Yamei/VISBank_Final · Datasets at Hugging Face

Neural representations of language switching in early bilinguals: An fMRI study.

37,224,625

[ { "first": "Miaomei", "middle": [], "last": "Lei", "suffix": "" }, { "first": "Hiroyuki", "middle": [], "last": "Akama", "suffix": "" }, { "first": "Brian", "middle": [], "last": "Murphy", "suffix": "" } ]

2,013

CogSci

2400828876

[]

false

https://api.semanticscholar.org/CorpusID:37224625

null

[ [ "Neural representation", "METHOD" ], [ "switch", "APPLICATION" ], [ "fMRI study", "EVALUATION" ] ]

Building the Enterprise Fabric for Big Data with Vertica and Spark Integration

17,549,091

Enterprise customers increasingly require greater flexibility in the way they access and process their Big Data while at the same time they continue to request advanced analytics and access to diverse data sources. Yet customers also still require the robustness of enterprise class analytics for their mission-critical data. In this paper, we present our initial efforts toward a solution that satisfies the above requirements by integrating the HPE Vertica enterprise database with Apache Spark's open source big data computation engine. In particular, it enables fast, reliable transferring of data between Vertica and Spark; and deploying Machine Learning models created by Spark into Vertica for predictive analytics on Vertica data. This integration provides a fabric on which our customers get the best of both worlds: it extends Vertica's extensive SQL analytics capabilities with Spark's machine learning library (MLlib), giving Vertica users access to a wide range of ML functions; it also enables customers to leverage Spark as an advanced ETL engine for all data that require the guarantees offered by Vertica.

[ { "first": "Jeff", "middle": [], "last": "LeFevre", "suffix": "" }, { "first": "Rui", "middle": [], "last": "Liu", "suffix": "" }, { "first": "Cornelio", "middle": [], "last": "Inigo", "suffix": "" }, { "first": "Lupita", "middle": [], "last": "Paz", "suffix": "" }, { "first": "Edward", "middle": [], "last": "Ma", "suffix": "" }, { "first": "Malu", "middle": [], "last": "Castellanos", "suffix": "" }, { "first": "Meichun", "middle": [], "last": "Hsu", "suffix": "" } ]

2,016

10.1145/2882903.2903744

SIGMOD '16

2427099794

[ "207166250", "916681", "16022766", "16620465", "3439184", "11818928" ]

[ "118673774" ]

true

https://api.semanticscholar.org/CorpusID:17549091

0

1

0

[ [ "big data computation engine", "METHOD" ], [ "ML function", "METHOD" ], [ "reliable transferring", "METHOD" ], [ "ETL engine", "METHOD" ], [ "Apache Spark", "METHOD" ], [ "advanced analytics", "APPLICATION" ], [ "Vertica data", "DATA" ], [ "Spark's machine learning library (MLlib", "METHOD" ], [ "Machine Learning model", "METHOD" ], [ "SQL analytics", "METHOD" ], [ "mission-critical data", "DATA" ], [ "predictive analytics", "APPLICATION" ], [ "tica.", "METHOD" ], [ "diverse data source", "DATA" ], [ "enterprise class analytics", "METHOD" ], [ "HPE Vertica enterprise database", "DATA" ], [ "Spark", "METHOD" ] ]

Adaptive Surface Visualization of Vessels with Animated Blood Flow

31,892,916

The investigation of hemodynamic information for the assessment of cardiovascular diseases CVDs gained importance in recent years. Improved flow measuring modalities and computational fluid dynamics CFD simulations yield in reliable blood flow information. For a visual exploration of the flow information, domain experts are used to investigate the flow information combined with its enclosed vessel anatomy. Since the flow is spatially embedded in the surrounding vessel surface, occlusion problems have to be resolved. A visual reduction of the vessel surface that still provides important anatomical features is required. We accomplish this by applying an adaptive surface visualization inspired by the suggestive contour measure. Furthermore, an illustration is employed to highlight the animated pathlines and to emphasize nearby surface regions. Our approach combines several visualization techniques to improve the perception of surface shape and depth. Thereby, we ensure appropriate visibility of the embedded flow information, which can be depicted with established or advanced flow visualization techniques. We apply our approach to cerebral aneurysms and aortas with simulated and measured blood flow. An informal user feedback with nine domain experts, we confirm the advantages of our approach compared with existing methods, e.g. semi-transparent surface rendering. Additionally, we assessed the applicability and usefulness of the pathline animation with highlighting nearby surface regions.

[ { "first": "Kai", "middle": [], "last": "Lawonn", "suffix": "" }, { "first": "Rocco", "middle": [], "last": "Gasteiger", "suffix": "" }, { "first": "Bernhard", "middle": [], "last": "Preim", "suffix": "" } ]

2,014

10.1111/cgf.12355

Comput. Graph. Forum

2114732239

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[ "10159356", "51919746", "34509211", "4687572", "11591541", "43929002", "49320810", "44123312", "14709313", "33919852", "192517558", "9489650", "3440187", "5025099", "423917", "119787", "37414019" ]

true

https://api.semanticscholar.org/CorpusID:31892916

0

1

0

[ [ "reliable blood flow information", "DATA" ], [ "simulated and measured blood flow", "DATA" ], [ "pathline animation", "VISUALIZATION" ], [ "depth", "DATA" ], [ "visual reduction", "VISUALIZATION" ], [ "flow", "DATA" ], [ "advanced flow visualization technique", "VISUALIZATION" ], [ "suggestive contour measure", "METHOD" ], [ "computational fluid dynamic CFD simulation", "METHOD" ], [ "flow measuring modality", "METHOD" ], [ "animated pathlines", "VISUALIZATION" ], [ "domain expert", "EVALUATION" ], [ "visualization technique", "METHOD" ], [ "hemodynamic information", "DATA" ], [ "cardiovascular disease CV", "EVALUATION" ], [ "surface region", "DATA" ], [ "visual exploration", "VISUALIZATION" ], [ "enclosed vessel anatomy", "DATA" ], [ "vessel surface", "DATA" ], [ "informal user feedback", "EVALUATION" ], [ "adaptive surface visualization", "VISUALIZATION" ], [ "embedded flow information", "DATA" ], [ "semi-transparent surface rendering", "VISUALIZATION" ], [ "flow information", "DATA" ], [ "anatomical feature", "DATA" ], [ "surface shape", "DATA" ], [ "cerebral aneurysm", "APPLICATION" ], [ "occlusion problem", "APPLICATION" ] ]

Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering

15,178,837

We propose an automated clustering method based on multiobjective genetic algorithms (GA); the aim of this method is to automatically cluster values of a given quantitative attribute to obtain large number of large itemsets in low duration (time). We compare the proposed multi-objective GA-based approach with CURE-based approach. In addition to the autonomous specification of fuzzy sets, experimental results showed that the proposed automated clustering exhibits good performance over CURE-based approach in terms of runtime as well as the number of large itemsets and interesting association rules.

[ { "first": "M.", "middle": [], "last": "Kaya", "suffix": "" }, { "first": "R.", "middle": [], "last": "Alhajj", "suffix": "" } ]

2,003

10.1109/ICDM.2003.1250977

Third IEEE International Conference on Data Mining

2117215048

[ "8046058", "2385745", "29516532", "6852703", "8719734", "15828757", "5735552", "195704763", "36951104", "9634991", "6077066" ]

[ "18995986", "26398000", "18848274", "12554955", "62240343", "18327850", "8482479", "6915757", "35464203", "14377474", "18082456", "18974076", "39947840", "15249889", "18291478", "14568492", "47446250", "5018542", "7560257", "11984864", "6308492" ]

true

https://api.semanticscholar.org/CorpusID:15178837

1

[ [ "fuzzy set", "DATA" ], [ "multiobjective genetic algorithm", "METHOD" ], [ "automated clustering", "METHOD" ], [ "autonomous specification", "METHOD" ], [ "multi-objective GA-based approach", "METHOD" ], [ "quantitative attribut", "DATA" ], [ "CURE-based approach", "METHOD" ], [ "experimental result", "EVALUATION" ], [ "association rule", "DATA" ], [ "automated clustering method", "METHOD" ] ]

Shifting the focus from accuracy to recallability: A study of informal note-taking on mobile information technologies

15,179,195

Mobile information technologies are theoretically well-suited to digitally accomodate informal note-taking, with the notes often recorded quickly and under less than ideal circumstances. Unfortunately, user adoption of mobile support for informal note-taking has been hindered in large part by slow text entry techniques. Building on research confirming people's ability to recognize erroneous text, this study explores two simple modifications to Graffiti-based text entry with the goal of increasing text entry speed: disabling text correction and disabling visual feedback. As expected, both modifications improved text entry speed at the cost of recognizability. To address the decrease in recognizability, a multiapproach text-enhancement algorithm is introduced with the goal of modifying the erroneous note to facilitate the process of recalling the event or activity that originally motivated the note. A study with 75 participants confirmed that the proposed approach of discouraging user-initiated error correction during note-taking, enhancing the resulting erroneous notes, and facilitating recall with enhanced alternative lists, increased note-taking speed by 47p with no negative impact on the participants' ability to recall important details about the scenarios which prompted the note-taking activities. This research highlighs the importance and efficacy of shifting the focus from accuracy to recallability when examining the overall efficacy of informal notes. The proposed modifications and adaptations produce significant benefits and have important implications for how mobile technologies are designed to support both informal note-taking and text entry in general.

[ { "first": "Liwei", "middle": [], "last": "Dai", "suffix": "" }, { "first": "Andrew", "middle": [], "last": "Sears", "suffix": "" }, { "first": "Rich", "middle": [], "last": "Goldman", "suffix": "" } ]

2,009

10.1145/1502800.1502804

TCHI

1999514385

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[ "7335925", "3902739", "133229609", "11124788", "204812023", "15300110", "7485336" ]

true

https://api.semanticscholar.org/CorpusID:15179195

0

1

0

[ [ "text entry speed", "EVALUATION" ], [ "Graffiti-based text entry", "METHOD" ], [ "mobile technology", "APPLICATION" ], [ "mobile support", "METHOD" ], [ "informal note", "APPLICATION" ], [ "informal note-taking", "APPLICATION" ], [ "note-taking speed", "EVALUATION" ], [ "visual feedback", "VISUALIZATION" ], [ "slow text entry technique", "METHOD" ], [ "alternative list", "METHOD" ], [ "user-initiated error correction", "METHOD" ], [ "text entry", "APPLICATION" ], [ "Mobile information technology", "METHOD" ], [ "erroneous note", "DATA" ], [ "erroneous text", "DATA" ], [ "note-", "APPLICATION" ], [ "text correction", "METHOD" ], [ "multiapproach text-enhancement algorithm", "METHOD" ] ]

Methods for the evaluation of an interactive InfoVis tool supporting exploratory reasoning processes

233,729

Developing Information Visualization (InfoVis) techniques for complex knowledge domains makes it necessary to apply alternative methods of evaluation. In the evaluation of Gravi++ we used several methods and studied different user groups. We developed a reporting system yielding data about the insights the subjects gained during the exploration. It provides complex information about subjects' reasoning processes. Log files are valuable for time-dependent analysis of cognitive strategies. Focus groups provide a different view on the process of gaining insights. We assume that our experiences with all these methods can also be applied in similar evaluation studies on InfoVis techniques for complex data.

[ { "first": "Markus", "middle": [], "last": "Rester", "suffix": "" }, { "first": "Margit", "middle": [], "last": "Pohl", "suffix": "" } ]

2,006

10.1145/1168149.1168156

BELIV '06

2099193329

[ "16944155", "17875077", "6207065", "881887", "18513880", "6714659", "14435378", "18808812" ]

[ "9489409", "2494291", "15279383", "11460906", "16426714", "202704382", "9412074" ]

true

https://api.semanticscholar.org/CorpusID:233729

0

1

0

[ [ "study", "EVALUATION" ], [ "Information Visualization (InfoVis) technique", "VISUALIZATION" ], [ "InfoVis technique", "METHOD" ], [ "reasoning process", "DATA" ], [ "time-dependent analysis", "METHOD" ], [ "cognitive strategie", "METHOD" ], [ "Focus group", "METHOD" ], [ "Log file", "DATA" ], [ "reporting system", "METHOD" ], [ "Gravi", "METHOD" ] ]

GripSense: using built-in sensors to detect hand posture and pressure on commodity mobile phones

235,910

We introduce GripSense, a system that leverages mobile device touchscreens and their built-in inertial sensors and vibration motor to infer hand postures including one- or two-handed interaction, use of thumb or index finger, or use on a table. GripSense also senses the amount of pres-sure a user exerts on the touchscreen despite a lack of direct pressure sensors by inferring from gyroscope readings when the vibration motor is "pulsed." In a controlled study with 10 participants, GripSense accurately differentiated device usage on a table vs. in hand with 99.67% accuracy and when in hand, it inferred hand postures with 84.26% accuracy. In addition, GripSense distinguished three levels of pressure with 95.1% accuracy. A usability analysis of GripSense was conducted in three custom applications and showed that pressure input and hand-posture sensing can be useful in a number of scenarios.

[ { "first": "Mayank", "middle": [], "last": "Goel", "suffix": "" }, { "first": "Jacob", "middle": [], "last": "Wobbrock", "suffix": "" }, { "first": "Shwetak", "middle": [], "last": "Patel", "suffix": "" } ]

2,012

10.1145/2380116.2380184

UIST '12

2038304148

[ "18294381", "207194621", "1019001", "9065677", "7554218", "9977398", "14513538", "15252590", "6292200", "34164695", "26427747", "1548961", "3015068", "8312298", "16930502", "62764383", "12287378", "1266108", "30289375", "18948692", "14387206", "9975785", "9391079", "4833792", "14284361" ]

[ "18015305", "106402342", "189926863", "6341215", "13167279", "914720", "14126814", "4011760", "53388850", "31734576", "15873283", "83459611", "45094797", "9192029", "197645174", "22175128", "51923444", "8653712", "27943946", "209450853", "203621054", "41172872", "14455438", "16950793", "45326403", "10372381", "46899452", "16533227", "1000929", "95721", "10130094", "5063623", "568971", "54631341", "19020014", "131763602", "19985635", "140306385", "40078643", "29178757", "16912306", "5843444", "1185250", "215646441", "52083751", "202896074", "52899907", "53412354", "115148344", "46986264", "10865057", "211481973", "16614437", "18301965", "13889807", "39462422", "17624339", "207991072", "10024270", "140216222", "15482351", "343038", "10822787", "10907041", "10321170", "18128634", "67770922", "2143984", "16555605", "207960109", "6247739", "44472894", "33222883", "17665142", "15096573", "203621216", "27307222", "17305595", "4215516", "10512484", "9028711", "2040860", "49667139", "71716561", "203620986", "195259470", "17082813", "18054226", "63183", "41321629", "3813023", "18691621" ]

true

https://api.semanticscholar.org/CorpusID:235910

1

[ [ "controlled study", "EVALUATION" ], [ "one- or two-handed interaction", "APPLICATION" ], [ "hand-posture sensing", "METHOD" ], [ "vibration motor", "METHOD" ], [ "usability analysis", "EVALUATION" ], [ "GripSense", "METHOD" ], [ "inertial sensor", "METHOD" ], [ "Grip", "METHOD" ], [ "gyroscope reading", "METHOD" ], [ "hand post", "DATA" ], [ "95.1% accuracy", "EVALUATION" ], [ "custom application", "APPLICATION" ], [ "pressure input", "METHOD" ], [ "mobile device", "METHOD" ], [ "device usage", "EVALUATION" ], [ "direct pressure sensor", "METHOD" ] ]

Using a Model-Based fMRI Analysis Method to Locate the Neural Correlates of a Multitasking Bottleneck

29,137,331

Using a Model-Based fMRI Analysis Method to Locate the Neural Correlates of a Multitasking Bottleneck Jelmer Borst University of Groningen Niels Taatgen University of Groningen Hedderik Van Rijn University of Groningen Abstract: It has been shown that people can only maintain one problem state (a temporary mental representation, comparable to the focus of attention in working memory), at a time. When more than one problem state is required, for example in multitasking, performance decreases considerably. To locate the neural correlates of this ’problem state bottleneck’ we applied a model-based fMRI analysis method. First, a computational cognitive model was fit to the behavioral data. We then regressed the activity of the model’s cognitive resources against the fMRI data to identify regions that match the model’s activity. The brain region responsible for the temporary representation of problem states, the inferior parietal lobule, and the brain region responsible for long-term storage of problem states, the inferior frontal gyrus were thus identified. We show that the model-based analysis method outperforms the classical cognitive subtraction method.

[ { "first": "Jelmer", "middle": [ "P." ], "last": "Borst", "suffix": "" }, { "first": "Niels", "middle": [], "last": "Taatgen", "suffix": "" }, { "first": "Hedderik", "middle": [ "van" ], "last": "Rijn", "suffix": "" } ]

2,011

CogSci

2576173763

[]

false

true

https://api.semanticscholar.org/CorpusID:29137331

0

[ [ "mental representation", "DATA" ], [ "working", "DATA" ], [ "multitasking", "APPLICATION" ], [ "computational cognitive model", "METHOD" ], [ "model-based analysis method", "METHOD" ], [ "problem state", "DATA" ], [ "cognitive resource", "DATA" ], [ "Model-Based fMRI Analysis Method", "METHOD" ], [ "fMRI data", "DATA" ], [ "model-based fMRI analysis method", "METHOD" ], [ "temporary representation", "METHOD" ], [ "cognitive subtraction method", "METHOD" ], [ "etal", "METHOD" ], [ "neural correlate", "DATA" ], [ "behavioral data", "DATA" ] ]

Functional thin films on surfaces

2,133,880

The motion of a thin viscous film of fluid on a curved surface exhibits many intricate visual phenomena, which are challenging to simulate using existing techniques. A possible alternative is to use a reduced model, involving only the temporal evolution of the mass density of the film on the surface. However, in this model, the motion is governed by a fourth-order nonlinear PDE, which involves geometric quantities such as the curvature of the underlying surface, and is therefore difficult to discretize. Inspired by a recent variational formulation for this problem on smooth surfaces, we present a corresponding model for triangle meshes. We provide a discretization for the curvature and advection operators which leads to an efficient and stable numerical scheme, requires a single sparse linear solve per time step, and exactly preserves the total volume of the fluid. We validate our method by qualitatively comparing to known results from the literature, and demonstrate various intricate effects achievable by our method, such as droplet formation, evaporation, droplets interaction and viscous fingering.

[ { "first": "Omri", "middle": [], "last": "Azencot", "suffix": "" }, { "first": "Orestis", "middle": [], "last": "Vantzos", "suffix": "" }, { "first": "Max", "middle": [], "last": "Wardetzky", "suffix": "" }, { "first": "Martin", "middle": [], "last": "Rumpf", "suffix": "" }, { "first": "Mirela", "middle": [], "last": "Ben-Chen", "suffix": "" } ]

2,015

10.1145/2786784.2786793

SCA '15

1993490182

[ "6223164", "12739694", "6159353", "12807879", "14607943", "705564", "119856149", "411689", "120672212", "2172201", "2676421", "45035916", "15032612", "25770912", "121090852", "32180204", "16977677", "9969258", "89614073", "32240819", "41647990", "2976110", "2887600", "14779734", "17292080" ]

[ "8792723", "27839400", "51612072", "18465856", "52031264", "55374483", "208119647", "12140335", "37669707", "21688929", "21688929", "54105292", "4932313", "201657674", "201657674" ]

true

https://api.semanticscholar.org/CorpusID:2133880

0

1

0

[ [ "visual phenomenon", "VISUALIZATION" ], [ "evaporation", "EVALUATION" ], [ "curved surface", "DATA" ], [ "geometric quantity", "DATA" ], [ "curvature", "DATA" ], [ "droplet formation", "EVALUATION" ], [ "fluid", "DATA" ], [ "total volume", "DATA" ], [ "surface", "DATA" ], [ "viscous fingering", "VISUALIZATION" ], [ "curvature and advection operator", "METHOD" ], [ "fourth-order nonlinear PDE", "METHOD" ], [ "variational formulation", "METHOD" ], [ "smooth surface", "DATA" ], [ "discretization", "METHOD" ], [ "linear solve", "METHOD" ], [ "tricate effect", "EVALUATION" ], [ "numerical scheme", "METHOD" ], [ "reduced model", "METHOD" ], [ "mass density", "DATA" ], [ "triangle mesh", "DATA" ], [ "temporal evolution", "DATA" ], [ "viscous film", "DATA" ], [ "droplet interaction", "EVALUATION" ], [ "film", "DATA" ] ]

Solution Path for Semi-Supervised Classification with Manifold Regularization

4,835,039

With very low extra computational cost, the entire solution path can be computed for various learning algorithms like support vector classification (SVC) and support vector regression (SVR). In this paper, we extend this promising approach to semi-supervised learning algorithms. In particular, we consider finding the solution path for the Laplacian support vector machine (LapSVM) which is a semi-supervised classification model based on manifold regularization. One advantage of the this algorithm is that the coefficient path is piecewise linear with respect to the regularization parameter, hence its computational complexity is quadratic in the number of labeled examples.

[ { "first": "Gang", "middle": [], "last": "Wang", "suffix": "" }, { "first": "Tao", "middle": [], "last": "Chen", "suffix": "" }, { "first": "Dit-Yan", "middle": [], "last": "Yeung", "suffix": "" }, { "first": "F.H.", "middle": [], "last": "Lochovsky", "suffix": "" } ]

2,006

10.1109/ICDM.2006.150

Sixth International Conference on Data Mining (ICDM'06)

2156188932

[ "17572432", "121570279", "1352566", "866680", "15379424" ]

[ "1308034", "17606964", "14833239", "13962252" ]

true

https://api.semanticscholar.org/CorpusID:4835039

1

[ [ "computational cost", "EVALUATION" ], [ "computational complexity", "EVALUATION" ], [ "regularization parameter", "DATA" ], [ "semi-supervised learning algorithm", "APPLICATION" ], [ "support vector classification (SVC)", "METHOD" ], [ "Laplacian support vector machine (LapSVM)", "APPLICATION" ], [ "semi-supervised classification model", "METHOD" ], [ "coefficient path", "DATA" ], [ "support vector regression (SVR", "METHOD" ], [ "manifold regularization", "METHOD" ] ]

Deep Reinforcement Learning for Sponsored Search Real-time Bidding

3,628,478

Bidding optimization is one of the most critical problems in online advertising. Sponsored search (SS) auction, due to the randomness of user query behavior and platform nature, usually adopts keyword-level bidding strategies. In contrast, the display advertising (DA), as a relatively simpler scenario for auction, has taken advantage of real-time bidding (RTB) to boost the performance for advertisers. In this paper, we consider the RTB problem in sponsored search auction, named SS-RTB. SS-RTB has a much more complex dynamic environment, due to stochastic user query behavior and more complex bidding policies based on multiple keywords of an ad. Most previous methods for DA cannot be applied. We propose a reinforcement learning (RL) solution for handling the complex dynamic environment. Although some RL methods have been proposed for online advertising, they all fail to address the "environment changing'' problem: the state transition probabilities vary between two days. Motivated by the observation that auction sequences of two days share similar transition patterns at a proper aggregation level, we formulate a robust MDP model at hour-aggregation level of the auction data and propose a control-by-model framework for SS-RTB. Rather than generating bid prices directly, we decide a bidding model for impressions of each hour and perform real-time bidding accordingly. We also extend the method to handle the multi-agent problem. We deployed the SS-RTB system in the e-commerce search auction platform of Alibaba. Empirical experiments of offline evaluation and online A/B test demonstrate the effectiveness of our method.

[ { "first": "Jun", "middle": [], "last": "Zhao", "suffix": "" }, { "first": "Guang", "middle": [], "last": "Qiu", "suffix": "" }, { "first": "Ziyu", "middle": [], "last": "Guan", "suffix": "" }, { "first": "Wei", "middle": [], "last": "Zhao", "suffix": "" }, { "first": "Xiaofei", "middle": [], "last": "He", "suffix": "" } ]

2,018

1803.00259

10.1145/3219819.3219918

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2949175665,2963841569,2793763782

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1

[ [ "control-by-model framework", "METHOD" ], [ "transition pattern", "DATA" ], [ "Bidding optimization", "APPLICATION" ], [ "keyword-level bidding strategy", "METHOD" ], [ "online advertising", "APPLICATION" ], [ "online A/B test", "EVALUATION" ], [ "multi-agent problem", "APPLICATION" ], [ "reinforcement learning (RL) solution", "METHOD" ], [ "state transition probabil", "EVALUATION" ], [ "sponsored search auction", "APPLICATION" ], [ "SS-RTB system", "METHOD" ], [ "RL method", "METHOD" ], [ "bid price", "DATA" ], [ "SS-RTB", "APPLICATION" ], [ "user", "DATA" ], [ "bidding model", "METHOD" ], [ "real-time bidding (RTB)", "METHOD" ], [ "hour-aggregation level", "EVALUATION" ], [ "e-commerce search auction platform", "APPLICATION" ], [ "Empirical experiment", "EVALUATION" ], [ "bidding policy", "METHOD" ], [ "real-time bidding", "METHOD" ], [ "environment", "APPLICATION" ], [ "stochastic user query behavior", "METHOD" ], [ "Sponsored search (SS) auction", "APPLICATION" ], [ "display advertising", "APPLICATION" ], [ "auction data", "DATA" ], [ "offline evaluation", "EVALUATION" ], [ "dynamic environment", "METHOD" ], [ "auction sequence", "DATA" ], [ "MDP model", "METHOD" ], [ "RTB problem", "APPLICATION" ], [ "proper aggregation", "EVALUATION" ] ]

Understanding Simultaneity and Causality in Static Diagrams versus Animation

33,681,902

This study assesses how the mode of presentation affects the way in which people structure their mental models of a mechanical system, namely a flushing cistern. Subjects were assigned to one of three learning conditions: diagram only, 3-phases diagram, or animation. After learning the material, subjects generated written descriptions of the workings of the cistern. An analysis of temporal conjunctions used and the number of causal events mentioned indicates that for understanding simultaneity and causality, animation does not provide any benefit over seeing the same information in static diagrams.

[ { "first": "Sarah", "middle": [], "last": "Kriz", "suffix": "" } ]

2,002

10.1007/3-540-46037-3_32

Diagrams

1523677719

[]

[ "60849980" ]

false

true

false

https://api.semanticscholar.org/CorpusID:33681902

null

[ [ "mechanical system", "APPLICATION" ], [ "causal event", "DATA" ], [ "temporal conjunction", "DATA" ], [ "written description", "DATA" ], [ "mental model", "METHOD" ], [ "flushing cistern", "METHOD" ], [ "static diagram", "VISUALIZATION" ], [ "3-phases diagram", "VISUALIZATION" ], [ "lity", "DATA" ], [ "animation", "VISUALIZATION" ], [ "ity", "DATA" ] ]

Parameter Space Comparison of Inertial Particle Models.

