Yamei/VISBank_Final · Datasets at Hugging Face

Active learning and search on low-rank matrices

7,766,009

Collaborative prediction is a powerful technique, useful in domains from recommender systems to guiding the scientific discovery process. Low-rank matrix factorization is one of the most powerful tools for collaborative prediction. This work presents a general approach for active collaborative prediction with the Probabilistic Matrix Factorization model. Using variational approximations or Markov chain Monte Carlo sampling to estimate the posterior distribution over models, we can choose query points to maximize our understanding of the model, to best predict unknown elements of the data matrix, or to find as many "positive" data points as possible. We evaluate our methods on simulated data, and also show their applicability to movie ratings prediction and the discovery of drug-target interactions.

[ { "first": "Dougal", "middle": [ "J." ], "last": "Sutherland", "suffix": "" }, { "first": "Barnabás", "middle": [], "last": "Póczos", "suffix": "" }, { "first": "Jeff", "middle": [], "last": "Schneider", "suffix": "" } ]

2,013

10.1145/2487575.2487627

KDD '13

2016342346

[ "408998", "2592820", "122377496", "518540", "16393297", "10660879", "12948548", "632612", "122533222", "10476513", "16798325", "7054948", "16095034", "17065002", "2023417", "2284921", "503367", "11569603", "9862002", "2420504", "14937958", "467086", "3260560", "5325699", "5048382", "14147390", "627438", "706026" ]

[ "201710375", "18548714", "35412312", "88516239", "15013533", "16137042", "4856866", "88515201", "53599768", "202541136", "14406993", "2401688", "209436400", "17774646", "22889797", "88521216", "53732021", "204766052", "202612928", "1090197", "13998324", "40771036", "3604371", "52107971", "7255923", "9291915", "3044479" ]

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[ [ "collaborative prediction", "APPLICATION" ], [ "query point", "DATA" ], [ "\"positive\" data point", "DATA" ], [ "movie rating prediction", "APPLICATION" ], [ "recommender system", "APPLICATION" ], [ "Collaborative prediction", "METHOD" ], [ "simulated data", "DATA" ], [ "Probabilistic Matrix Factorization model", "METHOD" ], [ "Markov chain Monte Carlo sampling", "METHOD" ], [ "drug-target interaction", "APPLICATION" ], [ "variational approximation", "METHOD" ], [ "Low-rank matrix factorization", "METHOD" ], [ "data matrix", "DATA" ], [ "scientific discovery process", "APPLICATION" ], [ "posterior distribution", "METHOD" ] ]

SmartMiner: a depth first algorithm guided by tail information for mining maximal frequent itemsets

10,447,344

Maximal frequent itemsets (MR) are crucial to many tasks in data mining. Since the MaxMiner algorithm first introduced enumeration trees for mining MR in 1998, several methods have been proposed to use depth first search to improve performance. To further improve the performance of mining MR, we proposed a technique that takes advantage of the information gathered from previous steps to discover new MR. More specifically, our algorithm called SmartMiner gathers and passes tail information and uses a heuristic select function which uses the tail information to select the next node to explore. Compared with Mafia and GenMax, SmartMiner generates a smaller search tree, requires a smaller number of support counting, and does not require superset checking. Using the datasets Mushroom and Connect, our experimental study reveals that SmartMiner generates the same MFI as Mafia and GenMax, but yields an order of magnitude improvement in speed.

[ { "first": "Qinghua", "middle": [], "last": "Zou", "suffix": "" }, { "first": "W.W.", "middle": [], "last": "Chu", "suffix": "" }, { "first": "Baojing", "middle": [], "last": "Lu", "suffix": "" } ]

2,002

10.1109/ICDM.2002.1184003

2002 IEEE International Conference on Data Mining, 2002. Proceedings.

2106021426

[ "18189609", "1158516", "9897773", "214764712", "212504238", "3489319", "10084934", "2317353", "10561854", "11259252" ]

[ "12268325", "1140539", "4472711", "8682490", "10003968", "7233159", "55794097", "26997837", "1121307", "17560845", "14947370", "1319637", "14738525", "6947277", "16970750", "206454396", "18086071", "18380444", "3204977", "1725218", "17822161", "18380314", "17875842", "8467505", "12450272", "17556868" ]

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ADMIT: anomaly-based data mining for intrusions

14,453,540

Security of computer systems is essential to their acceptance and utility. Computer security analysts use intrusion detection systems to assist them in maintaining computer system security. This paper deals with the problem of differentiating between masqueraders and the true user of a computer terminal. Prior efficient solutions are less suited to real time application, often requiring all training data to be labeled, and do not inherently provide an intuitive idea of what the data model means. Our system, called ADMIT, relaxes these constraints, by creating user profiles using semi-incremental techniques. It is a real-time intrusion detection system with host-based data collection and processing. Our method also suggests ideas for dealing with concept drift and affords a detection rate as high as 80.3% and a false positive rate as low as 15.3%.

[ { "first": "Karlton", "middle": [], "last": "Sequeira", "suffix": "" }, { "first": "Mohammed", "middle": [], "last": "Zaki", "suffix": "" } ]

2,002

10.1145/775047.775103

KDD '02

1979877030

[ "207670313", "12151424", "14327047", "2941443", "10028835", "17572268", "1666274", "125866813", "58843371", "61010993", "12305305", "10631594", "2191016", "7175720", "18900231", "2430214", "59717655" ]

[ "27719286", "36487201", "49349668", "35842716", "17316958", "6960953", "8934270", "67020444", "10076021", "210970531", "6362344", "3807574", "70030846", "70208528", "7585122", "14247622", "5477051", "18190017", "61811776", "17392352", "707033", "88524430", "10574408", "202349426", "16979786", "14165986", "51887805", "7206567", "44146492", "53398669", "42168435", "41515867", "28292522", "11481045", "14593421", "1887239", "15047316", "15340432", "4806351", "18427897", "40657143", "5472514", "52285867", "17773258", "13569241", "16615974", "16774809", "206577177", "14070482", "196059470", "214595961", "4937576", "15655528", "14881580", "14006795", "18566576", "145829981", "16341591", "42074260", "52213134", "14900426", "1824706", "210954027", "2686204", "3054963", "54586890", "1776767", "2768613", "2703866", "2273859", "14612530", "54054137", "28567886", "15982493", "9574885", "49864028", "49564741", "10355824", "14859085", "1565926", "50458504", "9562623", "16282953", "14415871", "56179059", "6807759", "6904263", "17385394", "18023732", "40946259", "34144379", "41927637", "16132583", "207191986", "16247079", "6428722", "16600904", "17645721", "208527535", "207112993", "19045857", "49688491", "18033661", "7085535", "209500650", "14370527", "16588614", "68232019", "17203933", "22051843", "1994416", "1016649", "7407773", "324008", "9385923", "9894749", "6732306", "2157817", "16754125", "56597699", "8642370" ]

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Conversational expectations account for apparent limits on theory of mind use.

18,153,063

[ { "first": "Robert", "middle": [ "X.", "D." ], "last": "Hawkins", "suffix": "" }, { "first": "Noah", "middle": [ "D." ], "last": "Goodman", "suffix": "" } ]

2,016

CogSci

2785399429

[ "1560164", "26797288", "147397593", "2804004", "2496804", "147274549", "11823067", "41817757", "34890492", "7635867", "10659981", "8523033", "97674389", "17214272", "141321709", "11269306" ]

[]

true

false

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1

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[ [ "mind use", "METHOD" ], [ "Conversational expectation", "METHOD" ] ]

