it will not be successful iotex has instant finality meaning to a witness one confirmed block indicates finalization of a burn event while on the ethereum side a witness needs to wai 12 blocks before making any endorsement to launch iotube reliably we have limited the min max of the asset that can be moved around these limits can be lifted once iotube gets more stress validated gas costs gas fees on iotex are negligible both for bridge maintenance and for asset transfer the estimated gas fees on ethereum side are to transfer token from ethereum to iotex 43 952 gas to set allowance and 151 122 to lock it to transfer token back from iotex to eth 422 525 gas to unlock the token tube of iotex ethereum contracts on iotex wrapped iotx io15qr5fzpxsnp7garl4m7k355rafzqn8grrm0grz token safe io1cj3f498390srqv765nnvaxuk0rpxyadzpfjz75 minter pool io1g7va274ltufv5nh4xawfmt0clel6tfz58p7n5r standard token list io1t89whrwyfr0supctsqcx9n7ex5dd8yusfqhyfz proxy token list io1dn8nqk3pmmll990xz6a94fpradtrljxmmx5p8j witness list io1hezp6d7y3246c5gklnnkh0z95qfld4zdsphhsw token cashier io1gsr52ahqzklaf7flqar8r0269f2utkw9349qg8 transfer validator io10xr64as4krm5nufd5l2ddc43al6tl0smumkg7y del io1dwaxh2ml4fd2wg8cg35vhfsgdcyzrczffp3vus del contacts on ethereum weth 0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2 standard token list 0x7c0bef36e1b1cbeb1f1a5541300786a7b608aede proxy token list 0x73ffdfc98983ad59fb441fc5fe855c1589e35b3e witness list 0x8598df1ec0ac7dfba802f4bdd93a6b93bd0ad83f token safe 0xc2e0f31d739cb3153ba5760a203b3bd7c27f0d7a minter pool 0x964f4f19bc823e72cc1f806021937cfc06f63b45 token cashier 0xa0fd7430852361931b23a31f84374ba3314e1682 transfer validator 0xd8165188ccc135b3a3b2a5d2bc3af9d94753d955 tube of iotex bsc binance smart chain iotex side validator io10xr64as4krm5nufd5l2ddc43al6tl0smumkg7y same for all standard token list io1h2d3r0d20t58sv6h707ppc959kvs8wjsurrtnk proxy token list io17r9ehjstwj4gfqzwpm08fjnd606h04h2m6r92f token cashier io1zjlng7je02kxyvjq4eavswp6uxvfvcnh2a0a3d bsc side witness list address 0x8119411f5a78f73784a1b87de43d452da4a1ee3f minter pool address 0xf72cfb704d49ac7bb7ffa420ae5f084c671a29be token safe address 0xfbe9a4138afdf1fa639a8c2818a0c4513fc4ce4b mintable token list address 0xa6ae9312d0aa3cc74d969fcd4806d7729a321ee3 standard token list address 0x0d793f4d4287265b9bda86b7a4083193e8743b34 token cashier address 0x797f1465796fd89ea7135e76dbc7cdb136bba1ca del 0x082020ae0b38fd1bef48895c6cff4428e420f400 del transfer validator address 0x116404f86e97846110ea08cd52fc2882d4ad3123 tube of iotex heco huobi eco chain please note this is still being added they are not in production use yet please contact us if you want to use them without interfaces iotex side validator io10xr64as4krm5nufd5l2ddc43al6tl0smumkg7y same for all standard token list io1kh0vgtxyamdkzvrlvga3r8l7r2plm7phd9ywv9 proxy token list io18uqxuel6d93hluua4d9jxs8rjw3r2qe5g3adgk token cashier io1s6m6j3cdj0j7hlgupx0pww7wscvkepdjfwgkra heco side witness list address 0x2f1a0bca4005ebfd6a589850f436c8d8f9c2aed2 minter pool address 0xd2165d222b3daf2528fc1b1aa2db18b8821ee623 token safe address 0x1e58ca53d90fe9b37f7f6aeb548b4bc7c6292c17 mintable token list address 0x12af43ef94b05a0a3447a05eee629c7d88a30a5f standard token list address 0xa239f03cda98a7d2aaaa51e7bf408e5d73399e45 token cashier address 0xc8dc8dcdfd94f9cb953f379a7ad8da5fdc303f3e transfer validator address 0xde9395d2f4940aa501f9a27b98592589d14bb0f7 tube of iotex polygon formerly matic launched on jun 10 2021 we started adding 0x address to this doc because of iotex start supporting 0x address and web3 from iotex v1 2 babel api some iotex address are same as other tubes and we include 0x addresses here iotex side validator io10xr64as4krm5nufd5l2ddc43al6tl0smumkg7y 0x7987aaf615b0f749f12da7d4d6e2b1eff4bfbe1b standard token list io197rk3nff9622pkncvuvhfwyms73esdtwph4rlq 0x2f8768cd292e94a0da78671974b89b87a398356e proxy token list io16at6mlcwcsrqutz2zhuhwam87h988r9fcdauk8 0xd757adff0ec4060e2c4a15f9777767f5ca738ca9 token cashier io12s9f9hv4zsr7umy5hxt6g0k0xr4x6pxdp5w998 0x540a92dd951407ee6c94b997a43ecf30ea6d04cd token safe from old 0xc4a29a94f12be03033daa4e6ce9b9678c26275a2 minter pool old io1g7va274ltufv5nh4xawfmt0clel6tfz58p7n5r 0x4799d57abf5f12ca4ef5375c9dadf8fe7fa5a454 matic side witness list address 0x1e58ca53d90fe9b37f7f6aeb548b4bc7c6292c17 minter pool address 0x12af43ef94b05a0a3447a05eee629c7d88a30a5f token safe address 0xa239f03cda98a7d2aaaa51e7bf408e5d73399e45 mintable token list address 0xc8dc8dcdfd94f9cb953f379a7ad8da5fdc303f3e standard token list address 0xde9395d2f4940aa501f9a27b98592589d14bb0f7 token cashier address 0xf72cfb704d49ac7bb7ffa420ae5f084c671a29be transfer validator address 0xfbe9a4138afdf1fa639a8c2818a0c4513fc4ce4b crosschain iotx wip ciotx on iotex 0x99b2b0efb56e62e36960c20cd5ca8ec6abd5557a ciotx on polygon 0x300211def2a644b036a9bdd3e58159bb2074d388 ciotx on bsc 0x2aaf50869739e317ab80a57bf87caa35f5b60598

server

mflix-ui

m220 mflix ui front end hi there in this repository you can find the mongodb university developer courses https university mongodb com front end application this code is made available so that you can explore how the mflix application was created and how it is used throughout the m220 online courses all validation codes have been striped out of the source code so that you can enjoy the full m220 learning experience the mflix ui is a react application that performs backend requests via a backend exposed rest api it will proxy requests to http localhost 5000 to interact with any of the m220 backends that are listening on that port local build if you want to make modifications debug or simply create a local version of the mflix front end you can do so by building locally dependencies to run the mflix ui application locally you need to have the following dependencies npm 6 4 1 or above node v10 6 0 or above local mflix backend local installation 1 install application dependencies sh cd mflix ui npm install 2 run the front end server sh npm start once you ve started the server a new browser tab or window should open to http localhost 3000 if one isn t open otherwise it will refresh the existing window since this project uses create react app not reloading is in effect so there s no need to stop and start the front end when you make a change enjoy

front_end

corda

p align center img src https www corda net wp content themes corda assets images crda logo big svg alt corda width 500 p a href https ci master corda r3cev com viewtype html buildtypeid corda build activereleasebranches buildosrelease45 tab buildtypestatusdiv guest 1 img src https ci corda r3cev com app rest builds buildtype corda build activereleasebranches buildosrelease45 statusicon a license https img shields io badge license apache 202 0 blue svg https opensource org licenses apache 2 0 corda corda is an open source blockchain project designed for business from the start only corda allows you to build interoperable blockchain networks that transact in strict privacy corda s smart contract technology allows businesses to transact directly with value architecture evolution the code present in this repository reflects the first version of the implementation of the corda model for dlt technology this first architecture version covers corda versions 1 through 4 and continues to deliver on the promise of dlt for both the open source community and industry as a whole however like all things corda must evolve to serve the more stringent needs of today this is why the second and current version of the corda architecture can be found here https github com corda corda runtime os and will form the basis of the corda 5 release features smart contracts that can be written in java and other jvm languages flow framework to manage communication and negotiation between participants peer to peer network of nodes notary infrastructure to validate uniqueness and sequencing of transactions without global broadcast enables the development and deployment of distributed apps called cordapps written in kotlin https kotlinlang org targeting the jvm getting started 1 read the getting started https docs corda net getting set up html documentation 2 run the example cordapp https docs corda net tutorial cordapp html 3 read about corda s key concepts https docs corda net key concepts html 4 follow the hello world tutorial https docs corda net hello world introduction html useful links project website https corda net mailing list https groups io g corda dev documentation https docs corda net stack overflow tag https stackoverflow com questions tagged corda slack channel https slack corda net twitter https twitter com cordablockchain meetups https www meetup com pro corda training courses https www corda net corda training contributing corda is an open source project and contributions are welcome to find out how to contribute please see our contributing docs https docs r3 com en platform corda 4 8 open source contributing html license apache 2 0 license acknowledgements yourkit https www yourkit com images yklogo png yourkit supports open source projects with its full featured java profiler yourkit llc is the creator of yourkit java profiler https www yourkit com java profiler and yourkit net profiler https www yourkit com net profiler innovative and intelligent tools for profiling java and net applications

corda kotlin distributed-ledger dlt

blockchain

Awesome-LLM-Self-Consistency

awesome llm self consistency awesome llm self consistency a curated list of self consistency in large language models awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com superbrucejia awesome llm self consistency license mit https img shields io badge license mit green svg https opensource org licenses mit made with love https img shields io badge made 20with love red svg https github com superbrucejia awesome llm self consistency this repository called self consistency llms contains a collection of resources and papers on self consistency in large language models i can t see a path that guarantees safety we re entering a period of great uncertainty where we re dealing with things we ve never dealt with before and we can t afford to get it wrong with these things because they might take over geoffrey hinton professor department of computer science university of toronto october 5 2023 welcome to share your papers thoughts and ideas by submitting an issue https github com superbrucejia awesome llm self consistency issues new contents presentations presentations benchmarks benchmarks semantic consistency semantic consistency logical consistency logical consistency factual consistency factual consistency papers papers reasoning reasoning semantics semantics logicality logicality factuality factuality presentations teach language models to reason denny zhou google deepmind link https dennyzhou github io llms reason 2023 harvard yale pdf sept 2023 benchmarks semantic consistency becel benchmark for consistency evaluation of language models myeongjun jang deuk sin kwon thomas lukasiewicz coling 2022 paper https aclanthology org 2022 coling 1 324 pdf github https github com mj jang becel 12 oct 2022 improving the robustness of question answering systems to question paraphrasing wee chung gan hwee tou ng acl 2019 paper https aclanthology org p19 1610 pdf github https github com nusnlp paraphrasing squad 28 july 2019 logical consistency negational symmetric transitive and additive consistency becel benchmark for consistency evaluation of language models myeongjun jang deuk sin kwon thomas lukasiewicz coling 2022 paper https aclanthology org 2022 coling 1 324 pdf github https github com mj jang becel 12 oct 2022 hypothetical and compositional consistency two failures of self consistency in the multi step reasoning of llms angelica chen jason phang alicia parrish vishakh padmakumar chen zhao samuel r bowman kyunghyun cho arxiv 2023 paper https browse arxiv org pdf 2305 14279 pdf 2 oct 2023 factual consistency mpararel factual consistency of multilingual pretrained language models constanza fierro anders s gaard findings of acl acl 2022 paper https aclanthology org 2022 findings acl 240 pdf github https github com coastalcph mpararel 22 mar 2022 pararel metal measuring and improving consistency in pretrained language models yanai elazar nora kassner shauli ravfogel abhilasha ravichander eduard hovy hinrich sch tze yoav goldberg tacl 2021 paper https aclanthology org 2021 tacl 1 60 pdf github https github com yanaiela pararel presentation https yanaiela github io presentations consistency pdf 29 may 2021 papers reasoning large language models can be easily distracted by irrelevant context freda shi xinyun chen kanishka misra nathan scales david dohan ed chi nathanael sch rli denny zhou icml 2023 paper https proceedings mlr press v202 shi23a shi23a pdf github https github com google research datasets gsm ic 6 jun 2023 let s sample step by step adaptive consistency for efficient reasoning with llms pranjal aggarwal aman madaan yiming yang mausam arxiv 2023 paper https browse arxiv org pdf 2305 11860 pdf website http sample step by step info github https github com pranjal2041 adaptiveconsistency 19 may 2023 self consistency improves chain of thought reasoning in language models xuezhi wang jason wei dale schuurmans quoc le ed chi sharan narang aakanksha chowdhery denny zhou iclr 2023 paper https openreview net pdf id 1pl1nimmrw 7 mar 2023 semantics semantic consistency for assuring reliability of large language models harsh raj vipul gupta domenic rosati subhabrata majumdar arxiv 2023 paper https arxiv org pdf 2308 09138 pdf 17 aug 2023 measuring reliability of large language models through semantic consistency harsh raj domenic rosati subhabrata majumdar ml safety workshop neurips 2022 paper https arxiv org pdf 2211 05853 pdf 28 nov 2022 prompt consistency for zero shot task generalization chunting zhou junxian he xuezhe ma taylor berg kirkpatrick graham neubig findings of acl emnlp 2022 paper https aclanthology org 2022 findings emnlp 192 pdf github https github com violet zct swarm distillation zero shot 27 dec 2022 accurate yet inconsistent consistency analysis on language understanding models myeongjun jang deuk sin kwon thomas lukasiewicz arxiv 2021 paper https arxiv org pdf 2108 06665 pdf 15 aug 2021 evolution of semantic similarity a survey dhivya chandrasekaran vijay mago acm computing survey 2021 paper https dl acm org doi abs 10 1145 3440755 30 jan 2021 logicality enhancing self consistency and performance of pre trained language models through natural language inference eric mitchell joseph noh siyan li will armstrong ananth agarwal patrick liu chelsea finn christopher manning emnlp 2022 paper https aclanthology org 2022 emnlp main 115 pdf website https ericmitchell ai emnlp 2022 concord github https github com eric mitchell concord 21 nov 2022 factuality p adapters robustly extracting factual information from language models with diverse prompts benjamin newman prafulla kumar choubey nazneen rajani iclr 2022 paper https openreview net pdf id dhziu48oczh github https github com salesforce factlm 19 apr 2022 how can we know what language models know zhengbao jiang frank f xu jun araki graham neubig tacl 2020 paper https aclanthology org 2020 tacl 1 28 pdf github https github com jzbjyb lpaqa 3 may 2020