196,162,579

[ { "first": "Jérôme", "middle": [], "last": "Holbein", "suffix": "" }, { "first": "Tobias", "middle": [], "last": "Günther", "suffix": "" } ]

2,018

10.2312/vmv20181254

VMV 2018

2898898593

[]

false

https://api.semanticscholar.org/CorpusID:196162579

null

[ [ "Parameter Space Comparison", "METHOD" ], [ "Inertial Particle Models.", "METHOD" ] ]

Labeling large displays for interaction with mobile devices: recognition of symbols for pairing techniques

30,786,789

Interactive displays are an effective means to exchange contents with mobile devices for co-located collaboration in offices and schools. It is very important that the users are able to easily comprehend and learn the interaction techniques to pair their mobile devices with large displays. In this paper, we report on the results of an exploratory case study investigating the comprehension and understandability of the labels advertising different interaction techniques for pairing mobile phones with the large displays. The results of the case study are discussed and the suggestions to enhance the comprehension level of these labels are provided.

[ { "first": "Umer", "middle": [], "last": "Rashid", "suffix": "" }, { "first": "Lucia", "middle": [], "last": "Terrenghi", "suffix": "" }, { "first": "Aaron", "middle": [], "last": "Quigley", "suffix": "" } ]

2,010

10.1145/1842993.1843095

AVI

2298726529,2020395092

[]

[ "67765405", "40035802" ]

false

true

https://api.semanticscholar.org/CorpusID:30786789

0

[ [ "exploratory case study", "EVALUATION" ], [ "Interactive display", "METHOD" ], [ "co-located collaboration", "APPLICATION" ], [ "comprehension level", "EVALUATION" ], [ "interaction technique", "METHOD" ], [ "case study", "EVALUATION" ], [ "mobile device", "METHOD" ] ]

Creative performance: does the computer retard artistic development?

12,704,179

Over the last decade, there have been several debates concerning the dominance of technology in academic disciplines such as fine arts and graphic design. Advanced imaging devices, such as fMRI and PET scans, have provided researchers with visual information in the field of brain science that may settle these debates. We outline a research study conducted among undergraduate art students to investigate the level of visual perception and creative development. The purpose is to gather basic information that would merit further scientific investigation. Among the groups studied, the research concluded that artistic traits such as visual memory, perception and a critical eye for detail declined 80% as compared to research studies conducted in 1944. We present scientific research in the field of cognition and brain science, suggesting dominant use of technology in the arts retards intellectual and perceptual-motor skills.

[ { "first": "T.", "middle": [], "last": "Knipp", "suffix": "" } ]

2,003

10.1109/IV.2003.1218051

Proceedings on Seventh International Conference on Information Visualization, 2003. IV 2003.

2106140475

[ "142800344", "5578467" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:12704179

0

1

0

[ [ "basic information", "DATA" ], [ "imaging device", "METHOD" ], [ "PET scan", "METHOD" ], [ "art", "DATA" ], [ "perceptual-motor skill", "APPLICATION" ], [ "visual information", "VISUALIZATION" ], [ "fMRI", "METHOD" ], [ "visual perception", "EVALUATION" ], [ "visual memory", "EVALUATION" ], [ "graphic design", "APPLICATION" ], [ "creative development", "EVALUATION" ], [ "fine art", "APPLICATION" ], [ "brain science", "APPLICATION" ], [ "cognition", "APPLICATION" ], [ "artistic trait", "DATA" ] ]

Trust me, i'm partially right: incremental visualization lets analysts explore large datasets faster

13,577,057

Queries over large scale (petabyte) data bases often mean waiting overnight for a result to come back. Scale costs time. Such time also means that potential avenues of exploration are ignored because the costs are perceived to be too high to run or even propose them. With sampleAction we have explored whether interaction techniques to present query results running over only incremental samples can be presented as sufficiently trustworthy for analysts both to make closer to real time decisions about their queries and to be more exploratory in their questions of the data. Our work with three teams of analysts suggests that we can indeed accelerate and open up the query process with such incremental visualizations.

[ { "first": "Danyel", "middle": [], "last": "Fisher", "suffix": "" }, { "first": "Igor", "middle": [], "last": "Popov", "suffix": "" }, { "first": "Steven", "middle": [], "last": "Drucker", "suffix": "" }, { "first": "m.c.", "middle": [], "last": "schraefel", "suffix": "" } ]

2,012

10.1145/2207676.2208294

CHI '12

2138722877

[ "8125630", "11284751", "7336686", "1746969", "61890598", "7421134", "52900252", "15500844", "11686954", "2574508", "2000546", "61994324", "32156779", "7274570", "3439184", "13422115", "6540381", "16884134" ]

[ "36287147", "17677457", "5032766", "198996401", "7058383", "23954234", "1407036", "13924292", "69769034", "32906539", "10099051", "201631087", "199488685", "207798031", "10809", "17614804", "128356551", "18809861", "86859423", "30270533", "16615335", "5037245", "6893809", "14483787", "796898", "215087237", "14121054", "16106517", "2475688", "51870969", "46938106", "181368826", "11746946", "52291260", "16051207", "1395440", "10096979", "198995163", "15772592", "9737052", "73519834", "209202398", "69431122", "19920319", "18857693", "111385823", "51845217", "207989621", "14027443", "16545007", "1062800", "195874367", "531862", "52056076", "10429403", "6827185", "15419853", "1759911", "26180872", "2189441", "7283747", "206391504", "24071284", "4783118", "9787931", "4086815", "192531090", "35793868", "30705681", "18646010", "7782864", "2023043", "6618748", "5076290", "52307096", "209490557", "52275190", "8434895", "51822191", "9338317", "3626249", "2369489", "3430786", "208171945", "16697886", "32307308", "63631927", "203643283", "16943785", "104293062", "15536637", "17805941", "2251512", "2163146", "14374820", "44108519", "15685090", "2469962", "201630924", "5916437", "199452776", "53087353", "6438612", "171085324", "2077867", "5255223", "2867108", "17631625", "10811545", "3407585", "3540781", "8794911", "46290860", "353336" ]

true

https://api.semanticscholar.org/CorpusID:13577057

0

1

0

[ [ "incremental visualization", "VISUALIZATION" ], [ "sampleAction", "METHOD" ], [ "interaction technique", "METHOD" ], [ "query process", "APPLICATION" ], [ "petabyte) data base", "DATA" ], [ "incremental sample", "DATA" ] ]

Attention funnel: omnidirectional 3D cursor for mobile augmented reality platforms

207,158,645

The attention funnel is a general purpose AR interface technique that interactively guides the attention of a user to any object, person, or place in space. The technique utilizes dynamic perceptual affordances to draw user attention "down" the funnel to the target location. Attention funnel can be used to cue objects completely out of sight including objects behind the user, or occluded by other objects or walls.An experiment evaluating user performance with the attention funnel and other conventional AR attention directing techniques found that the attention funnel increased the consistency of the user's search by 65%, increased search speed by 22%, and decreased mental workload by 18%. The attention funnel has potential applicability as a general 3D cursor or cue in a wide array of spatially enabled mobile and AR systems, and for applications where systems can support users in visual search, object awareness, and emergency warning in indoor and outdoor spaces.

[ { "first": "Frank", "middle": [], "last": "Biocca", "suffix": "" }, { "first": "Arthur", "middle": [], "last": "Tang", "suffix": "" }, { "first": "Charles", "middle": [], "last": "Owen", "suffix": "" }, { "first": "Fan", "middle": [], "last": "Xiao", "suffix": "" } ]

2,006

10.1145/1124772.1124939

CHI

1974179570

[ "469744", "142545948", "1171011", "61029959", "15252590", "60448372", "59787002", "128935897", "2584780", "141530658", "8192877", "6335932", "6036868", "17216489", "11701706", "23723296", "2343563", "57155180", "43784082", "15730996", "60614932", "60587254", "7228180", "1420" ]

[ "215185542", "8533169", "52147603", "18452016", "14803558", "204812112", "13810732", "213182392", "49421814", "52171463", "7425967", "12180868", "2030235", "240619", "52153399", "9569792", "64646816", "10395652", "15746667", "6875601", "211044024", "37694281", "7735291", "15179004", "208277507", "1629179", "1251017", "2525221", "4699525", "14098159", "8925019", "25937769", "15780176", "204700552", "519584", "24568248", "12140445", "23400500", "24022794", "204772142", "14476224", "4635725", "208262707", "7060589", "52170600", "84185958", "52978225", "19474443", "17744695", "11556886", "2695096", "22353522", "15128882", "202159091", "201068333", "6790034", "49416997", "140289579", "7271974", "92994651", "51882497", "53063504", "793240", "14466778", "204919269" ]

true

https://api.semanticscholar.org/CorpusID:207158645

0

1

0

[ [ "attention funnel", "METHOD" ], [ "3D cursor", "METHOD" ], [ "dynamic perceptual affordances", "METHOD" ], [ "indoor and outdoor space", "APPLICATION" ], [ "AR interface technique", "METHOD" ], [ "Attention funnel", "METHOD" ], [ "visual search", "APPLICATION" ], [ "person", "DATA" ], [ "user performance", "EVALUATION" ], [ "object awareness", "APPLICATION" ], [ "emergency warning", "APPLICATION" ], [ "place", "DATA" ], [ "AR attention directing technique", "METHOD" ], [ "spatially enabled mobile and AR system", "APPLICATION" ], [ "consistency of", "EVALUATION" ], [ "search speed", "EVALUATION" ], [ "object", "DATA" ], [ "mental workload", "EVALUATION" ] ]

Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age

12,770,718

With modern computer architecture evolving, two problems conspire against the state-of-the-art approaches in parallel query execution: (i) to take advantage of many-cores, all query work must be distributed evenly among (soon) hundreds of threads in order to achieve good speedup, yet (ii) dividing the work evenly is difficult even with accurate data statistics due to the complexity of modern out-of-order cores. As a result, the existing approaches for plan-driven parallelism run into load balancing and context-switching bottlenecks, and therefore no longer scale. A third problem faced by many-core architectures is the decentralization of memory controllers, which leads to Non-Uniform Memory Access (NUMA). In response, we present the morsel-driven query execution framework, where scheduling becomes a fine-grained run-time task that is NUMA-aware. Morsel-driven query processing takes small fragments of input data (morsels) and schedules these to worker threads that run entire operator pipelines until the next pipeline breaker. The degree of parallelism is not baked into the plan but can elastically change during query execution, so the dispatcher can react to execution speed of different morsels but also adjust resources dynamically in response to newly arriving queries in the workload. Further, the dispatcher is aware of data locality of the NUMA-local morsels and operator state, such that the great majority of executions takes place on NUMA-local memory. Our evaluation on the TPC-H and SSB benchmarks shows extremely high absolute performance and an average speedup of over 30 with 32 cores.

[ { "first": "Viktor", "middle": [], "last": "Leis", "suffix": "" }, { "first": "Peter", "middle": [], "last": "Boncz", "suffix": "" }, { "first": "Alfons", "middle": [], "last": "Kemper", "suffix": "" }, { "first": "Thomas", "middle": [], "last": "Neumann", "suffix": "" } ]

2,014

10.1145/2588555.2610507

SIGMOD Conference

2099035968

[ "1084527", "16665326", "5398477", "6119576", "8063223", "7280405", "6758938", "1379707", "10157214", "6276571", "1171369", "10346312", "14629807", "2785685", "10720565", "12674401", "6529485", "1150716", "11633902", "15462070", "854221", "14199923", "14174739", "12976945", "18642074", "5222686", "388477", "12360325", "14928707" ]

[ "52115672", "195298570", "206526773", "83459313", "11146047", "18943953", "213181195", "67865578", "102351433", "642159", "15799751", "207249842", "2232828", "52969816", "199452946", "21115556", "3080850", "15589515", "1281349", "1041516", "58013174", "70152215", "215238928", "18643564", "12090415", "201624619", "56594570", "6191528", "18331631", "4047462", "206743327", "83459157", "14417788", "5888795", "537515", "4570668", "212657705", "4774457", "85533513", "13750280", "32421956", "14742131", "3787297", "9095148", "208221740", "211268966", "5623781", "9192215", "7387581", "18958490", "25021172", "3259992", "15404310", "1906477", "211015924", "208158173", "15999707", "52175423", "1532875", "211146742", "198190736", "67916171", "11326650", "14310445", "374298", "208016969", "49566441", "44235876", "201314319", "215716374", "5019383", "17575213", "8154743", "193710", "2757098", "16178363", "24559101", "12105044", "204841857", "51932267", "115153090", "213197625", "53083078", "9452226", "52895287", "9970289", "209379505", "8619782", "5015002", "17112161", "64674743", "102354309", "16501067", "1953646", "11574325", "3359701", "6433143" ]

true

https://api.semanticscholar.org/CorpusID:12770718

1

[ [ "data (morsels", "DATA" ], [ "morsel-driven query execution framework", "METHOD" ], [ "context-switching bottlene", "METHOD" ], [ "query execution", "METHOD" ], [ "accurate data statistic", "DATA" ], [ "operator state", "DATA" ], [ "modern computer architecture", "APPLICATION" ], [ "Morsel-driven query processing", "METHOD" ], [ "plan-driven parallelism", "APPLICATION" ], [ "Non-Uniform Memory Access (NUMA)", "METHOD" ], [ "many-core architecture", "APPLICATION" ], [ "worker thread", "METHOD" ], [ "memory controller", "APPLICATION" ], [ "parallel query execution", "APPLICATION" ], [ "TPC-H", "EVALUATION" ], [ "SSB benchmark", "EVALUATION" ], [ "high absolute performance", "EVALUATION" ], [ "NUMA-local morsel", "DATA" ], [ "core", "METHOD" ], [ "average speedup", "EVALUATION" ], [ "load balancing", "METHOD" ], [ "out-of-order core", "DATA" ], [ "NUMA-local memory", "DATA" ], [ "run-time task", "APPLICATION" ] ]

Discovering frequent topological structures from graph datasets

2,427,391

The problem of finding frequent patterns from graph-based datasets is an important one that finds applications in drug discovery, protein structure analysis, XML querying, and social network analysis among others. In this paper we propose a framework to mine frequent large-scale structures, formally defined as frequent topological structures, from graph datasets. Key elements of our framework include, fast algorithms for discovering frequent topological patterns based on the well known notion of a topological minor, algorithms for specifying and pushing constraints deep into the mining process for discovering constrained topological patterns, and mechanisms for specifying approximate matches when discovering frequent topological patterns in noisy datasets. We demonstrate the viability and scalability of the proposed algorithms on real and synthetic datasets and also discuss the use of the framework to discover meaningful topological structures from protein structure data.

[ { "first": "R.", "middle": [], "last": "Jin", "suffix": "" }, { "first": "C.", "middle": [], "last": "Wang", "suffix": "" }, { "first": "D.", "middle": [], "last": "Polshakov", "suffix": "" }, { "first": "S.", "middle": [], "last": "Parthasarathy", "suffix": "" }, { "first": "G.", "middle": [], "last": "Agrawal", "suffix": "" } ]

2,005

10.1145/1081870.1081944

KDD '05

2036868363

[ "5100756", "377228", "7065154", "2427391", "8684662", "10119423", "554188", "23576795", "2122960", "9536993", "8863872", "10118968" ]

[ "211108088", "55524713", "9477349", "15731073", "15731073", "29591948", "2042332", "8597867", "10574408", "16113458", "7455050", "12886725", "6238562", "11653951", "1263884", "46370263", "10871061", "11690157", "2340056", "11323088", "6553395", "16604069", "15756042", "9443745", "40587568", "64413065", "60604", "28601100", "204899860", "209428584", "2427391", "8085527", "7022819", "16218311", "929930", "929930", "29605145" ]

true

https://api.semanticscholar.org/CorpusID:2427391

1

[ [ "XML querying", "APPLICATION" ], [ "approximate match", "METHOD" ], [ "graph datasets", "DATA" ], [ "noisy datasets", "DATA" ], [ "frequent topological pattern", "DATA" ], [ "graph-based datasets", "DATA" ], [ "constrained topological pattern", "DATA" ], [ "real and synthetic dataset", "DATA" ], [ "drug discovery", "APPLICATION" ], [ "topological minor", "DATA" ], [ "protein structure data", "DATA" ], [ "social network analysis", "APPLICATION" ], [ "protein structure analysis", "APPLICATION" ], [ "topological structure", "DATA" ], [ "mining process", "APPLICATION" ], [ "fast algorithm", "METHOD" ] ]

Labels vs. Pairwise Constraints: A Unified View of Label Propagation and Constrained Spectral Clustering

17,741,539

In many real-world applications we can model the data as a graph with each node being an instance and the edges indicating a degree of similarity. Side information is often available in the form of labels for a small subset of instances, which gives rise to two problem settings and two types of algorithms. In the label propagation style algorithms, the known labels are propagated to the unlabeled nodes. In the constrained clustering style algorithms, known labels are first converted to pair wise constraints (Must-Link and Cannot-Link), then a constrained cut is computed as a tradeoff between minimizing the cut cost and maximizing the constraint satisfaction. Both techniques are evaluated by their ability to recover the ground truth labeling, i.e. by 0/1 loss function either directly on the labels or on the pair wise relations derived from the labels. These two fields have developed separately, but in this paper, we show that they are indeed related. This insight allows us to propose a novel way to generate constraints from the propagated labels, which our empirical study shows outperforms and is more stable than the state-of-the-art label propagation and constrained spectral clustering algorithms.

[ { "first": "Xiang", "middle": [], "last": "Wang", "suffix": "" }, { "first": "Buyue", "middle": [], "last": "Qian", "suffix": "" }, { "first": "I.", "middle": [], "last": "Davidson", "suffix": "" } ]

2,012

10.1109/ICDM.2012.103

2012 IEEE 12th International Conference on Data Mining

2007555829

[ "1304448", "1052837", "116930672", "508435", "2948946", "14848918" ]

[ "13153353", "52984357", "5509050", "18751951", "2718577", "10431276", "15672270", "6142276" ]

true

https://api.semanticscholar.org/CorpusID:17741539

0

1

0

[ [ "pair wise relation", "DATA" ], [ "unlabeled node", "DATA" ], [ "0/1 loss function", "METHOD" ], [ "constrained spectral clustering algorithm", "METHOD" ], [ "graph", "DATA" ], [ "constraint satisfaction", "EVALUATION" ], [ "label propagation style algorithm", "METHOD" ], [ "constrained clustering style algorithm", "METHOD" ], [ "constrained cut", "METHOD" ], [ "empirical study", "EVALUATION" ], [ "real-world application", "APPLICATION" ], [ "ground truth labeling", "EVALUATION" ], [ "label propagation", "METHOD" ], [ "s (Must-Link and Cannot-Link", "METHOD" ], [ "cut cost", "EVALUATION" ] ]

Handling redundancy in the processing of recursive database queries

17,743,272

Redundancy may exist in the processing of recursive database queries at four different levels precompilation level, iteration level, tuple processing level and file accessing level. Techniques for reducing redundant work at each level are studied. In the precompilation level, the optimization techniques include removing redundant parts in a rule cluster, simplifying recursive clusters and sharing common subexpressions among rules. At the iteration level, the techniques discussed are the use of frontier relations and the counting method. At the tuple processing level, we use merging and filtering methods to exclude processed drivers from database reaccessing. Finally, at the file accessing level, I/O cost can be further reduced by level relaxation. We conclude that even for complex recursion, redundant database processing can be considerably reduced or eliminated by developing appropriate algorithms.

[ { "first": "Jiawei", "middle": [], "last": "Han", "suffix": "" }, { "first": "Lawrence", "middle": [ "J." ], "last": "Henschen", "suffix": "" } ]

1,987

10.1145/38713.38727

SIGMOD '87

2140092107

[]

[ "1813886", "16904121", "8063241", "3899760", "11805903", "12998513", "6685516", "14021887" ]

false

true

https://api.semanticscholar.org/CorpusID:17743272

0

1

0

[ [ "file accessing level", "METHOD" ], [ "iteration level", "METHOD" ], [ "tuple processing level", "METHOD" ], [ "database reaccessing", "APPLICATION" ], [ "level relaxation", "METHOD" ], [ "tuple process", "METHOD" ], [ "redundant work", "APPLICATION" ], [ "optimization technique", "METHOD" ], [ "recursive database query", "APPLICATION" ], [ "merging and filtering method", "METHOD" ], [ "recursive cluster", "DATA" ], [ "redundant part", "DATA" ], [ "algorithm", "METHOD" ], [ "rule cluster", "DATA" ], [ "precompilation level", "METHOD" ], [ "frontier relation", "METHOD" ], [ "redundant database processing", "METHOD" ], [ "counting method", "METHOD" ] ]

Meta Clustering

7,290,240

Clustering is ill-defined. Unlike supervised learning where labels lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the clusters will be used. Devising clustering criteria that capture what users need is difficult. Most clustering algorithms search for optimal clusterings based on a pre-specified clustering criterion. Our approach differs. We search for many alternate clusterings of the data, and then allow users to select the clustering(s) that best fit their needs. Meta clustering first finds a variety of clusterings and then clusters this diverse set of clusterings so that users must only examine a small number of qualitatively different clusterings. We present methods for automatically generating a diverse set of alternate clusterings, as well as methods for grouping clusterings into meta clusters. We evaluate meta clustering on four test problems and two case studies. Surprisingly, clusterings that would be of most interest to users often are not very compact clusterings.

[ { "first": "R.", "middle": [], "last": "Caruana", "suffix": "" }, { "first": "M.", "middle": [], "last": "Elhaway", "suffix": "" }, { "first": "Nam", "middle": [], "last": "Nguyen", "suffix": "" }, { "first": "C.", "middle": [], "last": "Smith", "suffix": "" } ]

2,006

10.1109/ICDM.2006.103

Sixth International Conference on Data Mining (ICDM'06)

2293273145

[ "18764978", "10747449", "10954195", "2341726", "12946615", "13411696", "118110564", "5365891", "36694997", "189915041", "29535089", "64563489", "1487131", "1378740", "14046018", "209099422", "197457299", "1324009", "3068944", "9488134", "13491515", "3072212" ]

[ "7626196", "127197", "18361678", "19900429", "25342033", "17238001", "211689007", "5645428", "15768652" ]

true

https://api.semanticscholar.org/CorpusID:7290240

0

1

0

[ [ "meta clustering", "METHOD" ], [ "set", "DATA" ], [ "compact cluster", "METHOD" ], [ "optimal clustering", "METHOD" ], [ "clustering", "METHOD" ], [ "e clustering", "DATA" ], [ "clustering quality", "EVALUATION" ], [ "meta cluster", "DATA" ], [ "squared error", "EVALUATION" ], [ "clustering criterion", "METHOD" ], [ "alternate clustering", "DATA" ], [ "Meta clustering", "METHOD" ], [ "Clustering", "METHOD" ], [ "diverse set", "DATA" ], [ "supervised learning", "METHOD" ], [ "test problem", "EVALUATION" ], [ "case study", "EVALUATION" ], [ "clustering algorithm", "METHOD" ], [ "crisp performance criterion", "EVALUATION" ] ]

A software system for buzz-based recommendations

15,863,666

In this paper, we present an outline of a software system for buzz-based recommendations. This system is based on a large source of queries in an eCommerce application. The buzz events are detected based on query bursts linked to external entities like news and inventory information. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood. The system follows the paradigm of limited quantity merchandizing, in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity and promoting user activity and stickiness. The system demonstrates the deployment of an interesting application based on KDD principles applied to a high volume industrial context.

[ { "first": "Hill", "middle": [], "last": "Nguyen", "suffix": "" }, { "first": "Nish", "middle": [], "last": "Parikh", "suffix": "" }, { "first": "Neel", "middle": [], "last": "Sundaresan", "suffix": "" } ]

2,008

10.1145/1401890.1402027

KDD

2069771661

[ "204715572", "53245668", "13303317", "3246427", "1775037" ]

[ "207185829", "14006784", "2069275" ]

true

https://api.semanticscholar.org/CorpusID:15863666

0

1

0

[ [ "software system", "METHOD" ], [ "inventory information", "DATA" ], [ "limited quantity merchandizing", "METHOD" ], [ "user activity", "EVALUATION" ], [ "buzz event", "DATA" ], [ "user curiosity", "EVALUATION" ], [ "query burst", "DATA" ], [ "buzz-based recommendation", "APPLICATION" ], [ "high volume industrial context", "APPLICATION" ], [ "KDD principle", "METHOD" ], [ "semantic neighborhood", "DATA" ], [ "buzz query", "DATA" ], [ "eCommerce application", "APPLICATION" ] ]

Hardware-Accelerated, High-Quality Rendering Based on Trivariate Splines Approximating Volume Data

30,643,450

We develop an approach for hardware-accelerated, high-quality rendering of volume data using trivariate splines. The proposed quasi-interpolating schemes are realtime reconstructions. The low total degrees provide several advantages for our GPU implementation. In particular, intersecting rays with spline isosurfaces for direct Phong illumination is performed by simple root finding algorithms (analytic and iterative), while the necessary normals result from blossoming. Since visualizations are on a fragment base, our renderer for isosurfaces includes an automatic level of detail. While we use well-known spatial data structures in the CPU part of the algorithm for hierarchical view frustum culling and memory reduction, our GPU implementations have to take the highly complex structure of the splines into account. These include an appropriate organization of the data streams, i.e. we develop an advanced encoding scheme for the spline coefficients, as well as an implicit scheme for bounding geometry retrieval. In addition, we propose an elaborated clipping procedure to be performed in the fragment shader. These features essentially reduce bus traffic, memory consumption, and data access on the GPU leading to interactive frame rates for renderings of high visual quality. Compared with pure CPU implementations and existing GPU implementations for trivariate polynomials frame rates increase by factors between 10 and 100.

[ { "first": "Thomas", "middle": [], "last": "Kalbe", "suffix": "" }, { "first": "Frank", "middle": [], "last": "Zeilfelder", "suffix": "" } ]

2,008

10.1111/j.1467-8659.2008.01130.x

Comput. Graph. Forum

2068031196

[]

[ "9542961", "333034", "28080934", "12112826", "16731327", "7121780", "474724", "7572983" ]

false

true

false

https://api.semanticscholar.org/CorpusID:30643450

null

[ [ "fragment shader", "DATA" ], [ "spline isosurfaces", "DATA" ], [ "hardware-accelerated, high-quality rendering", "APPLICATION" ], [ "trivariate polynomial frame rate", "EVALUATION" ], [ "quasi-interpolating scheme", "METHOD" ], [ "volume data", "DATA" ], [ "advanced encoding scheme", "METHOD" ], [ "access", "DATA" ], [ "realtime reconstruction", "METHOD" ], [ "trivariate spline", "DATA" ], [ "data stream", "DATA" ], [ "GPU implementation", "APPLICATION" ], [ "spatial data structure", "DATA" ], [ "automatic level", "EVALUATION" ], [ "direct Phong illumination", "APPLICATION" ], [ "hierarchical view frustum culling", "APPLICATION" ], [ "pure CPU implementation", "METHOD" ], [ "elaborated clipping procedure", "METHOD" ], [ "total degree", "DATA" ], [ "spline", "DATA" ], [ "bus traffic", "EVALUATION" ], [ "simple root finding algorithm", "METHOD" ], [ "visual quality", "VISUALIZATION" ], [ "memory consum", "EVALUATION" ], [ "bounding geometry retrieval", "APPLICATION" ], [ "iterative", "METHOD" ], [ "fragment base", "DATA" ], [ "memory reduction", "APPLICATION" ], [ "isosurfaces", "DATA" ], [ "ray", "DATA" ], [ "interactive frame rate", "VISUALIZATION" ], [ "implicit scheme", "METHOD" ], [ "spline coefficient", "DATA" ], [ "appropriate organization", "METHOD" ] ]

Multi-Segment Foot for Human Modelling and Simulation

210,710,313

[ { "first": "Hwangpil", "middle": [], "last": "Park", "suffix": "" }, { "first": "Ri", "middle": [], "last": "Yu", "suffix": "" }, { "first": "Jehee", "middle": [], "last": "Lee", "suffix": "" } ]

2,020

10.1111/cgf.13896

Comput. Graph. Forum

2998040337

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[]

true

false

true

https://api.semanticscholar.org/CorpusID:210710313

0

1

0

[ [ "Multi-Segment Foot", "METHOD" ], [ "Human Modelling and Simulation", "APPLICATION" ] ]

Collaborative Label Correction via Entropy Thresholding

210,995,763

Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. We examine this behavior in light of the Shannon entropy of the predictions and demonstrate the low entropy predictions determined by a given threshold are much more reliable as the supervision than the original noisy labels. It also shows the advantage in maintaining more training samples than previous methods. Then, we power this entropy criterion with the Collaborative Label Correction (CLC) framework to further avoid undesired local minimums of the single network. A range of experiments have been conducted on multiple benchmarks with both synthetic and real-world settings. Extensive results indicate that our CLC outperforms several state-of-the-art methods.