Spatial Reasoning within Visible Functional Constraints

9,163,218

Spatial Reasoning within Visible Functional Constraints Georg Jahn ([email protected]) University of Greifswald, Department of Psychology, Franz-Mehring-Str. 47, 17489 Greifswald, Germany Miriam Muller-Bardorff ([email protected]) University of Greifswald, Department of Psychology, Franz-Mehring-Str. 47, 17489 Greifswald, Germany Kenny R. Coventry ([email protected]) Cognition and Communication Research Centre, School of Life Sciences, Northumbria University Newcastle upon Tyne, NE1 8ST, UK Abstract Spatial relational reasoning problems vary in difficulty depending on the number of possible interpretations of a set of premises. Visible context and functional constraints should take effect in the interpretation of extended spatial descriptions in reasoning problems as in the comprehension of single utterances. In this paper we demonstrate that visible and functional constraints mesh together on-line with verbal information to limit the number of interpretations considered by participants to solve spatial reasoning problems. In an eye tracking experiment, the interplay of visual and verbal information was studied in the domain of spatial relational reasoning. Standard verbal two-dimensional reasoning problems were presented auditorily along with visible context. In one condition, the visual-verbal interplay was designed to limit the number of interpretations that participants should consider for a set of premises. Past research has shown that visual context does not appear to limit the number of interpretations participants produce in this domain. In the present study, however, participants’ responses, premise processing times, and gaze behavior confirmed that the interplay of visual and verbal information successfully directed participants towards a single interpretation when functional constraints disambiguated spatial relations. The results corroborate theories of situated language processing and demonstrate perceptual grounding and functional modulation in spatial reasoning. Keywords: Spatial relational integration; mental models. reasoning; Spatial Relational Reasoning visual-verbal Situated Language Comprehension Comprehending language that refers to visible context involves rapid and even predictive shifts of attention towards likely referents in a visible scene. In situated language comprehension, perceptual and linguistic processing are closely intertwined (Altmann & Kamide, 1999; Knoeferle & Crocker, 2006). For example, perceptual-linguistic integration has been shown to quickly disambiguate syntactic structure, referents of noun phrases, and the interpretation of verbs. In spatial language, semantic uncertainty concerns locations and spatial relations. For example, “right of” often denotes a region rather than a specific location. Multiple additional constraints can take effect in spatial language processing to sharpen the interpretation of spatial descriptions. This includes the visible context as well as functional constraints (Coventry & Garrod, 2004). Whereas the interplay of perceptual, linguistic, and semantic processing has been studied extensively for the comprehension of single utterances including statements of spatial relations, there have been few attempts to demonstrate its effect on reasoning with spatial premises. In experiments on spatial relational reasoning, reasoners are asked to infer or evaluate spatial relations based on several stated spatial relations. For example: The apple is to the left of the banana, the carrot is to the right of the banana. Where is the apple with respect to the carrot? The spatial array or spatial mental model that satisfies the relations stated in the example is: A(pple) B(anana) C(arrot) The model yields the relation that holds between the apple and the carrot and thus the sought inference: The apple is to the left of the carrot. Such one-dimensional three-term series problems have been studied extensively with spatial and non-spatial relations (e.g., better and worse). Two- dimensional problems (Byrne & Johnson-Laird, 1989) have become standard tasks for studying spatial relational reasoning as well. Such problems are used here embedded in the context of planning seating arrangements for guests at tables. Table 1 shows three types of two-dimensional problems, which differ with regard to the number of alternative seating arrangements fulfilling the set of spatial premises. One arrangement is possible for one-model problems. Two arrangements are possible for the two-model problems, in which the second premise introduces this indeterminacy. In determinate two-model problems, the spatial relation between the guests in the bottom row (D and E) is the same in both possible arrangements (D sits to the left of E), whereas this relation differs between the arrangements for indeterminate two-model problems. Thus, the correct

[ { "first": "Georg", "middle": [], "last": "Jahn", "suffix": "" }, { "first": "Miriam", "middle": [], "last": "Müller-Bardorff", "suffix": "" }, { "first": "Kenny", "middle": [ "R." ], "last": "Coventry", "suffix": "" } ]

2,011

CogSci

2572311376

[ "1660615", "10127043", "124469332", "18129212" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:9163218

0

1

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[ [ "predict", "METHOD" ], [ "visible context", "DATA" ], [ "single u", "DATA" ], [ "spatial reasoning problem", "APPLICATION" ], [ "spatial relational reasoning", "APPLICATION" ], [ "gaze behavior", "EVALUATION" ], [ "seating arrangement", "APPLICATION" ], [ "spatial language", "APPLICATION" ], [ "ating", "VISUALIZATION" ], [ "functional modulation", "METHOD" ], [ "two-dimensional problem", "APPLICATION" ], [ "one-model problem", "APPLICATION" ], [ "noun phrase", "DATA" ], [ "spatial language processing", "METHOD" ], [ "two-model problem", "APPLICATION" ], [ "Functional", "APPLICATION" ], [ "perceptual grounding", "METHOD" ], [ "visual-verbal interplay", "METHOD" ], [ "reasoning problem", "APPLICATION" ], [ "syntactic structure", "DATA" ], [ "relational reasoning problem", "APPLICATION" ], [ "visible scene", "DATA" ], [ "determinate two-model problem", "APPLICATION" ], [ "information", "DATA" ], [ "visible and functional constraint", "DATA" ], [ "semantic uncertainty", "APPLICATION" ], [ "single utterance", "DATA" ], [ "spatial reasoning", "METHOD" ], [ "spatial description", "DATA" ], [ "visual context", "VISUALIZATION" ], [ "functional constraint", "METHOD" ], [ "premise processing time", "EVALUATION" ], [ "Spatial Relational Reason", "METHOD" ], [ "one-dimensional three-term series problem", "APPLICATION" ], [ "situated language comprehension", "APPLICATION" ], [ "spatial premise", "DATA" ], [ "Spatial Reasoning", "METHOD" ], [ "eye tracking experiment", "EVALUATION" ], [ "indeterminate two-model problem", "APPLICATION" ], [ "verbal information", "DATA" ], [ "situated language processing", "METHOD" ], [ "spatial relation", "DATA" ], [ "spatial and non-spatial relation", "METHOD" ], [ "spatial array", "METHOD" ], [ "spatial mental model", "METHOD" ], [ "perceptual-linguistic integration", "METHOD" ], [ "perceptual and linguistic processing", "METHOD" ], [ "integration; mental model", "METHOD" ], [ "Two- dimensional problem", "APPLICATION" ], [ "two-dimensional reasoning problem", "APPLICATION" ], [ "Visible context", "METHOD" ], [ "perceptual, linguistic, and semantic processing", "METHOD" ] ]

Vague One-Class Learning for Data Streams

16,919,654

In this paper, we formulate a new research problem of learning from vaguely labeled one-class data streams, where the main objective is to allow users to label instance groups, instead of single instances, as positive samples for learning. The batch-labeling, however, raises serious issues because labeled groups may contain non-positive samples, and users may change their labeling interests at any time. To solve this problem, we propose a Vague One-Class Learning (VOCL) framework which employs a double weighting approach, at both instance and classifier levels, to build an ensembling framework for learning. At instance level, both local and global filterings are considered for instance weight adjustment. Two solutions are proposed to take instance weight values into the classifier training process. At classifier level, a weight value is assigned to each classifier of the ensemble to ensure that learning can quickly adapt to users’ interests. Experimental results on synthetic and real-world data streams demonstrate that the proposed VOCL framework significantly outperforms other methods for vaguely labeled one-class data streams.

[ { "first": "Xingquan", "middle": [], "last": "Zhu", "suffix": "" }, { "first": "Xindong", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Chengqi", "middle": [], "last": "Zhang", "suffix": "" } ]

2,009

10.1109/ICDM.2009.70

2009 Ninth IEEE International Conference on Data Mining

2169797554

[ "1662330", "15112547", "18856538", "2545890", "1720166", "118335233", "2110475", "3958127", "4095446", "6416602", "11868540", "5874117", "1582462", "56776745", "53915833", "15489782", "5944615", "9929910", "140109802", "961425" ]

[ "1481577", "17384966", "185617", "3121576", "11532872", "11702328", "11623844", "7674290", "1828513", "11300870", "32551158" ]

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https://api.semanticscholar.org/CorpusID:16919654

0

1

0

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Versions and workspaces in Microsoft repository

11,963,942

This paper describes the version and workspace features of Microsoft Repository, a layer that implements fine-grained objects and relationships on top of Microsoft SQL Server. It supports branching and merging of versions, delta storage, checkout-checkin, and single-version views for version-unaware applications.

[ { "first": "Thomas", "middle": [], "last": "Bergstraesser", "suffix": "" }, { "first": "Philip", "middle": [ "A." ], "last": "Bernstein", "suffix": "" }, { "first": "Shankar", "middle": [], "last": "Pal", "suffix": "" }, { "first": "David", "middle": [], "last": "Shutt", "suffix": "" } ]

1,999

10.1145/304182.304248

SIGMOD '99

2003981282

[ "2013843", "10250124", "960864" ]

[ "526474", "15551650", "520140", "32949677", "27952629", "15001576", "12984231", "20114754", "1616351" ]

true

https://api.semanticscholar.org/CorpusID:11963942

0

1

0

[ [ "single-version view", "VISUALIZATION" ], [ "SQL Server", "METHOD" ], [ "version-unaware application", "APPLICATION" ], [ "relationship", "DATA" ], [ "Microsoft Repository", "METHOD" ], [ "delta storage", "METHOD" ], [ "fine-grained object", "DATA" ], [ "checkout-checkin", "METHOD" ] ]

U-Air: when urban air quality inference meets big data

2,826,277

Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.