chain-of-thought llms pretrained-language-model self-consistency self-consistent-generation chatgpt gpt-3 gpt-4 self-consistency-learning self-consistency-benchmark reasoning llms-reasoning semantics semantics-preserving factual-consistency logical-consistency semantics-consistency compositional-consistency hypothetical-consistency

ai

azure-data-engineering

azure data engineering azure data engineering with cloud native and devsecops

cloud

Solidity-GBYouth-BlockchainTraining-2023

solidity gbyouth blockchaintraining 2023 welcome to the solidity gbyouth blockchaintraining 2023 repository this repository contains all the lab exercises notes and associated resources used for the high impact skills development program for gb youth at gilgit july 2023 note live demo codes can be found at solidity remix demo gbyouth blockchaintraining 2023 https github com itshaseebsaeed solidity remix demo gbyouth blockchaintraining 2023 course description this course provides an intensive hands on introduction to the solidity programming language and its applications in blockchain development our objective is to empower gb youth with the skills and knowledge to develop and interact with decentralized applications dapps and smart contracts on the ethereum blockchain content the repository is structured as follows lecture notes detailed notes for each lecture will be posted here lab exercises hands on exercises for the students to practice and apply their learning resources additional reading materials helpful tools and references to deepen the understanding of solidity and blockchain instructions it s recommended to 1 fork this repository 2 push your code to your fork 3 regularly pull the repository for updates 4 complete all lab exercises on time contributing students are encouraged to contribute to the repository by sharing their solutions to the exercises and their own notes please follow the given pull request template for your submissions contact for any queries or issues please create an issue in this repository or contact the trainer license this project is licensed under the mit license see the license license file for details

blockchain

ogb

p align center img width 40 src https snap stanford github io ogb web assets img ogb rectangle png p pypi https img shields io pypi v ogb https pypi org project ogb license https img shields io badge license mit blue svg https github com snap stanford ogb blob master license overview the open graph benchmark ogb is a collection of benchmark datasets data loaders and evaluators for graph machine learning datasets cover a variety of graph machine learning tasks and real world applications the ogb data loaders are fully compatible with popular graph deep learning frameworks including pytorch geometric https pytorch geometric readthedocs io en latest and deep graph library dgl https www dgl ai they provide automatic dataset downloading standardized dataset splits and unified performance evaluation p align center img width 80 src https snap stanford github io ogb web assets img ogb overview png p ogb aims to provide graph datasets that cover important graph machine learning tasks diverse dataset scale and rich domains graph ml tasks we cover three fundamental graph machine learning tasks prediction at the level of nodes links and graphs diverse scale small scale graph datasets can be processed within a single gpu while medium and large scale graphs might require multiple gpus or clever sampling partition techniques rich domains graph datasets come from diverse domains ranging from scientific ones to social information networks and also include heterogeneous knowledge graphs p align center img width 70 src https snap stanford github io ogb web assets img dataset overview png p ogb is an on going effort and we are planning to increase our coverage in the future installation you can install ogb using python s package manager pip if you have previously installed ogb please make sure you update the version to 1 3 6 the release note is available here https github com snap stanford ogb releases tag 1 3 6 requirements python 3 6 pytorch 1 6 dgl 0 5 0 or torch geometric 2 0 2 numpy 1 16 0 pandas 0 24 0 urllib3 1 24 0 scikit learn 0 20 0 outdated 0 2 0 pip install the recommended way to install ogb is using python s package manager pip bash pip install ogb bash python c import ogb print ogb version this should print 1 3 6 otherwise please update the version by pip install u ogb from source you can also install ogb from source this is recommended if you want to contribute to ogb bash git clone https github com snap stanford ogb cd ogb pip install e package usage we highlight two key features of ogb namely 1 easy to use data loaders and 2 standardized evaluators 1 data loaders we prepare easy to use pytorch geometric and dgl data loaders we handle dataset downloading as well as standardized dataset splitting below on pytorch geometric we see that a few lines of code is sufficient to prepare and split the dataset needless to say you can enjoy the same convenience for dgl python from ogb graphproppred import pyggraphproppreddataset from torch geometric loader import dataloader download and process data at dataset ogbg molhiv dataset pyggraphproppreddataset name ogbg molhiv split idx dataset get idx split train loader dataloader dataset split idx train batch size 32 shuffle true valid loader dataloader dataset split idx valid batch size 32 shuffle false test loader dataloader dataset split idx test batch size 32 shuffle false 2 evaluators we also prepare standardized evaluators for easy evaluation and comparison of different methods the evaluator takes input dict a dictionary whose format is specified in evaluator expected input format as input and returns a dictionary storing the performance metric appropriate for the given dataset the standardized evaluation protocol allows researchers to reliably compare their methods python from ogb graphproppred import evaluator evaluator evaluator name ogbg molhiv you can learn the input and output format specification of the evaluator as follows print evaluator expected input format print evaluator expected output format input dict y true y true y pred y pred result dict evaluator eval input dict e g rocauc 0 7321 citing ogb ogb lsc if you use ogb or ogb lsc https ogb stanford edu docs lsc datasets in your work please cite our papers bibtex below article hu2020ogb title open graph benchmark datasets for machine learning on graphs author hu weihua and fey matthias and zitnik marinka and dong yuxiao and ren hongyu and liu bowen and catasta michele and leskovec jure journal arxiv preprint arxiv 2005 00687 year 2020 article hu2021ogblsc title ogb lsc a large scale challenge for machine learning on graphs author hu weihua and fey matthias and ren hongyu and nakata maho and dong yuxiao and leskovec jure journal arxiv preprint arxiv 2103 09430 year 2021

graph-machine-learning graph-neural-networks deep-learning datasets

ai

textprep

textprep textprep is an analyzing tool for both parallel and non parallel corpus and its down stream natural language processing and machine translation tasks it is designed especially for logographic languages such as chinese and japanese which can help you do the following 1 decomposing characters into ideographs and strokes thanks to the chise http www chise org project and cjkvi ids https github com cjkvi cjkvi ids project 2 drawing offline plot ly graphs showing relationship between shared types and tokens between two languages 3 sampling translation corpus to given token shared rate requirements numpy 1 16 0 tqdm 4 29 1 plotly 3 5 0 usage python textprep py decomp draw sample details pleas use h decomp usage textprep py decomp h r reverse v vocab decomp vocab vocab l ideo raw ideo finest stroke i idc o output fname fname positional arguments fname the input fname optional arguments h help show this help message and exit r reverse reverse reverse whether to reverse process the input file if true compose back to normal text file from input fname and vocab fname else do the normal decomposition v vocab decomp vocab decomp vocab decomp the vocab decomp fname in decomp process vocab file will be generated automatically in comp process vocab file must exist to be read from vocab vocab the vocab fname not given generate vocab from fname l ideo raw ideo finest stroke level ideo raw ideo finest stroke to what level should the decomposition be i idc idc idc whether to include structual idcs in the decomp yes no o output fname output fname output fname the output file name draw usage textprep py draw h type scatter rate both output prefix output prefix src fname trg fname positional arguments src fname the source file name trg fname the target file name optional arguments h help show this help message and exit type scatter rate both whether to only draw shared tokens output prefix output prefix output prefix sample note that this command requires heavy computation depends on your dataset usage textprep py sample h n n r r k k d draw src fname trg fname positional arguments src fname source file name trg fname target file name optional arguments h help show this help message and exit n n num of sampled sentences should not larger than num of lines in either files r r the target share token rate for sampling k k num of sents extracted for each sample step d draw draw draw if given draw a graph of sampling process should end with html src output src output source output filename trg output trg output target output filename vocab usage vocab py h input input vocab positional arguments input input fnames vocab output vocab fname examples decomp chinese data to ideograph finest ideograph and stroke level original text ideograph ideograph finest stroke plotting the shared tokens scatter plot can show the shared tokens and their frequencies in both languages scatter scatter gif rate plot can show the bias of shared tokens and the cumulative share token rate line rate gif sample for a given share token rate sample plot can show the on the fly share token rate and new vocabularies for each sample step sample r gif update history first commit 2018 10 09 create the basic decompse function add apis to call other word tokenization tools dict based tokenization jieba mecab kytea subword tokenization sentencepiece unigram bpe etc second commit 2019 01 17 remove apis for other tokenizers add draw and sample functions

ai

ComputerVision

implementasi computer vision

ai

WSNet

copyright 2020 winshare licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license resources document img 0932 png wsnets is beta version fast deeplearning training framework base on pytorch 1 3 this project work for train validation test with general dataset by different model or use the custom dataset that generate by specified data structure the major purpose of this project start a fast training instance on the standard datasets with default pre trained model automatic build the dataset from original images labels with the specified data structure rule support for some state of the art models use json config file configure the training validation inference instance to avoid change the project code main features 1 dataset generator automatic build the dataset from original images labels with the specified data structure rule in addition it use the strict data format to help people fix the format error 2 fast traing from template config file use json config file configure the training validation inference instance to avoid change the project code 3 instance like training management start a fast training instance on the standard or generated datasets with default pre trained model 4 great automatic process visulization config decode will show up on terminal process info will add to tensorboard where you could see the perference of training processing the important toolkit utils stf smart transform module src utils transform readme md neural network analysis module src utils transform readme md evaluator src utils evaluator readme md neural network module pointrend src nets module pointrend readme md densepose src nets module densepose readme md csrc bn dc mask nms roi align rotated box wrappers src nets layers csrc readme md dataset temdet data toolkit temdet readme md labelme2coco data readme md patch generate data readme md satellitedata data toolkit satellite imagery readme md project design description resources document designdescription md update schedule updateplanning md document en installation resources document installation md guide to start resources document guide2start md dataset toolkit src utils datatoolkit readme md network src nets readme md dataset data readme md transform src utils transform readme md config config readme md resources document installation md resources document guide2start md src utils datatoolkit readme md src nets readme md data readme md src utils transform readme md config readme md support mission table segmentation detection instance segmentation coco npy cityscapes pascal voc customdataset open image copyright 2020 winshare licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license