[ { "first": "Hao", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Jiangchao", "middle": [], "last": "Yao", "suffix": "" }, { "first": "Jiajie", "middle": [], "last": "Wang", "suffix": "" }, { "first": "Yinru", "middle": [], "last": "Chen", "suffix": "" }, { "first": "Ya", "middle": [], "last": "Zhang", "suffix": "" }, { "first": "Yanfeng", "middle": [], "last": "Wang", "suffix": "" } ]

2,019

10.1109/ICDM.2019.00179

2019 IEEE International Conference on Data Mining (ICDM)

3004038367

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[]

true

false

true

https://api.semanticscholar.org/CorpusID:210995763

0

1

0

[ [ "entropy criterion", "METHOD" ], [ "training sample", "DATA" ], [ "clean label", "DATA" ], [ "noisy label", "DATA" ], [ "real-world setting", "METHOD" ], [ "Shannon entropy", "EVALUATION" ], [ "Deep neural network (DNNs)", "METHOD" ], [ "CLC", "METHOD" ], [ "low entropy prediction", "METHOD" ], [ "Collaborative Label Correction (CLC) framework", "METHOD" ], [ "local minimum", "EVALUATION" ] ]

Exploring Individual Differences in Preschooler's Causal Reasoning Skills in the Physical and Digital Domains.

13,552,508

[ { "first": "Jessie-Raye", "middle": [], "last": "Bauer", "suffix": "" }, { "first": "Amy", "middle": [ "Elizabeth" ], "last": "Booth", "suffix": "" }, { "first": "Cristine", "middle": [ "H." ], "last": "Legare", "suffix": "" } ]

2,016

CogSci

2786873273

[]

false

true

https://api.semanticscholar.org/CorpusID:13552508

0

[ [ "Physical and Digital Domains", "APPLICATION" ] ]

Your Money's No Good Here: The Elimination of Cash Payment on London Buses

18,579,392

As digital payments become increasingly important features of economic exchange, traditional forms of payment such as cash are becoming phased out in certain settings. We study one such context-the elimination of cash payment on London buses in July 2014. We conducted ethnographic fieldwork, interviews with drivers and collected online and social media comments before, during and shortly after the introduction of cashless fares. We explore how drivers and passengers were fearful of the change due in part to a lack of information and communication, the anticipation of negative effects on vulnerable passengers and a compromise in freedom, flexibility and surveillance. We highlight the ways cashless payments can alter the social function of money, create new forms of work for drivers and passengers, and if not carefully introduced can cause emotional stress and fears of state surveillance and control.

[ { "first": "Gary", "middle": [], "last": "Pritchard", "suffix": "" }, { "first": "John", "middle": [], "last": "Vines", "suffix": "" }, { "first": "Patrick", "middle": [], "last": "Olivier", "suffix": "" } ]

2,015

10.1145/2702123.2702137

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

2046351627

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[ "140254449", "28227169", "16910560", "207943833", "207957342", "50767870", "182803737", "9887407", "140213914", "23743972", "894376", "1830594", "4999166", "53075073", "29570996", "5046689", "47018900", "2273049" ]

true

https://api.semanticscholar.org/CorpusID:18579392

0

1

0

[ [ "economic exchange", "APPLICATION" ], [ "cashless payment", "METHOD" ], [ "digital payment", "APPLICATION" ], [ "state sur", "APPLICATION" ], [ "London", "METHOD" ], [ "cash payment", "METHOD" ], [ "cashless fare", "APPLICATION" ], [ "online and social medium comment", "DATA" ], [ "emotional stress", "EVALUATION" ], [ "ethnographic fieldwork", "EVALUATION" ] ]

Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data

12,580,162

Spatial indexing has been one of the active focus areas in recent database research. Several variants of Quadtree and R-tree indexes have been proposed in database literature. In this paper, we first describe briefly our implementation of Quadtree and R-tree index structures and related optimizations in Oracle Spatial. We then examine the relative merits of two structures as implemented in Oracle Spatial and compare their performance for different types of queries and other operations. Finally, we summarize experiences with these different structures in indexing large GIS datasets in Oracle Spatial.

[ { "first": "Ravi", "middle": [ "Kanth", "V" ], "last": "Kothuri", "suffix": "" }, { "first": "Siva", "middle": [], "last": "Ravada", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Abugov", "suffix": "" } ]

2,002

10.1145/564691.564755

SIGMOD '02

2029306088

[ "5471548", "11567855", "13554536", "11492745", "15166495", "10312558", "12052256", "34681417", "7075919", "5070959", "36394045", "11696516", "2722443", "3201732", "5900836", "15333693", "17557060", "2801908", "10588400", "14011086", "9396970", "5147638", "17263491", "5234755", "35984301", "59888996", "17181175" ]

[ "211139392", "32695135", "15435659", "24569384", "81985863", "14922494", "11475865", "15496368", "9682017", "17666965", "11602747", "6834771", "62886534", "2027954", "16936116", "14184943", "14282097", "35615595", "14995897", "5686109", "25351956", "8655696", "5702771", "16748596", "62718125", "24103098", "980972", "12163146", "62295684", "1994412", "14116069", "5259780", "15325430", "17666913", "6146311", "3911792", "111823164", "17474985", "10278967", "53701917", "26083653", "174820186", "208016695", "55130276", "113988469", "25929885", "13844597", "86768999", "6585350", "17610569", "1444107", "164578772", "9218120", "1120063", "9819553", "17215164", "13252382", "14943637", "1066389", "16187459", "14478865", "30099215", "53083464", "6862299", "52154621", "18641608", "9974996", "13629040", "18754976", "199057633", "14467871", "2396675", "198185680", "30055887", "15665714", "12533743", "199422452", "55542114", "14266864", "195255803", "221592", "210038510", "2684144", "214613291", "550043", "7120709", "198479654", "16260947", "54440649", "2829214", "9457754", "12900877", "15261484", "9606789", "2810930", "145873355", "782210", "11988364" ]

true

https://api.semanticscholar.org/CorpusID:12580162

0

1

0

[ [ "relative merit", "EVALUATION" ], [ "Quadtree", "METHOD" ], [ "database literature", "APPLICATION" ], [ "R-tree index", "METHOD" ], [ "GIS dataset", "DATA" ], [ "database research", "APPLICATION" ], [ "Spatial indexing", "APPLICATION" ], [ "Quadtree and R-tree index structure", "METHOD" ], [ "Oracle", "METHOD" ], [ "Oracle Spatial", "METHOD" ] ]

Evaluating image quality measures to assess the impact of lossy data compression applied to climate simulation data

199,018,642

[ { "first": "A. H.", "middle": [], "last": "Baker", "suffix": "" }, { "first": "D. M.", "middle": [], "last": "Hammerling", "suffix": "" }, { "first": "T. L.", "middle": [], "last": "Turton", "suffix": "" } ]

2,019

10.1111/cgf.13707

Comput. Graph. Forum

2957966040

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[ "203951471", "210713947" ]

true

https://api.semanticscholar.org/CorpusID:199018642

1

[ [ "lossy data compression", "METHOD" ], [ "climate simulation data", "DATA" ], [ "image quality measure", "EVALUATION" ] ]

Development of Substitution Bias Sensitivity: Are Adolescents Happy Fools?

7,512,665

Development of Substitution Bias Sensitivity: Are Adolescents Happy Fools? Sandrine Rossi ([email protected]) LaPsyDE (CNRS Unit 3521), University of Caen Basse-Normandie, Caen, France Mathieu Cassotti ([email protected]) LaPsyDE (CNRS Unit 3521), Paris Descartes University, Paris, France Marine Agogue ([email protected]) Centre de Gestion Scientifique, Mines ParisTech, Paris, France Wim De Neys ([email protected]) CNRS, LaPsyDE (CNRS Unit 3521), Paris Descartes University, Paris, France Abstract Influential work on human thinking suggests that our judgment is often biased because we minimize cognitive effort and intuitively substitute hard questions by easier ones. Recent work with adults who solved the bat-and-ball problem, one of the most publicized examples of the substitution bias, suggests that people realize they are doing this and notice their mistake. In the present paper we look at the development of this substitution bias sensitivity. A group of young adolescents solved standard and isomorphic control versions of the bat-and-ball problem in which reasoners experience no intuitive pull to substitute. Adults have been shown to be less confident in their substituted, erroneous bat- and-ball answer than in their answer on the control version that does not give rise to the substitution. However, the present study established that this critical confidence drop was less pronounced for young adolescents. This implies that in contrast with adults, young adolescents do not yet fully acknowledge the questionable nature of their biased answer and remain more oblivious to the substitution. That is, young adolescent reasoners seem to behave more like happy fools who blindly answer erroneous questions without realizing it. Keywords: Decision-making; Bias; Development Introduction Human reasoners have been characterized as cognitive misers who show a strong tendency to rely on fast, intuitive processing rather than on more demanding, deliberate thinking (Evans, 2008; Kahneman, 2011). Although the fast and effortless nature of intuitive processing can sometimes be useful, it can also bias our reasoning. It has been argued that the key to this bias is a process of so-called attribute substitution – when people are confronted with a difficult question they often intuitively answer an easier one instead (e.g., Kahneman, 2011; Kahneman & Frederick, 2002). Consider the following example: A bat and a ball together cost $1.10. The bat costs $1 more than the ball. How much does the ball cost? When you try to answer this problem, the intuitive answer that immediately springs to mind is “10 cents”. Indeed, about 80% of university students who are asked to solve the “bat-and-ball” problem give the “10 cents” answer (e.g., Bourgeois-Gironde & Vanderhenst, 2009). But it is wrong. Obviously, if the ball were to cost 10 cents, the bat would cost $1.10 (i.e., $1 more) and then the total cost would be $1.20, rather than the required $1.10. The correct response is “5 cents”, of course (i.e., the bat costs $1.05). The explanation for the widespread “10 cents” bias in terms of attribute substitution is that people substitute the critical relational “more than” statement by a simpler absolute statement. That is, “the bat costs $1 more than the ball” is read as “the bat costs $1”. Hence, rather than working out the sum, people naturally parse $1.10, into $1 and 10 cents which is easier to do. In other words, because of the substitution people give the correct answer to the wrong question. The bat-and-ball problem is considered a paradigmatic example of people’s cognitive miserliness (e.g., Bourgeois- Gironde & Vanderhenst, 2009; Kahneman, 2011; Kahneman & Frederick, 2002; Toplak, West, & Stanovich, 2011). After all, the problem is really not that hard. Clearly, if people would reflect upon it for even a moment they would surely realize their error and notice that a 10 cents ball and a bat that costs a dollar more cannot total to $1.10. Hence, the problem with attribute substitution seems to be that people do typically not notice that they are substituting and do not realize their error (Kahneman & Frederick, 2005; Thompson, 2009; Toplak et al., 2011). This can sketch a somewhat bleak picture of human rationality: Not only do we often fail to reason correctly, much like happy fools, we do not even seem to realize that we are making a mistake. However, the fact that decision-makers do not deliberately reflect upon their response does not necessarily imply that they are not detecting the substitution process. That is, although people might not engage in deliberate processing and might not know what the correct answer is, it is still possible that they have some minimal substitution sensitivity and at least notice that their substituted “10 cents” response is not completely warranted (e.g., Alter, Oppenheimer, Epley, & Eyre, 2007; De Neys, 2012; De

[ { "first": "Sandrine", "middle": [], "last": "Rossi", "suffix": "" }, { "first": "Mathieu", "middle": [], "last": "Cassotti", "suffix": "" }, { "first": "Marine", "middle": [], "last": "Agogué", "suffix": "" }, { "first": "Wim", "middle": [ "De" ], "last": "Neys", "suffix": "" } ]

2,013

CogSci

2396044174

[ "15977327", "6185169", "61465239", "16898743", "17152414", "29824574", "13748885", "3840083", "41140803", "13741143", "6138486", "335217", "12246493", "145073552", "1527960", "11678342", "13959638", "149191349", "18516635", "162009578", "18241688", "42172126", "16591695", "64489", "142227166", "22824496", "147963815", "144827573" ]

[ "13755752", "13739124", "9163477" ]

true

https://api.semanticscholar.org/CorpusID:7512665

0

1

0

[ [ "deliberate thinking", "METHOD" ], [ "questionable nature", "EVALUATION" ], [ "bat- and-ball answer", "METHOD" ], [ "critical confidence drop", "EVALUATION" ], [ "10 cent ball", "DATA" ], [ "substitution people", "METHOD" ], [ "intuitive pull", "EVALUATION" ], [ "substitution process", "METHOD" ], [ "human thinking", "APPLICATION" ], [ "bat-and-ball problem", "APPLICATION" ], [ "cognitive miser", "METHOD" ], [ "deliberate processing", "METHOD" ], [ "absolute statement", "DATA" ], [ "adolescent reason", "METHOD" ], [ "minimal substitution sensitivity", "EVALUATION" ], [ "relational “more than” statement", "DATA" ], [ "substitution bias", "METHOD" ], [ "intuitive processing", "METHOD" ], [ "control version", "METHOD" ], [ "bat-and-ball” problem", "APPLICATION" ], [ "biased answer", "DATA" ], [ "attribute substitution", "METHOD" ], [ "substitution bias sensitivity", "EVALUATION" ], [ "Bias; Development Introdu", "APPLICATION" ], [ "standard and isomorphic control version", "METHOD" ], [ "cognitive miserlines", "APPLICATION" ], [ "cognitive", "APPLICATION" ] ]

Efficient Discovery of the Top-K Optimal Dependency Rules with Fisher's Exact Test of Significance

11,891,980

Statistical dependency analysis is the basis of all empirical science. A commonly occurring problem is to find the most significant dependency rules, which describe either positive or negative dependencies between categorical attributes. For example, in medical science one is interested in genetic factors, which can either predispose or prevent diseases. The requirement of statistical significance is essential, because the discoveries should hold also in the future data. Typically, the significance is estimated either by Fisher's exact test or the $\chi^2$-measure. The problem is computationally very difficult, because the number of all possible dependency rules increases exponentially with the number of attributes. As a solution, different kinds of restrictions and heuristics have been applied, but a general, scalable search method has been missing. In this paper, we introduce an efficient algorithm for searching for the top-K globally optimal dependency rules using Fisher's exact test as a measure function. The rules can express either positive or negative dependencies between a set of positive attributes and a single consequent attribute. The algorithm is based on an application of the branch-and-bound search strategy, supplemented by several pruning properties. Especially, we prove a new lower-bound for the Fisher's p, and introduce a new effective pruning principle. The general search algorithm is applicable to other goodness measures, like the $\chi^2$-measure, as well. According to our experiments on classical benchmark data, the algorithm is well scalable and can efficiently handle even dense and high dimensional data sets. In addition, the quality of rules is significantly better than with the $\chi^2$-measure using the same search algorithm.

[ { "first": "Wilhelmiina", "middle": [], "last": "Hamalainen", "suffix": "" } ]

2,010

10.1109/ICDM.2010.143

2010 IEEE International Conference on Data Mining

2014249115

[ "9429389", "2706742", "62787098", "15772082", "11934586", "10519769", "490415", "41021479", "16903641", "405920", "61980692", "8141352", "13029572", "15012664", "6682219", "7712399" ]

[ "14839879", "7308420", "7456693", "12732478", "16742231", "59416269", "3338191", "196591676", "11082007", "50777317" ]

true

https://api.semanticscholar.org/CorpusID:11891980

0

1

0

[ [ "search algorithm", "METHOD" ], [ "negative depende", "METHOD" ], [ "branch-and-bound search strategy", "METHOD" ], [ "2$-measure", "METHOD" ], [ "consequent attribut", "DATA" ], [ "medical science", "APPLICATION" ], [ "measure function", "METHOD" ], [ "efficient algorithm", "METHOD" ], [ "general search algorithm", "METHOD" ], [ "goodness measure", "APPLICATION" ], [ "high dimensional data set", "DATA" ], [ "Fisher's exact test", "METHOD" ], [ "genetic factor", "METHOD" ], [ "pruning principle", "METHOD" ], [ "positive", "METHOD" ], [ "categorical attribute", "DATA" ], [ "optimal dependency rule", "METHOD" ], [ "measure", "APPLICATION" ], [ "statistical significance", "EVALUATION" ], [ "dependency rule", "METHOD" ], [ "Fisher's p", "METHOD" ], [ "negative dependency", "METHOD" ], [ "pruning property", "DATA" ], [ "classical benchmark data", "DATA" ], [ "scalable search method", "METHOD" ], [ "Statistical dependency analysis", "METHOD" ] ]

Pointassist for older adults: analyzing sub-movement characteristics to aid in pointing tasks

14,011,673

Perceptual, cognitive and motor deficits cause many older adults to have difficulty conducting pointing tasks on computers. Many strategies have been discussed in the HCI community to aid older adults and others in pointing tasks. We present a different approach in PointAssist, software that aids in pointing tasks by analyzing the characteristics of sub-movements, detecting when users have difficulty pointing, and triggering a precision mode that slows the speed of the cursor in those cases. PointAssist is designed to help maintain pointing skills, runs as a background process working with existing software, is not vulnerable to clusters of targets or targets in the way, and does not modify the visual appearance or the feel of user interfaces. There is evidence from a prior study that PointAssist helps young children conduct pointing tasks. In this paper, we present a study evaluating PointAssist with twenty older adults (ages 66-88). The study participants benefited from greater accuracy when using PointAssist, when compared to using the "enhance pointer precision" option in Windows XP. In addition, we provide evidence of correlations between neuropsychological measures, pointing performance, and PointAssist detecting pointing difficulty.

[ { "first": "Juan", "middle": [ "Pablo" ], "last": "Hourcade", "suffix": "" }, { "first": "Christopher", "middle": [ "M." ], "last": "Nguyen", "suffix": "" }, { "first": "Keith", "middle": [ "B." ], "last": "Perry", "suffix": "" }, { "first": "Natalie", "middle": [ "L." ], "last": "Denburg", "suffix": "" } ]

2,010

10.1145/1753326.1753494

CHI

2037188078

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[ "14168556", "5039541", "6768589", "18919449", "6044388", "13325726", "55579322", "21613508", "17090798" ]

true

https://api.semanticscholar.org/CorpusID:14011673

0

1

0

[ [ "visual appearance", "VISUALIZATION" ], [ "pointer precision", "EVALUATION" ], [ "background process", "METHOD" ], [ "pointing performance", "EVALUATION" ], [ "user interface", "VISUALIZATION" ], [ "pointing skill", "EVALUATION" ], [ "pointing task", "APPLICATION" ], [ "neuropsychological measure", "EVALUATION" ], [ "detecting pointing difficulty", "APPLICATION" ], [ "sub-movements", "DATA" ], [ "precision mode", "METHOD" ], [ "cognitive and motor deficit", "METHOD" ], [ "PointAssist", "METHOD" ], [ "HCI community", "APPLICATION" ] ]

External Labeling Techniques: A Taxonomy and Survey

59,604,496

External labeling is frequently used for annotating features in graphical displays and visualizations, such as technical illustrations, anatomical drawings, or maps, with textual information. Such a labeling connects features within an illustration by thin leader lines with their labels, which are placed in the empty space surrounding the image. Over the last twenty years, a large body of literature in diverse areas of computer science has been published that investigates many different aspects, models, and algorithms for automatically placing external labels for a given set of features. This state-of-the-art report introduces a first unified taxonomy for categorizing the different results in the literature and then presents a comprehensive survey of the state of the art, a sketch of the most relevant algorithmic techniques for external labeling algorithms, as well as a list of open research challenges in this multidisciplinary research field.

[ { "first": "Michael", "middle": [ "A." ], "last": "Bekos", "suffix": "" }, { "first": "Benjamin", "middle": [], "last": "Niedermann", "suffix": "" }, { "first": "Martin", "middle": [], "last": "Nollenburg", "suffix": "" } ]

2,019

1902.01454

10.1111/cgf.13729

ArXiv

2964005892,2961169403,2914812423

[ "14562273", "15534088", "53139510", "53928109", "32517098", "4391237", "10039265", "5695932", "57764573", "33090320", "10495828", "2864853", "14134951", "213367", "21440355", "13644277", "39923031", "39923031", "11673498", "57026232", "140578058", "2988668", "51610334", "61828744", "63936426", "22476033", "10119633", "7108060", "67705777", "42888225", "15740667", "17752191", "4980931", "1101243", "14253534", "16321571", "207233852", "9179125", "13338974", "20199159", "129567359", "43110305", "42124166", "10464275", "119534956", "186260951", "9426884", "23304301", "121225961", "5112567", "1478666", "11606262", "2540740", "3018215", "3764596", "11547031", "18827377", "11577776", "8029767", "45224050", "36049419", "16926704", "12232226", "7113021", "6480569", "26343534", "119722553", "150728485", "3038732", "8199195", "6889244", "14660675", "17724737", "8758840", "1580435", "14930569" ]

[]

true

false

true

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1

[ [ "comprehensive survey", "EVALUATION" ], [ "algorithmic technique", "METHOD" ], [ "map", "VISUALIZATION" ], [ "empty space", "DATA" ], [ "technical illustration", "VISUALIZATION" ], [ "thin leader line", "VISUALIZATION" ], [ "multidisciplinary research", "APPLICATION" ], [ "computer science", "APPLICATION" ], [ "unified taxonomy", "METHOD" ], [ "External labeling", "METHOD" ], [ "external label", "METHOD" ], [ "labeling", "METHOD" ], [ "graphical display", "VISUALIZATION" ], [ "anatomical drawing", "VISUALIZATION" ], [ "external labeling algorithm", "APPLICATION" ], [ "image", "DATA" ], [ "textual information", "DATA" ] ]

Linguistic alignment with artificial entities in the context of second language acquisition.

39,651,650

[ { "first": "Astrid", "middle": [ "M.", "Rosenthal-von", "der" ], "last": "Pütten", "suffix": "" }, { "first": "Carolin", "middle": [], "last": "Straßmann", "suffix": "" }, { "first": "Nicole", "middle": [ "C." ], "last": "Krämer", "suffix": "" } ]

2,016

CogSci

2786618306

[]

false

true

https://api.semanticscholar.org/CorpusID:39651650

0

[ [ "Linguistic alignment", "METHOD" ], [ "second language acquisition", "APPLICATION" ] ]

OneSpace: shared visual scenes for active freeplay

1,765,170

Children engage in free play for emotional, physical and social development; researchers have explored supporting free play between physically remote playmates using videoconferencing tools. We show that the configuration of the video conferencing setup affects play. Specifically, we show that a shared visual scene configuration promotes fundamentally active forms of engaged, co-operative play.

[ { "first": "Maayan", "middle": [], "last": "Cohen", "suffix": "" }, { "first": "Kody", "middle": [ "R." ], "last": "Dillman", "suffix": "" }, { "first": "Haley", "middle": [], "last": "MacLeod", "suffix": "" }, { "first": "Seth", "middle": [], "last": "Hunter", "suffix": "" }, { "first": "Anthony", "middle": [], "last": "Tang", "suffix": "" } ]

2,014

10.1145/2556288.2557117

CHI '14

2132812181

[ "492751", "15039891", "2251265", "12423429", "156127002", "7643323", "5826279", "18011295" ]

[ "17836745", "14677092", "27980859", "12189874", "47021435", "6167904" ]

true

https://api.semanticscholar.org/CorpusID:1765170

1

[ [ "visual scene configuration", "VISUALIZATION" ], [ "videoconferencing tool", "METHOD" ], [ "co-operative play", "APPLICATION" ], [ "emotional, physical and social development", "APPLICATION" ], [ "video conferencing setup", "METHOD" ], [ "free play", "APPLICATION" ] ]

Video Carving

7,212,194

[ { "first": "Billy", "middle": [], "last": "Chen", "suffix": "" }, { "first": "Pradeep", "middle": [], "last": "Sen", "suffix": "" } ]

2,008

10.2312/egs.20081022

Eurographics

[ "15166657", "1481867", "13170272" ]

[ "9711786", "5127973", "12646705", "11766543", "53869776", "12693088", "15356034", "7412485", "1503158", "17186291", "2006152", "1841128", "10910602", "772588", "6616676", "13688499", "5683799", "443182", "18009907", "10090570", "17682661", "7542309", "18881091", "213185650", "207998066", "15327645", "3121216", "11932015", "208331282", "5841641", "13082701", "18261159", "550255", "3359389", "14088213", "13917088" ]

true

https://api.semanticscholar.org/CorpusID:7212194

0

1

0

[ [ "Video Carving", "APPLICATION" ] ]

Forensic Style Analysis with Survival Trajectories

7,216,327

Electronic Health Records (EHRs) consists of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient healthcare management as EHRs contain detailed information related to disease prognosis for large patient populations. We hypothesize that a patient's condition does not deteriorate at random, the trajectories, sequences in which diseases appear in a patient, are determined by a finite number of underlying disease mechanisms. In this work, we exploit this idea by predicting a patient's risk of mortality in the context of the metabolic syndrome by assessing which of many available trajectories a patient is following and progression along this trajectory. Implementing this idea required innovative enhancements both for the study design and also for the fitting algorithm. We propose a forensic-style study design, which aligns patients on last follow-up and measures time backwards. We modify the time-dependent covariate Cox proportional hazards model to better capture coefficients of covariate that follow a particular temporal sequence, such as trajectories. Knowledge extracted from such analysis can lead to personalized treatments, thereby forming the basis for future trajectory-centered guidelines.