[ { "first": "Yu", "middle": [], "last": "Zheng", "suffix": "" }, { "first": "Furui", "middle": [], "last": "Liu", "suffix": "" }, { "first": "Hsun-Ping", "middle": [], "last": "Hsieh", "suffix": "" } ]

2,013

10.1145/2487575.2488188

KDD '13

1971402834

[ "140703572", "5852377", "277918", "15374171", "16237925", "7464925", "55939985", "127961113", "3329660", "10977786", "207189603", "14906936", "17026487" ]

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true

https://api.semanticscholar.org/CorpusID:2826277

0

1

0

[ [ "artificial neural network (ANN),", "METHOD" ], [ "classical dispersion model", "METHOD" ], [ "CRF", "METHOD" ], [ "human mobility", "DATA" ], [ "linear-chain conditional random field (CRF", "METHOD" ], [ "air quality", "EVALUATION" ], [ "tensive experiment", "EVALUATION" ], [ "separated classifier", "METHOD" ], [ "spatial correlation", "METHOD" ], [ "spatial classifier", "METHOD" ], [ "time and fine-grained air quality information", "DATA" ], [ "traffic volume", "DATA" ], [ "real-time) air quality data", "DATA" ], [ "semi-supervised learning approach", "METHOD" ], [ "classification model", "METHOD" ], [ "temporal classifier", "METHOD" ], [ "network", "DATA" ], [ "ANN", "METHOD" ], [ "feature", "DATA" ], [ "air pollution", "APPLICATION" ], [ "air-quality-monitor-station", "METHOD" ], [ "linear/Gaussian interpolation", "METHOD" ], [ "traffic flow", "DATA" ], [ "decision tree", "METHOD" ], [ "temporal dependency", "DATA" ], [ "co-training framework", "METHOD" ], [ "real data source", "DATA" ], [ "meteorology", "DATA" ], [ "urban air quality", "EVALUATION" ], [ "point of interest", "DATA" ] ]

Codex: a dual screen tablet computer

1,420,227

The Codex is a dual-screen tablet computer, about the size of a 4"x 6 day planner, with a self-supporting binding and embedded sensors. The device can be oriented in a variety of postures to support different nuances of individual work, ambient display, or collaboration with another user. In the context of a pen-operated note taking application, we demonstrate interaction techniques that support a fluid division of labor for tasks and information across the two displays while minimizing disruption to the primary experience of authoring notes.

[ { "first": "Ken", "middle": [], "last": "Hinckley", "suffix": "" }, { "first": "Morgan", "middle": [], "last": "Dixon", "suffix": "" }, { "first": "Raman", "middle": [], "last": "Sarin", "suffix": "" }, { "first": "Francois", "middle": [], "last": "Guimbretiere", "suffix": "" }, { "first": "Ravin", "middle": [], "last": "Balakrishnan", "suffix": "" } ]

2,009

10.1145/1518701.1518996

CHI

2155107494

[ "1098368", "207200039", "16017604", "6343977", "110094395", "107987430", "8389547", "9203531", "144954448", "14513538", "3032531", "207631536", "7115124", "33257537", "7370189", "6197597", "15062698", "27801713", "207177544", "28962932", "8631092", "53248660", "1914460", "6286353" ]

[ "15959211", "14530839", "140268364", "10127879", "13325391", "14765743", "11279365", "54739007", "17783610", "28390562", "2711064", "2158025", "140239258", "108314455", "1332914", "18304693", "54598341", "5041134", "9352852", "49376237", "10570398", "86849518", "214736388", "11982723", "52979365", "19020014", "30340928", "201706283", "17446667", "85504503", "1208034", "5694680", "19550226", "40525170", "7621127", "190229598", "47020385", "6046124", "9831421", "142747", "11124788", "2260787", "204970856", "9283908", "1944068", "1439559", "52096407" ]

true

https://api.semanticscholar.org/CorpusID:1420227

0

1

0

[ [ "fluid division", "METHOD" ], [ "ambient display", "APPLICATION" ], [ "interaction technique", "METHOD" ], [ "dual-screen tablet computer", "METHOD" ], [ "pen-operated note taking application", "APPLICATION" ], [ "self-supporting binding", "METHOD" ], [ "embedded sensor", "METHOD" ] ]

Adaptive multi-stage distance join processing

214,792,581

A spatial distance join is a relatively new type of operation introduced for spatial and multimedia database applications. Additional requirements for ranking and stopping cardinality are often combined with the spatial distance join in on-line query processing or internet search environments. These requirements pose new challenges as well as opportunities for more efficient processing of spatial distance join queries. In this paper, we first present an efficient k-distance join algorithm that uses spatial indexes such as R-trees. Bi-directional node expansion and plane-sweeping techniques are used for fast pruning of distant pairs, and the plane-sweeping is further optimized by novel strategies for selecting a sweeping axis and direction. Furthermore, we propose adaptive multi-stage algorithms for k-distance join and incremental distance join operations. Our performance study shows that the proposed adaptive multi-stage algorithms outperform previous work by up to an order of magnitude for both k-distance join and incremental distance join queries, under various operational conditions.

[ { "first": "Hyoseop", "middle": [], "last": "Shin", "suffix": "" }, { "first": "Bongki", "middle": [], "last": "Moon", "suffix": "" }, { "first": "Sukho", "middle": [], "last": "Lee", "suffix": "" } ]

2,000

10.1145/335191.335428

SIGMOD 2000

2072145795

[]

[ "911775", "15915928", "61614043", "8510271", "5871901", "2805562", "15070519", "5824969", "17354070", "767303", "16830366", "16763059", "7497876", "16245057", "1463169", "3042735", "17660967", "3193010", "5141661", "211081420", "12232617", "9935858", "1441781", "7498688", "8546436", "14572621", "2396675", "16817872", "3524875", "16053054", "2383234", "2266285", "6734608", "5875070", "17011627", "9850672", "41898016", "5722926", "8155733", "892301", "5458340", "29047802", "1744642", "28617102" ]

false

true

false

https://api.semanticscholar.org/CorpusID:214792581

null

[ [ "plane-sweeping technique", "METHOD" ], [ "adaptive multi-stage algorithm", "METHOD" ], [ "incremental distance join operation", "APPLICATION" ], [ "k-distance join", "APPLICATION" ], [ "incremental distance join", "APPLICATION" ], [ "performance study", "EVALUATION" ], [ "internet search environment", "APPLICATION" ], [ "plane-sweeping", "METHOD" ], [ "Bi-directional node expansion", "METHOD" ], [ "R-trees", "METHOD" ], [ "fast pruning of distant pair", "APPLICATION" ], [ "order of magnitud", "EVALUATION" ], [ "spatial distance join", "METHOD" ], [ "k-distance join algorithm", "METHOD" ], [ "spatial and multimedia database application", "APPLICATION" ], [ "spatial index", "METHOD" ], [ "spatial distance join query", "APPLICATION" ], [ "on-line query processing", "APPLICATION" ] ]

Influence sets based on reverse nearest neighbor queries

214,792,706

Inherent in the operation of many decision support and continuous referral systems is the notion of the “influence” of a data point on the database. This notion arises in examples such as finding the set of customers affected by the opening of a new store outlet location, notifying the subset of subscribers to a digital library who will find a newly added document most relevant, etc. Standard approaches to determining the influence set of a data point involve range searching and nearest neighbor queries. In this paper, we formalize a novel notion of influence based on reverse neighbor queries and its variants. Since the nearest neighbor relation is not symmetric, the set of points that are closest to a query point (i.e., the nearest neighbors) differs from the set of points that have the query point as their nearest neighbor (called the reverse nearest neighbors). Influence sets based on reverse nearest neighbor (RNN) queries seem to capture the intuitive notion of influence from our motivating examples. We present a general approach for solving RNN queries and an efficient R-tree based method for large data sets, based on this approach. Although the RNN query appears to be natural, it has not been studied previously. RNN queries are of independent interest, and as such should be part of the suite of available queries for processing spatial and multimedia data. In our experiments with real geographical data, the proposed method appears to scale logarithmically, whereas straightforward sequential scan scales linearly. Our experimental study also shows that approaches based on range searching or nearest neighbors are ineffective at finding influence sets of our interest.

[ { "first": "Flip", "middle": [], "last": "Korn", "suffix": "" }, { "first": "Shanmugavelayutham", "middle": [], "last": "Muthukrishnan", "suffix": "" } ]

2,000

10.1145/335191.335415

SIGMOD 2000

2076287166

[]