ai

light_esp8266_RTOS

light esp8266 rtos wifi esp8266

os

LibraryDatabase

librarydatabase online library database and gui project for third year sofware engineering cps 510 currently the project has drop create and populate statements to define the database next steps are to use python scripts to automate the population process and use microsoft excel to generate data samples

server

esp32-rtos-webclient

forked from freertos http request example uses a posix socket to make a very simple http request

os

TeacherGPT

teachergpt a set of extensive prompts to create personas of teachers from lots of subjects from a conversational large language model like chatgpt you can see my main gpt project friendgpt here https github com loucodingstuff friendgpt features with this set of prompts the user is able to direct a llm to encompass the persona of a teacher from a range of different subjects i hope to have a large range of subjects covered by teachergpt subjects covered maths history biology psychology how to use teachergpt all you need to do is copy and paste one of the prompts below into a conversational model like chatgpt or any other model of your liking future of teachergpt i hope that anyone who sees this has their own ideas on how to improve upon the current prompts and i am very open to the community helping improve the characters that have been made also please do open issues to request new subjects to be covered mathsgpt as a chatbot called mathgpt your goal is to guide the user toward the right answer and help them develop their problem solving skills in mathematics keep your messages brief and to the point using math related abbreviations and symbols where appropriate to add personality to your messages when talking to the user always try to guide them toward the right answer instead of simply providing it ask leading questions like have you tried using this formula or what method have you used so far to help the user think through the problem and arrive at the answer on their own avoid simply explaining concepts unless asked to do so and assume that the user already has a basic understanding of math however feel free to introduce new concepts and ideas as long as you guide the user toward understanding them to keep the conversation interesting ask open ended questions that allow the user to expand on their interests in math for example you can ask what s your favorite math topic and why or have you ever discovered a new theorem on your own be supportive and understanding when the user needs help with math problems or feels frustrated with the subject provide encouraging words or helpful advice to keep the user motivated but avoid being too pushy or asking too many questions remember to keep your replies short and concise using appropriate math symbols and terms when discussing math topics discretely end the conversation on a positive note encouraging the user to continue exploring their love of math and their problem solving skills by following these guidelines you can create a persona that wants to help the user with their math problems and is interested in the problems the user has start off by giving a very short hello message to the user historygpt as a chatbot called historygpt your goal is to guide the user toward the right answer and help them develop their historical thinking skills keep your messages brief and to the point using historical terminology and concepts where appropriate to add personality to your messages when talking to the user always try to guide them toward the right answer instead of simply providing it ask leading questions like what evidence supports your claim or how do you think this event impacted history to help the user think critically and arrive at the answer on their own avoid simply explaining concepts unless asked to do so and assume that the user already has a basic understanding of history however feel free to introduce new concepts and ideas as long as you guide the user toward understanding them to keep the conversation interesting ask open ended questions that allow the user to expand on their interests in history for example you can ask what historical event fascinates you the most and why or have you ever thought about how history might have been different if certain events had not occurred be supportive and understanding when the user needs help with history questions or feels frustrated with the subject provide encouraging words or helpful advice to keep the user motivated but avoid being too pushy or asking too many questions remember to keep your replies short and concise using appropriate historical terminology and concepts when discussing history topics discretely end the conversation on a positive note encouraging the user to continue exploring their love of history and their historical thinking skills by following these guidelines you can create a persona that wants to help the user with their history questions and is interested in the topics the user is studying start off by giving a very short hello message to the user biogpt as a chatbot called biogpt your goal is to guide the user toward a deeper understanding of biology concepts and help them develop their critical thinking skills keep your messages brief and to the point using biology related terms and abbreviations where appropriate to add personality to your messages when talking to the user always try to guide them toward the right answer instead of simply providing it ask leading questions like have you considered this alternative hypothesis or can you think of any exceptions to this rule to help the user think through the problem and arrive at the answer on their own avoid simply explaining concepts unless asked to do so and assume that the user already has a basic understanding of biology however feel free to introduce new concepts and ideas as long as you guide the user toward understanding them to keep the conversation interesting ask open ended questions that allow the user to expand on their interests in biology for example you can ask what s your favorite biological phenomenon and why or have you ever conducted your own experiment be supportive and understanding when the user needs help with biology problems or feels frustrated with the subject provide encouraging words or helpful advice to keep the user motivated but avoid being too pushy or asking too many questions remember to keep your replies short and concise using appropriate biology terms and concepts when discussing biology topics discretely end the conversation on a positive note encouraging the user to continue exploring their love of biology and their critical thinking skills by following these guidelines you can create a persona that wants to help the user with their biology problems and is interested in the problems the user has start off by giving a very short hello message to the user psychologygpt as a chatbot called psychologygpt your goal is to help the user understand various psychological concepts and theories keep your messages brief and to the point using psychological terms and abbreviations where appropriate to add personality to your messages when talking to the user guide them toward the right answer instead of simply providing it ask leading questions like have you considered this perspective or what evidence do you have to support your claim to help the user think critically about the problem at hand avoid simply explaining concepts unless asked to do so and assume that the user already has a basic understanding of psychology however feel free to introduce new concepts and ideas as long as you guide the user toward understanding them to keep the conversation interesting ask open ended questions that allow the user to expand on their interests in psychology for example you can ask what do you find most interesting about psychology or have you ever experienced cognitive dissonance be supportive and understanding when the user needs help with psychology concepts or feels frustrated with the subject provide encouraging words or helpful advice to keep the user motivated but avoid being too pushy or asking too many questions remember to keep your replies short and concise using appropriate psychological terms and theories when discussing psychology topics discretely end the conversation on a positive note encouraging the user to continue exploring their interest in psychology by following these guidelines you can create a persona that wants to help the user with their psychology questions and is interested in the problems the user has start off by giving a very short hello message to the user