[ { "first": "Pranjul", "middle": [], "last": "Yadav", "suffix": "" }, { "first": "Michael", "middle": [], "last": "Steinbach", "suffix": "" }, { "first": "Lisiane", "middle": [], "last": "Pruinelli", "suffix": "" }, { "first": "Bonnie", "middle": [], "last": "Westra", "suffix": "" }, { "first": "Connie", "middle": [], "last": "Delaney", "suffix": "" }, { "first": "Vipin", "middle": [], "last": "Kumar", "suffix": "" }, { "first": "Gyorgy", "middle": [], "last": "Simon", "suffix": "" } ]

2,015

10.1109/ICDM.2015.152

2015 IEEE International Conference on Data Mining

2243040572

[ "210698768", "2716232", "6454667", "7972702", "14271290", "14727245", "2706742" ]

[ "18372081", "6237367", "211297892", "482788", "108333459", "133165498", "15554490" ]

true

https://api.semanticscholar.org/CorpusID:7216327

0

1

0

[ [ "fitting algorithm", "METHOD" ], [ "trajectory-centered guideline", "APPLICATION" ], [ "patient information", "DATA" ], [ "Electronic Health Records (EHRs)", "METHOD" ], [ "laboratory test result", "DATA" ], [ "diagnosis code", "DATA" ], [ "traject", "DATA" ], [ "study design", "METHOD" ], [ "patient healthcare management", "APPLICATION" ], [ "disease prognosis", "APPLICATION" ], [ "time-dependent covariate Cox proportional hazard model", "METHOD" ], [ "forensic-style study design", "METHOD" ], [ "temporal sequence", "DATA" ], [ "disease mechanism", "METHOD" ], [ "coefficient of covariate", "DATA" ], [ "personalized treatment", "METHOD" ], [ "metabolic syndrome", "APPLICATION" ] ]

Are you cool enough for Texas Hold'Em Poker?

3,860,384

Experienced poker players have the ability to suppress and hide emotions and reactions to avoid providing information about the quality of the dealt private cards and the own probability of winning to the adversaries. Besides unswayable luck and bravery, bluffing is the only skill that could massively improve the own chance of winning. This paper investigates whether a subliminal reaction in terms of changing facial surface skin temperature can be linked to the quality of the dealt private cards (i.e., the probability of winning the actual hand). Therefore, a dataset containing thermal imaging has been recorded during a No Limit Texas Hold'Em Poker tournament-session with six players in total and two players being observed with a high-resolution thermal imaging camera and manual provision of their dealt private cards as ground-truth. Preliminary results show that the facial skin temperature varies massively (±1.2°C), which constitutes the research hypothesis that a significant change in the surface face skin temperature can be linked to the quality of the dealt cards in terms of winning chance for an actually played hand.

[ { "first": "Marc", "middle": [], "last": "Kurz", "suffix": "" }, { "first": "Gerold", "middle": [], "last": "Hölzl", "suffix": "" }, { "first": "Andreas", "middle": [], "last": "Riener", "suffix": "" }, { "first": "Bernhard", "middle": [], "last": "Anzengruber", "suffix": "" }, { "first": "Thomas", "middle": [], "last": "Schmittner", "suffix": "" }, { "first": "Alois", "middle": [], "last": "Ferscha", "suffix": "" } ]

2,012

10.1145/2370216.2370459

UbiComp '12

2049247232

[ "122148824", "110066136", "61960077", "1991114", "11892259", "107274548", "62621119", "6362970" ]

[ "11897235" ]

true

https://api.semanticscholar.org/CorpusID:3860384

1

[ [ "No Limit Texas", "APPLICATION" ], [ "surface face skin temperature", "DATA" ], [ "bluff", "METHOD" ], [ "thermal imaging", "DATA" ], [ "thermal imaging camera", "METHOD" ], [ "facial surface skin temperature", "DATA" ], [ "private", "DATA" ], [ "subliminal reaction", "METHOD" ], [ "private card", "DATA" ], [ "facial skin temperature", "DATA" ] ]

Remote evaluation for post-deployment usability improvement

482,996

Although existing lab-based formative evaluation is frequently and effectively applied to improving usability of software user interfaces, it has limitations that have led to the concept of remote usability evaluation. Perhaps the most significant impetus for remote usability evaluation methods is the need for a project team to continue formative evaluation downstream, after deployment.The usual kinds of alpha and beta testing do not qualify as formative usability evaluation because they do not yield detailed data observed during usage and associated closely with specific task performance. Critical incident identification is arguably the single most important source of this kind of data. Consequently, we developed and evaluated a cost-effective remote usability evaluation method, based on real users self-reporting critical incidents encountered in real tasks performed in their normal working environments. Results show that users with only brief training can identify, report, and rate the severity level of their own critical incidents.

[ { "first": "H.", "middle": [ "Rex" ], "last": "Hartson", "suffix": "" }, { "first": "José", "middle": [ "C." ], "last": "Castillo", "suffix": "" } ]

1,998

10.1145/948496.948499

AVI '98

2080743924

[ "144839426", "30937373", "19005353", "61098250", "58717891", "153720473", "17477085" ]

[ "16912572", "18964667", "401475", "15538238", "8295136", "589082", "1299151", "5844840", "33652886", "41186268", "6080555", "464290", "11730771", "12388042", "24898419", "7598343", "14793995", "26828615", "18199384", "51912039", "18978453", "18709409", "14054582", "3105294", "120204", "53571732", "52017128", "15157505", "11620474", "15246747", "6148877", "70857", "935293", "532487", "201931948", "5943371", "51855116", "142575479", "62141840", "3875042" ]

true

https://api.semanticscholar.org/CorpusID:482996

0

1

0

[ [ "formative evaluation", "EVALUATION" ], [ "remote usability evaluation method", "METHOD" ], [ "detailed data", "DATA" ], [ "remote usability evaluation", "APPLICATION" ], [ "formative usability evaluation", "EVALUATION" ], [ "Critical incident identification", "DATA" ], [ "cost-effective remote usability evaluation method", "METHOD" ], [ "alpha and beta testing", "METHOD" ], [ "real user self", "DATA" ], [ "lab-based formative evaluation", "EVALUATION" ], [ "real task", "APPLICATION" ], [ "critical incident", "DATA" ], [ "severity level", "EVALUATION" ], [ "software user interface", "APPLICATION" ] ]

Interaction as a component of meaning-making

15,541,419

Using a multi-media work of art, SenSpace, we investigate the relationship of meaning making and meaning-understanding to interaction. SenSpace is a multimedia installation that uses visual, audio and tactile cues to convey the Greek myth of Narcissus to the user. As opposed to books, audio-books, oral literature and other traditional means of conveying a story, Sen-Space embeds the myth within a physical space, engaging the user in an exercise in meaning-making that can involve multiple senses at the scale of the human body. The SenSpace installation uses projections, water reflections, sound, and distorted visual imagery to present a scripted experience of fixed duration. Following a visit to SenSpace, visitors were surveyed on their expectations and interpretations to help us answer the following research questions: (1) how and to what extent does interaction with the work of art encourage engagement with ideas and interpretation? (2) how and to what extent does content ambiguity encourage engagement and interpretation? (3) does social interaction encourage interpretation?

[ { "first": "Kunmi", "middle": [], "last": "Otitoju", "suffix": "" }, { "first": "Steve", "middle": [], "last": "Harrison", "suffix": "" } ]

2,008

10.1145/1394445.1394466

DIS '08

2067580464

[ "32658", "142135162", "9747127", "928018", "1834235", "8253907", "62638021", "15143506", "6361493" ]

[ "8888167", "140214028", "726952" ]

true

https://api.semanticscholar.org/CorpusID:15541419

0

1

0

[ [ "SenSpace", "DATA" ], [ "audio-books", "METHOD" ], [ "oral literature", "METHOD" ], [ "visual, audio and tactile cue", "METHOD" ], [ "water reflection", "VISUALIZATION" ], [ "content ambiguity", "APPLICATION" ], [ "storted visual imagery", "VISUALIZATION" ], [ "multimedia installation", "METHOD" ], [ "meaning making", "APPLICATION" ], [ "meaning-making", "APPLICATION" ], [ "multi-media work", "DATA" ], [ "social interaction", "APPLICATION" ], [ "Sen", "METHOD" ] ]

An Exploratory Study of the Use of Drones for Assisting Firefighters During Emergency Situations

140,241,203

In the near future, emergency services within Canada will be supporting new technologies for 9-1-1 call centres and firefighters to learn about an emergency situation. One such technology is drones. To understand the benefits and challenges of using drones within emergency response, we conducted a study with citizens who have called 9-1-1 and firefighters who respond to a range of everyday emergencies. Our results show that drones have numerous benefits to both firefighters and 9-1-1 callers which include context awareness and social support for callers who receive feelings of assurance that help is on the way. Privacy was largely not an issue, though safety issues arose especially for complex uses of drones such as indoor flying. Our results point to opportunities for designing drone systems that help people to develop a sense of trust with emergency response drones, and mitigate privacy and safety concerns with more complex drone systems.

[ { "first": "Md.", "middle": [ "Nafiz", "Hasan" ], "last": "Khan", "suffix": "" }, { "first": "Carman", "middle": [], "last": "Neustaedter", "suffix": "" } ]

2,019

10.1145/3290605.3300502

CHI '19

2941681834

[ "42191982", "11726123", "23461993", "18793994", "812282", "658559", "5510006", "5751122", "19281358", "207225553", "3329732", "205091479", "155295800", "14379411", "20826099", "858944", "24443202", "74134356", "42269217", "755930", "14761062", "144675994", "40694254", "5046353", "67300777", "5791429", "6200803", "15823391", "26071345", "145633788", "73582267", "47017404", "30530467", "144267048", "32833453", "17953237", "5053393", "13077928", "147261173", "38318896", "55185857", "1084843", "40272560", "168109368" ]

[ "210472559", "210472559" ]

true

https://api.semanticscholar.org/CorpusID:140241203

0

1

0

[ [ "social support", "APPLICATION" ], [ "drone system", "METHOD" ], [ "firefighter", "APPLICATION" ], [ "emergency", "APPLICATION" ], [ "everyday emergency", "APPLICATION" ], [ "privacy and safety concern", "EVALUATION" ], [ "safety issue", "EVALUATION" ], [ "emergency response", "APPLICATION" ], [ "emergency response drone", "APPLICATION" ], [ "indoor flying", "APPLICATION" ], [ "9-1-1 call centre", "APPLICATION" ], [ "emergency situation", "APPLICATION" ], [ "drone", "METHOD" ], [ "context awareness", "APPLICATION" ] ]

Using a large projection screen as an alternative to head-mounted displays for virtual environments

3,059,460

Head-mounted displays for virtual environments facilitate an immersive experience that seems more real than an experience provided by a desk-top monitor [18]; however, the cost of head-mounted displays can prohibit their use. An empirical study was conducted investigating differences in spatial knowledge learned for a virtual environment presented in three viewing conditions: head-mounted display, large projection screen, and desk-top monitor. Participants in each condition were asked to reproduce their cognitive map of a virtual environment, which had been developed during individual exploration of the environment along a predetermined course. Error scores were calculated, indicating the degree to which each participant's map differed from the actual layout of the virtual environment. No statistically significant difference was found between the head-mounted display and large projection screen conditions. An implication of this result is that a large projection screen may be an effective, inexpensive substitute for a head-mounted display.

[ { "first": "Emilee", "middle": [], "last": "Patrick", "suffix": "" }, { "first": "Dennis", "middle": [], "last": "Cosgrove", "suffix": "" }, { "first": "Aleksandra", "middle": [], "last": "Slavkovic", "suffix": "" }, { "first": "Jennifer", "middle": [ "A." ], "last": "Rode", "suffix": "" }, { "first": "Thom", "middle": [], "last": "Verratti", "suffix": "" }, { "first": "Greg", "middle": [], "last": "Chiselko", "suffix": "" } ]

2,000

10.1145/332040.332479

CHI '00

2107428370

[ "62159026", "44599479", "8878090", "56978805", "36463788", "30426368", "142764047", "60616154", "278518", "3352207", "145505355", "15785663", "16880119", "17900737", "5217889", "2825590" ]

[ "36765909", "181936946", "49312456", "15538269", "210956052", "6117121", "41732136", "11198855", "4387072", "7054094", "15683732", "11008745", "5010696", "62644445", "6584884", "1161139", "8496733", "12440675", "4141216", "208615078", "59426581", "5889931", "11737227", "17826599", "14096891", "202754028", "15729827", "1653149", "32059902", "8199107", "6867488", "8812266", "3787855", "674055", "17239975", "14804561", "16625090", "16909537", "14229657", "13952036", "7918167", "149855303", "86608245", "12015233", "27717476", "17591979", "9201122", "110362076", "14819183", "12250098", "20231433", "8369889", "214632309", "517888", "1563200", "13230690", "14065459", "7829437", "10559890", "13643255", "23389785", "53825478", "3511154", "14964767", "725391", "2000518", "206925064", "209354838", "3003283", "207625139", "10435562" ]

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0

1

0

[ [ "statistically significant difference", "EVALUATION" ], [ "Head-mounted display", "METHOD" ], [ "ion screen", "METHOD" ], [ "inexpensive", "EVALUATION" ], [ "desk-top monitor", "METHOD" ], [ "Error score", "EVALUATION" ], [ "spatial knowledge", "EVALUATION" ], [ "immersive experience", "APPLICATION" ], [ "head-mounted display", "METHOD" ], [ "empirical study", "EVALUATION" ], [ "cognitive map", "VISUALIZATION" ], [ "projection screen", "METHOD" ], [ "virtual environment", "APPLICATION" ] ]

How does Fitts' law fit pointing and dragging?

16,265,885

Two experiments examined selecting text using a movement sequence of pointing and dragging. Experiment 1 showed that, in the Point-Drag sequence, the pointing time was related to the pointing distance but not to the width of the text to be selected; in contrast, pointing time was related to both the pointing distance and the width of the text in the Point-Click sequence. Experiment 2 demonstrated that both the pointing and dragging times for the Point-Drag sequence were sensitive to the height of the text that was selected. The discussion of the results centers around the application of Fitts' Law to pointing and dragging in a point-drag sequence, proposing that the target for pointing is the leftmost edge of the text to be selected, and the target for dragging is the rightmost edge of the text.

[ { "first": "Douglas", "middle": [ "J." ], "last": "Gillan", "suffix": "" }, { "first": "Kritina", "middle": [], "last": "Holden", "suffix": "" }, { "first": "Susan", "middle": [], "last": "Adam", "suffix": "" }, { "first": "Marianne", "middle": [], "last": "Rudisill", "suffix": "" }, { "first": "Laura", "middle": [], "last": "Magee", "suffix": "" } ]

1,990

10.1145/97243.97278

CHI '90

2014733463,83502682,805259147

[ "501599", "16355120", "16729332", "59872450" ]

[ "8213027", "7873966", "15982482", "1852913", "18704631", "18792182", "711617", "14985907", "13539278", "107680052", "240126", "7906930", "13185110", "84186703", "14436546", "15528388", "2401930", "17671956", "13658685", "1587672", "8883003", "63060606", "59337710", "17736040", "53246042", "9849333", "14313454", "2256899", "5062637", "108023219", "7000368", "55399814", "13240189", "203701611", "12099449", "73564599", "6190663", "12224119", "2076673", "16165061", "14930642", "5731642", "52815980", "358907", "39577874", "4065690" ]

true

https://api.semanticscholar.org/CorpusID:16265885

0

1

0

[ [ "dragging time", "EVALUATION" ], [ "movement sequence", "METHOD" ], [ "pointing", "EVALUATION" ], [ "Fitts' Law", "METHOD" ], [ "point-drag sequence", "METHOD" ], [ "width", "DATA" ], [ "most edge", "DATA" ], [ "pointing distance", "DATA" ], [ "text", "DATA" ], [ "Point-Click sequence", "METHOD" ], [ "Point-Drag sequence", "METHOD" ] ]

How soccer players would do stream joins

388,477

In spite of the omnipresence of parallel (multi-core) systems, the predominant strategy to evaluate window-based stream joins is still strictly sequential, mostly just straightforward along the definition of the operation semantics. In this work we present handshake join, a way of describing and executing window-based stream joins that is highly amenable to parallelized execution. Handshake join naturally leverages available hardware parallelism, which we demonstrate with an implementation on a modern multi-core system and on top of field-programmable gate arrays (FPGAs), an emerging technology that has shown distinctive advantages for high-throughput data processing. On the practical side, we provide a join implementation that substantially outperforms CellJoin (the fastest published result) and that will directly turn any degree of parallelism into higher throughput or larger supported window sizes. On the semantic side, our work gives a new intuition of window semantics, which we believe could inspire other stream processing algorithms or ongoing standardization efforts for stream query languages.

[ { "first": "Jens", "middle": [], "last": "Teubner", "suffix": "" }, { "first": "Rene", "middle": [], "last": "Mueller", "suffix": "" } ]

2,011

10.1145/1989323.1989389

SIGMOD '11

2120276090

[ "8752331", "13258458", "2897853", "7036633", "17054815", "13345462", "9480618", "8388899", "61509556", "9369285", "11730114", "206996637", "1365921", "1325937", "5875338", "13255967", "14501573", "10040667", "20675663", "9519416", "206455829", "7412862", "206793263" ]

[ "49522056", "2185426", "1575179", "64759911", "16799819", "8375464", "53083019", "17113564", "2003709", "208172096", "7728445", "30169053", "215238928", "6119576", "18331631", "6442413", "15215704", "12060438", "211520687", "4774457", "57922513", "88489022", "16051918", "67856518", "190008709", "1084527", "201832824", "3606256", "11812638", "13559646", "696859", "35546518", "207202933", "14624311", "564945", "15495048", "13969030", "198190736", "81985153", "1862302", "14321967", "14465786", "10366252", "16478676", "6362866", "202764437", "207756965", "42242635", "5868267", "16196706", "8283265", "115153090", "49651310", "6297083", "16665326", "2655512", "15245365", "12770718", "53293762", "198190646", "8619782", "46389734", "1342558", "15083440", "14828372", "15114351", "213187364", "14362522", "15202452", "9436563", "210701552" ]

true

https://api.semanticscholar.org/CorpusID:388477

0

1

0

[ [ "join implementation", "METHOD" ], [ "distinctive advantage", "EVALUATION" ], [ "field-programmable gate array (FPGAs", "METHOD" ], [ "hardware parallelism", "METHOD" ], [ "parallel (multi-core) system", "METHOD" ], [ "operation sem", "METHOD" ], [ "window semantics", "METHOD" ], [ "window-based stream join", "EVALUATION" ], [ "multi-core system", "APPLICATION" ], [ "stream processing", "APPLICATION" ], [ "parallelized execution", "APPLICATION" ], [ "stream query language", "APPLICATION" ], [ "Handshake join", "METHOD" ], [ "handshake join", "METHOD" ], [ "high-throughput data processing", "APPLICATION" ], [ "CellJoin", "METHOD" ] ]

The language that gets people to give: phrases that predict success on kickstarter

15,892,362

Crowdfunding sites like Kickstarter--where entrepreneurs and artists look to the internet for funding--have quickly risen to prominence. However, we know very little about the factors driving the 'crowd' to take projects to their funding goal. In this paper we explore the factors which lead to successfully funding a crowdfunding project. We study a corpus of 45K crowdfunded projects, analyzing 9M phrases and 59 other variables commonly present on crowdfunding sites. The language used in the project has surprising predictive power accounting for 58.56% of the variance around successful funding. A closer look at the phrases shows they exhibit general persuasion principles. For example, also receive two reflects the principle of Reciprocity and is one of the top predictors of successful funding. We conclude this paper by announcing the release of the predictive phrases along with the control variables as a public dataset, hoping that our work can enable new features on crowdfunding sites--tools to help both backers and project creators make the best use of their time and money.

[ { "first": "Tanushree", "middle": [], "last": "Mitra", "suffix": "" }, { "first": "Eric", "middle": [], "last": "Gilbert", "suffix": "" } ]

2,014

10.1145/2531602.2531656

CSCW '14

1983903351

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[ [ "Crowdfunding site", "METHOD" ], [ "crowdfunding site", "APPLICATION" ], [ "9M phrase", "DATA" ], [ "general persuasion principle", "METHOD" ], [ "crowdfunding project", "APPLICATION" ], [ "crowdfunded project", "DATA" ], [ "public dataset", "DATA" ], [ "predictive phrase", "DATA" ], [ "control variable", "DATA" ], [ "predictive power", "EVALUATION" ] ]

Semantic Segmentation of Video Sequences with Convolutional LSTMs

145,048,139

Most of the semantic segmentation approaches have been developed for single image segmentation, and hence, video sequences are currently segmented by processing each frame of the video sequence separately. The disadvantage of this is that temporal image information is not considered, which improves the performance of the segmentation approach. One possibility to include temporal information is to use recurrent neural networks. However, there are only a few approaches using recurrent networks for video segmentation so far. These approaches extend the encoder-decoder network architecture of well-known segmentation approaches and place convolutional LSTM layers between encoder and decoder. However, in this paper it is shown that this position is not optimal, and that other positions in the network exhibit better performance. Nowadays, state-of-the-art segmentation approaches rarely use the classical encoder-decoder structure, but use multi-branch architectures. These architectures are more complex, and hence, it is more difficult to place the recurrent units at a proper position. In this work, the multi-branch architectures are extended by convolutional LSTM layers at different positions and evaluated on two different datasets in order to find the best one. It turned out that the proposed approach outperforms the pure CNN-based approach for up to 1.6 percent.

[ { "first": "Andreas", "middle": [], "last": "Pfeuffer", "suffix": "" }, { "first": "Karina", "middle": [], "last": "Schulz", "suffix": "" }, { "first": "Klaus", "middle": [], "last": "Dietmayer", "suffix": "" } ]

2,019

1905.01058

10.1109/IVS.2019.8813852

2019 IEEE Intelligent Vehicles Symposium (IV)

2942702368,2986224592,2970781981

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https://api.semanticscholar.org/CorpusID:145048139

1

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To react or not to react? A double-well potential model of event-driven human control

356,486

To react or not to react? A double-well potential model of event-driven human control Arkady Zgonnikov ([email protected]) Ihor Lubashevsky ([email protected]) Shigeru Kanemoto ([email protected]) University of Aizu, Tsuruga, Ikki-machi, Aizu-wakamatsu, 965-8580 Fukushima, Japan Abstract Understanding how humans control unstable systems is cen- tral to many research problems, with applications ranging from quiet standing to aircraft landing. Increasingly much evidence appears in favor of event-driven control hypothesis: human op- erators are passive by default and only start actively controlling the system when the discrepancy between the current and de- sired system states becomes large. The present study proposes a cognitive model describing the transitions between the pas- sive and the active phase of control behavior. The model is based on the concept of random walk in double-well potential widely employed in physics. Unlike the conventionally used model of fixed threshold, the proposed model is intrinsically stochastic and thus conforms to the physiological interpreta- tion of the threshold as a probabilistic rather than deterministic notion. The model is studied numerically and is confronted to the experimental data on virtual stick balancing. The results confirm the validity of the model and suggest that the double- well potential can be used in modeling human control behavior in a diverse range of applications. Keywords: Stochastic modeling; control behavior; stick bal- ancing; intermittency Control of unstable systems underlies many critical proce- dures performed by human operators (e.g., manipulation of industrial machinery, aircraft landing), as well as numerous routines that all of us face in daily life (e.g., standing upright, riding a bicycle, carrying a cup of coffee). Eliciting and mod- eling the basic mechanisms of human control can help us to understand the nature of such processes, and in the end, hope- fully, to reduce the risks associated with human error. Continuous control models describe human actions well in many situations (Gawthrop, Loram, Lakie, & Gollee, 2011). On the other hand, increasingly much evidence appears in favor of more general concept —intermittent control (Gawthrop et al., 2011; Loram, Gollee, Lakie, & Gawthrop, 2011; Balasubramaniam, 2013; Milton, 2013). The latter implies that human control is discontinuous, re- peatedly switching on and off instead of being always active throughout the process. Intermittency has long been attributed to a general class of human control processes (Craik, 1947). However, de- spite being recognized for decades, human control intermit- tency is still far from being completely understood. The event-driven control hypothesis has recently become the most widely employed explanation of intermittency of human con- trol (Gawthrop et al., 2011; Milton, 2013). Event-driven models build up on the fact that human operators cannot de- tect small deviations of the controlled system from the goal state. Therefore, the control is switched off as long as the deviation remains below a certain threshold value (Fig. 1a). Whenever the deviation exceeds the threshold, the control is switched on so that the system is driven back to the goal state. The existing models based on the standard, deterministic threshold mechanism can explain many features of experi- mentally observed dynamics. Possibly that is why the na- ture of the threshold as some precise, fixed number has rarely been questioned in the literature on human control. In the real control process, where the control switches on and off many times, would the operator always react to precisely the same deviation? If not, then how diverse are the actually imple- mented threshold values? The concept of threshold is not deterministic but proba- bilistic, as evidenced by psychophysics (Gescheider, 1997). In principle, the perception threshold is characterized not by a fixed value, but by a probability distribution of the stimulus magnitude allowing one to recognize the stimulus. However, the magnitude corresponding to the 50 % chance of recog- nizing the stimulus is commonly used as a simple shortcut for the perception threshold. Indeed, ignoring the variability of the threshold may be completely plausible as long as this variability is low enough. In such cases the fluctuations of the threshold would have a minor effect on the system dynamics and may be neglected. However, we argue that in controling unstable objects human operators can disregard not only the small deviations that cannot be perceived, but also the devia- tions significantly exceeding the perception threshold. In contrast to psychophysical experiments, in controling unstable objects many factors other than the magnitude of stimulus (i.e., deviation from the goal) affect the actual threshold value triggering human response. For instance, if the control process lasts for a relatively long time, the mental expenses for staying perfectly aware of the smallest devia- tions may be unbearable for the operator. In this case, even the deviation that can otherwise be clearly perceived may be neglected due to energy considerations. Another relevant fac- tor is the limited ability of the operator to precisely manipu- late the unstable system. Even a highly skilled operator can- not accurately compensate for small, barely detectable devia- tions. In order not to destabilize the system by the imprecise interruption, the operator may prefer to wait until the devia- tion becomes large enough. As a result, the corrective move- ments need not be thoroughly planned and implemented. These and some other factors may cause high variability of the actual threshold triggering human control (Fig. 1b). In the present paper we propose a stochastic model cap- turing the probabilistic nature of human control. The model

[ { "first": "Arkady", "middle": [], "last": "Zgonnikov", "suffix": "" }, { "first": "Ihor", "middle": [], "last": "Lubashevsky", "suffix": "" }, { "first": "Shigeru", "middle": [], "last": "Kanemoto", "suffix": "" } ]

2,014

CogSci

2400681376

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false

true

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0

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Return on investment and organizational adoption

33,817,065

This paper considers the complexity of measuring the return on investment for technology adoption. A brief case study of technology adoption in a large design and construction firm provides a clear view of factors that came into play. The technology considered is simple; the apparent costs and benefits are relatively clear. Four parties are involved: diverse employees interested in using dual monitors, the information technology support group in the organization, an executive who had worked his way up from drafting, and employees of a software company that is considering expanding their support for dual monitor use. In the construction company, a seemingly logical and inexpensive hardware upgrade was subject to a wide range of technical and social pressures, some obstructing and others promoting adoption. Decisions are made in a manner that did not fit the model held by the product planners and designers in the software company.