[ "12258838", "1093040", "54602068", "347597", "14949826", "11843277", "10065543", "18534911", "16638315", "10129324", "15988909", "14310824", "12715487", "9503429", "15435659", "14672854", "13645146", "59327184", "17776759", "33144465", "16695141", "14442760", "6007133", "17005453", "44051347", "17343815", "51983293", "7618612", "16381760", "30365816", "18930638", "307925", "1995156", "9979072", "214594232", "39979431", "15893402", "18932460", "456619", "6541952", "18330324", "3102692", "11055048", "808572", "12140608", "786487", "16828522", "54457237", "17032745", "6620946", "14666229", "14216516", "13294355", "53373328", "8332299", "1106179", "10139613", "2718376", "18594508", "5056267", "15527391", "16925769", "14764858", "15100529", "13883217", "21695271", "26647541", "1275479", "65436044", "1666760", "14428128", "17527449", "15585367", "54999049", "13979208", "18967862", "208617801", "208329594", "38635570", "18656966", "7033410", "7272352", "27408913", "9423980", "17771531", "14115110", "11395596", "208285", "6618797", "4785107", "12020004", "32073151", "13894465", "8594166", "16477785", "9738057", "1362162", "61779", "197671882", "2533373", "9129300", "17406209", "327672", "10487566", "27363851", "43405754", "65474290", "16485281", "10989643", "11126642", "14195730", "34647625", "11562571", "3482767", "11582351", "17090648", "12269116", "29641412", "3724162", "212703446", "199509317", "53040591", "53024759", "37120641", "15249587", "214596537", "54702214", "15826374", "67856357", "3526672", "18784585", "1308429", "39032089", "1698110", "10152376", "15616859", "1693693", "3102988", "33643804", "199510938", "3398773", "53716607", "14199865", "17085083", "4518551", "16471794", "10381798", "52161057", "14607273", "16117092", "18175648", "6000340", "13042186", "198986097", "888929", "19522445", "28330014", "38860317", "32797214", "3262795", "7502009", "14825845", "18534364", "1213876", "42761740", "16031296", "15237478", "33148171", "28286452", "15454668", "29751203", "17575248", "23549573", "7199270", "10561015", "5908978", "3484707", "5762162", "205217477", "1805432", "201694011", "16580963", "889152", "7562649", "34834764", "16621455", "13951707", "12287599", "6957054", "2991428", "62141315", "14351790", "18377520", "52214027", "53034458", "88841", "1446769", "15491140", "17516901", "14676219", "13430516", "30312999", "17223086", "15708215", "11741647", "252867", "8088856", "207728458", "6683972", "10114115", "11766406", "2052482", "24750603", "23115512", "10844954", "16693257", "14663232", "106410506", "3524571", "17902885", "53079957", "1582891", "7395393", "17133399", "8912490", "10742401", "17188733", "18785567", "214691670", "18777935", "21742297", "12394943", "56455186", "14045762", "52278826", "17727463", "5363634", "124440846", "16053054", "8965090", "6784759", "3455572", "44639234", "17105104", "8244696", "16917848", "17003435", "6726385", "9595072", "6116682", "49680", "56532253", "21938862", "8880310", "174820869", "6304926", "86297", "1845119", "2704486", "13066204", "16536579", "13424530", "200373", "15205301", "10440027", "16545062", "35485335", "29169648", "14012169", "135453732", "4900811", "16144033", "2480508", "16996362", "61412194", "1923526", "17158486", "10399782", "9022362", "15136990", "17507860", "7099899", "11321839", "10960242", "1917822", "3175057", "42532243", "16855877", "8834947", "6144732", "22887149", "17436435", "10854550", "20712943", "591989", "1338783", "1196574", "201733038", "5212244", "52074529", "42740602", "5963138", "16142861", "10463643", "16096996", "3182095", "9558652", "195294807", "559318", "1644240", "31900020", "9494436" ]

false

true

false

https://api.semanticscholar.org/CorpusID:214792706

null

[ [ "data point", "DATA" ], [ "influence set", "DATA" ], [ "RNN", "METHOD" ], [ "set", "DATA" ], [ "reverse neighbor query", "METHOD" ], [ "RNN query", "APPLICATION" ], [ "experimental study", "EVALUATION" ], [ "reverse nearest neighbor", "METHOD" ], [ "R-tree based method", "METHOD" ], [ "nearest neighbor query", "METHOD" ], [ "document", "DATA" ], [ "decision support", "APPLICATION" ], [ "continuous referral system", "APPLICATION" ], [ "digital library", "DATA" ], [ "spatial and multimedia data", "DATA" ], [ "reverse nearest neighbor (RNN) query", "METHOD" ], [ "sequential scan", "METHOD" ], [ "nearest neighbor", "METHOD" ], [ "geographical data", "DATA" ], [ "nearest neighbor relation", "METHOD" ], [ "range searching", "METHOD" ] ]

A study of transitive closure as a recursion mechanism

38,874,672

We show that every linearly recursive query can be expressed as a transitive closure possibly preceded and followed by operations already available in relational algebra. This reduction is possible even if there are repeated variables in the recursive literals and if some of the arguments in the recursive literals are constants. Such an equivalence has significant theoretical and practical ramifications. One the one hand it influences the design of expressive notations to capture recursion as an augmentation of relational query languages. On the other hand implementation of deductive databases is impacted in that the design does not have to provide the generality that linear recursion would demand. It suffices to study the single problem of transitive closure and to provide an efficient implementation for it.

[ { "first": "H.", "middle": [ "V." ], "last": "Jagadish", "suffix": "" }, { "first": "Rakesh", "middle": [], "last": "Agrawal", "suffix": "" }, { "first": "Linda", "middle": [], "last": "Ness", "suffix": "" } ]

1,987

10.1145/38713.38750

SIGMOD '87

2013951929

[]

[ "15218136", "592840", "8207294", "7755027", "8465220", "9019095", "18626062", "160010021", "3121193", "13384535", "184482858", "12750309", "31262762", "18097391", "14689245", "35506421", "13389512", "6998870", "60821235", "8936046", "6685516", "16233955", "11134002", "3391477", "14701584", "13744316", "37712575", "14679554", "8684515", "18780660", "60747095", "15201118", "13570661", "556308", "20860892", "14342131" ]

false

true

https://api.semanticscholar.org/CorpusID:38874672

0

[ [ "linear recursion", "METHOD" ], [ "relational query language", "METHOD" ], [ "efficient implementation", "METHOD" ], [ "recursive", "APPLICATION" ], [ "recursive literal", "METHOD" ], [ "expressive notation", "METHOD" ], [ "relational algebra", "APPLICATION" ], [ "deductive database", "DATA" ], [ "transitive closure", "VISUALIZATION" ], [ "ed variable", "DATA" ], [ "practical ram", "EVALUATION" ] ]

Hubbub: a sound-enhanced mobile instant messenger that supports awareness and opportunistic interactions

9,705,357

There have been many attempts to support awareness and lightweight interactions using video and audio, but few have been built on widely available infrastructure. Text-based systems have become more popular, but few support awareness, opportunistic conversations, and mobility, three important elements of distributed collaboration. We built on the popularity of text-based Instant Messengers (IM) by building a mobile IM called Hubbub that tries to provide all three, notably through the use of earcons. In a 5.5-month use study, we found that Hubbub helped people feel connected to colleagues in other locations and supported opportunistic interactions. The sounds provided effective awareness cues, although some found them annoying. It was more important to support graceful transitions between multiple fixed locations than to support wireless access, although both were useful

[ { "first": "Ellen", "middle": [], "last": "Isaacs", "suffix": "" }, { "first": "Alan", "middle": [], "last": "Walendowski", "suffix": "" }, { "first": "Dipti", "middle": [], "last": "Ranganthan", "suffix": "" } ]

2,002

10.1145/503376.503409

CHI '02

2037390419

[ "207200039", "14845169", "1387183", "110478504", "1746888", "59766166", "10512266", "44252376", "7837039", "3356174", "18155214", "18062230", "5539580", "23001061", "166946338", "2652975", "10355154", "2909871", "5530079" ]

[ "17006818", "18358202", "16317343", "62204935", "16205064", "1834235", "6856924", "15080976", "5398686", "17299072", "15421436", "8155763", "2304209", "12208853", "113240477", "30760677", "16840544", "16162696", "26215582", "6255940", "8873031", "17633871", "18750599", "15924625", "1681916", "5213310", "17718597", "13427624", "15400558", "9152765", "10791855", "9985969", "14652186", "13383955", "55227274", "17326299", "6006584", "16207936", "15005167", "1464964", "17757568", "3148516", "18023718", "61450803", "3131066", "16758183", "154863891", "1609696", "7329904", "6672787", "3899470", "14606722", "16421358", "3090976", "2857026", "12745299", "8929091", "8873689", "16875469", "16945358", "15459424", "32642211", "18602167", "2744523", "14663364", "14663364", "2103297", "17701914", "15215642", "1636860", "2992344", "202123071", "14969032", "10031857", "9767272", "6412905", "39172646", "7288673", "38166992", "11148312", "1042556", "8184140", "6661751", "2325165", "1634889", "2656", "59945927", "43606302", "52066859", "110100246" ]

true

https://api.semanticscholar.org/CorpusID:9705357

0

1

0

[ [ "opportunistic interaction", "APPLICATION" ], [ "distributed collaboration", "APPLICATION" ], [ "opportunistic conversation", "APPLICATION" ], [ "5.5-month use study", "EVALUATION" ], [ "awareness cue", "EVALUATION" ], [ "mobile IM", "APPLICATION" ], [ "Text-based system", "METHOD" ], [ "lightweight interaction", "APPLICATION" ], [ "graceful transition", "APPLICATION" ], [ "earcons", "METHOD" ], [ "wireless access", "APPLICATION" ], [ "text-based Instant Messengers (IM)", "METHOD" ] ]

Jumping in Japanese: Converting linguistic instructions into physical performances.