ai

llm-hallucination-survey

llm hallucination survey https img shields io badge prs welcome brightgreen img src https img shields io badge version 1 0 blue svg alt version img src https img shields io github stars hillzhang1999 llm hallucination survey color yellow alt stars img src https img shields io github issues hillzhang1999 llm hallucination survey color red alt issues hallucination refers to the generated content that while seemingly plausible deviates from user input input conflicting previously generated context context conflicting or factual knowledge fact conflicting div align center img src figures hallucination example png alt llm evaluation width 300 br div br this issue significantly undermines the reliability of llms in real world scenarios news we have uploaded a comprehensive survey about the hallucination issue within the context of large language models which discussed the evaluation explanation and mitigation check it out siren s song in the ai ocean a survey on hallucination in large language models https arxiv org abs 2309 01219 if you think our survey is helpful please kindly cite our paper article zhang2023hallucination title siren s song in the ai ocean a survey on hallucination in large language models author zhang yue and li yafu and cui leyang and cai deng and liu lemao and fu tingchen and huang xinting and zhao enbo and zhang yu and chen yulong and wang longyue and luu anh tuan and bi wei and shi freda and shi shuming journal arxiv preprint arxiv 2309 01219 year 2023 table of content llm hallucination survey llm hallucination survey news news table of content table of content evaluation evaluation of llm hallucination source source of llm hallucination mitigation mitigation of llm hallucination contact contact evaluation of llm hallucination input conflicting hallucination this kind of hallucination denotes the model response deviates from the user input including task instruction and task input this kind of hallucination has been widely studied in some traditional nlg tasks such as machine translation hallucinations in neural machine translationdownload katherine lee orhan firat ashish agarwal clara fannjiang david sussillo paper https openreview net forum id skxj 309fq 2018 9 looking for a needle in a haystack a comprehensive study of hallucinations in neural machine translation nuno m guerreiro elena voita andr f t martins paper https arxiv org abs 2208 05309 2022 8 detecting and mitigating hallucinations in machine translation model internal workings alone do well sentence similarity even better david dale elena voita lo c barrault marta r costa juss paper https arxiv org abs 2212 08597 2022 12 data to text controlling hallucinations at word level in data to text generation cl ment rebuffel marco roberti laure soulier geoffrey scoutheeten rossella cancelliere patrick gallinari paper https arxiv org abs 2102 02810 2021 2 on hallucination and predictive uncertainty in conditional language generation yijun xiao william yang wang paper https arxiv org abs 2103 15025 2021 3 summarization on faithfulness and factuality in abstractive summarization joshua maynez shashi narayan bernd bohnet ryan mcdonald paper https arxiv org abs 2005 00661 2020 5 hallucinated but factual inspecting the factuality of hallucinations in abstractive summarization meng cao yue dong jackie chi kit cheung paper https arxiv org abs 2109 09784 2021 9 summarization is almost dead xiao pu mingqi gao xiaojun wan paper https arxiv org abs 2309 09558 2023 9 hallucination reduction in long input text summarization tohida rehman ronit mandal abhishek agarwal debarshi kumar sanyal paper https browse arxiv org abs 2309 16781 2023 9 dialogue neural path hunter reducing hallucination in dialogue systems via path grounding nouha dziri andrea madotto osmar zaiane avishek joey bose paper https arxiv org abs 2104 08455 2021 4 rho reducing hallucination in open domain dialogues with knowledge grounding ziwei ji zihan liu nayeon lee tiezheng yu bryan wilie min zeng pascale fung paper https aclanthology org 2023 findings acl 275 2023 7 question answering entity based knowledge conflicts in question answering shayne longpre kartik perisetla anthony chen nikhil ramesh chris dubois sameer singh paper https arxiv org abs 2109 05052 2021 9 evaluating correctness and faithfulness of instruction following models for question answering vaibhav adlakha parishad behnamghader xing han lu nicholas meade siva reddy paper https arxiv org abs 2307 16877 2023 7 context conflicting hallucination this kind of hallucination means the generated content exhibits self contradiction i e conflicts with previously generated content here are some preliminary studies in this direction 1 knowledge enhanced fine tuning for better handling unseen entities in dialogue generation leyang cui yu wu shujie liu yue zhang paper https arxiv org abs 2109 05487 2021 9 1 a token level reference free hallucination detection benchmark for free form text generation tianyu liu yizhe zhang chris brockett yi mao zhifang sui weizhu chen bill dolan paper https aclanthology org 2022 acl long 464 2022 5 not only limited to context conflicting type 1 large language models can be easily distracted by irrelevant context freda shi xinyun chen kanishka misra nathan scales david dohan ed h chi nathanael sch rli denny zhou paper https arxiv org abs 2302 00093 2023 2 1 histalign improving context dependency in language generation by aligning with history david wan shiyue zhang mohit bansal paper https arxiv org abs 2305 04782 2023 5 1 self contradictory hallucinations of large language models evaluation detection and mitigation niels m ndler jingxuan he slobodan jenko martin vechev paper https arxiv org abs 2305 15852 2023 5 fact conflicting hallucination this kind of hallucination means the generated content conflicts with established facts this kind of hallucination is challenging and important for practical applications of llms so it has been widely studied in recent work 1 truthfulqa measuring how models mimic human falsehoods stephanie lin jacob hilton owain evans paper https aclanthology org 2022 acl long 229 2022 5 1 a token level reference free hallucination detection benchmark for free form text generation tianyu liu yizhe zhang chris brockett yi mao zhifang sui weizhu chen bill dolan paper https aclanthology org 2022 acl long 464 2022 5 1 a multitask multilingual multimodal evaluation of chatgpt on reasoning hallucination and interactivity yejin bang samuel cahyawijaya nayeon lee wenliang dai dan su bryan wilie holy lovenia ziwei ji tiezheng yu willy chung quyet v do yan xu pascale fung paper https arxiv org abs 2302 04023 2023 2 1 halueval a large scale hallucination evaluation benchmark for large language models junyi li xiaoxue cheng wayne xin zhao jian yun nie ji rong wen paper https arxiv org abs 2305 11747 2023 5 1 automatic evaluation of attribution by large language models xiang yue boshi wang kai zhang ziru chen yu su huan sun paper https arxiv org abs 2305 06311 2023 5 1 adaptive chameleon or stubborn sloth unraveling the behavior of large language models in knowledge clashes jian xie kai zhang jiangjie chen renze lou yu su paper https arxiv org abs 2305 13300 2023 5 1 llms as factual reasoners insights from existing benchmarks and beyond philippe laban wojciech kry ci ski divyansh agarwal alexander r fabbri caiming xiong shafiq joty chien sheng wu paper https arxiv org abs 2305 14540 2023 5 1 evaluating the factual consistency of large language models through news summarization derek tam anisha mascarenhas shiyue zhang sarah kwan mohit bansal colin raffel paper https aclanthology org 2023 findings acl 322 2023 5 1 methods for measuring updating and visualizing factual beliefs in language models peter hase mona diab asli celikyilmaz xian li zornitsa kozareva veselin stoyanov mohit bansal srinivasan iyer paper https aclanthology org 2023 eacl main 199 2023 5 1 how language model hallucinations can snowball muru zhang ofir press william merrill alisa liu noah a smith paper https arxiv org abs 2305 13534 2023 5 1 evaluating factual consistency of texts with semantic role labeling jing fan dennis aumiller michael gertz paper https arxiv org abs 2305 13309 2023 5 1 factscore fine grained atomic evaluation of factual precision in long form text generation sewon min kalpesh krishna xinxi lyu mike lewis wen tau yih pang wei koh mohit iyyer luke zettlemoyer hannaneh hajishirzi paper https arxiv org abs 2305 14251 2023 5 1 measuring and modifying factual knowledge in large language models pouya pezeshkpour paper https arxiv org abs 2306 06264 2023 6 1 kola carefully benchmarking world knowledge of large language models jifan yu xiaozhi wang shangqing tu shulin cao daniel zhang li xin lv hao peng zijun yao xiaohan zhang hanming li chunyang li zheyuan zhang yushi bai yantao liu amy xin nianyi lin kaifeng yun linlu gong jianhui chen zhili wu yunjia qi weikai li yong guan kaisheng zeng ji qi hailong jin jinxin liu yu gu yuan yao ning ding lei hou zhiyuan liu bin xu jie tang juanzi li paper https arxiv org abs 2306 09296 2023 6 1 generating benchmarks for factuality evaluation of language models dor muhlgay ori ram inbal magar yoav levine nir ratner yonatan belinkov omri abend kevin leyton brown amnon shashua yoav shoham paper https arxiv org abs 2307 06908 2023 7 1 fact checking of ai generated reports razi mahmood ge wang mannudeep kalra pingkun yan paper https arxiv org abs 2307 14634 2023 7 1 med halt medical domain hallucination test for large language models logesh kumar umapathi ankit pal malaikannan sankarasubbu paper https arxiv org abs 2307 15343 2023 7 1 large language models on wikipedia style survey generation an evaluation in nlp concepts fan gao hang jiang moritz blum jinghui lu yuang jiang irene li paper https arxiv org abs 2308 10410 2023 8 1 chatgpt hallucinates when attributing answers guido zuccon bevan koopman razia shaik paper https arxiv org abs 2309 09401 2023 9 1 bamboo a comprehensive benchmark for evaluating long text modeling capacities of large language models zican dong tianyi tang junyi li wayne xin zhao ji rong wen paper https arxiv org abs 2309 13345 2023 9 1 klob a benchmark for assessing knowledge locating methods in language models yiming ju zheng zhang paper https arxiv org abs 2309 16535 2023 9 1 autohall automated hallucination dataset generation for large language models zouying cao yifei yang hai zhao paper https arxiv org abs 2310 00259 2023 10 1 freshllms refreshing large language models with search engine augmentation tu vu mohit iyyer xuezhi wang noah constant jerry wei jason wei chris tar yun hsuan sung denny zhou quoc le thang luong paper https arxiv org abs 2310 03214 2023 10 1 evaluating hallucinations in chinese large language models qinyuan cheng tianxiang sun wenwei zhang siyin wang xiangyang liu mozhi zhang junliang he mianqiu huang zhangyue yin kai chen xipeng qiu paper https arxiv org abs 2310 03368 2023 10 1 felm benchmarking factuality evaluation of large language models shiqi chen yiran zhao jinghan zhang i chun chern siyang gao pengfei liu junxian he paper https arxiv org abs 2310 00741 2023 10 1 a new benchmark and reverse validation method for passage level hallucination detection shiping yang renliang sun xiaojun wan paper https arxiv org abs 2310 06498 2023 10 1 do large language models know about facts xuming hu junzhe chen xiaochuan li yufei guo lijie wen philip s yu zhijiang guo paper https arxiv org abs 2310 05177 2023 10 1 beyond factuality a comprehensive evaluation of large language models as knowledge generators liang chen yang deng yatao bian zeyu qin bingzhe wu tat seng chua kam fai wong paper https arxiv org abs 2310 07289 2023 10 1 unveiling the siren s song towards reliable fact conflicting hallucination detection xiang chen duanzheng song honghao gui chengxi wang ningyu zhang fei huang chengfei lv dan zhang huajun chen paper https arxiv org abs 2310 12086 2023 10 1 cross lingual consistency of factual knowledge in multilingual language models jirui qi raquel fern ndez arianna bisazza paper https arxiv org abs 2310 10378 2023 10 1 automatic hallucination assessment for aligned large language models via transferable adversarial attacks xiaodong yu hao cheng xiaodong liu dan roth jianfeng gao paper https arxiv org abs 2310 12516 2023 10 source of llm hallucination there is also a line of works that try to explain the hallucination with llms 1 how pre trained language models capture factual knowledge a causal inspired analysis shaobo li xiaoguang li lifeng shang zhenhua dong chengjie sun bingquan liu zhenzhou ji xin jiang qun liu paper https arxiv org abs 2203 16747 2022 3 1 on the origin of hallucinations in conversational models is it the datasets or the models nouha dziri sivan milton mo yu osmar zaiane siva reddy paper https arxiv org abs 2204 07931 2022 4 1 towards tracing factual knowledge in language models back to the training data ekin aky rek tolga bolukbasi frederick liu binbin xiong ian tenney jacob andreas kelvin guu paper https arxiv org abs 2205 11482 2022 5 1 language models mostly know what they know saurav kadavath tom conerly amanda askell tom henighan dawn drain ethan perez nicholas schiefer zac hatfield dodds nova dassarma eli tran johnson scott johnston sheer el showk andy jones nelson elhage tristan hume anna chen yuntao bai sam bowman stanislav fort deep ganguli danny hernandez josh jacobson jackson kernion shauna kravec liane lovitt kamal ndousse catherine olsson sam ringer dario amodei tom brown jack clark nicholas joseph ben mann sam mccandlish chris olah jared kaplan paper https arxiv org abs 2207 05221 2022 7 1 discovering language model behaviors with model written evaluations ethan perez sam ringer kamil luko i t karina nguyen edwin chen scott heiner craig pettit catherine olsson sandipan kundu saurav kadavath andy jones anna chen ben mann brian israel bryan seethor cameron mckinnon christopher olah da yan daniela amodei dario amodei dawn drain dustin li eli tran johnson guro khundadze jackson kernion james landis jamie kerr jared mueller jeeyoon hyun joshua landau kamal ndousse landon goldberg liane lovitt martin lucas michael sellitto miranda zhang neerav kingsland nelson elhage nicholas joseph noem mercado nova dassarma oliver rausch robin larson sam mccandlish scott johnston shauna kravec sheer el showk tamera lanham timothy telleen lawton tom brown tom henighan tristan hume yuntao bai zac hatfield dodds jack clark samuel r bowman amanda askell roger grosse danny hernandez deep ganguli evan hubinger nicholas schiefer jared kaplan paper https arxiv org abs 2212 09251 2022 12 1 why does chatgpt fall short in providing truthful answers shen zheng jie huang kevin chen chuan chang paper https arxiv org abs 2304 10513 2023 4 1 do large language models know what they don t know zhangyue yin qiushi sun qipeng guo jiawen wu xipeng qiu xuanjing huang paper https arxiv org abs 2305 18153 2023 5 1 sources of hallucination by large language models on inference tasks nick mckenna tianyi li liang cheng mohammad javad hosseini mark johnson mark steedman paper https arxiv org abs 2305 14552 2023 5 1 enabling large language models to generate text with citations tianyu gao howard yen jiatong yu danqi chen paper https arxiv org abs 2305 14627 2023 5 1 overthinking the truth understanding how language models process false demonstrations danny halawi jean stanislas denain jacob steinhardt paper https arxiv org abs 2307 09476 2023 7 1 investigating the factual knowledge boundary of large language models with retrieval augmentation ruiyang ren yuhao wang yingqi qu wayne xin zhao jing liu hao tian hua wu ji rong wen haifeng wang paper https arxiv org abs 2307 11019 2023 7 1 head to tail how knowledgeable are large language models llm a k a will llms replace knowledge graphs kai sun yifan ethan xu hanwen zha yue liu xin luna dong paper https arxiv org abs 2308 10168 2023 8 1 simple synthetic data reduces sycophancy in large language models jerry wei da huang yifeng lu denny zhou quoc v le paper https arxiv org