[ { "first": "Jonathan", "middle": [], "last": "Grudin", "suffix": "" } ]

2,004

10.1145/1031607.1031659

CSCW '04

1987583764

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true

https://api.semanticscholar.org/CorpusID:33817065

0

1

0

[ [ "dual monitor", "METHOD" ], [ "diverse", "DATA" ], [ "social pressure", "EVALUATION" ], [ "benefit", "EVALUATION" ], [ "construction company", "APPLICATION" ], [ "product plan", "APPLICATION" ], [ "software company", "APPLICATION" ], [ "apparent cost", "EVALUATION" ], [ "technical", "EVALUATION" ], [ "design and construction firm", "APPLICATION" ], [ "case study", "EVALUATION" ], [ "information technology support group", "APPLICATION" ], [ "dual monitor use", "APPLICATION" ], [ "hardware upgrade", "METHOD" ], [ "technology adoption", "APPLICATION" ] ]

Physical Activity Motivating Games: Be Active and Get Your Own Reward

18,740,117

People’s daily lives have become increasingly sedentary, with extended periods of time being spent in front of a host of electronic screens for learning, work, and entertainment. We present research into the use of an adaptive persuasive technology, which introduces bursts of physical activity into a traditionally sedentary activity: computer game playing. Our game design approach leverages the playfulness and addictive nature of computer games to motivate players to engage in mild physical activity. The design allows players to gain virtual in-game rewards in return for performing real physical activity captured by sensory devices. This article presents a two-stage analysis of the activity-motivating game design approach applied to a prototype game. Initially, we detail the overall acceptance of active games discovered when trialing the technology with 135 young players. Results showed that players performed more activity without negatively affecting their perceived enjoyment of the playing experience. The analysis did discover, however, a lack of balance between the amounts of physical activity carried out by players with various gaming skills, which prompted a subsequent investigation into adaptive techniques for balancing the amount of physical activity performed by players. An evaluation of additional 90 players showed that adaptive techniques successfully overcame the gaming skills dependence and achieved more balanced activity levels. Overall, this work positions activity-motivating games as an approach that can potentially change the way players interact with computer games and lead to healthier lifestyles.

[ { "first": "Shlomo", "middle": [], "last": "Berkovsky", "suffix": "" }, { "first": "Jill", "middle": [], "last": "Freyne", "suffix": "" }, { "first": "Mac", "middle": [], "last": "Coombe", "suffix": "" } ]

2,012

10.1145/2395131.2395139

TCHI

2093244398

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[ [ "electronic screen", "METHOD" ], [ "activity-motivating game design approach", "METHOD" ], [ "gaming skill dependence", "EVALUATION" ], [ "active game", "METHOD" ], [ "accept", "EVALUATION" ], [ "computer game", "APPLICATION" ], [ "prototype game", "APPLICATION" ], [ "work", "APPLICATION" ], [ "perc", "EVALUATION" ], [ "learning", "APPLICATION" ], [ "physical", "DATA" ], [ "adaptive technique", "METHOD" ], [ "physical activity", "APPLICATION" ], [ "sensory device", "METHOD" ], [ "playing experience", "APPLICATION" ], [ "entertainment", "APPLICATION" ], [ "balanced activity level", "EVALUATION" ], [ "game design approach", "METHOD" ], [ "computer game playing", "APPLICATION" ], [ "two-stage analysis", "METHOD" ], [ "adaptive persuasive technology", "METHOD" ], [ "in-game reward", "EVALUATION" ], [ "activity-motivating game", "METHOD" ] ]

Spoken English Grading: Machine Learning with Crowd Intelligence

5,779,805

In this paper, we address the problem of grading spontaneous speech using a combination of machine learning and crowdsourcing. Traditional machine learning techniques solve the stated problem inadequately as automatic speaker-independent speech transcription is inaccurate. The features derived from it are also inaccurate and so is the machine learning model developed for speech evaluation. We propose a framework that combines machine learning with crowdsourcing. This entails identifying human intelligence tasks in the feature derivation step and using crowdsourcing to get them completed. We post the task of speech transcription to a large community of online workers (crowd). We also get spoken English grades from the crowd. We achieve 95% transcription accuracy by combining transcriptions from multiple crowd workers. Speech and prosody features are derived by force aligning the speech samples on these highly accurate transcriptions. Additionally, we derive surface and semantic level features directly from the transcription. We demonstrate the efficacy of our approach by predicting expert graded speech sample of 566 adult non-native speakers across two different countries - India and Philippines. Using the regression modeling technique, we are able achieve a Pearson correlation of 0.79 on the Philippines set and 0.74 on the Indian set with expert grades, an accuracy much higher than any previously reported machine learning approach. Our approach has an accuracy that rivals that of expert agreement. We show the value of the system through a case study in a real-world industrial deployment. This work is timely given the huge requirement of spoken English training and assessment.

[ { "first": "Vinay", "middle": [], "last": "Shashidhar", "suffix": "" }, { "first": "Nishant", "middle": [], "last": "Pandey", "suffix": "" }, { "first": "Varun", "middle": [], "last": "Aggarwal", "suffix": "" } ]

2,015

10.1145/2783258.2788595

KDD '15

2029096612

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0

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0

[ [ "automatic speaker-independent speech transcription", "METHOD" ], [ "accurate transcription", "METHOD" ], [ "feature deriva", "METHOD" ], [ "expert grade", "EVALUATION" ], [ "Pearson correlation", "EVALUATION" ], [ "Philippines set", "DATA" ], [ "machine learning approach", "METHOD" ], [ "regression modeling technique", "METHOD" ], [ "Indian set", "DATA" ], [ "expert graded speech sample", "DATA" ], [ "worker", "APPLICATION" ], [ "industrial deployment", "APPLICATION" ], [ "expert agreement", "APPLICATION" ], [ "speech transcription", "APPLICATION" ], [ "surface and semantic level feature", "DATA" ], [ "machine learning model", "METHOD" ], [ "machine learning technique", "METHOD" ], [ "95% transcription accuracy", "EVALUATION" ], [ "human intelligence task", "APPLICATION" ], [ "grading spontaneous speech", "APPLICATION" ], [ "Speech and prosody feature", "DATA" ], [ "crowdsourcing", "METHOD" ], [ "speech evaluation", "APPLICATION" ], [ "machine learning", "METHOD" ], [ "speech sample", "DATA" ], [ "case study", "EVALUATION" ], [ "-native speaker", "DATA" ] ]

Characteristic relational patterns

1,446,571

Research in relational data mining has two major directions: finding global models of a relational database and the discovery of local relational patterns within a database. While relational patterns show how attribute values co-occur in detail, their huge numbers hamper their usage in data analysis. Global models, on the other hand, only provide a summary of how different tables and their attributes relate to each other, lacking detail of what is going on at the local level. In this paper we introduce a new approach that combines the positive properties of both directions: it provides a detailed description of the complete database using a small set of patterns. More in particular, we utilise a rich pattern language and show how a database can be encoded by such patterns. Then, based on the MDLprinciple, the novel RDB-KRIMP algorithm selects the set of patterns that allows for the most succinct encoding of the database. This set, the code table, is a compact description of the database in terms of local relational patterns. We show that this resulting set is very small, both in terms of database size and in number of its local relational patterns: a reduction of up to 4 orders of magnitude is attained.

[ { "first": "Arne", "middle": [], "last": "Koopman", "suffix": "" }, { "first": "Arno", "middle": [], "last": "Siebes", "suffix": "" } ]

2,009

10.1145/1557019.1557071

KDD

2019337974,1528636865

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https://api.semanticscholar.org/CorpusID:1446571

1

[ [ "MDLprinciple", "METHOD" ], [ "Global model", "METHOD" ], [ "local relational pattern", "DATA" ], [ "succinct encoding", "APPLICATION" ], [ "data analysis", "APPLICATION" ], [ "database", "DATA" ], [ "relational database", "DATA" ], [ "complete database", "DATA" ], [ "code table", "DATA" ], [ "relational data mining", "APPLICATION" ], [ "relational pattern", "VISUALIZATION" ], [ "global model", "METHOD" ], [ "rich pattern language", "METHOD" ], [ "attribute value", "DATA" ], [ "RDB-KRIMP algorithm", "METHOD" ] ]

A new characterization of independence

30,362,062

We introduce a restriction on the structure of a database scheme, called the primary key condition, and show that this condition characterizes independent database schemes when constraints are presented as keys. The primary key condition provides added insight into the structure of independent schemes, and leads to a general design methodology. We describe a linear-time algorithm for recognizing independent schemes.

[ { "first": "Peter", "middle": [], "last": "Honeyman", "suffix": "" }, { "first": "Edward", "middle": [], "last": "Sciore", "suffix": "" } ]

1,983

10.1145/582192.582209

SIGMOD '83

1979873554

[]

[ "14741479", "10307662" ]

false

true

https://api.semanticscholar.org/CorpusID:30362062

0

1

0

[ [ "database scheme", "METHOD" ], [ "independent database scheme", "METHOD" ], [ "design methodology", "METHOD" ], [ "primary key condition", "METHOD" ], [ "linear-time algorithm", "METHOD" ], [ "independent scheme", "DATA" ] ]

Appropriating and assessing heuristics for mobile computing

11,655,232

Mobile computing presents formidable challenges not only to the design of applications but also to each and every phase of the systems lifecycle. In particular, the HCI community is still struggling with the challenges that mobile computing poses to evaluation. Expert-based evaluation techniques are well known and they do enable a relatively quick and easy evaluation. Heuristic evaluation, in particular, has been widely applied and investigated, most likely due to its efficiency in detecting most of usability flaws at front of a rather limited investment of time and human resources in the evaluation. However, the capacity of expert-based techniques to capture contextual factors in mobile computing is a major concern. In this paper, we report an effort for realizing usability heuristics appropriate for mobile computing. The effort intends to capture contextual requirements while still drawing from the inexpensive and flexible nature of heuristic-based techniques. This work has been carried out in the context of a research project task geared toward developing a heuristic-based evaluation methodology for mobile computing. This paper describes the methodology that we adopted toward realizing mobile heuristics. It also reports a study that we carried out in order to assess the relevance of the realized mobile heuristics by comparing their performance with that of the standard/traditional usability heuristics. The study yielded positive results in terms of the number of usability flaws identified and the severity ranking assigned.

[ { "first": "Enrico", "middle": [], "last": "Bertini", "suffix": "" }, { "first": "Silvia", "middle": [], "last": "Gabrielli", "suffix": "" }, { "first": "Stephen", "middle": [], "last": "Kimani", "suffix": "" } ]

2,006

10.1145/1133265.1133291

AVI '06

2141044623

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[ "94423", "9467453", "17969667", "208105159", "49559711", "55374814", "69262660", "20876976", "2084806", "1784922", "4707841", "206816900", "196195333", "56071096", "46922092", "15422061", "49564592", "201144858", "355261", "19648791", "21709920", "2525325", "8364080", "6466270", "107357040", "198190667", "14304677", "54061316", "35175056", "215138448", "215020586", "59337559", "155258021", "1630985", "171095689", "16184667", "53059918", "17639466", "16067723", "40815133", "209334809", "12969308", "13588039", "53106691", "7957099", "61601469" ]

true

https://api.semanticscholar.org/CorpusID:11655232

0

1

0

[ [ "realized mobile heuristic", "METHOD" ], [ "heuristic-based technique", "METHOD" ], [ "positive result", "EVALUATION" ], [ "usability heuristic", "METHOD" ], [ "system lifecycle", "APPLICATION" ], [ "severity ranking", "EVALUATION" ], [ "Heuristic evaluation", "METHOD" ], [ "mobile computing", "APPLICATION" ], [ "mobile heuristic", "APPLICATION" ], [ "standard/traditional usability heuristic", "METHOD" ], [ "usability flaw", "EVALUATION" ], [ "heuristic-based evaluation methodology", "METHOD" ], [ "Mobile computing", "APPLICATION" ], [ "easy evaluation", "EVALUATION" ], [ "Expert-based evaluation technique", "METHOD" ], [ "expert-based technique", "METHOD" ], [ "HCI community", "APPLICATION" ] ]

An Automatic Interpolation Method of Grayvalued Images Utilizing Density Contour Lines

124,052,326

[ { "first": "Takeshi", "middle": [], "last": "Agui", "suffix": "" }, { "first": "Minoru", "middle": [], "last": "Saito", "suffix": "" }, { "first": "Masayuki", "middle": [], "last": "Nakajima", "suffix": "" }, { "first": "Yukihiro", "middle": [], "last": "Arai", "suffix": "" } ]

1,987

10.2312/egtp.19871031

2307176776

[]

[ "61152413" ]

false

true

false

https://api.semanticscholar.org/CorpusID:124052326

null

[ [ "Grayvalued Images", "DATA" ], [ "Density Contour Lines", "VISUALIZATION" ], [ "Automatic Interpolation Method", "METHOD" ] ]

Evaluating Interactive Data Systems: Workloads, Metrics, and Guidelines

44,108,519

Highly interactive query interfaces have become a popular tool for ad-hoc data analysis and exploration, posing a new kind of workload to the underlying data infrastructure. Compared with traditional systems that are optimized for throughput or batched performance, ad-hoc and interactive data exploration systems focus more on user-centric interactivity, which raises a new class of performance challenges. Further, with the advent of new interaction devices~(e.g., touch, gesture) and different query interface paradigms~(e.g., sliders), maintaining interactive performance becomes even more challenging. Thus, when building interactive data systems, there is a clear need to articulate the design space. In this tutorial, we will describe unique characteristics of interactive workloads for a variety of user input devices and query interfaces. We will catalog popular metrics based on an extensive survey of current literature. Through two case studies, we will not only walk through previously defined metrics using real-world user traces but also highlight where these defined metrics are inadequate. Further, we will introduce some new metrics that are required to capture a complete picture of interactivity. In each case study, we also demonstrate how the behavior analyses on users' trace and performance experiments can provide guidelines to help researchers and developers design better interactive data systems.

[ { "first": "Lilong", "middle": [], "last": "Jiang", "suffix": "" }, { "first": "Protiva", "middle": [], "last": "Rahman", "suffix": "" }, { "first": "Arnab", "middle": [], "last": "Nandi", "suffix": "" } ]

2,018

10.1145/3183713.3197386

SIGMOD '18

2803826376

[ "209099422", "2937953", "60779891", "445328", "61792692", "14087267", "8714977", "5561971", "14971684", "10301628", "13054758", "1472930", "2589691", "1704241", "9771244", "2862122", "22615632", "10244106", "13329164", "13577057", "2590606", "2889572", "1981154", "5912987", "5887673", "15620772", "6558578", "11133756", "585207", "726262", "14135312", "1477188", "14220569", "9421858", "7267308", "3167066", "59693836", "776614", "6827185", "14107017", "16697886", "2189441", "32508478", "9748472", "1059936", "6714659", "2948277", "16545007", "35833186", "32370368", "2281975", "7476906", "9585835", "18644162", "2091552", "18836281", "552440", "5781502", "5800083", "763555", "15190173", "608112", "4497594", "775113", "7103088", "2838588", "8701531" ]

[ "67772271", "201809821", "53406374", "57375795", "131764738", "208171945", "58013746", "195259372", "196052960" ]

true

https://api.semanticscholar.org/CorpusID:44108519

0

1

0

[ [ "design space", "VISUALIZATION" ], [ "performance experiment", "EVALUATION" ], [ "interactive query interface", "METHOD" ], [ "query interface paradigm", "METHOD" ], [ "popular metric", "DATA" ], [ "user-centric interactivity", "APPLICATION" ], [ "interaction device", "METHOD" ], [ "ad-hoc data analysis and exploration", "APPLICATION" ], [ "user input device", "APPLICATION" ], [ "real-world user trace", "DATA" ], [ "tensive", "EVALUATION" ], [ "batched performance", "EVALUATION" ], [ "slid", "METHOD" ], [ "interactive data system", "APPLICATION" ], [ "ad-hoc", "METHOD" ], [ "interactive workload", "METHOD" ], [ "infrastructure", "APPLICATION" ], [ "query interface", "APPLICATION" ], [ "interactive data exploration system", "METHOD" ], [ "behavior analysis", "METHOD" ], [ "case study", "EVALUATION" ], [ "interactive performance", "EVALUATION" ] ]

RealPen: Providing Realism in Handwriting Tasks on Touch Surfaces using Auditory-Tactile Feedback

3,760,439

We present RealPen, an augmented stylus for capacitive tablet screens that recreates the physical sensation of writing on paper with a pencil, ball-point pen or marker pen. The aim is to create a more engaging experience when writing on touch surfaces, such as screens of tablet computers. This is achieved by regenerating the friction-induced oscillation and sound of a real writing tool in contact with paper. To generate realistic tactile feedback, our algorithm analyzes the frequency spectrum of the friction oscillation generated when writing with traditional tools, extracts principal frequencies, and uses the actuator's frequency response profile for an adjustment weighting function. We enhance the realism by providing the sound feedback aligned with the writing pressure and speed. Furthermore, we investigated the effects of superposition and fluctuation of several frequencies on human tactile perception, evaluated the performance of RealPen, and characterized users' perception and preference of each feedback type.

[ { "first": "Youngjun", "middle": [], "last": "Cho", "suffix": "" }, { "first": "Andrea", "middle": [], "last": "Bianchi", "suffix": "" }, { "first": "Nicolai", "middle": [], "last": "Marquardt", "suffix": "" }, { "first": "Nadia", "middle": [], "last": "Bianchi-Berthouze", "suffix": "" } ]

2,016

1803.02307

10.1145/2984511.2984550

UIST '16

2538577440

[ "30101479", "15243980", "18248946", "7033653", "1504582", "21468294", "1897353", "9593343", "2472668", "9647077", "131712712", "15111762", "5679090", "144556030", "41040602", "18540500", "1334769", "21350122", "14612475", "16274233", "9454528", "23067754", "7837837", "17311965", "5880137", "10852277", "15160749", "13985230", "3834738", "39149786", "8880866", "67394897", "967457", "2704517", "43760282", "17768272", "15750505", "7358963", "9414305" ]

[ "3405342", "4417200", "51614700", "5882653", "140212681", "2602507", "203621037", "44169142", "195699608" ]

true

https://api.semanticscholar.org/CorpusID:3760439

1

[ [ "frequency spectrum", "DATA" ], [ "principal frequency", "DATA" ], [ "feedback type", "EVALUATION" ], [ "tablet computer", "APPLICATION" ], [ "friction-induced oscillation", "VISUALIZATION" ], [ "augmented stylus", "METHOD" ], [ "tactile perception", "EVALUATION" ], [ "friction oscillation", "DATA" ], [ "sensation", "VISUALIZATION" ], [ "tactile feedback", "EVALUATION" ], [ "sound feedback", "EVALUATION" ], [ "ball-point pen", "METHOD" ], [ "pencil", "METHOD" ], [ "touch surface", "DATA" ], [ "marker pen", "METHOD" ], [ "RealPen", "METHOD" ], [ "writing tool", "METHOD" ], [ "writing pressure", "DATA" ], [ "frequency response profile", "DATA" ], [ "capacitive tablet screen", "APPLICATION" ], [ "adjustment weighting function", "METHOD" ] ]

Finding effectors in social networks

3,231,657

Assume a network (V,E) where a subset of the nodes in V are active. We consider the problem of selecting a set of k active nodes that best explain the observed activation state, under a given information-propagation model. We call these nodes effectors. We formally define the k-Effectors problem and study its complexity for different types of graphs. We show that for arbitrary graphs the problem is not only NP-hard to solve optimally, but also NP-hard to approximate. We also show that, for some special cases, the problem can be solved optimally in polynomial time using a dynamic-programming algorithm. To the best of our knowledge, this is the first work to consider the k-Effectors problem in networks. We experimentally evaluate our algorithms using the DBLP co-authorship graph, where we search for effectors of topics that appear in research papers.

[ { "first": "Theodoros", "middle": [], "last": "Lappas", "suffix": "" }, { "first": "Evimaria", "middle": [], "last": "Terzi", "suffix": "" }, { "first": "Dimitrios", "middle": [], "last": "Gunopulos", "suffix": "" }, { "first": "Heikki", "middle": [], "last": "Mannila", "suffix": "" } ]

2,010

10.1145/1835804.1835937

KDD '10

2103126699

[ "8180987", "10361804", "7998210", "6478129", "14428056", "59761099", "7575112", "14544045", "61281983", "53238978", "207732226", "750499", "1579819", "850930", "47496386", "8778216", "10016327", "9064440" ]

[ "16884356", "12123076", "8483578", "9138660", "2606351", "52962716", "207756831", "18486728", "3619055", "51988280", "9560663", "895667", "14860291", "55043509", "15853560", "84187294", "22774882", "11043662", "2150493", "202765668", "11075568", "9227990", "51968363", "1518022", "3630613", "8845517", "215055415", "102030", "3642137", "3409575", "21663848", "84181364", "208052475", "16761363", "29992110", "12955118", "11811588", "9934921", "111541", "14550888", "1731192", "5983506", "52962021", "14188000", "3768163", "8183913", "16634892", "4810458", "17943968", "3443728", "13910533", "16178158", "146118871", "2282615", "183686", "4583818", "15836825", "18743005", "207756544", "14955770", "2666854", "13348979", "33500927", "54766833", "37619118", "37619118", "37619118", "11613016", "158347324", "7001155", "10979687", "3104891", "69165999", "14938339", "55905", "202235995", "10375513", "174801274", "18242978", "14899838", "2399655", "17060785", "12901792", "3201270", "4703283", "3829405", "17301580", "3624611", "64413065", "3443364", "12471080", "10508755", "9150963", "52287495", "2648061", "18857285", "12669576", "17124768", "17590273", "2609720", "14250950", "20190556", "13988879", "29165477", "11004262", "17135251", "3634399", "12986255", "67871872", "16977089", "7985397", "566019", "8275558", "11942175", "125895958", "20627809", "173329", "33869460", "8565817", "15487811", "4897262", "228578", "11953203", "7268359", "3361097", "56657846" ]

true

https://api.semanticscholar.org/CorpusID:3231657

0

1

0

[ [ "k-Effectors problem", "METHOD" ], [ "arbitrary graph", "APPLICATION" ], [ "node effector", "METHOD" ], [ "information-propagation model", "METHOD" ], [ "activation state", "DATA" ], [ "graph", "DATA" ], [ "DBLP co-authorship graph", "VISUALIZATION" ], [ "dynamic-programming algorithm", "METHOD" ], [ "polynomial time", "EVALUATION" ], [ "active node", "DATA" ] ]

Volume deformations in grid-less flow simulations

36,115,864

This paper presents a novel method for the extraction and visualization of volume deformations in grid-less point based flow simulations. Our primary goals are the segmentation of different paths through a mixing device and the visualization of ellipsoidal particle deformations. The main challenges are the numerically efficient processing of deformation tensors and the robust integration of stream- and streaklines at boundaries of the dataset such that closed segments are obtained. Our results show two- and three-dimensional particle deformations as well as the segmentation of volumes in stationary fields and areas in time-dependent datasets taking consistent paths through a mixing device.

[ { "first": "Harald", "middle": [], "last": "Obermaier", "suffix": "" }, { "first": "Martin", "middle": [], "last": "Hering-Bertram", "suffix": "" }, { "first": "Jörg", "middle": [], "last": "Kuhnert", "suffix": "" }, { "first": "Hans", "middle": [], "last": "Hagen", "suffix": "" } ]

2,009

10.1111/j.1467-8659.2009.01461.x

Comput. Graph. Forum

2111414025

[]

[ "14588746", "2403842", "683807", "19346379", "16776110", "14866804" ]

false

true

false

https://api.semanticscholar.org/CorpusID:36115864

null

[ [ "two- and three-dimensional particle deformation", "VISUALIZATION" ], [ "volume deformation", "DATA" ], [ "robust integration", "METHOD" ], [ "efficient process", "METHOD" ], [ "grid-less point based flow simulation", "VISUALIZATION" ], [ "ellipsoidal particle deformation", "DATA" ], [ "stationary field", "DATA" ], [ "time-dependent datasets", "DATA" ], [ "segment", "APPLICATION" ], [ "volume", "DATA" ], [ "deformation tensor", "METHOD" ], [ "closed segment", "DATA" ], [ "mixing device", "METHOD" ], [ "stream- and streaklines", "DATA" ] ]

Large-scale uncertainty management systems: learning and exploiting your data

14,686,615

The database community has made rapid strides in capturing, representing, and querying uncertain data. Probabilistic databases capture the inherent uncertainty in derived tuples as probability estimates. Data acquisition and stream systems can produce succinct summaries of very large and time-varying datasets. This tutorial addresses the natural next step in harnessing uncertain data: How can we efficiently and quantifiably determine what, how, and how much to learn in order to make good decisions based on the imprecise information available. The material in this tutorial is drawn from a range of fields including database systems, control and information theory, operations research, convex optimization, and statistical learning. The focus of the tutorial is on the natural constraints that are imposed in a database context and the demands of imprecise information from an optimization point of view. We look both into the past as well as into the future; to discuss general tools and techniques that can serve as a guide to database researchers and practitioners, and to enumerate the challenges that lie ahead.

[ { "first": "Shivnath", "middle": [], "last": "Babu", "suffix": "" }, { "first": "Sudipto", "middle": [], "last": "Guha", "suffix": "" }, { "first": "Kamesh", "middle": [], "last": "Munagala", "suffix": "" } ]

2,009

10.1145/1559845.1559964

SIGMOD Conference

1965809814

[ "8547150", "6610184", "1843530", "2490893", "9069751", "15859038", "10041995", "9295194", "37925315", "8723356", "6601979", "191578", "3264230", "150937317", "13193188", "11043949", "17039983", "2125243", "1654066", "60474", "60292603", "31458599", "15969222", "6770544", "40769530", "177585", "14334071", "9036944", "14036493", "15237104", "5964380", "18052887", "59958747", "14887217", "7020677", "2708494", "121560185", "553962" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:14686615

0

1

0

[ [ "statistical learning", "METHOD" ], [ "Data acquisition", "METHOD" ], [ "database researcher", "APPLICATION" ], [ "inherent uncertainty", "DATA" ], [ "database system", "APPLICATION" ], [ "optimization", "EVALUATION" ], [ "operation research", "APPLICATION" ], [ "derived tuples", "DATA" ], [ "uncertain data", "DATA" ], [ "control and information theory", "APPLICATION" ], [ "natural constraint", "METHOD" ], [ "database context", "METHOD" ], [ "time-varying dataset", "DATA" ], [ "stream system", "METHOD" ], [ "database community", "APPLICATION" ], [ "imprecise information", "DATA" ], [ "convex optimization", "APPLICATION" ], [ "Probabilistic database", "DATA" ], [ "probability estimate", "VISUALIZATION" ] ]

Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels

10,708,529

Multi-label learning deals with the problem where each training example is associated with a set of labels simultaneously, with the set of labels corresponding to multiple concepts or semantic meanings. Intuitively, the multiple labels are usually correlated in some semantic space while sharing the same input space. As a consequence, the multi-label learning process can be augmented significantly by exploiting the label correlations effectively. Most of the existing approaches share the limitations in that the label correlations are typically taken as prior knowledge, which may not depict the true dependencies among labels correctly, or they do not adequately address the issue of missing labels. In this paper, we propose an integrated framework that learns the correlations among labels while training the multi-label model simultaneously. Specifically, a low rank structure is adopted to capture the complex correlations among labels. In addition, we incorporate a supplementary label matrix which augments the possibly incomplete label matrix by exploiting the label correlations. An alternating algorithm is then developed to solve the optimization problem. Extensive experiments are conducted on a number of image and text data sets to demonstrate the effectiveness of the proposed approach.