36,313,595

[ { "first": "Chie", "middle": [], "last": "Fukada", "suffix": "" }, { "first": "Noriyuki", "middle": [], "last": "Kida", "suffix": "" }, { "first": "Hiromichi", "middle": [], "last": "Hagihara", "suffix": "" }, { "first": "Takatsugu", "middle": [], "last": "Kojima", "suffix": "" } ]

2,017

CogSci

2785422776

[]

false

true

https://api.semanticscholar.org/CorpusID:36313595

0

[ [ "physical performance", "APPLICATION" ], [ "linguistic instruction", "METHOD" ] ]

'A bit like British Weather, I suppose': Design and Evaluation of the Temperature Calendar

2,217,085

In this paper we present the design and evaluation of the Temperature Calendar -- a visualization of temperature variation within a workplace over the course of the past week. This highlights deviation from organizational temperature policy, and aims to bring staff "into the loop" of understanding and managing heating, and so reduce energy waste. The display was deployed for three weeks in five public libraries. Analysis of interaction logs, questionnaires and interviews shows that staff used the displays to understand heating in their buildings, and took action reflecting this new understanding. Bringing together our results, we discuss design implications for workplace displays, and an analysis of carbon emissions generated in constructing and operating our design. More in general, the findings helped us to reflect on the role of policy on energy consumption, and the potential for the HCI community to engage with its application, as well as its definition or modification.

[ { "first": "Enrico", "middle": [], "last": "Costanza", "suffix": "" }, { "first": "Ben", "middle": [], "last": "Bedwell", "suffix": "" }, { "first": "Michael", "middle": [ "O." ], "last": "Jewell", "suffix": "" }, { "first": "James", "middle": [], "last": "Colley", "suffix": "" }, { "first": "Tom", "middle": [], "last": "Rodden", "suffix": "" } ]

2,016

10.1145/2858036.2858367

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

2408072812

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true

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1

[ [ "interaction log", "DATA" ], [ "carbon emission", "DATA" ], [ "energy waste", "EVALUATION" ], [ "Temperature Calendar", "VISUALIZATION" ], [ "questionnaire", "EVALUATION" ], [ "HCI community", "APPLICATION" ], [ "energy consump", "APPLICATION" ], [ "temperature variation", "DATA" ], [ "heat", "APPLICATION" ], [ "organizational temperature policy", "METHOD" ], [ "workplace display", "APPLICATION" ] ]

Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses

569,848

We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes. In sentence comprehension, it is widely assumed that the distance between linguistic co-dependents affects the latency of dependency resolution: the longer the distance, the longer the retrieval time (the distance-based account). An alternative theory, direct-access, assumes that retrieval times are a mixture of two distributions: one distribution represents successful retrievals (these are independent of dependency distance) and the other represents an initial failure to retrieve the correct dependent, followed by a reanalysis that leads to successful retrieval. We implement both models as Bayesian hierarchical models and show that the direct-access model explains Chinese relative clause reading time data better than the distance account.

[ { "first": "Shravan", "middle": [], "last": "Vasishth", "suffix": "" }, { "first": "Nicolas", "middle": [], "last": "Chopin", "suffix": "" }, { "first": "Robin", "middle": [], "last": "Ryder", "suffix": "" }, { "first": "Bruno", "middle": [], "last": "Nicenboim", "suffix": "" } ]

2,017

1702.00564

ArXiv

2952759491,2962824719,2587488591

[ "122830292", "15192218", "8389938", "15875009", "6434882", "15495668", "17259421", "1446984", "36568061" ]

[]

true

false

true

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1

[ [ "sentence comprehension", "APPLICATION" ], [ "case-study", "EVALUATION" ], [ "linguis", "DATA" ], [ "dependency distance", "DATA" ], [ "distance-based account", "METHOD" ], [ "direct-access model", "METHOD" ], [ "direct-access", "METHOD" ], [ "Bayesian hierarchical mixture modelling", "METHOD" ], [ "dependent", "DATA" ], [ "Chinese relative clause reading time data", "DATA" ], [ "Bayesian hierarchical model", "METHOD" ], [ "reanalys", "METHOD" ], [ "dependency resolution", "APPLICATION" ], [ "distance account", "METHOD" ], [ "cognitive process", "APPLICATION" ] ]

Experimental Analysis of Barehand Mid-air Mode-Switching Techniques in Virtual Reality

140,448,398

We present an empirical comparison of eleven bare hand, mid-air mode-switching techniques suitable for virtual reality in two experiments. The first evaluates seven techniques spanning dominant and non-dominant hand actions. Techniques represent common classes of actions selected by a methodical examination of 56 examples of prior art. The standard "subtraction method" protocol is adapted for 3D interfaces, with two baseline selection methods, bare hand pinch and device controller button. A second experiment with four techniques explores more subtle dominant-hand techniques and the effect of using a dominant hand device for selection. Results provide guidance to practitioners when choosing bare hand, mid-air mode-switching techniques, and for researchers when designing new mode-switching methods in VR.

[ { "first": "Hemant", "middle": [ "Bhaskar" ], "last": "Surale", "suffix": "" }, { "first": "Fabrice", "middle": [], "last": "Matulic", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Vogel", "suffix": "" } ]

2,019

10.1145/3290605.3300426

CHI '19

2941563158

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[ "214691248" ]

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0

1

0

[ [ "mid-air mode-switching technique", "METHOD" ], [ "subtraction method\"", "METHOD" ], [ "dominant-hand technique", "METHOD" ], [ "virtual reality", "APPLICATION" ], [ "dominant hand device", "METHOD" ], [ "methodical examination", "EVALUATION" ], [ "bare hand, mid-air mode-switching technique", "METHOD" ], [ "3D interface", "APPLICATION" ], [ "empirical comparison", "EVALUATION" ], [ "baseline selection method", "METHOD" ], [ "mode-switching method", "METHOD" ], [ "bare hand pinch", "METHOD" ], [ "device controller button", "METHOD" ], [ "dominant and non-dominant hand action", "METHOD" ] ]

Effects of experience in a developmental model of reading

3,066,265

There is considerable evidence showing that age of acquisition (AoA) is an important factor influencing lexical processing. Early-learned words tend to be processed more quickly compared to later-learned words. The effect could be due to the gradual reduction in plasticity as more words are learned. Alternatively, it could originate from differences within semantic representations. We implemented the triangle model of reading including orthographic, phonological and semantic processing layers, and trained it according to experience of a language learner to explore the AoA effects in both naming and lexical decision. Regression analyses on the model’s performance showed that AoA was a reliable predictor of naming and lexical decision performance, and the effect size was larger for lexical decision than for naming. The modelling results demonstrate that AoA operates differentially on concrete and abstract words, indicating that both the mapping and the representation accounts of AoA were contributing to the model’s performance.

[ { "first": "Ya-Ning", "middle": [], "last": "Chang", "suffix": "" }, { "first": "Padraic", "middle": [], "last": "Monaghan", "suffix": "" } ]

2,016

CogSci

2565541063

[ "8415125", "4835404", "34985620", "30175694", "14652785", "5816266", "6667873", "4837948", "3712075", "11754544", "18274480", "252796", "2146137", "16753815", "14470408", "14769856", "6000627", "41089024", "210791166", "144705169", "59786205", "8030958", "14426294" ]

[]

true

false

true

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0

1

0

[ [ "semantic representation", "METHOD" ], [ "ity", "EVALUATION" ], [ "lexical decision", "APPLICATION" ], [ "lexical decision performance", "EVALUATION" ], [ "abstract word", "DATA" ], [ "lexical processing", "APPLICATION" ], [ "gradual reduction", "EVALUATION" ], [ "representation account", "METHOD" ], [ "triangle model", "METHOD" ], [ "gression analyse", "EVALUATION" ], [ "orthographic, phonological and semantic processing layer", "METHOD" ], [ "modelling result", "EVALUATION" ], [ "AoA", "METHOD" ], [ "concrete", "DATA" ], [ "naming", "APPLICATION" ] ]

Eye Strain from Switching Focus in Optical See-Through Displays

44,363,074

The optical see-through (OST) display is one of the key enabling devices for augmented reality. Despite the latest craze such as with the Google Glass, there are still many ergonomic problems associated with the OST displays. One of the already well known such problem is the “refocusing” problem, in which the user has to switch one’s focus between the distant real world and see-through display up front. Such refocusing, for one, is bound to cause significant strain and fatigue to the eyes. However, there are not many studies, nor guidelines devoted to this issue. In this preliminary work, we ran experiments to measure the degree for eye strain and its pattern at different refocusing distances and durations (or number of focused targets). The findings should serve as one guideline in designing OST glass based interaction and applications.

[ { "first": "Jaeun", "middle": [], "last": "Yu", "suffix": "" }, { "first": "Gerard", "middle": [ "J." ], "last": "Kim", "suffix": "" } ]

2,015

10.1007/978-3-319-22723-8_59

INTERACT

1772442425

[]

[ "22977381" ]

false

true

false

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null

[ [ "augmented reality", "APPLICATION" ], [ "Google Glass", "METHOD" ], [ "ergonomic problem", "EVALUATION" ], [ "see-through display", "VISUALIZATION" ], [ "optical see-through (OST) display", "METHOD" ], [ "work", "EVALUATION" ], [ "OST glass based interaction", "APPLICATION" ], [ "significant strain and fatigue", "EVALUATION" ], [ "refocusing” problem", "METHOD" ], [ "OST display", "METHOD" ], [ "eye strain", "EVALUATION" ] ]

Motion event expressions in language and gesture: Evidence from Persian.