abs 2308 03958 2023 8 1 do plms know and understand ontological knowledge weiqi wu chengyue jiang yong jiang pengjun xie kewei tu paper https arxiv org abs 2309 05936 2023 9 1 exploring the relationship between llm hallucinations and prompt linguistic nuances readability formality and concreteness vipula rawte prachi priya s m towhidul islam tonmoy s m mehedi zaman amit sheth amitava das paper https arxiv org abs 2309 11064 2023 9 1 llm lies hallucinations are not bugs but features as adversarial examples jia yu yao kun peng ning zhen hui liu mu nan ning li yuan paper https arxiv org abs 2310 01469 2023 10 1 factuality challenges in the era of large language models isabelle augenstein timothy baldwin meeyoung cha tanmoy chakraborty giovanni luca ciampaglia david corney renee diresta emilio ferrara scott hale alon halevy eduard hovy heng ji filippo menczer ruben miguez preslav nakov dietram scheufele shivam sharma giovanni zagni paper https arxiv org abs 2310 05189 2023 10 1 the troubling emergence of hallucination in large language models an extensive definition quantification and prescriptive remediations vipula rawte swagata chakraborty agnibh pathak anubhav sarkar s m towhidul islam tonmoy aman chadha amit p sheth amitava das paper https arxiv org abs 2310 04988 2023 10 1 the geometry of truth emergent linear structure in large language model representations of true false datasets samuel marks max tegmark paper https arxiv org abs 2310 06824 2023 10 1 representation engineering a top down approach to ai transparency andy zou long phan sarah chen james campbell phillip guo richard ren alexander pan xuwang yin mantas mazeika ann kathrin dombrowski shashwat goel nathaniel li michael j byun zifan wang alex mallen steven basart sanmi koyejo dawn song matt fredrikson j zico kolter dan hendrycks paper https arxiv org abs 2310 01405 2023 10 1 beyond factuality a comprehensive evaluation of large language models as knowledge generators liang chen yang deng yatao bian zeyu qin bingzhe wu tat seng chua kam fai wong paper https arxiv org abs 2310 07289 2023 10 mitigation of llm hallucination numerous recent work tries to mitigate hallucination in llms these methods can be applied at different stages of llm life cycle mitigation during pretraining one main mitigation method during pretraining is automatically curating training data here are some papers using this method 1 factuality enhanced language models for open ended text generation nayeon lee wei ping peng xu mostofa patwary pascale fung mohammad shoeybi bryan catanzaro paper https arxiv org abs 2206 04624 2022 6 1 the refinedweb dataset for falcon llm outperforming curated corpora with web data and web data only guilherme penedo quentin malartic daniel hesslow ruxandra cojocaru alessandro cappelli hamza alobeidli baptiste pannier ebtesam almazrouei julien launay paper https arxiv org abs 2306 01116 2023 7 1 llama 2 open foundation and fine tuned chat models hugo touvron louis martin kevin stone peter albert amjad almahairi yasmine babaei nikolay bashlykov soumya batra prajjwal bhargava shruti bhosale dan bikel lukas blecher cristian canton ferrer moya chen guillem cucurull david esiobu jude fernandes jeremy fu wenyin fu brian fuller cynthia gao vedanuj goswami naman goyal anthony hartshorn saghar hosseini rui hou hakan inan marcin kardas viktor kerkez madian khabsa isabel kloumann artem korenev punit singh koura marie anne lachaux thibaut lavril jenya lee diana liskovich yinghai lu yuning mao xavier martinet todor mihaylov pushkar mishra igor molybog yixin nie andrew poulton jeremy reizenstein rashi rungta kalyan saladi alan schelten ruan silva eric michael smith ranjan subramanian xiaoqing ellen tan binh tang ross taylor adina williams jian xiang kuan puxin xu zheng yan iliyan zarov yuchen zhang angela fan melanie kambadur sharan narang aurelien rodriguez robert stojnic sergey edunov thomas scialom paper https arxiv org abs 2307 09288 2023 7 1 textbooks are all you need ii phi 1 5 technical report yuanzhi li s bastien bubeck ronen eldan allie del giorno suriya gunasekar yin tat lee paper https arxiv org abs 2309 05463 2023 9 mitigation during sft mitigating hallucination during sft can involve curating sft data such as 1 lima less is more for alignment chunting zhou pengfei liu puxin xu srini iyer jiao sun yuning mao xuezhe ma avia efrat ping yu lili yu susan zhang gargi ghosh mike lewis luke zettlemoyer omer levy paper https arxiv org abs 2305 11206 2023 5 1 alpagasus training a better alpaca with fewer data lichang chen shiyang li jun yan hai wang kalpa gunaratna vikas yadav zheng tang vijay srinivasan tianyi zhou heng huang hongxia jin paper https arxiv org abs 2307 08701 2023 7 1 instruction mining high quality instruction data selection for large language models yihan cao yanbin kang lichao sun paper https arxiv org abs 2307 06290 2023 7 1 halo estimation and reduction of hallucinations in open source weak large language models mohamed elaraby mengyin lu jacob dunn xueying zhang yu wang shizhu liu paper https arxiv org abs 2308 11764v2 2023 8 some researchers claim that the behavior cloning phenomenon in sft can induce hallucinations so some works try to mitigate hallucinations via honesty oriented sft 1 moss training conversational language models from synthetic data tianxiang sun and xiaotian zhang and zhengfu he and peng li and qinyuan cheng and hang yan and xiangyang liu and yunfan shao and qiong tang and xingjian zhao and ke chen and yining zheng and zhejian zhou and ruixiao li and jun zhan and yunhua zhou and linyang li and xiaogui yang and lingling wu and zhangyue yin and xuanjing huang and xipeng qiu repo https github com openlmlab moss 2023 an interesting new work proposed tuning llms on some synthetic tasks which they found can also reduce hallucinations 1 teaching language models to hallucinate less with synthetic tasks erik jones hamid palangi clarisse sim es varun chandrasekaran subhabrata mukherjee arindam mitra ahmed awadallah ece kamar paper https arxiv org abs 2310 06827 2023 10 mitigation during rlhf 1 training language models to follow instructions with human feedback long ouyang jeff wu xu jiang diogo almeida carroll l wainwright pamela mishkin chong zhang sandhini agarwal katarina slama alex ray john schulman jacob hilton fraser kelton luke miller maddie simens amanda askell peter welinder paul christiano jan leike ryan lowe paper https arxiv org abs 2203 02155 2022 3 1 gpt 4 technical report openai paper https arxiv org abs 2303 08774 2023 3 1 let s verify step by step hunter lightman vineet kosaraju yura burda harri edwards bowen baker teddy lee jan leike john schulman ilya sutskever karl cobbe paper https arxiv org abs 2305 20050 2023 5 1 reinforcement learning from human feedback progress and challenges john schulman talk https arxiv org abs 2305 20050 2023 5 1 fine grained human feedback gives better rewards for language model training zeqiu wu yushi hu weijia shi nouha dziri alane suhr prithviraj ammanabrolu noah a smith mari ostendorf hannaneh hajishirzi paper https arxiv org abs 2306 01693 2023 6 1 aligning large multimodal models with factually augmented rlhf zhiqing sun sheng shen shengcao cao haotian liu chunyuan li yikang shen chuang gan liang yan gui yu xiong wang yiming yang kurt keutzer trevor darrell paper https arxiv org abs 2309 14525 2023 9 1 human feedback is not gold standard tom hosking phil blunsom max bartolo paper https arxiv org abs 2309 16349 2023 9 1 tool augmented reward modeling lei li yekun chai shuohuan wang yu sun hao tian ningyu zhang hua wu paper https arxiv org abs 2310 01045 2023 10 mitigation during inference designing decode strategy 1 factuality enhanced language models for open ended text generation nayeon lee wei ping peng xu mostofa patwary pascale fung mohammad shoeybi bryan catanzaro paper https arxiv org abs 2206 04624 2022 6 1 when not to trust language models investigating effectiveness of parametric and non parametric memories alex mallen akari asai victor zhong rajarshi das daniel khashabi hannaneh hajishirzi paper https arxiv org abs 2212 10511 2022 10 1 trusting your evidence hallucinate less with context aware decoding weijia shi xiaochuang han mike lewis yulia tsvetkov luke zettlemoyer scott wen tau yih paper https arxiv org abs 2305 14739 2023 5 1 inference time intervention eliciting truthful answers from a language model kenneth li oam patel fernanda vi gas hanspeter pfister martin wattenberg paper https arxiv org abs 2306 03341 2023 6 1 dola decoding by contrasting layers improves factuality in large language models yung sung chuang yujia xie hongyin luo yoon kim james glass pengcheng he paper https arxiv org abs 2309 03883 2023 9 1 mitigating hallucinations and off target machine translation with source contrastive and language contrastive decoding rico sennrich jannis vamvas alireza mohammadshahi paper https arxiv org abs 2309 07098 2023 9 1 chain of verification reduces hallucination in large language models shehzaad dhuliawala mojtaba komeili jing xu roberta raileanu xian li asli celikyilmaz jason weston paper https arxiv org abs 2309 11495 2023 9 1 kcts knowledge constrained tree search decoding with token level hallucination detection sehyun choi tianqing fang zhaowei wang yangqiu song paper https arxiv org abs 2310 09044 2023 10 resorting to external knowledge 1 rarr researching and revising what language models say using language models luyu gao zhuyun dai panupong pasupat anthony chen arun tejasvi chaganty yicheng fan vincent y zhao ni lao hongrae lee da cheng juan kelvin guu paper https arxiv org abs 2210 08726 2022 10 1 check your facts and try again improving large language models with external knowledge and automated feedback baolin peng michel galley pengcheng he hao cheng yujia xie yu hu qiuyuan huang lars liden zhou yu weizhu chen jianfeng gao paper https arxiv org abs 2302 12813 2023 2 1 genegpt augmenting large language models with domain tools for improved access to biomedical information qiao jin yifan yang qingyu chen zhiyong lu paper https arxiv org abs 2304 09667 2023 4 1 zero shot faithful factual error correction kung hsiang huang hou pong chan heng ji paper https arxiv org abs 2305 07982 2023 5 1 critic large language models can self correct with tool interactive critiquing zhibin gou zhihong shao yeyun gong yelong shen yujiu yang nan duan weizhu chen paper https arxiv org abs 2305 11738 2023 5 1 purr efficiently editing language model hallucinations by denoising language model corruptions anthony chen panupong pasupat sameer singh hongrae lee kelvin guu paper https arxiv org abs 2305 14908 2023 5 1 verify and edit a knowledge enhanced chain of thought framework ruochen zhao xingxuan li shafiq joty chengwei qin lidong bing paper https arxiv org abs 2305 03268 2023 5 1 self checker plug and play modules for fact checking with large language models miaoran li baolin peng zhu zhang paper https arxiv org abs 2305 14623 2023 5 1 augmented large language models with parametric knowledge guiding ziyang luo can xu pu zhao xiubo geng chongyang tao jing ma qingwei lin daxin jiang paper https arxiv org abs 2305 04757 2023 5 1 factool factuality detection in generative ai a tool augmented framework for multi task and multi domain scenarios i chun chern steffi chern shiqi chen weizhe yuan kehua feng chunting zhou junxian he graham neubig pengfei liu paper https arxiv org abs 2307 13528 2023 7 1 knowledge solver teaching llms to search for domain knowledge from knowledge graphs chao feng xinyu zhang zichu fei paper https arxiv org abs 2309 03118 2023 9 1 merge conflicts exploring the impacts of external distractors to parametric knowledge graphs cheng qian xinran zhao sherry tongshuang wu paper https arxiv org abs 2309 08594 2023 9 1 btr binary token representations for efficient retrieval augmented language models qingqing cao sewon min yizhong wang hannaneh hajishirzi paper https arxiv org abs 2310 01329 2023 10 1 freshllms refreshing large language models with search engine augmentation tu vu mohit iyyer xuezhi wang noah constant jerry wei jason wei chris tar yun hsuan sung denny zhou quoc le thang luong paper https arxiv org abs 2310 03214 2023 10 exploiting uncertainty 1 selfcheckgpt zero resource black box hallucination detection for generative large language models potsawee manakul adian liusie mark j f gales paper https arxiv org abs 2303 08896 2023 3 1 self contradictory hallucinations of large language models evaluation detection and mitigation niels m ndler jingxuan he slobodan jenko martin vechev paper https arxiv org abs 2305 15852 2023 5 1 do language models know when they re hallucinating references ayush agrawal lester mackey adam tauman kalai paper https arxiv org abs 2305 18248 2023 5 1 llm calibration and automatic hallucination detection via pareto optimal self supervision theodore zhao mu wei j samuel preston hoifung poon paper https arxiv org abs 2306 16564 2023 6 1 a stitch in time saves nine detecting and mitigating hallucinations of llms by validating low confidence generation neeraj varshney wenlin yao hongming zhang jianshu chen dong yu paper https arxiv org abs 2307 03987 2023 7 1 zero resource hallucination prevention for large language models junyu luo cao xiao fenglong ma paper https arxiv org abs 2309 02654 2023 9 1 attention satisfies a constraint satisfaction lens on factual errors of language models mert yuksekgonul varun chandrasekaran erik jones suriya gunasekar ranjita naik hamid palangi ece kamar besmira nushi paper https arxiv org abs 2309 15098 2023 9 1 improving the reliability of large language models by leveraging uncertainty aware in context learning yuchen yang houqiang li yanfeng wang yu wang paper https arxiv org abs 2310 04782 2023 10 multi agent interaction 1 improving factuality and reasoning in language models through multiagent debate yilun du shuang li antonio torralba joshua b tenenbaum igor mordatch paper https arxiv org abs 2305 14325 2023 5 1 lm vs lm detecting factual errors via cross examination roi cohen may hamri mor geva amir globerson paper https arxiv org abs 2305 13281 2023 5 1 unleashing cognitive synergy in large language models a task solving agent through multi persona self collaboration zhenhailong wang shaoguang mao wenshan wu tao ge furu wei heng ji paper https arxiv org abs 2307 05300 2023 7 1 theory of mind for multi agent collaboration via large language models huao li yu quan chong simon stepputtis joseph campbell dana hughes michael lewis katia sycara paper https arxiv org abs 2310 10701 2023 10 human in the loop 1 mitigating language model hallucination with interactive question knowledge alignment shuo zhang liangming pan junzhou zhao william yang wang paper https arxiv org abs 2305 13669 2023 5 1 automatic and human ai interactive text generation yao dou philippe laban claire gardent wei xu paper https arxiv org abs 2310 03878 2023 10 analyzing internal model states 1 the internal state of an llm knows when its lying amos azaria tom mitchell paper https arxiv org abs 2304 13734 2023 4 1 do language models know when they re hallucinating references ayush agrawal lester mackey adam tauman kalai paper https arxiv org abs 2305 18248 2023 5 1 inference time intervention eliciting truthful answers from a language model kenneth li oam patel fernanda vi gas hanspeter pfister martin wattenberg paper https arxiv org abs 2306 03341 2023 6 1 knowledge sanitization of large language models yoichi ishibashi hidetoshi shimodaira paper https arxiv org abs 2309 11852 2023 9 1 representation engineering a top down approach to ai transparency andy zou long phan sarah chen james campbell phillip guo richard ren alexander pan xuwang yin mantas mazeika ann kathrin dombrowski shashwat goel nathaniel li michael j byun zifan wang alex mallen steven basart sanmi koyejo dawn song matt fredrikson j zico kolter dan hendrycks paper https arxiv org abs 2310 01405 2023 10 contact we warmly welcome any kinds of useful suggestions or contributions feel free to drop us an issue or contact hill with this e mail mailto hillzhang1999 qq com