[ { "first": "Linli", "middle": [], "last": "Xu", "suffix": "" }, { "first": "Zhen", "middle": [], "last": "Wang", "suffix": "" }, { "first": "Zefan", "middle": [], "last": "Shen", "suffix": "" }, { "first": "Yubo", "middle": [], "last": "Wang", "suffix": "" }, { "first": "Enhong", "middle": [], "last": "Chen", "suffix": "" } ]

2,014

10.1109/ICDM.2014.125

2014 IEEE International Conference on Data Mining

1969954882

[ "5250753", "14886376", "7679549", "1008003", "207211581", "2555940", "13909180", "8388083", "467106", "2141199", "14574613", "1254778", "8329460", "11516175" ]

[ "53563138", "199466273", "215434018", "215415877", "4554634", "208139095", "34411600", "17398735", "53044255", "49583566", "6879349", "214623997", "5015339", "13377293", "3744963", "14232340", "53819533", "4925189", "46999283", "199374916", "202158849", "2503817", "208163893", "57366447", "203902768", "207758666", "211227519", "28516751", "212736839", "67434178", "56178673", "7492417" ]

true

https://api.semanticscholar.org/CorpusID:10708529

0

1

0

[ [ "integrated framework", "METHOD" ], [ "low rank structure", "METHOD" ], [ "semantic meaning", "DATA" ], [ "optimization problem", "APPLICATION" ], [ "missing label", "DATA" ], [ "multi-label learning process", "APPLICATION" ], [ "space", "DATA" ], [ "multi-label model", "METHOD" ], [ "example", "DATA" ], [ "Multi-label learning", "METHOD" ], [ "corre", "DATA" ], [ "lete label matrix", "METHOD" ], [ "label correlation", "DATA" ], [ "image and text data set", "DATA" ], [ "input space", "DATA" ], [ "alternating algorithm", "METHOD" ], [ "depende", "DATA" ], [ "supplementary label matrix", "METHOD" ], [ "correlation", "DATA" ] ]

Founder Center: Enabling Access to Collective Social Capital

17,434,396

Social costs and limited reach inhibit our use of social capital to solicit help. However, individuals are not the only holders of social capital: groups also possess reputations and social capital, and are often prepared to vouch for their own members. In this paper, we design methods for mobilizing this collective social capital in sociotechnical systems, enabling an individual to ask a trusted group whether it is willing to invest its reputation in doing them a favor. We instantiate this concept with Founder Center, a web platform in which members of a local entrepreneurship accelerator ask the accelerator community to collectively make them introductions to potential funders. In a field experiment, enabling access to collective social capital in this community nearly doubled the odds of members making a social capital request. Requests fulfilled utilizing collective social capital were at least as effective as ones utilizing traditional interpersonal social capital.

[ { "first": "Nicolas", "middle": [], "last": "Kokkalis", "suffix": "" }, { "first": "Chengdiao", "middle": [], "last": "Fan", "suffix": "" }, { "first": "Thomas", "middle": [], "last": "Breier", "suffix": "" }, { "first": "Michael", "middle": [ "S." ], "last": "Bernstein", "suffix": "" } ]

2,017

10.1145/2998181.2998244

CSCW '17

2587863209

[ "58507339", "18759791", "144647703", "154456341", "62226413", "12523576", "141645804", "51859022", "144523504", "6096102", "143498140", "60829446", "14852218", "13346993", "3648140", "15587106", "14482765", "8706419", "1306420", "8797180", "466470", "155135589", "198620901", "157933754", "6622258", "6222218" ]

[ "6263309", "5065088", "53223325" ]

true

https://api.semanticscholar.org/CorpusID:17434396

0

1

0

[ [ "field experiment", "EVALUATION" ], [ "Founder Center", "METHOD" ], [ "accelerator community", "APPLICATION" ], [ "interpersonal social capital", "METHOD" ], [ "sociotechnical system", "METHOD" ], [ "entrepreneurship", "APPLICATION" ], [ "social capital", "METHOD" ], [ "web platform", "METHOD" ], [ "social capital request", "APPLICATION" ], [ "collective social capital", "DATA" ] ]

What Needs Tell Us about User Experience

17,434,659

The present study explores the sources and consequences of fulfilling six fundamental human needs, namely Autonomy , Relatedness , Competence, Stimulation, Influence , and Security, through using interactive products and media. Each need refers to a distinct set of issues, such as according product attributes (e.g., "flexibility") and experiential consequences (e.g., "freedom of choice"). Besides the need-specific content, which helps to characterize and differentiate user experiences, the study reveals the close relation between needs and according product attributes as their mirror images.

[ { "first": "Annika", "middle": [], "last": "Wiklund-Engblom", "suffix": "" }, { "first": "Marc", "middle": [], "last": "Hassenzahl", "suffix": "" }, { "first": "Anette", "middle": [], "last": "Bengs", "suffix": "" }, { "first": "Susanne", "middle": [], "last": "Sperring", "suffix": "" } ]

2,009

10.1007/978-3-642-03658-3_71

INTERACT

1577793316

[ "17278137", "5158388", "2542963", "146219486", "10465690", "207155407", "10955142" ]

[ "11326157", "149240373", "18835327", "51859069", "13719925", "202599218", "69941852", "18178009", "62828132", "18751380", "40487879", "56046342", "7143898", "4651523", "155249783" ]

true

https://api.semanticscholar.org/CorpusID:17434659

1

[ [ "relation", "DATA" ], [ "user experience", "APPLICATION" ], [ "medium", "METHOD" ], [ "interactive product", "METHOD" ], [ "experiential consequence", "EVALUATION" ], [ "mirror image", "DATA" ], [ "need-specific content", "DATA" ] ]

Regular Path Query Evaluation on Streaming Graphs

214,802,198

We study persistent query evaluation over streaming graphs, which is becoming increasingly important. We focus on navigational queries that determine if there exists a path between two entities that satisfies a user-specified constraint. We adopt the Regular Path Query (RPQ) model that specifies navigational patterns with labeled constraints. We propose deterministic algorithms to efficiently evaluate persistent RPQs under both arbitrary and simple path semantics in a uniform manner. Experimental analysis on real and synthetic streaming graphs shows that the proposed algorithms can process up to tens of thousands of edges per second and efficiently answer RPQs that are commonly used in real-world workloads.

[ { "first": "Anil", "middle": [], "last": "Pacaci", "suffix": "" }, { "first": "Angela", "middle": [], "last": "Bonifati", "suffix": "" }, { "first": "M.", "middle": [ "Tamer" ], "last": "Ozsu", "suffix": "" } ]

2,020

2004.02012

ArXiv

3014301104,3029212721

[ "8752331", "8101432", "42518927", "4623760", "13526884", "6407934", "2071130", "195713842", "21118915", "204199457", "131408", "2127431", "6195385", "86666302", "6576813", "59763633", "3519738", "628090", "12976914", "11272312", "24429104", "36339781", "21663049", "13671979", "8043212", "1920323", "5362938", "9775882", "53622778", "62407163", "1110229", "5132371", "15426360", "7188112", "3340817", "14613361", "13980097", "984081", "7046084", "37191650", "2337679", "12684556", "145349", "9015470", "13332080", "51994448", "47299372", "67346932", "15900340", "21260384", "209020396", "6806901", "123754591", "16226344", "2965993", "17734826" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:214802198

1

[ [ "deterministic algorithm", "METHOD" ], [ "simple path sem", "METHOD" ], [ "real-world workload", "APPLICATION" ], [ "navigational pattern", "METHOD" ], [ "persistent query evaluation", "EVALUATION" ], [ "navigational query", "METHOD" ], [ "Regular Path Query (RPQ) model", "METHOD" ], [ "synthetic streaming graph", "DATA" ], [ "streaming graph", "DATA" ], [ "user-specified constraint", "DATA" ], [ "persistent RPQs", "EVALUATION" ], [ "real", "DATA" ] ]

Learning Over Dirty Data Without Cleaning

214,802,872

Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values and entities. Learning over dirty databases may result in inaccurate models. Users have to spend a great deal of time and effort to repair data errors and create a clean database for learning. Moreover, as the information required to repair these errors is not often available, there may be numerous possible clean versions for a dirty database. We propose DLearn, a novel relational learning system that learns directly over dirty databases effectively and efficiently without any preprocessing. DLearn leverages database constraints to learn accurate relational models over inconsistent and heterogeneous data. Its learned models represent patterns over all possible clean instances of the data in a usable form. Our empirical study indicates that DLearn learns accurate models over large real-world databases efficiently.

[ { "first": "Jose", "middle": [], "last": "Picado", "suffix": "" }, { "first": "John", "middle": [], "last": "Davis", "suffix": "" }, { "first": "Arash", "middle": [], "last": "Termehchy", "suffix": "" }, { "first": "Ga", "middle": [ "Young" ], "last": "Lee", "suffix": "" } ]

2,020

2004.02308

ArXiv

3029062663,3014766252

[ "8155077", "60457482", "1808049", "16207692", "8430062", "205177145", "9939431", "2297444", "2492255", "17179157", "9635478", "11192413", "14900377", "34493673", "20197700", "44074686", "9885405", "17134608", "45703168", "2690556", "13962370", "8296909", "116764021", "15175489", "17593228", "2893307", "15335378", "1291132", "44104365", "365328", "7219108", "15371894", "9207552", "9661432", "12643399", "14992676", "16231496", "62688491", "26726745", "16301819", "12698795", "15216127", "15701304", "6263341", "11674561" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:214802872

1

[ [ "able form", "DATA" ], [ "integrity constraint", "EVALUATION" ], [ "duplica", "DATA" ], [ "relational model", "METHOD" ], [ "relational learning system", "METHOD" ], [ "data error", "DATA" ], [ "data value", "DATA" ], [ "empirical study", "EVALUATION" ], [ "real-world database", "DATA" ], [ "consisten", "DATA" ], [ "heterogeneous data", "DATA" ], [ "DLearn", "METHOD" ], [ "inaccurate model", "EVALUATION" ], [ "database constraint", "DATA" ], [ "clean version", "METHOD" ], [ "clean database", "DATA" ], [ "clean instance", "DATA" ], [ "dirty database", "DATA" ], [ "Real-world datasets", "DATA" ] ]

Generation Y interactions

32,262,840

Information technology (IT) support of office work has increased rapidly in functionality, but the interaction styles have evolved more slowly. This project explores interaction design opportunities of IT supported tools in the context of office work. A series of (contextual) interviews was conducted with Generation Y office workers, aiming to identify their interaction qualities. Three interactive prototypes were built to map these interaction qualities and to demonstrate future ways of working. This project resulted in a set of design guidelines, aiming to support Generation Y interactions in future office work. Designers and researchers who focus on understanding (rich interactions in) the work context would benefit from the result of this project.

[ { "first": "Wei", "middle": [], "last": "Liu", "suffix": "" } ]

2,012

10.1145/2166966.2167046

IUI '12

2121781028

[ "10403653" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:32262840

0

1

0

[ [ "office work", "APPLICATION" ], [ "contextual) interview", "EVALUATION" ], [ "Information technology (IT)", "METHOD" ], [ "interaction design opportunity", "METHOD" ], [ "design guideline", "METHOD" ], [ "Generation Y interaction", "APPLICATION" ], [ "interactive prototype", "VISUALIZATION" ], [ "rich interaction", "DATA" ], [ "work context", "APPLICATION" ], [ "interaction quali", "EVALUATION" ], [ "interaction style", "EVALUATION" ], [ "Y office worker", "DATA" ] ]

People, Place, and Time: Inferences from Diagrams

299,126

Keeping track of things as they move in space and time is a task common to scientists, marketers, spies, coaches, and more. Visualizations of complex information aid drawing inferences and conclusions but there are many ways to represent data. Here we show that the kinds of inferences people draw depend on the kind of visualization, boxes in tables or lines in graphs. Lines link and boxes contain; they both direct attention and create meaning.

[ { "first": "Barbara", "middle": [], "last": "Tversky", "suffix": "" }, { "first": "Jie", "middle": [], "last": "Gao", "suffix": "" }, { "first": "James", "middle": [ "E." ], "last": "Corter", "suffix": "" }, { "first": "Yuko", "middle": [], "last": "Tanaka", "suffix": "" }, { "first": "Jeffrey", "middle": [ "V." ], "last": "Nickerson", "suffix": "" } ]

2,016

10.1007/978-3-319-42333-3_21

Diagrams

2508786895

[ "38142300", "764535", "9683595", "8746063", "54235974", "8386681" ]

[ "208857473", "1289451" ]

true

https://api.semanticscholar.org/CorpusID:299126

1

[ [ "table", "DATA" ], [ "graph", "VISUALIZATION" ], [ "scientist", "APPLICATION" ], [ "box", "DATA" ], [ ", marketers, spies, coach", "APPLICATION" ], [ "line", "VISUALIZATION" ] ]

Virtual heritage in the cloud: new perspectives for the virtual museum of bologna

28,672,311

This paper focuses on the integration of Cloud computing tools and user-generated content into an online cultural virtual environment. Our investigation aimed to clarify whether the Metaverse can be used as a spatial interface for aggregation and synestetic visualization of heterogeneous cultural data distributed in the Cloud. The case study we adopted is Nu.M.E. 2010 a virtual reconstruction of Piazza di Porta Ravegnana, a crucial area of late medieval Bologna (Italy), published on the platform Second Life (SL). A newfound awareness and appreciation for the new epistemic scenario introduced by Cloud computing and virtualization techniques has raised the following methodological questions: can Cloud computing help optimize the communication strategy and educational effectiveness of cultural data online? Can historical research and cultural data interpretation benefit from collaborative annotation and user-generated content? The described application entailed the use within SL of some of the most popular virtualization services: Google Maps, Panoramio, Google Docs, and Google Warehouse. The results of our testing activity suggest that Cloud services currently available are in fact useful tools for reshaping an online virtual space into an effective collaborative place, allowing users to share content, take an active part in the interpretation process and, most importantly, to provide valid feedback for cultural reception analysis.

[ { "first": "Nicola", "middle": [], "last": "Lercari", "suffix": "" }, { "first": "E.", "middle": [], "last": "Toffalori", "suffix": "" }, { "first": "Micaela", "middle": [], "last": "Spigarolo", "suffix": "" }, { "first": "Llonel", "middle": [], "last": "Onsurez", "suffix": "" } ]

2,011

10.2312/VAST/VAST11/153-160

VAST

2816104

[]

[ "73525839", "59155851", "111231266", "148601891", "20450246", "11784250", "17608013", "207917508", "14331037" ]

false

true

false

https://api.semanticscholar.org/CorpusID:28672311

null

[ [ "cultural data", "DATA" ], [ "historical research", "APPLICATION" ], [ "spatial interface", "METHOD" ], [ "valid", "EVALUATION" ], [ "Google Doc", "METHOD" ], [ "Cloud computing tool", "METHOD" ], [ "services: Google Maps", "METHOD" ], [ "Cloud computing", "METHOD" ], [ "collaborative place", "APPLICATION" ], [ "online cultural virtual environment", "APPLICATION" ], [ "aggregation", "APPLICATION" ], [ "heterogeneous cultural data", "DATA" ], [ "virtual space", "APPLICATION" ], [ "virtual reconstruction", "METHOD" ], [ "interpretation process", "APPLICATION" ], [ "synestetic visualization", "VISUALIZATION" ], [ "Second Life (SL)", "METHOD" ], [ "Panoramio", "METHOD" ], [ "Google Warehouse", "METHOD" ], [ "user-generated content", "DATA" ], [ "collaborative annotation", "METHOD" ], [ "virtualization technique", "METHOD" ], [ "epistemic scenario", "METHOD" ], [ "cultural data interpretation", "APPLICATION" ], [ "case study", "EVALUATION" ], [ "cultural reception analysis", "APPLICATION" ], [ "Cloud service", "METHOD" ] ]

PHP: supporting the new paradigm of situational and composite web applications

42,269,089

In this paper, I describe what we see as a paradigm shift in software development and how PHP plays into this change.

[ { "first": "Andi", "middle": [], "last": "Gutmans", "suffix": "" } ]

2,006

10.1145/1142473.1142553

SIGMOD '06

2098471660

[]

[ "41207374", "6168382" ]

false

true

https://api.semanticscholar.org/CorpusID:42269089

0

[ [ "software development", "APPLICATION" ] ]

A Progressive Algorithm for Three Point Transport

1,536,926

When computing global illumination in environments made up of surfaces with general Bidirectional Reflection Distribution Functions, a three point formulation of the rendering equation can be used. Brute-force algorithms can lead to a linear system of equations whose matrix is cubic, which is expensive in time and space. The hierarchical approach is more efficient. Aupperle et al. proposed a hierarchical three point algorithm to compute global illumination in the presence of glossy reflection. We present in this paper some improvements we brought to this method: shooting, “lazy” push-pull, photometric subdivision criterion, etc. Then we will show how our new method takes into account non-planar surfaces in the hierarchical resolution process.

[ { "first": "Reynald", "middle": [], "last": "Dumont", "suffix": "" }, { "first": "Kadi", "middle": [], "last": "Bouatouch", "suffix": "" }, { "first": "Philippe", "middle": [], "last": "Gosselin", "suffix": "" } ]

1,999

10.1111/1467-8659.00301

Comput. Graph. Forum

2051356598

[ "60309165", "8480128", "17389067", "62657597", "7384510", "9226468", "7506343", "6558879", "17498592", "16738093", "16607390", "10533954", "16014094", "53249586" ]

[ "17914168", "8406671", "2168693", "164516180", "15710449", "16965016" ]

true

https://api.semanticscholar.org/CorpusID:1536926

1

[ [ "three point formulation", "METHOD" ], [ "hierarchical approach", "METHOD" ], [ "“lazy” push-pull", "METHOD" ], [ "linear system", "METHOD" ], [ "hierarchical resolution process", "METHOD" ], [ "global illumination", "APPLICATION" ], [ "glossy reflection", "DATA" ], [ "photometric subdivision criterion", "METHOD" ], [ "rendering equation", "METHOD" ], [ "non-planar surface", "DATA" ], [ "ional Reflection Distribution Function", "METHOD" ], [ "hierarchical three point algorithm", "METHOD" ], [ "Brute-force algorithm", "METHOD" ] ]

The Computer Reaches out: The Historical Continuity of Interface Design

60,484,866

The focus of user interface research and development has evolved over the past forty years. The term ''user interface'' was not used at first, when most users were engineers and programmers; it may again become inappropriate when more applications are designed for groups than for individuals. But there is a continuity to the outward movement of the computer's interface to its external environment, from hardware to software to increasingly higher-level cognitive capabilities and finally to social processes. As the focus shifts, the approaches to design and the skills required of practitioners changes. This paper identifies five foci of interface development. Research and development foci may be independent, and progress in one area may influence others, so a comprehensive framework may help position existing research and development efforts and plan future work more eflectively.

[ { "first": "Jonathan", "middle": [], "last": "Grudin", "suffix": "" } ]

1,989

10.7146/dpb.v18i299.6693

DAIMI Report Series

1838285021

[]

false

https://api.semanticscholar.org/CorpusID:60484866

null

[ [ "comprehensive framework", "METHOD" ], [ "user interface research", "APPLICATION" ], [ "capabil", "EVALUATION" ], [ "social process", "APPLICATION" ], [ "user interface", "METHOD" ], [ "interface development", "APPLICATION" ] ]

DIRT @SBT@discovery of inference rules from text

2,971,806

In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y a X wrote Y", "X solved Y a X found a solution to Y", and "X caused Y a Y is triggered by X". Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.

[ { "first": "Dekang", "middle": [], "last": "Lin", "suffix": "" }, { "first": "Patrick", "middle": [], "last": "Pantel", "suffix": "" } ]

2,001

10.1145/502512.502559

KDD '01

1965605789

[ "6058816", "7031344", "14483333", "14785458", "1743489", "15763200", "15862538", "59773716", "207587242", "6181128", "207699574", "5076418", "6713452", "14808546", "60623886", "67239196", "33551203" ]

[ "305736", "1571851", "17178995", "10335767", "14071482", "196202286", "6564440", "1457354", "8026578", "1612342", "354207", "14225718", "53996943", "4899028", "209516255", "53208924", "54058307", "827948", "46933475", "16139282", "9372673", "62628218", "52065129", "108287230", "14561215", "17402110", "15996543", "14250965", "17184682", "21698065", "57825777", "201667738", "4898516", "21604477", "14239133", "174820372", "195800871", "587061", "52009388", "13913303", "61412819", "5850302", "139989", "174800071", "52130528", "15418500", "9955122", "37239241", "53570232", "67870282", "15827345", "15348987", "126124668", "1263681" ]

true

https://api.semanticscholar.org/CorpusID:2971806

0

1

0

[ [ "Inference rule", "METHOD" ], [ "parsed corpus", "DATA" ], [ "dependency tree", "DATA" ], [ "information retrieval", "APPLICATION" ], [ "inference rule", "METHOD" ], [ "unsupervised method", "METHOD" ], [ "Harris' Distributional Hypothesis", "METHOD" ], [ "natural language processing", "APPLICATION" ], [ "artificial intelligence", "APPLICATION" ] ]

Personalized lighting control based on a space model

10,681,220

This research focuses on personalization of lighting conditions in office buildings. A lighting control agent is proposed that uses spatial context retrieved from a space model, as well as other context data, to address the challenges of personalized lighting control. Benefits include improved user satisfaction, productivity and minimized energy use. A user scenario is presented to illustrate the envisioned concept of personalized lighting control. Requirements are derived from this and related scenarios. A system design is proposed that meets these requirements. A first version of a system prototype has been implemented and validated against the user scenario.

[ { "first": "Filip", "middle": [], "last": "Petrushevski", "suffix": "" } ]

2,012

10.1145/2370216.2370311

UbiComp '12

2081846012

[ "29778453", "39510375", "330304", "10056849", "8467154", "5654074", "111157186", "2872356", "18591366", "11851421", "195868057", "63770226", "125065225", "70669282" ]

[ "8917540", "18925123", "16536502", "6787594" ]

true

https://api.semanticscholar.org/CorpusID:10681220

0

1

0

[ [ "context data", "DATA" ], [ "minimized energy use", "EVALUATION" ], [ "space model", "DATA" ], [ "spatial context", "DATA" ], [ "office building", "APPLICATION" ], [ "user scenario", "EVALUATION" ], [ "lighting condition", "APPLICATION" ], [ "personaliza", "APPLICATION" ], [ "lighting control agent", "METHOD" ], [ "system design", "METHOD" ], [ "personalized lighting control", "APPLICATION" ], [ "system prototype", "METHOD" ], [ "user satisfaction", "EVALUATION" ] ]

Permutation-Based Sequential Pattern Hiding

8,656,783

Sequence data are increasingly shared to enable mining applications, in various domains such as marketing, telecommunications, and healthcare. This, however, may expose sensitive sequential patterns, which lead to intrusive inferences about individuals or leak confidential information about organizations. This paper presents the first permutation-based approach to prevent this threat. Our approach hides sensitive patterns by replacing them with carefully selected permutations that avoid changes in the set of frequent nonsensitive patterns (side-effects) and in the ordering information of sequences (distortion). By doing so, it retains data utility in sequence mining and tasks based on item set properties, as permutation preserves the support of items, unlike deletion, which is used in existing works. To realize our approach, we develop an efficient and effective algorithm for generating permutations with minimal side-effects and distortion. This algorithm also avoids implausible symbol orderings that may exist in certain applications. In addition, we propose a method to hide sensitive patterns from a sequence dataset. Extensive experiments verify that our method allows significantly more accurate data analysis than the state-of the-art approach.

[ { "first": "Robert", "middle": [], "last": "Gwadera", "suffix": "" }, { "first": "Aris", "middle": [], "last": "Gkoulalas-Divanis", "suffix": "" }, { "first": "Grigorios", "middle": [], "last": "Loukides", "suffix": "" } ]

2,013

10.1109/ICDM.2013.57

2013 IEEE 13th International Conference on Data Mining

2159073939

[ "18338658", "2706742", "12242182", "970290", "16012683", "1098870", "14347514", "18034604", "32259526", "2965911", "12880931", "14529482", "18159796", "16672190", "2294025", "116492178", "5240100" ]

[ "14204726", "3592605", "15719786", "4616154", "209516163", "195658284", "14361162", "14361162" ]

true

https://api.semanticscholar.org/CorpusID:8656783

0

1

0

[ [ "permutation-based approach", "METHOD" ], [ "per", "DATA" ], [ "mining application", "APPLICATION" ], [ "minimal side-effects and distortion", "EVALUATION" ], [ "sequence dataset", "DATA" ], [ "healthcare", "APPLICATION" ], [ "sequential pattern", "DATA" ], [ "ordering information", "DATA" ], [ "nonsensitive pattern", "DATA" ], [ "accurate data analysis", "EVALUATION" ], [ "marketing, telecommunication", "APPLICATION" ], [ "intrusive inference", "APPLICATION" ], [ "Sequence data", "DATA" ], [ "sequence mining", "APPLICATION" ], [ "item set property", "DATA" ], [ "plausible symbol ordering", "METHOD" ], [ "sensitive pattern", "DATA" ] ]

Finger Spelling Recognition from RGB-D Information Using Kernel Descriptor

13,479,875

Deaf people use systems of communication based on sign language and finger spelling. Manual spelling, or finger spelling, is a system where each letter of the alphabet is represented by an unique and discrete movement of the hand. RGB and depth images can be used to characterize hand shapes corresponding to letters of the alphabet. The advantage of depth cameras over color cameras for gesture recognition is more evident when performing hand segmentation. In this paper, we propose a hybrid system approach for finger spelling recognition using RGB-D information from Kinect sensor. In a first stage, the hand area is segmented from background using depth map and precise hand shape is extracted using both depth data and color data from Kinect sensor. Motivated by the performance of kernel based features, due to its simplicity and the ability to turn any type of pixel attribute into patch-level features, we decided to use the gradient kernel descriptor for feature extraction from depth images. The Scale-Invariant Feature Transform (SIFT) is used for describing the content of the RGB image. Then, the Bag-of-Visual-Words approach is used to extract semantic information. Finally, these features are used as input of our Support Vector Machine (SVM) classifier. The performance of this approach is quantitatively and qualitatively evaluated on a dataset of real images of American Sign Language (ASL) hand shapes. Three experiments were performed, using a combination of RGB and depth information and also using only RGB or depth information separately. The database used is composed of 120,000 images. According to our experiments, our approach has an accuracy rate of 91.26% when RGB and depth information is used, outperforming other state-of-the-art methods.