4,687,474

How do people conceptualize motion events and talk about them? The current study examines how gestural representations of motion events arise from linguistic expressions in Persian, which has characteristics of both Talmy’s satelliteand verb-framed languages. We examined native Persian speakers’ speech and gestures in describing 20 motion events. We focused on two motion event components: path (trajectory of motion like up) and manner (how the action is performed like jumping). Results indicated that when expressing motion, Persian speakers produced path in both speech and gesture, whereas manner was conveyed only through speech (mostly as adverbs). Additionally, dynamic gestures tended to occur in the same order they were uttered. The difference between path and manner findings asks for further research to examine language-gesture interaction in detail among different languages. Results also suggest refinement in gesture theories that argue for one-to-one correspondence between speech and gesture.

[ { "first": "Niloofar", "middle": [], "last": "Akhavan", "suffix": "" }, { "first": "Nazbanou", "middle": [], "last": "Nozari", "suffix": "" }, { "first": "Tilbe", "middle": [], "last": "Göksun", "suffix": "" } ]

2,015

CogSci

2402637811

[ "42104976", "145604008", "19783538", "32102175", "70536953", "210502881", "60964547", "371637", "140524717", "55508743", "45181499", "143170727", "142797419", "2372003", "141252321", "59628367" ]

[ "12330411" ]

true

https://api.semanticscholar.org/CorpusID:4687474

0

1

0

[ [ "Persi", "DATA" ], [ "gesture theory", "APPLICATION" ], [ "dynamic gesture", "METHOD" ], [ "one-to-one correspondence", "METHOD" ], [ "gestural representation", "METHOD" ], [ "verb-framed language", "METHOD" ], [ "language-gesture interaction", "METHOD" ], [ "motion event component", "DATA" ], [ "manner finding", "DATA" ], [ "linguistic expression", "DATA" ], [ "motion event", "DATA" ] ]

A weakly supervised activity recognition framework for real-time synthetic biology laboratory assistance

972,312

We describe the design of a hybrid system -- a combination of a Dynamic Graphical Model (DGM) with a Deep Neural Network (DNN) -- to identify activities performed during synthetic biology experiments. The purpose is to provide real-time feedback to experimenters, thus helping to reduce human errors and improve experimental reproducibility. The data consists of unlabeled videos of recorded experiments and "weakly supervised" information (i.e., "theoretical" and asynchronous knowledge of sets of high level activity sequences in the experiment) used to train the system. Multiple activity sequences are modeled using a trellis, and deep features are extracted from video images. Model performance is accessed using real-time online statistical inference. The trellis incorporates variations during experiment execution, making our model very general and capable of high performance.

[ { "first": "Chandrashekhar", "middle": [], "last": "Lavania", "suffix": "" }, { "first": "Sunil", "middle": [], "last": "Thulasidasan", "suffix": "" }, { "first": "Anthony", "middle": [], "last": "LaMarca", "suffix": "" }, { "first": "Jeffrey", "middle": [], "last": "Scofield", "suffix": "" }, { "first": "Jeff", "middle": [], "last": "Bilmes", "suffix": "" } ]

2,016

10.1145/2971648.2971716

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

2510505172

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[ "201894324", "36367356", "3814964", "51927950" ]

true

https://api.semanticscholar.org/CorpusID:972312

0

1

0

[ [ "trellis", "METHOD" ], [ "real-time online statistical inference", "METHOD" ], [ "activity se", "DATA" ], [ "experiment execut", "APPLICATION" ], [ "Deep Neural Network (DNN)", "METHOD" ], [ "deep feature", "DATA" ], [ "unlabeled video", "DATA" ], [ "experimental reproducibility", "EVALUATION" ], [ "weakly supervised\"", "DATA" ], [ "Dynamic Graphical Model (DGM)", "METHOD" ], [ "real-time feedback", "EVALUATION" ], [ "hybrid system", "METHOD" ], [ "high level activity sequence", "DATA" ], [ "human error", "EVALUATION" ], [ "synthetic biology experiment", "APPLICATION" ], [ "tre", "METHOD" ], [ "video image", "DATA" ] ]

Staying afloat on Neurath's boat - Heuristics for sequential causal learning

975,617

Causal models are key to flexible and efficient exploitation of the environment. However, learning causal structure is hard, with massive spaces of possible models, hard-to-compute marginals and the need to integrate diverse evidence over many instances. We report on two experiments in which participants learnt about probabilistic causal systems involving three and ::: four variables from sequences of interventions. Participants were broadly successful, albeit exhibiting sequential dependence and floundering under high background noise. We capture their behavior with a simple model, based on the “Neurath’s ship” metaphor for scientific progress, that neither maintains a probability distribution, nor computes exact likelihoods.

[ { "first": "Neil", "middle": [], "last": "Bramley", "suffix": "" }, { "first": "Peter", "middle": [], "last": "Dayan", "suffix": "" }, { "first": "David", "middle": [ "A." ], "last": "Lagnado", "suffix": "" } ]

2,015

CogSci

2407314176

[ "18779283", "18008446", "6844972", "2438767", "57701457", "28498", "21180723", "27948848", "117273529", "15817579", "10871785", "7469096", "60507168", "5839957", "14889432", "16175785", "13979657", "1356273" ]

[ "40618249", "1921621", "46928908", "13683966", "102505151", "27244888", "14275499", "8306085", "85522043" ]

true

https://api.semanticscholar.org/CorpusID:975617

0

1

0

[ [ "high background noise", "DATA" ], [ "sequential dependence", "EVALUATION" ], [ "probability distribution", "DATA" ], [ "Causal model", "METHOD" ], [ "causal structure", "METHOD" ], [ "diverse evidence", "DATA" ], [ "exact like", "DATA" ], [ "Neurath’s ship” metaphor", "METHOD" ], [ "scientific progress", "APPLICATION" ], [ "simple model", "METHOD" ], [ "probabilistic causal system", "METHOD" ] ]

Towards context-based search engine selection

975,990

A well-known problem for web search is targeting search on information that satisfies users' information needs. User queries tend to be short, and hence often ambiguous, which can lead to inappropriate results from general-purpose search engines. This has led to a number of methods for narrowing queries by adding information. This paper presents an alternative approach that aims to improve query results by using knowledge of a user's current activities to select search engines relevant to their information needs, exploiting the proliferation of high-quality special-purpose search services. The paper introduces the PRISM source selection system and describes its approach. It then describes two initial experiments testing the system's methods.

[ { "first": "David", "middle": [ "B." ], "last": "Leake", "suffix": "" }, { "first": "Ryan", "middle": [], "last": "Scherle", "suffix": "" } ]

2,001

10.1145/359784.360301

IUI '01

2086051332

[ "10413529", "1604208", "10555462", "3090485", "15198171", "546155", "29646978", "2056547", "8852794", "295254" ]

[ "14640428", "14551240", "8108552", "14757586", "12847021", "202639679", "14462918", "10630523", "222386", "1967031", "14474053", "17143945", "18144458", "18397169", "264079" ]

true

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1

[ [ "PRISM source selection system", "METHOD" ], [ "special-purpose search service", "METHOD" ], [ "general-purpose search engine", "METHOD" ], [ "initial experiment", "EVALUATION" ], [ "web search", "APPLICATION" ], [ "query result", "EVALUATION" ] ]