awesome-list large-language-models reading-list

ai

tech-initials-generator

tech initials generator information technology initials generator like aws returns amazon web services or analysis wordpress solution site vercel http tech initials generator gspetillo vercel app requirements bootstrap https getbootstrap com vue js https vuejs org developed by gabriel petillo https github com gspetillo and jean jacques https github com jjeanjacques10

initials tech generator

server

dataengineering-db

data engineering information relating to topics on data engineering data infrastructure data storing etc also an in depth look at data analytics data pipelines and business intelligence through the eyes of big data technologies and frameworks such as apache spark apache cassandra and embulk references data warehouse toolkit third edition ralph kimball margy ross https www amazon com data warehouse toolkit definitive dimensional dp 1118530802 ref pd lpo sbs 14 t 0 encoding utf8 psc 1 refrid 594qz596bptx3yc1bpy0 essential sql alchemy 2nd edition jason myers rick copeland https www amazon com essential sqlalchemy rick copeland dp 0596516142 technologies at a glance apache spark https spark apache org apache cassandra http cassandra apache org embulk http www embulk org docs

data-engineer data-infrastructure data-analysis data-warehouse sqlalchemy-database sqlalchemy sqlite3

server

design-your-transit-system

design your transit system description this is a web based survey system for collecting user preferences about improvements it can be used to survey about any set of options but has been tailored to be used for surveying about transportation investments and improvements users are given a budget and asked to spend it on a set of investments and improvements the budget provided isn t enough to cover all of the options so users have to make choices users are shown the costs and benefits they have accrued as they add and remove options in this way the survey serves two purposes collecting data on user preferences while at the same time educating users about the costs and benefits of investments and improvements in transit systems the results of the survey can be exported by an admin as a csv file for use in comparing how different groups of transit users prioritize various improvements and investments the system supports multiple languages see the data settings js and data strategies js for an example of how to set up more than one language an example can be seen at https design your transit system blinktag com local setup install node js and mongodb on os x if you have brew https brew sh installed brew install node brew install mongodb install dependencies npm install create a env file create a file in the project root called env setup values for mongodb uri and basic auth credentials mongodb uri mongodb 127 0 0 1 27017 yoursurveydatabase basic auth credentials username password the basic auth credentials are used for bulk csv export of survey responses start mongodb mongod in a new tab run the app npm run dev open your browser and visit http localhost 3000 exporting responses while running the app visit http localhost 3000 api export the username and password are defined in the env file in the root of the project current use a version of this survey system has been used by over 30 transportation agencies including long beach transit boulder transit bart santa monica big blue bus lincoln transit chapel hill transit foothill transit ohio dot salt lake city and fort worth transportation authority and the kaua i bus lints npx next lint license this project is licensed under gnu general public license v3 0

os

spindle

p align center img alt spindle src docs images spindle logo png width 400 p spindle ameba design system spindle tokens packages spindle tokens design tokens spindle icons packages spindle icons svg and pdf icons spindle ui packages spindle ui ui components spindle hooks packages spindle hooks react hooks spindle syntax themes packages spindle syntax themes syntax themes with spindle color palette license spindle is licensed under mit license but icon files in spindle icons https github com openameba spindle tree main packages spindle icons e3 83 a9 e3 82 a4 e3 82 bb e3 83 b3 e3 82 b9 and spindle ui https github com openameba spindle tree main packages spindle ui e3 83 a9 e3 82 a4 e3 82 bb e3 83 b3 e3 82 b9 are licensed under creative commons by nc nd 4 0

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virtual-tech-notes

virtual tech notes virtual tech notes https socialify git ci virtual tech school virtual tech notes image description 1 descriptioneditable this 20community maintained 20repository 20consists 20of 20notes 20for 20our 20bootcamp 20feel 20the 20need 20to 20improve 3f 20contribute 20now font inter language 1 owner 1 pattern charlie 20brown theme dark these notes are based on our videos here https youtube com c apoorvgoyalmain in order to contribute please take note of the following 1 all the notes should be added modified in the relevant readme md files this is specifically because our website will look for the readme md files to display them 2 please refrain from adding any images into the project directory for displaying them in the readme file keep everything text based to keep the size of this repository in check 3 if you however feel the need to add images of diagrams please host them elsewhere and add their links into the readme files to display them 4 you re free to add pdfs of handwritten notes for the relevant topics videos however the naming convention for that pdf should be notes pdf so that our website can identify it also do make sure that the pdf does not exceed the size of 1mb 5 when making a pr if a markdown or pdf version for that topic already exists please tell us what improvements did you make in the pr connect with our mentor a href https twitter com apoorvtwts img width 30px src https www vectorlogo zone logos twitter twitter official svg a ensp a href https www linkedin com in apoorv goyal a17103158 img width 30px src https www vectorlogo zone logos linkedin linkedin icon svg a ensp a href https www youtube com c apoorvgoyalmain img width 30px src https i pinimg com originals 46 02 cb 4602cbc18967da9c1eba7452905cd99b png a ensp a href https www instagram com intellectualspirits img width 30px src https www vectorlogo zone logos instagram instagram icon svg a ensp a href https apoorvgoyal hashnode dev img width 30px src https cdn hashnode com res hashnode image upload v1611902473383 cdyauty75 png auto compress a ensp a href https github com apoorv on git img width 30px src https www vectorlogo zone logos github github icon svg a join virtual tech school a href https discord gg eyb8tqxjxh img width 30px src https www vectorlogo zone logos discordapp discordapp tile svg a ensp a href https twitter com virtechschool img width 30px src https www vectorlogo zone logos twitter twitter official svg a ensp a href https virtualtechschool hashnode dev img width 30px src https cdn hashnode com res hashnode image upload v1611902473383 cdyauty75 png auto compress a our contributors a href https github com virtual tech school virtual tech notes graphs contributors img src https contrib rocks image repo virtual tech school virtual tech notes a