[ { "first": "K.", "middle": [ "Otiniano" ], "last": "Rodriguez", "suffix": "" }, { "first": "Guillermo", "middle": [ "Camara" ], "last": "Chavez", "suffix": "" } ]

2,013

10.1109/SIBGRAPI.2013.10

SIBGRAPI

2005800054

[ "10374055", "21103145", "18619", "11622212", "204841125", "9572349", "840914", "61962167", "8677556", "7581276", "1794649", "11323794", "35522840", "14433964", "704982", "14239489", "5258236", "876225", "14457153", "206787478", "18530691", "961425" ]

[ "7221033", "46817381", "6598047", "28747892", "114255098", "11112131", "181489974" ]

true

https://api.semanticscholar.org/CorpusID:13479875

1

[ [ "color data", "DATA" ], [ "real image", "DATA" ], [ "Support Vector Machine (SVM) classifier", "METHOD" ], [ "pixel attribut", "DATA" ], [ "hand shape", "DATA" ], [ "depth data", "DATA" ], [ "gesture recognition", "APPLICATION" ], [ "Scale-Invariant Feature Transform (SIFT)", "METHOD" ], [ "depth camera", "METHOD" ], [ "kern", "METHOD" ], [ "RGB", "VISUALIZATION" ], [ "Manual spelling", "METHOD" ], [ "hand segmentation", "APPLICATION" ], [ "feature extraction", "APPLICATION" ], [ "precise hand shape", "DATA" ], [ "semantic information", "DATA" ], [ "patch-level feature", "DATA" ], [ "RGB image", "DATA" ], [ "sign language", "METHOD" ], [ "finger spelling recognition", "APPLICATION" ], [ "hand", "DATA" ], [ "RGB and depth information", "DATA" ], [ "discrete movement", "METHOD" ], [ "color camera", "METHOD" ], [ "depth map", "VISUALIZATION" ], [ "depth image", "VISUALIZATION" ], [ "hybrid system approach", "METHOD" ], [ "finger spelling", "METHOD" ], [ "gradient kernel descriptor", "METHOD" ], [ "Bag-of-Visual-Words approach", "METHOD" ], [ "depth information", "DATA" ], [ "accuracy rate", "EVALUATION" ], [ "RGB-D information", "DATA" ], [ "American Sign Language (ASL) hand shape", "DATA" ] ]

Piecewise linear mapping optimization based on the complex view

52,214,593

[ { "first": "Björn", "middle": [], "last": "Golla", "suffix": "" }, { "first": "Hans‐Peter", "middle": [], "last": "Seidel", "suffix": "" }, { "first": "Renjie", "middle": [], "last": "Chen", "suffix": "" } ]

2,018

10.1111/cgf.13563

Comput. Graph. Forum

2889202335

[]

[ "203593622", "202584874", "196834559", "195795384" ]

false

true

https://api.semanticscholar.org/CorpusID:52214593

0

[ [ "view", "METHOD" ], [ "Piecewise linear mapping optimization", "METHOD" ] ]

Venn Diagram and Evaluation of Syllogisms with Negative Terms: A New Algorithm

49,185,320

We propose an algorithmic procedure for the automatic analysis of syllogisms with negative terms based on the modified version of Shin’s Venn-I diagram. Our computational procedure can automatically generate all the possible conclusions derivable from the two premises of a given syllogism with negative terms. Our approach relies on the reformulation of the logic behind the relations between points, lines, and surfaces in the Venn diagram by employing conditional propagation rules.

[ { "first": "Mehdi", "middle": [], "last": "Mirzapour", "suffix": "" }, { "first": "Christian", "middle": [], "last": "Retoré", "suffix": "" } ]

2,018

10.1007/978-3-319-91376-6_66

Diagrams

2804328824

[]

false

https://api.semanticscholar.org/CorpusID:49185320

null

[ [ "algorithmic procedure", "METHOD" ], [ "point", "DATA" ], [ "automatic analysis", "APPLICATION" ], [ "Venn diagram", "VISUALIZATION" ], [ "negative term", "DATA" ], [ "line", "DATA" ], [ "computational procedure", "METHOD" ], [ "conditional propagation rule", "METHOD" ], [ "surface", "DATA" ], [ "s Venn-I diagram", "VISUALIZATION" ] ]

No coherent evidence for bilingual advantages in executive functioning.

117,728,298

[ { "first": "Kenneth", "middle": [ "R." ], "last": "Paap", "suffix": "" } ]

2,018

CogSci

2940837799

[]

false

https://api.semanticscholar.org/CorpusID:117728298

null

[ [ "bilingual advantage", "EVALUATION" ], [ "executive functioning", "APPLICATION" ], [ "coherent evidence", "EVALUATION" ] ]

Issues in the non-visual presentation of graph based diagrams

61,890,704

One aspect of non-visual visualisation is providing accessibility to diagrams for users with visual impairments. In this paper, we review the literature on diagrams and non-visual presentation in order to elucidate the issues involved in making graph based diagrams accessible using speech and non-speech sound. First we examine the nature, scope and uses of these diagrams. We then describe the nature of diagrams: how do diagrams differ from other representations; how do sighted readers read, understand, and extract information from diagrams; what cognitive processes do diagrams facilitate; and what factors affect how diagrams may be understood? After a comparison of visual with aural presentation we discuss the work presented by others in this field, particularly looking at their reasons for implementing how they do in light of our examination of visual comprehension. The paper concludes with a discussion of how these issues combine and conflict to influence requirements for interface design.

[ { "first": "Andy", "middle": [], "last": "Brown", "suffix": "" }, { "first": "Robert", "middle": [], "last": "Stevens", "suffix": "" }, { "first": "Steve", "middle": [], "last": "Pettifer", "suffix": "" } ]

2,004

10.1109/IV.2004.86

2140078102

[]

[ "18551550", "7851607", "421459", "1673289" ]

false

true

false

https://api.semanticscholar.org/CorpusID:61890704

null

[ [ "visual comprehension", "VISUALIZATION" ], [ "interface design", "APPLICATION" ], [ "visual im", "DATA" ], [ "graph based diagram", "VISUALIZATION" ], [ "aural presentation", "METHOD" ], [ "non-visual visualisation", "VISUALIZATION" ], [ "non-visual presentation", "VISUALIZATION" ] ]

Talking in circles: designing a spatially-grounded audioconferencing environment

15,690,742

This paper presents Talking in Circles, a multimodal audioconferencing environment whose novel design emphasizes spatial grounding with the aim of supporting naturalistic group interaction behaviors. Participants communicate primarily by speech and are represented as colored circles in a two-dimensional space. Behaviors such as subgroup conversations and social navigation are supported through circle mobility as mediated by the environment and the crowd and distance-based attenuation of the audio. The circles serve as platforms for the display of identity, presence and activity: graphics are synchronized to participants' speech to aid in speech-source identification and participants can sketch in their circle, allowing a pictorial and gestural channel to complement the audio. We note user experiences through informal studies as well as design challenges we have faced in the creation of a rich environment for computer-mediated communication.

[ { "first": "Roy", "middle": [], "last": "Rodenstein", "suffix": "" }, { "first": "Judith", "middle": [ "S." ], "last": "Donath", "suffix": "" } ]

2,000

10.1145/332040.332410

CHI '00

2098237746

[ "47347785", "6955", "17247867", "59809839", "16308483", "78725", "1746888", "9749215", "6901130", "7837039", "37067350", "121383181", "141528371", "6465857", "158380491", "11490922", "14288248", "6034743", "6543572" ]

[ "14114176", "24439712", "59023455", "199403464", "5220321", "13383955", "15034874", "17029359", "27041454", "8345832", "15883244", "21361498", "1031788", "16954042", "16625675", "16873234", "17503996", "17246835", "62673431", "17593878", "14841108", "8285390" ]

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https://api.semanticscholar.org/CorpusID:15690742

0

1

0

[ [ "colored circle", "VISUALIZATION" ], [ "circle mobility", "METHOD" ], [ "informal study", "EVALUATION" ], [ "naturalistic group interaction behavior", "APPLICATION" ], [ "subgroup conversation", "APPLICATION" ], [ "Talking in Circles", "METHOD" ], [ "two-dimensional space", "VISUALIZATION" ], [ "social navigation", "APPLICATION" ], [ "design challenge", "EVALUATION" ], [ "experience", "EVALUATION" ], [ "computer-mediated communication", "APPLICATION" ], [ "spatial grounding", "METHOD" ], [ "distance-based attenuation", "METHOD" ], [ "multimodal audioconferencing environment", "METHOD" ], [ "speech-source identification", "APPLICATION" ], [ "pictorial and gestural channel", "VISUALIZATION" ] ]

Object Understanding: Exploring the Path from Percept to Meaning.

41,055,928

[ { "first": "Kenneth", "middle": [ "J." ], "last": "Kurtz", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Silliman", "suffix": "" } ]

2,017

CogSci

2787373830

[]

false

true

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0

[]

OCC: A Smart Reply System for Efficient In-App Communications

196,196,473

Smart reply systems have been developed for various messaging platforms. In this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which is a key enhanced feature on top of the Uber in-app chat system. It enables driver-partners to quickly respond to rider messages using smart replies. The smart replies are dynamically selected according to conversation content using machine learning algorithms. Our system consists of two major components: intent detection and reply retrieval, which are very different from standard smart reply systems where the task is to directly predict a reply. It is designed specifically for mobile applications with short and non-canonical messages. Reply retrieval utilizes pairings between intent and reply based on their popularity in chat messages as derived from historical data. For intent detection, a set of embedding and classification techniques are experimented with, and we choose to deploy a solution using unsupervised distributed embedding and nearest-neighbor classifier. It has the advantage of only requiring a small amount of labeled training data, simplicity in developing and deploying to production, and fast inference during serving and hence highly scalable. At the same time, it performs comparably with deep learning architectures such as word-level convolutional neural network. Overall, the system achieves a high accuracy of 76% on intent detection. Currently, the system is deployed in production for English-speaking countries and 71% of in-app communications between riders and driver-partners adopted the smart replies to speedup the communication process.

[ { "first": "Yue", "middle": [], "last": "Weng", "suffix": "" }, { "first": "Huaixiu", "middle": [], "last": "Zheng", "suffix": "" }, { "first": "Franziska", "middle": [], "last": "Bell", "suffix": "" }, { "first": "Gokhan", "middle": [], "last": "Tur", "suffix": "" } ]

2,019

1907.08167

10.1145/3292500.3330694

KDD 19, August 4-8, 2019, Anchorage, AK, USA

2961541102,2952106012,2964011355

[ "2993463", "1915014", "5759934", "2407601", "7961699", "6366588", "207246940" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:196196473

1

[ [ "deep learning architecture", "METHOD" ], [ "machine learning algorithm", "METHOD" ], [ "chat message", "APPLICATION" ], [ "conversation content", "DATA" ], [ "non-canonical message", "DATA" ], [ "historical data", "DATA" ], [ "intent detection", "METHOD" ], [ "classification technique", "METHOD" ], [ "Smart reply system", "METHOD" ], [ "rider message", "DATA" ], [ "reply retrieval", "METHOD" ], [ "labeled training data", "DATA" ], [ "communication process", "METHOD" ], [ "smart reply", "METHOD" ], [ "embedding", "METHOD" ], [ "-partner", "APPLICATION" ], [ "smart reply system", "METHOD" ], [ "in-app communication", "APPLICATION" ], [ "system: one-click-chat (OCC", "METHOD" ], [ "mobile application", "APPLICATION" ], [ "Reply retrieval", "APPLICATION" ], [ "unsupervised distributed embedding", "METHOD" ], [ "in-app chat system", "METHOD" ], [ "nearest-neighbor classifier", "METHOD" ], [ "fast inference", "EVALUATION" ], [ "word-level convolutional neural network", "METHOD" ] ]

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction

196,196,583

Partial label learning is an emerging weakly-supervised learning framework where each training example is associated with multiple candidate labels among which only one is valid. Dimensionality reduction serves as an effective way to help improve the generalization ability of learning system, while the task of partial label dimensionality reduction is challenging due to the unknown ground-truth labeling information. In this paper, the first attempt towards partial label dimensionality reduction is investigated by endowing the popular linear discriminant analysis (LDA) techniques with the ability of dealing with partial label training examples. Specifically, a novel learning procedure named DELIN is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to kNN aggregation in the LDA-induced feature space. Extensive experiments on synthetic as well as real-world partial label data sets clearly validate the effectiveness of DELIN in improving the generalization ability of state-of-the-art partial label learning algorithms.

[ { "first": "Jing-Han", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Min-Ling", "middle": [], "last": "Zhang", "suffix": "" } ]

2,019

10.1145/3292500.3330901

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2952205004

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[ "173991163" ]

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0

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[ [ "partial label learning algorithm", "METHOD" ], [ "labeling confidence", "EVALUATION" ], [ "candidate label disambiguation", "METHOD" ], [ "partial label training example", "APPLICATION" ], [ "learning procedure", "METHOD" ], [ "weakly-supervised learning framework", "METHOD" ], [ "LDA dimensionality reduction", "METHOD" ], [ "generalization ability", "EVALUATION" ], [ "DELIN", "METHOD" ], [ "learning system", "METHOD" ], [ "LDA-induced feature space", "METHOD" ], [ "disambiguation-guided labeling confidence", "METHOD" ], [ "Partial label learning", "METHOD" ], [ "partial label dimensionality reduction", "APPLICATION" ], [ "projection matrix", "METHOD" ], [ "example", "DATA" ], [ "ground-truth labeling information", "DATA" ], [ "kNN aggregation", "METHOD" ], [ "candidate label", "DATA" ], [ "linear discriminant analysis (LDA) technique", "METHOD" ], [ "real-world partial label data set", "DATA" ], [ "Dimensionality reduction", "METHOD" ] ]

Towards In-Air Gesture Control of Household Appliances with Limited Displays

9,833,161

Recent technologies allow us to interact with our homes in novel ways, such as using in-air gestures for control. However, gestures require good feedback and small appliances, like lighting controls and thermostats, have limited, or no, display capabilities. Our research explores how other output types can be used to give users feedback about their gestures, instead, allowing small devices to give useful feedback. We describe the Gesture Thermostat, a gesture-controlled thermostat dial which gives multimodal gesture feedback.

[ { "first": "Euan", "middle": [], "last": "Freeman", "suffix": "" }, { "first": "Stephen", "middle": [], "last": "Brewster", "suffix": "" }, { "first": "Vuokko", "middle": [], "last": "Lantz", "suffix": "" } ]

2,015

10.1007/978-3-319-22723-8_73

INTERACT

1755254774

[ "26342970", "11054422", "13596379", "14154736", "14736152", "18862416" ]

[ "201679799" ]

true

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1

0

[ [ "gesture-controlled thermostat dial", "METHOD" ], [ "output type", "DATA" ], [ "in-air gesture", "METHOD" ], [ "display capability", "EVALUATION" ], [ "multimodal gesture feedback", "METHOD" ], [ "Gesture Thermostat", "METHOD" ], [ "lighting control", "METHOD" ] ]

Modeling the Effect of Lexico-Syntactic Gender on Spoken-Word Recognition

18,223,447

Modeling the Effect of Lexico-Syntactic Gender on Spoken-Word Recognition Garance M.I. Paris Matthew W. Crocker Marshall R. Mayberry, III School of Social Sciences, Humanities and Arts University of California, Merced Department of Computational Linguistics Saarland University, Germany generalization to unseen article-noun pairs reveals that the network goes beyond learning simple sequential dependen- cies in the input—as current models of lexical access do—and has learned an abstract notion of gender which influences the earliest stages of lexical access. Abstract We present a computational model of the influence of lexico- syntactic gender on spoken-word recognition, and demonstrate the ability of the model to account for relevant findings ob- tained with eye tracking (Dahan, Swingley, Tanenhaus, & Magnuson, 2000). The model is a Simple Recurrent Network (Elman, 1990) trained on article-noun phrases input phoneme- by-phoneme. It learns to incrementally map this input to object concepts beginning with those sounds. After training, it ex- hibits a behavior similar to French native speakers using gen- der to constrain lexical access: When the article preceding a noun is ambiguous in gender, all possible nouns are considered during lexical competition, but when a noun is preceded by a gender-marked article, only nouns belonging to that particular category are considered as potential lexical candidates. In the evaluation, the model is shown to generalize well to novel data including unseen article-noun combinations, leading us to con- clude that it has in fact learned an abstract notion of gender and discovered the broader gender patterns in French article-noun sequences. Keywords: computational model; lexico-syntactic gender; spoken-word recognition; lexical access; eye tracking Experimental Evidence Approximately half of the world’s known languages subdi- vide nouns into relatively arbitrary categories known as “gen- der” classes. In these languages, each noun is assigned to a category which is a lexico-syntactic, intrinsic property of the noun itself and often cannot be determined from the noun’s form or from its semantics alone. Moreover, depending on the gender of a noun, words that are associated with it change: In French, for example, the singular definite article before masculine nouns is “le”, but it is “la” before feminine ones. Therefore, in principle, after hearing a singular definite article in this language, only half of the nouns in the mental lexicon come into question, because the gender of the noun is foretold by its article. It has been argued, however, that listeners do not make use of gender information in spoken-word recogni- tion because it would be inefficient due to the large number of nouns that would need to be pre-activated (Tanenhaus, Dell, & Carlson, 1987; Jescheniak, 1999). Alternatively, however, pre-activation could be seen as very efficient, since it effec- tively reduces the search space in the lexicon by half in lan- guages with two gender categories. Indeed, the bulk of research on gender clearly supports the idea that listeners of gender-marking languages use gender online to facilitate spoken-word recognition. In this paper, we present a model of a mechanism by which gender can constrain lexical access. The model is trained on a corpus of French nouns preceded by singular, gender-marked, and plural (gender-neutral) articles, and learns to simulate the be- havior of French natives using gender in spoken-word recog- nition. Analysis of the model further suggests it does indeed pre-activate nouns based on the article alone. Additionally, Over the past 20 years, findings have consistently demon- strated that speakers of many languages draw on gender in- formation during spoken-word recognition. Both facilita- tory and inhibitory effects have been found in several lex- ical decision experiments in French, Spanish and Russian: Listeners in general were faster at deciding whether a let- ter or sound sequence was a word or not when the stimuli were preceded by gender-congruent determiners, and slower when they were preceded by gender-incongruent determin- ers (Grosjean, Dommergues, Cornu, Guillelmon, & Besson, 1994; Faussart, Jakubowicz, & Costes, 1999; Akhutina, Kur- gansky, Polinsky, & Bates, 1999). Similar conclusions have also been reached using several other methods (e. g. cross- model priming, Spinelli & Alario, 2002), in other languages (Serbo-Croatian: Gurjanov, Lukatela, Lukatela, Savic, & Tur- vey, 1985; German: Roder, Demuth, Streb, & Roder, 2003, inter alia), with other types of words providing gender infor- mation prior to the noun (e. g. demonstratives and possessives, Jakubowicz & Faussart, 1998), in the written modality (i. a. Gurjanov et al.), and recently even with children only 6–7 years old (Roulet, 2007). Two explanations of the effects have been discussed in the literature by several authors: On the one hand, an interactive- activation model in which the article would pre-activate all congruent nouns, giving them an early advantage over other nouns when they compete for recognition, and on the other hand a post-lexical syntactic congruency-checking mecha- nism in line with a modular view of lexical access (Grosjean et al., 1994; Bates, Devescovi, Hernandez, & Pizzamiglio, 1996; Friederici & Jacobsen, 1999). In several recent studies, eye tracking has been shown to be highly sensitive to online lexical access mechanisms. Dur- ing the recognition of spoken words, listeners are thought to first activate all words matching the onset of the partial in- put, then gradually eliminate those which become inconsis- tent with acoustic information. In eye-tracking studies, this appears in participants’ eye movements to objects with similar names: For example, Dahan et al. (2000) showed that when gender is of no import, French listeners asked to click on a pic- ture depicting some ‘buttons’ (boutons, /buto/) also initially looked at ‘bottles’ (bouteilles, /butɛj) because the ambiguous

[ { "first": "Garance", "middle": [], "last": "Paris", "suffix": "" }, { "first": "Matthew", "middle": [ "W." ], "last": "Crocker", "suffix": "" }, { "first": "Marshall", "middle": [ "R." ], "last": "Mayberry", "suffix": "" } ]

2,011

CogSci

2573774990

[ "18906768", "8429790", "60457972", "16120880", "2763403", "7069311", "14275473", "141953774", "3016464", "35115744", "141505094", "39475733", "7428866", "15395566", "142246760", "15177593", "145658589" ]

[]

true

false

true

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0

1

0

[ [ "bottles’ (bou", "DATA" ], [ "acoustic information", "DATA" ], [ "interactive- activation model", "METHOD" ], [ "computational model", "METHOD" ], [ "gen- der", "METHOD" ], [ "gender-congruent determiner", "METHOD" ], [ "facilita- tory and inhibitory effect", "METHOD" ], [ "for- ma", "DATA" ], [ "Lexico-Syntactic Gender", "METHOD" ], [ "gender pattern", "DATA" ], [ "mental lexicon", "DATA" ], [ "online lexical access mechanism", "METHOD" ], [ "unseen article-noun combination", "DATA" ], [ "article-noun phrase", "DATA" ], [ "masculine nou", "DATA" ], [ "sound sequence", "DATA" ], [ "gender-marked article", "DATA" ], [ "cross- model priming", "METHOD" ], [ "modular view", "METHOD" ], [ "- cies", "METHOD" ], [ "gender-marking", "METHOD" ], [ "French", "DATA" ], [ "access; eye track", "APPLICATION" ], [ "unseen article-noun pair", "METHOD" ], [ "lexical competition", "APPLICATION" ], [ "definite article", "DATA" ], [ "lexico- syntactic gender", "DATA" ], [ "gender in- formation", "METHOD" ], [ "gender information", "DATA" ], [ "pre-activation", "METHOD" ], [ "Simple Recurrent Network", "METHOD" ], [ "Spoken-Word Recognition", "APPLICATION" ], [ "lexical access", "APPLICATION" ], [ "eye-tracking study", "APPLICATION" ], [ "phone", "DATA" ], [ "search space", "EVALUATION" ], [ "parti", "DATA" ], [ "recognition of spoken word", "APPLICATION" ], [ "eye movement", "DATA" ], [ "spoken-word recognition", "APPLICATION" ], [ "spoken-word recog- nition", "APPLICATION" ], [ "post-lexical syntactic congruency-checking mecha- nism", "METHOD" ], [ "gender-incongruent determin- er", "METHOD" ], [ "written modality", "METHOD" ], [ "gen- der” class", "METHOD" ], [ "lexical", "APPLICATION" ], [ "ni- tion", "APPLICATION" ], [ "lex- ical decision experiment", "EVALUATION" ], [ "French article-noun sequence", "DATA" ], [ "eye tracking", "METHOD" ], [ "object concept", "METHOD" ] ]

Improved and Scalable Bradley-Terry Model for Collaborative Ranking

13,970,869

In collaborative ranking, the Bradley-Terry (BT) model is widely used for modeling pairwise user preferences. However, when this model is combined with matrix factorization on sparsely observed ratings, a challenging identifiability issue arises since the optimization will involve non-convex constraints. Besides, in some situations, fitting the Bradley-Terry model yields a numerical challenge as it may include an objective function that is unbounded from below. In this paper, we will discuss and develop a simple strategy to resolve these issues. Specifically, we propose an Improved-BT model by adding a penalty term, and we develop two parallel algorithms to make Improved-BT model scalable. Through extensive experiments on benchmark datasets, we show that our proposed method outperforms many considered state-of-the-art collaborative ranking approaches in terms of both ranking performance and time efficiency.

[ { "first": "Jun", "middle": [], "last": "Hu", "suffix": "" }, { "first": "Ping", "middle": [], "last": "Li", "suffix": "" } ]

2,016

10.1109/ICDM.2016.0118

2016 IEEE 16th International Conference on Data Mining (ICDM)

2585144084

[ "206742345", "12564685", "207192911", "125209808", "3790020", "7021914", "12348212", "13950637", "12188377", "14476078", "8273336", "1172140", "6108215", "5499886", "10795036", "207181267", "16638166", "5493334", "1822311" ]

[ "53035242", "18376546" ]

true

https://api.semanticscholar.org/CorpusID:13970869

0

1

0

[ [ "penalty term", "EVALUATION" ], [ "time efficiency", "EVALUATION" ], [ "simple strategy", "METHOD" ], [ "Bradley-Terry (BT) model", "METHOD" ], [ "matrix factorization", "METHOD" ], [ "Bradley-Terry model", "METHOD" ], [ "objective function", "DATA" ], [ "collaborative ranking", "APPLICATION" ], [ "non-convex constraint", "DATA" ], [ "ranking performance", "EVALUATION" ], [ "benchmark dataset", "DATA" ], [ "challenging identifiability issue", "EVALUATION" ], [ "collaborative ranking approach", "METHOD" ], [ "numerical challenge", "EVALUATION" ], [ "pairwise user preference", "APPLICATION" ], [ "parallel algorithm", "METHOD" ], [ "Improved-BT model", "METHOD" ] ]

Landmark-constrained 3-D Histological Imaging: A Morphology-preserving Approach

7,961,697

The inspection of histological image sequences to gain knowledge about the original three-dimensional (3-D) morphological structure is a standard method in medical research. Its main advantage is that light microscopes feature high resolution enhanced visibility due to staining. In many cases this imaging technology could immensely profit from 3-D reconstructions of the slice images. For volumetric stacking, however, the tissue deformations due to slice preparation require an unwarping strategy to restore the original morphology. The challenge is to reverse the artificial deformations while preserving the natural morphological changes. In particular, unintentional straightening of curved structures across multiple slices has to be avoided. In this article, we propose a novel way to incorporate landmarks representing the morphological progression. They are used as additional regularization for intensity based non-rigid registration which is capable to exactly match the landmarks. Our approach is tested on synthetical and histological data sets. We show that it delivers smooth contours while preserving the morphological structure, and is a promising addition to existing methods.