Motivations and Goals in Developing Integrative Models of Human Cognition

27,024,570

Motivations and Goals in Developing Integrative Models of Human Cognition Glenn Gunzelmann ([email protected]) Cognitive Models & Agents Branch, Air Force Research Laboratory 711 HPW/RHAC, 2620 Q St., Building 852 Wright Patterson Air Force Base, OH 85212-6061 USA Abstract Goals and Scope There has been tremendous growth recently in theories that attempt to provide more comprehensive accounts of the foundational mechanisms of human cognition. Such theories have taken a variety of forms, and have focused on different levels of analysis. The diversity is important and necessary, but can serve as a barrier to interaction, comparison, and integration, even at venues like the Annual Meeting of the Cognitive Science Society that should foster such dialogue. This workshop is intended to bring together individuals working on integrative models of human cognition, to emphasize shared motivations and goals. Ultimately, building scientific communities that bridge levels of analysis, methodologies, and theoretical approaches to work toward more comprehensive theories will be critical to the addressing the central goal of the Cognitive Science Society – understanding the nature of the human mind. The need to bring together this community of researchers was expressed by Newell (1990). Newell explicitly and deliberately referred to Unified Theories of Cognition in the plural, noting that multiple implementations are important for progress in the science. More recently, McClelland (2009) emphasized that “different simplifications are required to explore different issues.” (p. 12). Interactions among cognitive scientists from different methodological and theoretical backgrounds are crucial to identifying common foundations and interconnections among levels of analysis and theoretical perspectives. To reinforce and further develop the identity of this scientific community, this workshop will create an important opportunity for interaction and discussion amongst researchers working toward more integrative theories of cognition. It will not focus on a debate about the merits of developing integrative models of human cognition. The participants share, in general, an appreciation of the value of developing such theories, which provides the unifying theme for the event. In addition, the workshop will not focus on the claims of particular integrative models. That is an important scientific activity, but the goal here is to build broader appreciation of shared motivations and goals, despite sometimes very different approaches and theories. All of the presenters seek unifying mechanisms that cut through the complexity of human cognition and enhance our understanding. Complementary perspectives and opportunities for integration will be highlighted to emphasize connections. In addition, contemporary challenges in this pursuit will be discussed, which will facilitate future scientific debates regarding particular claims and mechanisms. Keywords: Integrated Models; Unified Theories of Cognition; Cognitive Models; Cognitive Architectures; Neural Architectures. Introduction The motivations for integrative models of human cognition have their roots in the origins of cognitive science as a scientific discipline. Even before cognitive psychology emerged, ideas about unifying principles to explain cognition were expressed in the scientific literature (e.g., Newell, Shaw, & Simon, 1958; Rosenblatt, 1961), including so-called grand psychological theories proposed during the first half of the 20 th century. The call for more comprehensive theories was explicitly made by Newell (1973), who expressed concern about the prospect that traditional, phenomenon-driven cognitive psychology would, by itself, lead to the kind of integrative understanding of the human mind that is the goal of cognitive science. In the decades since, integrative theories of human cognition have become increasingly prevalent in cognitive science. These theories now represent an exciting diversity of theoretical approaches and levels of analysis, better reflecting the diversity of the cognitive science community as a whole. As noted by McClelland (2009), this growth has been tied in important ways to sustained increases in computing power that enable cognitive modeling at a scale and resolution that was unimaginable half a century ago. The participants in the workshop have been selected to capture much of this theoretical diversity. The current state of the art in this area makes this workshop a timely and important contribution to the Annual Meeting of the Cognitive Science Society and the broader cognitive science community. Workshop Organization The workshop will be organized around a set of presentations and opportunities for discussion. The focus will not be on theoretical overviews. Instead, contributors will comment on the role of integrative models in cognitive science, including understanding the fundamental principles of human cognition broadly, and integrating across components of cognition to perform complex tasks. Presenters will highlight links to alternative approaches and methodologies, and discuss current challenges in developing integrative models of the human mind. Speakers will be given approximately 25 minutes, with no more than 15 minutes of presentation material. This will allow significant opportunity for questions, comments, and discussion. In addition, the closing session of the workshop will consist of a panel discussion with the goal of

[ { "first": "Glenn", "middle": [], "last": "Gunzelmann", "suffix": "" } ]

2,013

CogSci

2295632889

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[ "67842257" ]

true

https://api.semanticscholar.org/CorpusID:27024570

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[ [ "scientific community", "APPLICATION" ], [ "Cognitive Science", "APPLICATION" ], [ "human", "APPLICATION" ], [ "integrative model", "METHOD" ], [ "ures; Neural Architectures", "METHOD" ], [ "integrative theory", "APPLICATION" ], [ "cognitive modeling", "APPLICATION" ], [ "human mind", "DATA" ], [ "Unified Theories of Cognition", "METHOD" ], [ "theoretical diversity", "DATA" ], [ "scientific debate", "APPLICATION" ], [ "theoretical overview", "APPLICATION" ], [ "cognitive science", "APPLICATION" ], [ "theoretical approach", "METHOD" ], [ "Integrated Models", "METHOD" ], [ "cognition", "APPLICATION" ], [ "cognitive psychology", "APPLICATION" ], [ "principle", "METHOD" ], [ "unifying mechanism", "METHOD" ], [ "phenomenon-driven cognitive psychology", "METHOD" ], [ "cognitive scientist", "APPLICATION" ], [ "integrative understanding", "APPLICATION" ], [ "panel discussion", "EVALUATION" ], [ "presentation material", "EVALUATION" ], [ "gnitive Science Society", "APPLICATION" ], [ "grand psychological theory", "METHOD" ], [ "Human Cogni", "APPLICATION" ], [ "comprehensive theo", "EVALUATION" ], [ "cognitive science community", "APPLICATION" ], [ "Integrative Models", "METHOD" ], [ "scientific literature", "APPLICATION" ], [ "human cognition", "APPLICATION" ] ]

Augmenting Code with In Situ Visualizations to Aid Program Understanding

5,046,245

Programmers must draw explicit connections between their code and runtime state to properly assess the correctness of their programs. However, debugging tools often decouple the program state from the source code and require explicitly invoked views to bridge the rift between program editing and program understanding. To unobtrusively reveal runtime behavior during both normal execution and debugging, we contribute techniques for visualizing program variables directly within the source code. We describe a design space and placement criteria for embedded visualizations. We evaluate our in situ visualizations in an editor for the Vega visualization grammar. Compared to a baseline development environment, novice Vega users improve their overall task grade by about 2 points when using the in situ visualizations and exhibit significant positive effects on their self-reported speed and accuracy.

[ { "first": "Jane", "middle": [], "last": "Hoffswell", "suffix": "" }, { "first": "Arvind", "middle": [], "last": "Satyanarayan", "suffix": "" }, { "first": "Jeffrey", "middle": [], "last": "Heer", "suffix": "" } ]

2,018

10.1145/3173574.3174106

CHI '18

2796075754

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[ "204897475", "211051922", "195820488", "210174639", "173168986", "201665929", "204789099", "52109765", "199502002", "59599677", "53285615", "196621775", "198953499" ]

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https://api.semanticscholar.org/CorpusID:5046245

0

1

0

[ [ "design space", "VISUALIZATION" ], [ "baseline development environment", "METHOD" ], [ "in situ visualization", "VISUALIZATION" ], [ "placement", "METHOD" ], [ "explicit connection", "METHOD" ], [ "self-reported speed and accuracy", "EVALUATION" ], [ "program state", "DATA" ], [ "normal execut", "APPLICATION" ], [ "Vega visualization grammar", "METHOD" ], [ "program understanding", "METHOD" ], [ "runtime state", "DATA" ], [ "overall task grade", "EVALUATION" ], [ "program variable", "DATA" ], [ "runtime behavior", "DATA" ], [ "positive effect", "EVALUATION" ], [ "program editing", "METHOD" ], [ "embedded visualization", "VISUALIZATION" ], [ "source code", "DATA" ], [ "debugging tool", "METHOD" ] ]

"We Are the Product": Public Reactions to Online Data Sharing and Privacy Controversies in the Media

5,047,338

As online platforms increasingly collect large amounts of data about their users, there has been growing public concern about privacy around issues such as data sharing. Controversies around practices perceived as surprising or even unethical often highlight patterns of privacy attitudes when they spark conversation in the media. This paper examines public reaction "in the wild" to two data sharing controversies that were the focus of media attention-regarding the social media and communication services Facebook and WhatsApp, as well as the email service unroll.me. These controversies instigated discussion of data privacy and ethics, accessibility of website policies, notions of responsibility for privacy, cost-benefit analyses, and strategies for privacy management such as non-use. An analysis of reactions and interactions captured by comments on news articles not only reveals information about pervasive privacy attitudes, but also suggests communication and design strategies that could benefit both platforms and users.

[ { "first": "Casey", "middle": [], "last": "Fiesler", "suffix": "" }, { "first": "Blake", "middle": [], "last": "Hallinan", "suffix": "" } ]

2,018

10.1145/3173574.3173627

CHI '18

2795543871

[ "600035", "6183005", "5452028", "145567023", "2432888", "8057540", "21031187", "20057069", "156791187", "1847126", "7691878", "16437499", "205019910", "167589211", "154600377", "24722931", "3916218", "276377", "2317842", "153861302", "19771152", "6314017", "1823156", "1881959", "156280458", "55033962", "145106540", "2633485", "14544368", "16256141", "154587047", "16233042", "17314767", "11716", "11716", "146833880", "18899338", "159266772", "12077882", "44851727", "18557695", "3159000" ]

[ "196834967", "209168185", "52841625", "171091694", "53245499", "204391125", "210473819", "96446656", "132342629", "214599448" ]

true

https://api.semanticscholar.org/CorpusID:5047338

0

1

0

[ [ "cost-benefit analysis", "METHOD" ], [ "social medium and communication service", "APPLICATION" ], [ "public reaction", "EVALUATION" ], [ "privacy attitude", "METHOD" ], [ "communication and design strategy", "METHOD" ], [ "data sharing", "APPLICATION" ], [ "non-use", "METHOD" ], [ "data sharing contro", "APPLICATION" ], [ "news article", "DATA" ], [ "online platform", "APPLICATION" ], [ "data privacy", "APPLICATION" ], [ "privacy management", "APPLICATION" ], [ "email service", "APPLICATION" ], [ "pervasive privacy attitude", "DATA" ], [ "website policy", "APPLICATION" ] ]

A comparative longitudinal study of non-verbal mouse pointer

11,839,833

A longitudinal study of two non-speech continuous cursor control systems is presented in this paper: Whistling User Interface (U3I) and Vocal Joystick (VJ). This study combines the quantitative and qualitative methods to get a better understanding of novice users' experience over time. Three hypotheses were tested in this study. The quantitative data show that U3I performed better in error rate and in simulating a mouse click; VJ was better on other measures. The qualitative data indicate that the participants' opinions regarding both tools improved day-by-day. U3I was perceived as less fatiguing than VJ. U3I approached the performance of VJ at the end of the study period, indicating that these two systems can achieve similar performances as users get more experienced in using them. This study supports two hypotheses but does not provide enough evidence to support one hypothesis.