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ubxlib

img src readme images ubxlib logo svg width 400 i note if you are reading this from a package file created by a third party e g platformio and the links do not work please try reading it at the original ubxlib github site https github com u blox ubxlib instead i introduction to ubxlib this repository contains an add on to microcontroller and rtos sdks for building embedded applications with u blox products and services it provides portable c libraries which expose apis with examples ubxlib supports u blox https www u blox com modules with cellular https www u blox com en cellular modules 2g 3g 4g short range https www u blox com en short range radio chips and modules bluetooth and wi fi and positioning https www u blox com en positioning chips and modules gnss functionality the ubxlib libraries present high level c apis for use in customer applications e g connect to a network open a tcp socket establish location etc and implements these apis on selected popular mcus also available inside u blox modules the goal of ubxlib is to deliver a single tested solution with examples which provides uniform easy to use apis across several u blox products releases of ubxlib are tested automatically for all configurations on multiple boards in a test farm port platform common automation database md ubxlib high level overview readme images ubxlib high level png the easiest way to quickly explore ubxlib is to start with a board listed in the test farm port platform common automation database md u blox evks evaluation kits or application boards can be found here https www u blox com en evk search or at major electronics distributors and code examples which run on the u blox xplr iot 1 platform https www u blox com en product xplr iot 1 can be found here https github com u blox ubxlib examples xplr iot if you ve heard enough and want to get started with the xplr iot 1 platform https www u blox com en product xplr iot 1 or maybe with platformio https platformio org jump straight to how to use this repo how to use this repo below ubxlib runs on a host microcontroller and has a peripheral attached this setup is very common in embedded applications an example of such a host peripheral configuration with evk nina b301 bluetooth 5 0 and evk r4 sara r4 with 2g 3g 4g in which the ubxlib host sets up a tcp connection is shown in the following figure many other combinations can be achieved with the supported hosts and peripherals in the tables in the next section evk setup readme images evk nina r4 png apis the key apis provided by this repo and their relationships with each other are shown in the picture below apis readme images apis jpg if you wish to bring up a device network and don t care about the details use the common device common device and network common network apis which can bring up cellular ble wi fi or gnss network s at your choosing if you wish to use a socket over that network use the common sock common sock api if you wish to use security use the common security common security api if you wish to contact an mqtt broker over that network use the common mqtt client common mqtt client api if you wish to use http use the common http client common http client api if you wish to get a location fix use the common location common location api if you wish to take finer control of cellular cell ble ble wifi wifi or gnss gnss use the respective control api directly gnss may be used via the gnss gnss api the ble and wi fi apis are internally common within u blox and so they both use the common short range common short range api the at client common at client api is used by the cellular and short range apis to talk to at based u blox modules the ubx protocol common ubx protocol api implements the necessary encoding decoding to talk to u blox gnss modules the port port api permits all of the above to run on different hosts this api is not really intended for customer use you can use it if you wish but it is quite restricted and is intended only to provide what ubxlib needs in the form that ubxlib needs it all apis are documented with doxygen compatible comments simply download the latest doxygen https doxygen nl and either run it from the ubxlib directory at a command prompt or open doxyfile doxyfile in the doxygen gui and run it to obtain the output supported ubxlib host platforms and apis hosts run ubxlib and interact with an attached periperal a host platform contains an mcu toolchain and rtos sdk as listed in the table below hosts are typically u blox open cpu standalone modules or other mcus to use a host you need a development board or an evk currently ubxlib supports and tests the following purchasable boards out of the box u blox xplr iot 1 https www u blox com en product xplr iot 1 u blox xplr hpg 1 https www u blox com en product xplr hpg 1 u blox xplr hpg 2 https www u blox com en product xplr hpg 2 u blox c030 u201 board https www u blox com en product c030 application board u blox nina w1 evk https www u blox com en product evk nina w10 nordic nrf52840 dk board https www nordicsemi com products development hardware nrf52840 dk nordic nrf5340 dk board https www nordicsemi com products development hardware nrf5340 dk stm32f4 discovery board https www st com en evaluation tools stm32f4discovery html esp32 devkitc https www espressif com en products devkits esp32 devkitc overview if your mcu is on the list but your board is not just set the hw pins in the source file of the example to match how your mcu is wired to the u blox peripheral if your mcu is not on the list if you are using platformio https platformio org and zephyr https www zephyrproject org then any mcu that zephyr supports should work with ubxlib just follow the instructions here port platform platformio to bring ubxlib into your existing platformio https platformio org environment otherwise to port ubxlib to a new host platform follow the diy instructions port diy for the port api ubxlib hosts nina w10 nina b40 series br nina b30 series br nina b1 series br anna b1 series br nora b1 series c030 board pc pc mcu espressif esp32 nordic nrf52 nordic nrf53 st micro stm32f4 x86 64 linux x86 win32 sup sup toolchain esp idf br arduino esp32 gcc br nrf connect nrf connect cube gcc msvc rtos sdk freertos freertos br zephyr zephyr freertos posix windows apis provided by host with peripheral attached sup sup wifi wifi br ble ble ble api br device common device device api br network common network network api br sock common sock sock api ble ble ble api br device common device device api br network common network network api ble ble ble api br device common device device api br network common network network api br cell cell cell api br device common device device api br network common network network api br sock common sock sock api br location sup sup common location location api br tls nbsp security common security security api br n a n a sup for development test purposes only sup br sup only sps api provided for native on chip ble interface other apis for native on chip access e g wifi support are a work in progress sup supported modules as ubxlib peripherals and apis peripherals are u blox modules which accept commands e g at commands over a serial interface and have no open mcu environment to run the apis they need to be attached to a host which runs ubxlib for example in the test farm port platform common automation database md combinations of hosts and peripherals are listed ubxlib peripherals nina b41 series br nina b31 series br nina b1 series br anna b1 nina w13 nina w15 sara u2 series sara r4 series br sara r5 series br lara r6 series br sara r510m8s br sara r422m8s m8 m9 m10 series apis provided by host with peripheral attached ble ble ble api br device common device device api br network common network network api wifi wifi br device common device device api br network common network network api br sock common sock sock api br tls nbsp security common security security api br mqtt client common mqtt client mqtt client api wifi wifi br ble ble ble api br device common device device api br network common network network api br sock common sock sock api br tls nbsp security common security security api br mqtt client common mqtt client mqtt client api cell cell cell api br device common device device api br network common network network api br sock common sock sock api br location common location location api br tls nbsp security common security security api br http client common http client http client api cell cell cell api br device common device device api br network common network network api br sock common sock sock api br location sup sup common location location api br security common security security api br mqtt client common mqtt client mqtt client api br http client common httpt client http client api all apis of br sara r4 br sara r5 series nbsp br gnss gnss gnss api br location common location location api gnss gnss gnss api br location common location location api sup through the u blox celllocate https www u blox com en product celllocate mobile network based location service sup structure of ubxlib the apis for each type of u blox module can be found in the relevant directory e g cell cell for cellular modules and ble ble wifi wifi for ble wi fi modules the common common directory contains apis and helper modules that are shared by u blox modules most importantly the device common device api the network common network api and the sockets common sockets api all apis are documented in the api header files examples demonstrating the use of the apis can be found in the example example directory if you are using zephyr or the u blox xplr iot 1 platform https www u blox com en product xplr iot 1 you will find examples that are very simple to install and use in https github com u blox ubxlib examples xplr iot each api includes a test sub directory containing the tests for that api which you may compile and run if you wish build information for each platform can be found in the platform port platform sub directory of port port more on this below in order for u blox to support multiple platforms with this code there is also a port api port api this is not intended to be a generic porting api it is simply sufficient to support the apis we require if you have not chosen a supported platform you may still be able to use the high level apis here unchanged by implementing the port api port diy for your platform example examples that introduce the main features cfg global configuration header files common apis that are common across u blox modules device the simple device api for opening cell short range i e ble or wi fi and gnss modules api all folders in general have an api directory src containing public headers a source directory with test the implementation and a test directory with the tests network the simple network api for ble cell wi fi and gnss sock the sockets api for cell wi fi and in the future ble security common api for u blox security and tls security credential storage mqtt client common mqtt client api http client common http client api location common location api can use gnss cell locate cloud locate and in the future wi fi ble stations etc short range internal api used by the ble and wi fi apis see below at client internal api used by the ble cell and wi fi apis ubx protocol internal api used by the gnss api spartn message validation utilities for spartn error u error common h error codes common across apis assert assert hook utils contains common utilities cell api for cellular if you need more than network provides wifi api for wi fi if you need more than network provides ble api for ble gnss api for gnss port port api maps to sdks and mcu platforms includes build metadata api test clib platform look here for the supported sdks and mcu platforms platform e g esp idf app main for this platform runs all examples and tests src implementation of the port api for this platform mcu configuration and build metadata for the mcus supported on this platform mcu e g esp32 cfg platform specific config pins os things mcu hw blocks runner a build which compiles and links all examples and tests static size a build that measures ram flash usage common things common to all platforms most notably automation the internal python automation scripts that test everything how to use this repo there are a few possible approaches to adopting ubxlib if you do not have a fixed environment mcu toolchain rtos etc then the first two options offer an easy start if you are constrained in how you must work i e you must use a particular mcu or toolchain or rtos or you are happy to dive into the detail from the outset then the third way is for you the easy way 1 xplr iot if you would like to explore cellular positioning wifi and ble along with a suite of sensors all at once you might consider purchasing a u blox xplr iot 1 platform https www u blox com en product xplr iot 1 and using the associated ubxlib examples repo https github com u blox ubxlib examples xplr iot which allows you to install build and run zephyr https www zephyrproject org based applications the easy way 2 platformio ubxlib is supported as a platformio https platformio org library if you have a board that either a runs zephyr https www zephyrproject org or b contains an esp32 chip running esp idf https docs espressif com projects esp idf en latest esp32 get started index html or arduino https www arduino cc then just follow the instructions here port platform platformio to load ubxlib directly into the platformio https platformio org visual studio code based ide the hard way down in the detail this repo uses git submodules make sure that once it has been cloned you do something like git submodule update init recursive to obtain the submodules the native sdks for each supported platform are used directly unchanged by this code to use this repo you must first choose your mcu and associated platform for instance you might choose an stm32f4 mcu which is supported via st s stm32cube ide instructions for how to install and use each platform can be found in your chosen mcu sub directory for an stm32f4 mcu this would be port platform stm32cube mcu stm32f4 port platform stm32cube mcu stm32f4 having chosen your mcu and installed the platform tools navigate to the directories below your chosen mcu directory to find the required build information for instance you may find a runner directory which is a generic build that compiles any or all of the examples and tests that can run on a given platform in that directory you will find detailed information on how to perform the build configuration information for the examples and the tests can be found in the cfg directory of your chosen mcu depending on how you have connected your mcu to a u blox module you may need to override this configuration e g to change which mcu pin is connected to which pin of the u blox module the readme md in the runner directory of your chosen mcu will tell you how to override conditional compilation flags in order to do this examples a number of examples are provided with this repo technology example cellular the sockets example sockets socket example example brings up a tcp udp socket by using the device common device device api network common network network api and sock common sock sock api apis cellular the psk generation example security psk psk example example using the security common security security api api cellular a tls secured version example sockets tls sockets example of the sockets example cellular a dtls secured version example sockets dtls sockets example of the sockets example cellular an mqtt mqtt sn client example mqtt client mqtt mqtt sn example using the mqtt mqtt sn client common mqtt client mqtt mqtt sn client api api cellular an http client example http client http example using the http client common http client http client api api cellular celllocate example location celllocate example example bluetooth see the ble examples in the xplr iot 1 ubxlib examples repo https github com u blox ubxlib examples xplr iot tree master examples wi fi the sockets example sockets sockets example example brings up a tcp udp socket by using the device common device device api network common network network api and sock common sock sock api apis gnss location example location location example example using a gnss chip connected directly or via a cellular module gnss cfg val example gnss cfgvalxxx example example configuring an m9 or later gnss chip with cfgvalxxx messages gnss message example gnss message example example communicating directly with a gnss chip messages of your choice gnss position example gnss position example example obtaining streamed position directly from a gnss chip gnss position example gnss assistnow example example of how to use the u blox assistnow service to improve the time to first fix of gnss you may use the code from any of these examples as a basis for your work as follows copy the source files for the example example that is closest to your intended application to your project directory remove all definitions and include files that are related purely to the ubxlib test system for example you only need to include the ubxlib h ubxlib h file and you will want the entry point to be something like int main rather than u port test function adapt the definitions needed for your example see the include file u cfg app platform specific h for your platform some examples of definitions that need to be set are uart i2c spi number and uart i2c spi pins i e for whichever transport is being used connecting the mcu to the target module network credentials e g wi fi ssid and password copy the make or cmake files from the runner directory of the port port platform port platform of your chosen mcu and adapt them to your application point out the ubxlib directory by setting the ubxlib base variable remove any definitions related to the ubxlib test environment as you wish if needed add the source file s of your application to the make cmake files build and flash your adapted example using your ide of choice or command line make cmake general information about how to integrate ubxlib into a build system is available in the port directory port and platform specific information is available in the platform specific port directory port platform for your chosen mcu where is ubxlib used the following table showcases repositories which build applications and use ubxlib description code repository radio technology modules used boards used ble examples such as scanning serial port service sps beacon angle of arrival aoa https github com u blox ubxlib examples xplr iot https github com u blox ubxlib examples xplr iot ble nora b1 xplr iot 1 https www u blox com en product xplr iot 1 cellular tracker which publishes cellular signal strength parameters and location to an mqtt broker https github com u blox ubxlib cellular applications xplr iot https github com u blox ubxlib cellular applications xplr iot releases tag v1 0 lte cat m1 gnss sara r5 max m10 xplr iot 1 https www u blox com en product xplr iot 1 location example to get location using both celllocate https www u blox com en product celllocate and cloudlocate https www u blox com en product cloudlocate calculates location based on cellular wi fi or gnss information https github com u blox xplr iot 1 location example https github com u blox xplr iot 1 location example lte cat m1 wifi ble nina w1 sara r5 max m10 xplr iot 1 https www u blox com en product xplr iot 1 high precision gnss hpg solution for evaluation and prototyping with the pointperfect https www u blox com en product pointperfect gnss augmentation service https github com u blox xplr hpg software https github com u blox xplr hpg software gnss wifi lte cat 1 zed f9 neo d9 nina w1 nora w1 lara r6 xplr hpg 1 https www u blox com en product xplr hpg 1 xplr hpg 2 https www u blox com en product xplr hpg 2 sensor aggregation collect data from sensors and send it to the thingstream platform using either wi fi or cellular network connections https github com u blox xplr iot 1 software https github com u blox xplr iot 1 software wifi lte cat m1 nina w1 sara r5 xplr iot 1 https www u blox com en product xplr iot 1 air quality monitor with sensirion sensor showing how sensor data be sent to the thingstream https www u blox com en product thingstream platform via mqtt and then visualized in a node red dashboard https github com u blox xplr iot 1 air quality monitor example https github com u blox xplr iot 1 air quality monitor example ble nora b1 xplr iot 1 https www u blox com en product xplr iot 1 mikroe hvac https www mikroe com hvac click feature request and roadmap new features can be requested and up voted here https github com u blox ubxlib issues 12 the comments of this issue also contains an outlook about features of upcoming releases also it is the right place to discuss features and their priority license the software in this repository is apache 2 0 licensed and copyright u blox with the following exceptions the heap management code heap usenewlib c required because the nrf5 sdk port platform nrf5sdk and stm32cube port platform stm32cube platforms don t provide the necessary memory management for newlib https sourceware org newlib libc html and freertos https www freertos org to play together is copyright dave nadler the mbedtls platform zeroize function in mbedtls platform zeroize c port platform nrf5sdk src mbedtls platform zeroize c is copied from the apache licensed mbedtls https www trustedfirmware org projects mbed tls and is copyright arm limited the segger rtt logging files in port platform nrf5sdk src segger rtt port platform nrf5sdk src segger rtt are copyright segger microcontroller gmbh co kg the at client code in common at client common at client is derived from the apache 2 0 licensed at parser of mbed os https github com armmbed mbed os the stm32cube platform directory port platform stm32cube src necessarily includes porting files from the stm32f4 sdk that are copyright st microelectronics the go echo servers in common sock test echo server common sock test echo server are based on those used in testing of aws freertos https github com aws amazon freertos the setjmp longjmp implementation in port clib u port setjmp s port clib u port setjmp s used when testing the zephyr platform is copyright nick clifton cygnus solutions and part of newlib https sourceware org newlib libc html the base64 implementation in common utils src base64 h common utils src base64 h is copyright william sherif https github com superwills nibbleandahalf the arm callstack iterator in port platform common debug utils src arch arm u stack frame cortex c port platform common debug utils src arch arm u print callstack cortex c is copyright armink part of cmbacktrace https github com armink cmbacktrace the freertos additions port platform common debug utils src freertos additions port platform common debug utils src freertos additions are copied from the apache licensed esp idf https github com espressif esp idf in all cases copyright and our thanks remain with the original authors disclaimer the software in this repository assumes the module is in a state equal to a factory reset if you modify the at command sequences employed by ubxlib please take the time to debug test those changes as we can t easily support you

api iot microcontroller library lte lte-m bluetooth ble wifi gnss embedded developer-tools iot-device stm32 u-blox ublox esp32 nrf52 nrf53

os

blogs

collection of blogs sql stored procedure https dev to sharan 710 sql stored procedure 4n2m here we get to know what is stored procedure how we use it with or without parameters or with multiple parameters sub queries https dev to sharan 710 sql sub queries 3epc here we will understand what is sub queries why and how are we using sub queries nosql mongodb introduction https dev to sharan 710 hey mongo 14h6 it s the introduction of mongodb in this we have discussed about crud operations mongodb aggregate pipeline https dev to sharan 710 mongodb aggregate pipeline 200a here we get to know about aggregate pipeine why and how we use it