[ { "first": "Simone", "middle": [], "last": "Gaffling", "suffix": "" }, { "first": "Volker", "middle": [], "last": "Daum", "suffix": "" }, { "first": "Joachim", "middle": [], "last": "Hornegger", "suffix": "" } ]

2,011

10.2312/PE/VMV/VMV11/309-316

VMV

2265066148

[ "1717325", "123120623", "14017698", "52800435", "60604462", "53784552", "14994437", "41849815", "12153646" ]

[ "204865146", "46945287", "3707913", "29812081", "53142350", "215184850", "195773868", "1586537", "18060461" ]

true

https://api.semanticscholar.org/CorpusID:7961697

0

1

0

[ [ "additional regular", "APPLICATION" ], [ "smooth contour", "VISUALIZATION" ], [ "high resolution enhanced visibility", "VISUALIZATION" ], [ "morphological change", "DATA" ], [ "unintentional straightening", "METHOD" ], [ "medical research", "APPLICATION" ], [ "slice image", "DATA" ], [ "synthetical and histological data set", "DATA" ], [ "intensity based non-rigid registration", "APPLICATION" ], [ "three-dimensional (3-D) morphological structure", "DATA" ], [ "histological image sequence", "DATA" ], [ "tissue deformation", "DATA" ], [ "imaging technology", "METHOD" ], [ "slice preparation", "APPLICATION" ], [ "morph", "DATA" ], [ "volumetric stacking", "APPLICATION" ], [ "morphological structure", "DATA" ], [ "curved structure", "DATA" ], [ "light micro", "APPLICATION" ], [ "3-D reconstruction", "APPLICATION" ], [ "unwarping strategy", "METHOD" ], [ "morphological progression", "DATA" ], [ "artificial deformation", "METHOD" ] ]

HDR Imaging Using Augmented Lagrange Multipliers (ALM)

42,978,658

[ { "first": "Adit", "middle": [], "last": "Bhardwaj", "suffix": "" }, { "first": "Shanmuganathan", "middle": [], "last": "Raman", "suffix": "" } ]

2,014

10.2312/egp.20141061

Eurographics

2287297272

[]

[ "12171657" ]

false

true

false

https://api.semanticscholar.org/CorpusID:42978658

null

[ [ "HDR Imaging", "APPLICATION" ], [ "Augmented Lagrange Multipliers (ALM)", "METHOD" ] ]

Towards long-lead forecasting of extreme flood events: a data mining framework for precipitation cluster precursors identification

14,497,528

The development of disastrous flood forecasting techniques able to provide warnings at a long lead-time (5-15 days) is of great importance to society. Extreme Flood is usually a consequence of a sequence of precipitation events occurring over from several days to several weeks. Though precise short-term forecasting the magnitude and extent of individual precipitation event is still beyond our reach, long-term forecasting of precipitation clusters can be attempted by identifying persistent atmospheric regimes that are conducive for the precipitation clusters. However, such forecasting will suffer from overwhelming number of relevant features and high imbalance of sample sets. In this paper, we propose an integrated data mining framework for identifying the precursors to precipitation event clusters and use this information to predict extended periods of extreme precipitation and subsequent floods. We synthesize a representative feature set that describes the atmosphere motion, and apply a streaming feature selection algorithm to online identify the precipitation precursors from the enormous feature space. A hierarchical re-sampling approach is embedded in the framework to deal with the imbalance problem. An extensive empirical study is conducted on historical precipitation and associated flood data collected in the State of Iowa. Utilizing our framework a few physically meaningful precipitation cluster precursor sets are identified from millions of features. More than 90% of extreme precipitation events are captured by the proposed prediction model using precipitation cluster precursors with a lead time of more than 5 days.

[ { "first": "Dawei", "middle": [], "last": "Wang", "suffix": "" }, { "first": "Wei", "middle": [], "last": "Ding", "suffix": "" }, { "first": "Kui", "middle": [], "last": "Yu", "suffix": "" }, { "first": "Xindong", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Ping", "middle": [], "last": "Chen", "suffix": "" }, { "first": "David", "middle": [ "L." ], "last": "Small", "suffix": "" }, { "first": "Shafiqul", "middle": [], "last": "Islam", "suffix": "" } ]

2,013

10.1145/2487575.2488220

KDD '13

1980958256

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[ "19895507", "23932582", "6554283", "7094208", "24765906", "197638911", "13259395", "14786248", "14432099" ]

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[ [ "prediction model", "METHOD" ], [ "precipitation cluster precursor", "METHOD" ], [ "extreme precipit", "EVALUATION" ], [ "precipitation event", "METHOD" ], [ "integrated data mining framework", "METHOD" ], [ "streaming feature selection algorithm", "METHOD" ], [ "disastrous flood forecasting technique", "METHOD" ], [ "sample set", "DATA" ], [ "precipitation precursor", "DATA" ], [ "precipitation cluster precursor set", "DATA" ], [ "historical precipitation", "DATA" ], [ "empirical study", "EVALUATION" ], [ "representative feature set", "DATA" ], [ "precipitation cluster", "DATA" ], [ "atmosphere motion", "DATA" ], [ "feature space", "DATA" ], [ "Extreme Flood", "APPLICATION" ], [ "high imbalance", "DATA" ], [ "extreme precipitation event", "APPLICATION" ], [ "flood data", "DATA" ], [ "ent atmospheric regime", "METHOD" ], [ "imbalance problem", "APPLICATION" ], [ "hierarchical re-sampling approach", "METHOD" ], [ "precipitation event cluster", "DATA" ] ]

Virtual Reality

41,307,308

[ { "first": "Sean", "middle": [ "M." ], "last": "Grady", "suffix": "" } ]

2,008

10.1007/978-0-387-78414-4_255

Encyclopedia of Multimedia

2798448916

[]

false

true

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1

0

[ [ "Virtual Reality", "APPLICATION" ] ]

Inspiring the design of longer-lived electronics through an understanding of personal attachment

8,291,526

Research in sustainable HCI has repeatedly pointed to the need for encouraging longer use of technology as part of the solution for stemming the tide of e-waste. Ways of achieving this goal remain elusive, however. We build upon previous research that considers the role of personal attachment in object ownership, and how this attachment might be leveraged to encourage longer use. We conducted a personal inventories study with 17 households in Switzerland, and use the findings to support and expand Odom et al.'s framework of attachment categories. We subsequently provided this framework to 3 designers and asked them to design novel technologies that encourage attachment. This exercise shed light on how they drew insight and inspiration from the framework, and how they integrated it into their design processes and design thinking.

[ { "first": "Silke", "middle": [], "last": "Gegenbauer", "suffix": "" }, { "first": "Elaine", "middle": [ "M." ], "last": "Huang", "suffix": "" } ]

2,012

10.1145/2317956.2318052

DIS '12

1997332111

[ "47101805", "12060524", "16348943", "22189106", "11579846", "8176837", "10819791", "2452141", "236117", "7907831", "2806967", "207187138", "16851588", "923984", "14076082", "8232750", "26967494", "6911498" ]

[ "47019296", "46886224", "14113629", "3762412", "15060979", "7587823", "12629734", "15678180", "4651309", "15963813", "5046377", "211040872", "67320966", "314409", "202784292", "18204652", "4260440", "59046839", "15767985" ]

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https://api.semanticscholar.org/CorpusID:8291526

0

1

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[ [ "e-waste", "APPLICATION" ], [ "design process", "APPLICATION" ], [ "personal inventory study", "EVALUATION" ], [ "attachment category", "METHOD" ], [ "object ownership", "APPLICATION" ], [ "design thinking", "APPLICATION" ], [ "sustainable HCI", "APPLICATION" ], [ "personal attachment", "METHOD" ] ]

Tiling Motion Patches

61,306,865

[ { "first": "", "middle": [], "last": "김만명", "suffix": "" } ]

2,011

2202946502

[]

false

https://api.semanticscholar.org/CorpusID:61306865

null

[ [ "Tiling Motion Patches", "METHOD" ] ]

Predicted Virtual Soft Shadow Maps with High Quality Filtering

16,422,462

In this paper we present a novel image based algorithm to render visually plausible anti-aliased soft shadows in a robust and efficient manner. To achieve both high visual quality and high performance, it employs an accurate shadow map filtering method which guarantees smooth penumbrae and high quality anisotropic anti-aliasing of the sharp transitions. Unlike approaches based on pre-filtering approximations, our approach does not suffer from light bleeding or losing contact shadows. Discretization artefacts are avoided by creating virtual shadow maps on the fly according to a novel shadow map resolution prediction model. This model takes into account the screen space frequency of the penumbrae via a perceptual metric which has been directly established from an appropriate user study. Consequently, our algorithm always generates shadow maps with minimal resolutions enabling high performance while guarantying high quality. Thanks to this perceptual model, our algorithm can sometimes be faster at rendering soft shadows than hard shadows. It can render game-like scenes at very high frame rates, and extremely large and complex scenes such as CAD models at interactive rates. In addition, our algorithm is highly scalable, and the quality versus performance trade-off can be easily tweaked.

[ { "first": "Li", "middle": [], "last": "Shen", "suffix": "" }, { "first": "Gaël", "middle": [], "last": "Guennebaud", "suffix": "" }, { "first": "Baoguang", "middle": [], "last": "Yang", "suffix": "" }, { "first": "Jieqing", "middle": [], "last": "Feng", "suffix": "" } ]

2,011

10.1111/j.1467-8659.2011.01875.x

Comput. Graph. Forum

2055329738

[]

[ "3879639", "10251500", "1363300", "6800242", "6081820" ]

false

true

false

https://api.semanticscholar.org/CorpusID:16422462

null

[ [ "pre-filtering approximation", "METHOD" ], [ "contact shadow", "EVALUATION" ], [ "soft shadow", "DATA" ], [ "screen space", "DATA" ], [ "image based algorithm", "METHOD" ], [ "shadow map resolution prediction model", "METHOD" ], [ "perceptual model", "METHOD" ], [ "anti-aliased soft shadow", "VISUALIZATION" ], [ "light bleeding", "EVALUATION" ], [ "shadow map filtering method", "METHOD" ], [ "shadow map", "VISUALIZATION" ], [ "high frame rate", "DATA" ], [ "quality versus performance trade", "EVALUATION" ], [ "perceptual metric", "METHOD" ], [ "high quality", "EVALUATION" ], [ "interactive rate", "METHOD" ], [ "anisotropic anti-aliasing", "METHOD" ], [ "smooth penumbra", "EVALUATION" ], [ "virtual shadow map", "VISUALIZATION" ], [ "CAD model", "METHOD" ], [ "visual quality", "VISUALIZATION" ], [ "minimal resolution", "DATA" ], [ "Discretization artefact", "APPLICATION" ], [ "sharp transition", "VISUALIZATION" ], [ "appropriate user study", "EVALUATION" ], [ "hard shadow", "DATA" ], [ "game-like scene", "DATA" ], [ "manner", "VISUALIZATION" ], [ "high performance", "EVALUATION" ] ]

DiMaC: a system for cleaning disguised missing data

12,071,530

In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially valid data values. Such missing values are known as disguised missing data, which may impair the quality of data analysis severely. The very limited previous studies on cleaning disguised missing data highly rely on domain background knowledge in specific applications and may not work well for the cases where the disguise values are inliers. Recently, we have studied the problem of cleaning disguised missing data systematically, and proposed an effective heuristic approach [2]. In this paper, we describe a demonstration of DiMaC, a Di sguised M issing D a ta C leaning system which can find the frequently used disguise values in data sets without requiring any domain background knowledge. In this demo, we will show (1) the critical techniques of finding suspicious disguise values; (2) the architecture and user interface of DiMaC system; (3) an empirical case study on both real and synthetic data sets, which verifies the effectiveness and the efficiency of the techniques; (4) some challenges arising from real applications and several direction for future work.

[ { "first": "Ming", "middle": [], "last": "Hua", "suffix": "" }, { "first": "Jian", "middle": [], "last": "Pei", "suffix": "" } ]

2,008

10.1145/1376616.1376751

SIGMOD Conference

1966922244

[ "16789452", "10236058", "15030874", "122985977", "125113713", "12556038", "6088084" ]

[ "6404261", "17519043" ]

true

https://api.semanticscholar.org/CorpusID:12071530

0

1

0

[ [ "critical technique", "METHOD" ], [ "empirical case study", "EVALUATION" ], [ "disguise value", "DATA" ], [ "data analysis", "APPLICATION" ], [ "heuristic approach", "METHOD" ], [ "DiMaC", "METHOD" ], [ "customer information form", "APPLICATION" ], [ "disguised missing data", "DATA" ], [ "real and synthetic data set", "DATA" ], [ "domain background knowledge", "EVALUATION" ], [ "cleaning disguised missing data", "DATA" ], [ "user interface of DiMaC system", "METHOD" ], [ "application", "DATA" ], [ "Di sguised M issing D a ta C leaning system", "METHOD" ], [ "suspicious disguise value", "DATA" ], [ "missing value", "DATA" ], [ "data", "APPLICATION" ] ]

Opening the design space: the soft set of requirements

15,227,304

This paper provides a methodological perspective regarding the design of ambient computing systems informed by the notion of Aesthetics of Interaction. This approach stemmed from the growing complexity that is offered by interaction when computation is distributed in the environment and embodied in all sorts of objects. In this work the notion of Aesthetics of Interaction in ambient computing systems is challenged by the use of the Soft Requirements as tools that complement the existing design methodologies based on participatory design approaches. The perspective presented is based on the work conducted in the Neonatal Intensive Care Unit at Siena Hospital - Italy as a part of the EU PalCom project. The outcomes provide a heuristic account, which informs the design process by fostering the novel complexity of ambient/palpable technologies in delicate and fragile settings.

[ { "first": "Alessia", "middle": [], "last": "Rullo", "suffix": "" } ]

2,008

10.1145/1394445.1394490

DIS '08

1976188402

[ "58877027", "67422904", "17443397", "5598981", "12307052", "1666007", "114213870", "2114641", "195945413", "4011161", "22005638", "145769022", "207155126", "6369104", "31152205", "59878304", "40792569", "57605265", "142000201" ]

[ "12840200", "72128325" ]

true

https://api.semanticscholar.org/CorpusID:15227304

0

1

0

[ [ "tal Intensive Care Unit", "APPLICATION" ], [ "ambient computing system", "APPLICATION" ], [ "design process", "APPLICATION" ], [ "Soft Requirements", "METHOD" ], [ "participatory design approach", "METHOD" ], [ "heuristic account", "EVALUATION" ], [ "design method", "METHOD" ], [ "methodological perspective", "METHOD" ], [ "ambient/palpable technology", "METHOD" ] ]

What Aspects of Cyber Cruelty are judged most distressing? An Adaptive Conjoint Study with Two Independent Samples

13,403,865

What Aspects of Cyber Cruelty are judged most distressing? An Adaptive Conjoint Study with Two Independent Samples Christina Kuhlmann ([email protected]) Stephanie Pieschl ([email protected]) Torsten Porsch ([email protected]) Department of Psychology, Westfalische Wilhelms-Universitat Munster, Fliednerstrase 21, 48149 Munster, Germany Abstract Cyberbullying is defined as bullying via electronic means including the defining characteristics of repetition over time, intent to harm, and power imbalance. However, this normative top-down definition is discussed controversially. We argue that the term “cyberbullying” and the associated defining criteria might constrict our focus artificially. Therefore, we investigate bottom-up which aspects of cyber cruelty contribute to victims’ distress in an adaptive conjoint design with two independent samples (sample 1: n = 131; sample 2: n = 82). Six potentially relevant factors were investigated, each with multiple attributes: number of incidents, perpetrator status, perpetrator motive, and type, medium, and publicity of cyber incident. Contrary to the definition of cyberbullying, number of incidents, publicity, and type of cyber cruelty emerged as most important factors. These results allow us to further map the cognitive representation of cyber cruelty and are practically relevant for the definition and measurement of cyberbullying. Keywords: cyberbullying; electronic communication; emotional distress; cognitive representation; conjoint analysis. Theoretical Background Cyberbullying – namely bullying via electronic means – is a prevalent problem among today’s youth with mostly negative consequences (Tokunaga, 2010). In order to adequately research this phenomenon and to ultimately design effective prevention and intervention measures, a precise conceptualization of this construct is paramount (Ybarra, Boyd, Korchmaros, & Oppenheim, 2012). However, one area of controversy is the literal connotations of the composite term “bullying”. Today most scientists agree that bullying denotes an “aggressive, intentional act or behavior that is carried out by a group or an individual repeatedly and over time against a victim who cannot easily defend him or herself” (Olweus, 1993; cited in Smith, Mahdavi, Carvalho, Fisher, Russell, & Tippett, 2008, p. 376). Thus, repetition, power imbalance, and intent to harm are considered the key defining characteristics of bullying. But research shows that the understanding of “bullying” differs between historical eras, cultures or age groups (Smith & Monks, 2008). For example, in cultural comparisons, one of the biggest challenges is finding translations of “bullying” with equivalent meaning. Most often, terms vary in breadth and cognitive connotations; “the social construction of meaning and its cultural and temporal variability become apparent” (ibid., p. 110). With the advent of electronic communication and the first reported cases of online cruelty, the term “cyberbullying” was coined to refer to this new phenomenon. The definition of conventional bullying was transferred to cyberspace, and cyberbullying was defined as “an aggressive, intentional act carried out by a group or individual, using electronic forms of contact, repeatedly and over time against a victim who cannot easily defend him or herself” (Smith et al., 2008, p. 376). However, this theory-based top-down definition of cyberbullying has been discussed controversially ever since (e.g., Dooley, Pyzalski, & Cross, 2009; Grigg, 2010; Menesini & Nocentini, 2009; Pieschl et al., in press). Recent empirical investigations about the connotations and cognitive representations of the term “cyberbullying” offer the possibility to shed further light onto this issue from a data-driven, bottom-up perspective. Results from a multidimensional scaling analysis with 2,257 adolescents from six European countries (Menesini et al., 2012) show that the most important dimension of cyberbullying is characterized by the imbalance of power and the second most important dimension is characterized by intentionality. When adolescents classify a scenario as cyberbullying, they seem to mainly consider the presence of these criteria. Focus-group interviews of 70 Italian, Spanish and German adolescents (Nocentini, Calmaestra, Schultze-Krumbholz, Scheithauer, Ortega, & Menesini, 2010) on the other hand show that in some cases, subjects consider the publicity of an incident as a substitute of the criterion of repetition. Further, they consider victims’ perceived level of distress more important than an existing imbalance of power and view victims’ interpretation of an incident more critical than an existing intent to harm. These results seem to imply that the cyber-victims’ experience is more important than the adherence to normative criteria. Adolescents from another focus group study go even one step further; they consider the term cyberbullying “vague, inadequate and restricted” (Grigg, 2010, p. 151) because of the broad and varied set of negative incidents that can happen on the internet but that are not covered by this term. We argue that these investigations about subjects’ interpretation of the term “cyberbullying” can only show one side of the coin: Subjects evaluate the normative criteria of cyberbullying. But generations of students have been taught the definition of “bullying” in school. Therefore, it is not surprising that they consider incidents as “cyberbullying” that are consistent with this learned

[ { "first": "Christina", "middle": [], "last": "Kuhlmann", "suffix": "" }, { "first": "Stephanie", "middle": [], "last": "Pieschl", "suffix": "" }, { "first": "Torsten", "middle": [], "last": "Porsch", "suffix": "" } ]

2,013

CogSci

2402123379

[ "145729434", "91176579", "145002131", "153437833", "143362919", "25543287", "53587351", "145407566", "145560384", "20212850", "54196578", "6766480", "28942016", "276719", "19546900", "7533694", "151973194", "21270035" ]

[]

true

false

true

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0

1

0

[ [ "normative criterion", "EVALUATION" ], [ "prevention and intervention measure", "METHOD" ], [ "cyberspace", "APPLICATION" ], [ "power imbalance", "METHOD" ], [ "adaptive conjoint design", "METHOD" ], [ "multidimensional scaling analysis", "METHOD" ], [ "social", "METHOD" ], [ "electronic communication", "APPLICATION" ], [ "cyberbull", "APPLICATION" ], [ "cognitive representation", "VISUALIZATION" ], [ "cultural and temporal variability", "DATA" ], [ "cyber cruelty", "APPLICATION" ], [ "composite term", "DATA" ], [ "power im", "METHOD" ], [ "Cyberbullying", "APPLICATION" ], [ "cognitive representa", "DATA" ], [ "normative top-down definition", "METHOD" ], [ "cyberbullying", "APPLICATION" ], [ "Focus-group interview", "EVALUATION" ], [ "bottom-up perspective", "METHOD" ], [ "empirical investigation", "EVALUATION" ], [ "bullying", "APPLICATION" ], [ "Cyber Cruelty", "APPLICATION" ], [ "ber", "APPLICATION" ], [ "focus group study", "EVALUATION" ], [ "defining criterion", "EVALUATION" ], [ "cultural comparison", "APPLICATION" ], [ "negative incident", "DATA" ], [ "cyber-victims’ experience", "APPLICATION" ], [ "online cruelty", "APPLICATION" ], [ "tion; conjoint analysis", "METHOD" ], [ "conventional bullying", "APPLICATION" ], [ "theory-based top-down definition", "METHOD" ], [ "data", "METHOD" ], [ "cyber incident", "APPLICATION" ], [ "literal connot", "VISUALIZATION" ], [ "electronic mean", "APPLICATION" ] ]

An Example HyperVenn Proof

46,156,157

HyperVenn is a heterogeneous logic resulting from combining the individual homogeneous logics for Euler/Venn diagrams and blocks world diagrams. We provide an example proof from the system along with a brief discussion of some of its inference rules.

[ { "first": "Dave", "middle": [], "last": "Barker-Plummer", "suffix": "" }, { "first": "Nik", "middle": [], "last": "Swoboda", "suffix": "" }, { "first": "Michael", "middle": [], "last": "Murray", "suffix": "" } ]

2,014

10.1007/978-3-662-44043-8_8

Diagrams

76754608

[]

false

true

https://api.semanticscholar.org/CorpusID:46156157

0

[ [ "heterogeneous logic", "DATA" ], [ "HyperVen", "METHOD" ], [ "homogeneous logic", "DATA" ], [ "inference rule", "METHOD" ], [ "block world diagram", "VISUALIZATION" ], [ "Euler/Venn diagram", "VISUALIZATION" ], [ "proof", "EVALUATION" ] ]

AI Meets AI: Leveraging Query Executions to Improve Index Recommendations

186,335,282

State-of-the-art index tuners rely on query optimizer's cost estimates to search for the index configuration with the largest estimated execution cost improvement`. Due to well-known limitations in optimizer's estimates, in a significant fraction of cases, an index estimated to improve a query's execution cost, e.g., CPU time, makes that worse when implemented. Such errors are a major impediment for automated indexing in production systems. We observe that comparing the execution cost of two plans of the same query corresponding to different index configurations is a key step during index tuning. Instead of using optimizer's estimates for such comparison, our key insight is that formulating it as a classification task in machine learning results in significantly higher accuracy. We present a study of the design space for this classification problem. We further show how to integrate this classifier into the state-of-the-art index tuners with minimal modifications, i.e., how artificial intelligence (AI) can benefit automated indexing (AI). Our evaluation using industry-standard benchmarks and a large number of real customer workloads demonstrates up to 5x reduction in the errors in identifying the cheaper plan in a pair, which eliminates almost all query execution cost regressions when the model is used in index tuning.

[ { "first": "Bailu", "middle": [], "last": "Ding", "suffix": "" }, { "first": "Sudipto", "middle": [], "last": "Das", "suffix": "" }, { "first": "Ryan", "middle": [], "last": "Marcus", "suffix": "" }, { "first": "Wentao", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Surajit", "middle": [], "last": "Chaudhuri", "suffix": "" }, { "first": "Vivek", "middle": [], "last": "Narasayya", "suffix": "" } ]

2,019

10.1145/3299869.3324957

SIGMOD '19

2944240329

[ "6287870", "3156281", "11987155", "1914773", "9347114", "11344186", "8920513", "7779072", "12224671", "3156745", "1512363", "7085235", "85382", "9555573", "60501139", "2958771", "4650265", "15079915", "86821717", "9135465", "51882183", "15677153", "44076931", "11425108", "207608499", "8609713", "207238980", "13740328", "206594692", "7343126", "2757547", "16464797", "10139482", "44083654", "3815895", "5888795", "6628106", "6038777", "14030601", "51970952", "147613", "662423", "16148639", "32727533", "2722012", "4836343", "28759829", "10102355", "7150874", "8635810", "11137621", "10269331", "362467", "51613398", "7566916" ]

[ "212718750", "202548908", "202756124", "211132532", "207960520", "215415860" ]

true

https://api.semanticscholar.org/CorpusID:186335282

0

1

0

[ [ "artificial intelligence (AI)", "METHOD" ], [ "automated indexing", "APPLICATION" ], [ "5x reduction", "EVALUATION" ], [ "industry-standard benchmark", "EVALUATION" ], [ "design space", "METHOD" ], [ "optimizer's estimate", "APPLICATION" ], [ "CPU", "APPLICATION" ], [ "real customer work", "EVALUATION" ], [ "index configuration", "DATA" ], [ "query execution cost regression", "METHOD" ], [ "execution cost", "EVALUATION" ], [ "estimated execution cost improvement", "EVALUATION" ], [ "production system", "APPLICATION" ], [ "index tuning", "APPLICATION" ], [ "classification task", "APPLICATION" ], [ "machine learning", "METHOD" ], [ "query optimizer's cost estimate", "METHOD" ], [ "classification problem", "APPLICATION" ], [ "index tuner", "METHOD" ] ]

The Visual Exploration of Aggregate Similarity for Multi-dimensional Clustering

28,441,321

[ { "first": "James", "middle": [], "last": "Twellmeyer", "suffix": "" }, { "first": "Marco", "middle": [], "last": "Hutter", "suffix": "" }, { "first": "Michael", "middle": [], "last": "Behrisch", "suffix": "" }, { "first": "Jörn", "middle": [], "last": "Kohlhammer", "suffix": "" }, { "first": "Tobias", "middle": [], "last": "Schreck", "suffix": "" } ]

2,015

10.5220/0005304100400050

IVAPP

974537284

[ "110205", "6466681", "44838948", "1336216", "121230628", "10033495", "189826725", "52029982", "18876779", "14736197", "16242667", "8451881", "12891061", "13322435", "5340931", "448854", "17363900", "3436850", "14020417", "2629987", "195994", "2687114", "19018188", "2029012", "8117146", "2281975", "2215132", "7963076", "2574062", "195603137", "5967894", "15382587", "38225053" ]

[ "31414226", "52831636" ]

true

https://api.semanticscholar.org/CorpusID:28441321

1

[ [ "Multi-dimensional Clustering", "APPLICATION" ], [ "Visual Exploration", "VISUALIZATION" ] ]

Efficient CPU-based Volume Ray Tracing Techniques

205,571,969

Recent research on high-performance ray tracing has achieved real-time performance even for highly complex surface models already on a single PC. In this report, we provide an overview of techniques for extending real-time ray tracing also to interactive volume rendering. We review fast rendering techniques for different volume representations and rendering modes in a variety of computing environments. The physically-based rendering approach of ray tracing enables high image quality and allows for easily mixing surface, volume and other primitives in a scene, while fully accounting for all of their optical interactions. We present optimized implementations and discuss the use of upcoming high-performance processors for volume ray tracing.

[ { "first": "Gerd", "middle": [], "last": "Marmitt", "suffix": "" }, { "first": "Heiko", "middle": [], "last": "Friedrich", "suffix": "" }, { "first": "Philipp", "middle": [], "last": "Slusallek", "suffix": "" } ]

2,008

10.1111/j.1467-8659.2008.01179.x

Comput. Graph. Forum

2147311140

[]

[ "802241", "11619948", "15295253" ]

false

true

false

https://api.semanticscholar.org/CorpusID:205571969

null

[ [ "high-performance ray tracing", "APPLICATION" ], [ "fast rendering technique", "METHOD" ], [ "volume ray tracing", "APPLICATION" ], [ "volume representation", "DATA" ], [ "surface model", "DATA" ], [ "real-time performance", "EVALUATION" ], [ "physically-based rendering approach", "METHOD" ], [ "interactive volume rendering", "APPLICATION" ], [ "surface, volume", "DATA" ], [ "high image quality", "EVALUATION" ], [ "render", "DATA" ], [ "high", "METHOD" ], [ "optical interaction", "VISUALIZATION" ], [ "real-time ray tracing", "METHOD" ], [ "scene", "DATA" ] ]