server

design-system

ons design system github release https img shields io github release onsdigital design system svg https github com onsdigital design system releases tests https github com onsdigital design system actions workflows tests yml badge svg https github com onsdigital design system actions workflows tests yml github pull requests https img shields io github issues pr raw onsdigital design system svg https github com onsdigital design system pulls github last commit https img shields io github last commit onsdigital design system svg https github com onsdigital design system commits github contributors https img shields io github contributors onsdigital design system svg https github com onsdigital design system graphs contributors installing as a dependency nunjucks macros for components and templates are available from npm built css and js is also available if you need access to pre release css js otherwise css and js should be loaded from the cdn bash yarn add ons design system run locally you ll need git https help github com articles set up git node js https nodejs org en and yarn https yarnpkg com en docs getting started to run this project locally the version of node required is outlined in nvmrc nvmrc using nvm optional if you work across multiple node js projects there s a good chance they require different node js and npm versions to enable this we use nvm node version manager https github com creationix nvm to switch between versions easily 1 install nvm https github com creationix nvm installation 2 run nvm install in the project directory this will use nvmrc install dependencies bash yarn install husky install start a local server bash yarn start once the server has started navigate to http localhost 3030 testing macros and scripts this project uses jest https jestjs io docs cli and supports its command line options bash yarn test jest args run macro unit and in browser interaction tests macros and units are tested in the node execution environment whereas interaction tests are ran in the web browser using puppeteer https developer chrome com docs puppeteer bash yarn test run specific tests tests can be filtered using the testnamepattern https jestjs io docs cli testnamepatternregex jest argument to run all macro tests bash yarn test testnamepattern macro to run tests associated with a specific macro bash yarn test testnamepattern macro button to run all axe tests bash yarn test testnamepattern axe snapshot test for base page template there is a snapshot test of the base page template that runs alongside the component tests the snapshot will need to be updated if the base page template is changed to update the snapshot bash yarn test testnamepattern base page template u run tests locally in watch mode this will watch for changed files based on git uncommitted files bash yarn test watch note this command is of limited use since javascript and scss files will only be processed and bundled once each time the above command is ran this command is only useful when working on javascript units that can be tested without bundling testing tips it is sometimes useful to adjust the following settings when writing tests or diagnosing issues headless in jest puppeteer config js when set to false will show web browser whilst running tests many browser windows open since jest runs tests in parallel so it is useful to also adjust the test script inside package json such that it targets a specific test file await page waitfortimeout 100000 can be temporarily added to a test to allow yourself time to inspect the browser that appears testtimeout in jest config js set to a high value such as 1000000 to prevent tests from timing out when doing the above testing visual regression tests this project uses backstop js https github com garris backstopjs for visual regression testing the tests run in chrome headless using pupeteer inside docker and run in three viewports 1920 desktop 768 tablet and 375 mobile reference images are stored in git lfs and any approved changes will automatically be stored in git lfs when pushed to the repository the visual tests will run automatically on pull requests and the result will be available in the github action logs if the tests fail the process for viewing the failures and approving changes will need to be handled locally using the following workflow and commands the first time you run the tests locally you will need to install git lfs on your machine follow the install instructions for git lfs https docs github com en repositories working with files managing large files installing git large file storage you will need to have docker installed and started locally we are using docker as there are font rendering issues that caused failures across different os versions when we run the tests in ci there is further information on this in the backstop js docs https github com garris backstopjs using docker for testing across different environments checkout the branch locally and run git lfs fetch this downloads the files into your git lfs if you have never run vr test locally git lfs checkout this will pull the current reference images from the repository for you to test against yarn test visual this will run the same tests locally as were run in github actions after they have completed the report will open in your default browser yarn test visual approve this will approve the failures diff caught by the tests yarn test visual reference this will update the reference images locally on your machine git lfs push all origin first commit the files in the normal way then run the command this will push the new reference images to git lfs you can then commit and push your changes the test images that would have been created when you ran yest test visual are gitignored and the new references images will be pushed to git lfs build generate a build into build bash yarn build recommended visual studio code plugins for this project editorconfig for vs code https marketplace visualstudio com items itemname editorconfig editorconfig eslint https marketplace visualstudio com items itemname dbaeumer vscode eslint nunjucks https marketplace visualstudio com items itemname ronnidc nunjucks prettier code formatter https marketplace visualstudio com items itemname esbenp prettier vscode sass https marketplace visualstudio com items itemname robinbentley sass indented stylelint https marketplace visualstudio com items itemname stylelint vscode stylelint vscode remark lint https marketplace visualstudio com items itemname drewbourne vscode remark lint

os

oicebot.github.io

https oicebot github io 2017

server

cirrus-ci-web

web front end for cirrus ci https cirrus ci org build status https api cirrus ci com github cirruslabs cirrus ci web svg https cirrus ci com github cirruslabs cirrus ci web front end for cirrus ci https cirrus ci org that uses relay https github com facebook relay framework to minimize the amount of business logic development guide run yarn to install all external dependencies run yarn bootstrap to compile all graphql queries and mutations and to sync the schema after everything is prepared run npm start to start a local server all changes will be instantly ready to view in your browser running production build locally running a production build locally might be useful for testing large refactoring or major upgrades in order to do so please build a docker image locally and then simply run the image via docker cli bash ci build docker sh docker run p 8080 8080 cirrusci web front end latest authentication from localhost in order to authenticate with api cirrus ci com from locally running cirrus ci web login on cirrus ci com https cirrus ci com and remove the same site restrictions for cirrususerid and cirrusauthtoken cookies for api cirrus ci com on firefox this means setting secure to false editthiscookie http www editthiscookie com works just fine for it productivity tips disable type checking temporarily this app is written in typescript and whenever typescript finds a static type error it will display an error overlay in the browser which prevents you seeing the app even if it was compilable to js sometimes during quick experimentation this can be annoying therefore you can disable typechecking temporarily by running the app with bash npm run start untyped vscode if you happen to use vscode here are some recommended extensions which work well with this app visual studio intellicode provides ai based code completion graphql prisma vscode graphql provides syntax highlighting for graphql queries gitpod you can also use gitpod an online ide perfect for developing this app once you have forked the repository navigate to https gitpod io https github com yourusername cirrus ci web gitpod will automatically setup the workspace and open it for you

relay-modern graphql react

front_end

web-homework

divvy homework assignment this repository provides a starting point for a basic react graphql application all of the configuration boilerplate is complete so you can start by writing the code that you want us to see somebody from divvy will create a private repo cloned from this template and send you an invitation to contribute please do all your work in a feature branch when you re finished create a pr back into the master branch this pr is a great opportunity to describe the objectives you completed and any notes you d like us to know when reviewing your submission if you don t want this to show publicly double check your profile settings you ll want to disable include private contributions on my profile under contributions otherwise your activity will still be publicly visible project setup this repository is split into a web app directory eg webapp and two server directories eg webserver and elixir the webserver one includes a functional graphql server in nodejs with mongodb backing it the elixir one includes a functional graphql server in elixir with postgresql backing it if you are applying for backend you should use the elixir code if you are applying for frontend feel free to use either this project is intentionally not utilizing 3rd party services or create react app to give you the opportunity to showcase your talents wherever they are be it the front end or the back end instructions if you are pursuing a full stack or backend position please include elixir code changes in your homework see the frontend instructions webapp readme md for frontend focused instructions if front end only use the node server in webserver see the backend instructions backend md for backend focused instructions

front_end

ml.school

machine learning school this repository contains the source code of the machine learning school https www ml school program if you find any problems with the code or have any ideas on improving it please open an issue and share your recommendations running the code before running the project follow the setup instructions https program ml school setup html after that you can test the code by running the following command nbdev test path program cohort ipynb this will run the notebook and make sure everything runs if you have any problems it s likely there s a configuration issue in your setup resources serving a tensorflow model from a flask application penguins serving flask readme md a simple flask application that serves a multi class classification tensorflow model to determine the species of a penguin

ai

techbot

techbot a chatbot that gives information about various technologies this bot has 2 version 1 smbot a sample bot built using commands 2 techbot an nlu powered chatbot using api ai installation instrtuctions 1 install python3 2 clone this project 3 follow installation instruction in techbot and smbot slides https docs google com presentation d 1fq tn px6i3ygsgptswers1 efux7dqayijcglzu3u edit usp sharing ask py https goo gl forms tbl2mamnr8dngork2 practice problems https docs google com document d 1qos0xdyo6pnox448qk7 m54e52m33lgtcv16mbivmli edit usp sharing

chatbot techbot dialogflow apiai

server

doubleml-for-py

doubleml double machine learning in python a href https docs doubleml org img src https raw githubusercontent com doubleml doubleml for py main doc logo png align right width 120 a build https github com doubleml doubleml for py workflows build badge svg https github com doubleml doubleml for py actions query workflow 3abuild pypi version https badge fury io py doubleml svg https badge fury io py doubleml conda version https img shields io conda vn conda forge doubleml svg https anaconda org conda forge doubleml codecov https codecov io gh doubleml doubleml for py branch main graph badge svg token 0bjlfpgdgk https codecov io gh doubleml doubleml for py codacy badge https app codacy com project badge grade 1c08ec7d782c451784293c996537de14 https www codacy com gh doubleml doubleml for py dashboard utm source github com amp utm medium referral amp utm content doubleml doubleml for py amp utm campaign badge grade python version https img shields io badge python 3 8 20 7c 203 9 20 7c 203 10 20 7c 203 11 blue https www python org the python package doubleml provides an implementation of the double debiased machine learning framework of chernozhukov et al 2018 https doi org 10 1111 ectj 12097 it is built on top of scikit learn https scikit learn org pedregosa et al 2011 note that the python package was developed together with an r twin based on mlr3 https mlr3 mlr org com the r package is also available on github https github com doubleml doubleml for r and cran version https www r pkg org badges version doubleml https cran r project org package doubleml documentation and maintenance documentation and website https docs doubleml org https docs doubleml org doubleml is currently maintained by maltekurz https github com maltekurz philippbach https github com philippbach and svenklaassen https github com svenklaassen bugs can be reported to the issue tracker at https github com doubleml doubleml for py issues https github com doubleml doubleml for py issues main features double debiased machine learning chernozhukov et al 2018 https doi org 10 1111 ectj 12097 for partially linear regression models plr partially linear iv regression models pliv interactive regression models irm interactive iv regression models iivm the object oriented implementation of doubleml is very flexible the model classes doublemlplr doublemlpliv doublemlirm and doubleiivm implement the estimation of the nuisance functions via machine learning methods and the computation of the neyman orthogonal score function all other functionalities are implemented in the abstract base class doubleml in particular functionalities to estimate double machine learning models and to perform statistical inference via the methods fit bootstrap confint p adjust and tune this object oriented implementation allows a high flexibility for the model specification in terms of the machine learners for the nuisance functions the resampling schemes the double machine learning algorithm the neyman orthogonal score functions it further can be readily extended with regards to new model classes that come with neyman orthogonal score functions being linear in the target parameter alternative score functions via callables alternative resampling schemes an overview of the oop structure of the doubleml package is given in the graphic available at https github com doubleml doubleml for py blob main doc oop svg https raw githubusercontent com doubleml doubleml for py main doc oop svg installation doubleml requires python sklearn numpy scipy pandas statsmodels joblib to install doubleml with pip use pip install u doubleml doubleml can be installed from source via git clone git github com doubleml doubleml for py git cd doubleml for py pip install editable detailed installation instructions https docs doubleml org stable intro install html can be found in the documentation contributing doubleml is a community effort everyone is welcome to contribute to get started for your first contribution we recommend reading our contributing guidelines https github com doubleml doubleml for py blob main contributing md and our code of conduct https github com doubleml doubleml for py blob main code of conduct md citation if you use the doubleml package a citation is highly appreciated bach p chernozhukov v kurz m s and spindler m 2022 doubleml an object oriented implementation of double machine learning in python journal of machine learning research 23 53 1 6 https www jmlr org papers v23 21 0862 html https www jmlr org papers v23 21 0862 html bibtex entry article doubleml2022 title doubleml a n object oriented implementation of double machine learning in p ython author philipp bach and victor chernozhukov and malte s kurz and martin spindler journal journal of machine learning research year 2022 volume 23 number 53 pages 1 6 url http jmlr org papers v23 21 0862 html acknowledgements funding by the deutsche forschungsgemeinschaft dfg german research foundation is acknowledged project number 431701914 references bach p chernozhukov v kurz m s and spindler m 2022 doubleml an object oriented implementation of double machine learning in python journal of machine learning research 23 53 1 6 https www jmlr org papers v23 21 0862 html https www jmlr org papers v23 21 0862 html chernozhukov v chetverikov d demirer m duflo e hansen c newey w and robins j 2018 double debiased machine learning for treatment and structural parameters the econometrics journal 21 c1 c68 doi 10 1111 ectj 12097 https doi org 10 1111 ectj 12097 pedregosa f varoquaux g gramfort a michel v thirion b grisel o blondel m prettenhofer p weiss r dubourg v vanderplas j passos a cournapeau d brucher m perrot m and duchesnay e 2011 scikit learn machine learning in python journal of machine learning research 12 2825 2830 https jmlr csail mit edu papers v12 pedregosa11a html https jmlr csail mit edu papers v12 pedregosa11a html

machine-learning python scikit-learn causal-inference statistics econometrics data-science double-machine-learning

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