[ { "first": "Murni", "middle": [], "last": "Mahmud", "suffix": "" }, { "first": "Adam", "middle": [ "J." ], "last": "Sporka", "suffix": "" }, { "first": "Sri", "middle": [], "last": "Kurniawan", "suffix": "" }, { "first": "Pavel", "middle": [], "last": "Slavik", "suffix": "" } ]

2,007

10.1007/978-3-540-74800-7_44

INTERACT

1575856916

[ "2819368", "6117729", "10009173", "501599", "14876250", "59697092", "55111096", "15178687", "2239491", "2847637", "54158765", "16911942", "7757602", "13353255", "14984710", "60248329", "13892336" ]

[ "2307692", "40718442", "2616978", "10559092" ]

true

https://api.semanticscholar.org/CorpusID:11839833

1

[ [ "quantitative and qualitative method", "METHOD" ], [ "Whistling User Interface (U3I)", "METHOD" ], [ "novice users' experience", "APPLICATION" ], [ "Vocal Joystick (VJ)", "METHOD" ], [ "study period", "EVALUATION" ], [ "error rate", "EVALUATION" ], [ "qualitative data", "EVALUATION" ], [ "U3I", "METHOD" ], [ "mouse click", "EVALUATION" ], [ "longitudinal study", "EVALUATION" ], [ "quantitative data", "EVALUATION" ], [ "continuous cursor control system", "METHOD" ] ]

Medusa: a proximity-aware multi-touch tabletop

8,765,934

We present Medusa, a proximity-aware multi-touch tabletop. Medusa uses 138 inexpensive proximity sensors to: detect a user's presence and location, determine body and arm locations, distinguish between the right and left arms, and map touch point to specific users and specific hands. Our tracking algorithms and hardware designs are described. Exploring this unique design, we develop and report on a collection of interactions enabled by Medusa in support of multi-user collaborative design, specifically within the context of Proxi-Sketch, a multi-user UI prototyping tool. We discuss design issues, system implementation, limitations, and generalizable concepts throughout the paper.

[ { "first": "Michelle", "middle": [], "last": "Annett", "suffix": "" }, { "first": "Tovi", "middle": [], "last": "Grossman", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Wigdor", "suffix": "" }, { "first": "George", "middle": [], "last": "Fitzmaurice", "suffix": "" } ]

2,011

10.1145/2047196.2047240

UIST '11

2100491474

[ "2477064", "6543426", "25539", "45271459", "36550963", "12191319", "9403351", "437099", "4128790", "2712188", "1445673", "17558748", "112927411", "17605776", "6230608", "9107462", "296753", "9540590", "4674094", "2378786", "112917204", "3075638", "5880925", "18311193" ]

[ "15952864", "106402342", "4640096", "5790428", "3952281", "111379646", "207659562", "6455979", "666305", "11386465", "15935420", "7565670", "54457121", "19003178", "11632071", "17768963", "140212681", "52895710", "21748020", "52296511", "39752839", "5920970", "12352031", "16382422", "18863164", "16573459", "204812191", "28852710", "7408288", "20748387", "17162610", "14100049", "15429229", "44025819", "706228", "64893736", "10091933", "14156066", "17112800", "37807496", "1093537", "14086833", "8681693", "18443873", "18694045", "5192373", "8437453", "208278144", "20759007" ]

true

https://api.semanticscholar.org/CorpusID:8765934

1

[ [ "proximity-aware multi-touch tabletop", "METHOD" ], [ "Proxi-Sketch", "METHOD" ], [ "proximity sensor", "METHOD" ], [ "multi-user collaborative design", "APPLICATION" ], [ "design issue", "APPLICATION" ], [ "system implementation", "METHOD" ], [ "tracking algorithm", "METHOD" ], [ "hardware design", "METHOD" ], [ "multi-user UI prototyping tool", "METHOD" ] ]

AiD: augmented information display

8,767,388

Augmented Information Display (AiD) is an LCD-based communicative display device that transmits both visible (RGB) and invisible (infrared) information using temporal and spectral multiplexing. A field-sequential backlight system switches between a standard white or RGB LED backlight and a near-infrared (NIR) LED backlight at 120Hz frequency. The visible and invisible information are transmitted through the same LCD electro-optics elements but during different time intervals that synchronize with the corresponding backlights. We implemented several prototype software systems to demonstrate the potential applications of this novel display platform, such as an augmenting digital signage display, an information beacon for positioning systems, and an accessibility system for people with hearing impairments.

[ { "first": "Shuguang", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Jun", "middle": [], "last": "Xiao", "suffix": "" } ]

2,014

10.1145/2632048.2632085

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

2142287946

[ "62731848", "17313680", "7876575", "2712188", "8192877", "15175360", "207165348", "135512963", "207197640", "207004469", "7240607", "9366293", "9929537", "10533554", "9018049", "37088833" ]

[ "15224486" ]

true

https://api.semanticscholar.org/CorpusID:8767388

0

1

0

[ [ "visible and invisible information", "DATA" ], [ "prototype software system", "METHOD" ], [ "augmenting digital signage display", "APPLICATION" ], [ "near-infrared (NIR) LED backlight", "METHOD" ], [ "field-sequential backlight system", "METHOD" ], [ "display", "METHOD" ], [ "temporal and spectral multiplexing", "METHOD" ], [ "RGB LED backlight", "METHOD" ], [ "Augmented Information Display (AiD)", "METHOD" ], [ "LCD-based ccommunicative display device", "METHOD" ], [ "information beacon", "METHOD" ], [ "accessibility system", "APPLICATION" ], [ "positioning system", "APPLICATION" ], [ "LCD electro-optics element", "METHOD" ], [ "standard white", "METHOD" ] ]

Designing Expressions of Movement Qualities

47,017,544

Tango is a form of partner dancing in which two bodies sense one another, and move accordingly, in a dynamic, physical dialogue that is known for its subtle complexities, beauty and intimate experience. In MoCap Tango, we explore how we can build on our skills as designers to highlight and unravel these embedded qualities and use them as inspiration in designing interactions. In this pictorial, we invite the reader to actively participate in the designerly engagement that turns objective data into subjective expressions; highlighting the qualities embedded in the movements of professional dancers.

[ { "first": "Jeroen", "middle": [], "last": "Peeters", "suffix": "" }, { "first": "Ambra", "middle": [], "last": "Trotto", "suffix": "" } ]

2,018

10.1145/3196709.3196805

DIS '18

2808453678

[]

false

https://api.semanticscholar.org/CorpusID:47017544

null

[ [ "intimate experience", "EVALUATION" ], [ "subjective expression", "DATA" ], [ "professional dancer", "APPLICATION" ], [ "physical dialogue", "METHOD" ], [ "partner dancing", "APPLICATION" ], [ "objective data", "DATA" ], [ "designing interaction", "APPLICATION" ], [ "designerly engagement", "METHOD" ], [ "Tango", "APPLICATION" ] ]

Context-Aware Skeletal Shape Deformation

9,372,718

We describe a system for the animation of a skeleton-controlled articulated object that preserves the fine geometric details of the object skin and conforms to the characteristic shapes of the object specified through a set of examples. The system provides the animator with an intuitive user interface and produces compelling results even when presented with a very small set of examples. In addition it is able to generalize well by extrapolating far beyond the examples.

[ { "first": "Ofir", "middle": [], "last": "Weber", "suffix": "" }, { "first": "Olga", "middle": [], "last": "Sorkine", "suffix": "" }, { "first": "Yaron", "middle": [], "last": "Lipman", "suffix": "" }, { "first": "Craig", "middle": [], "last": "Gotsman", "suffix": "" } ]

2,007

10.1111/j.1467-8659.2007.01048.x

Comput. Graph. Forum

2119878875

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[ "2019744", "56717359", "204734958", "15255461", "16198866", "195899706", "2324973", "1068057", "18420878", "16049223", "207167285", "27717896", "15747808", "1515539", "6540443", "562670", "16360139", "15140499", "14695686", "7069842", "1004057", "16672566", "10707031", "3156486", "37245237", "14566502", "19541840", "19541840", "9708823", "13946088", "18996579", "60061", "15157774", "1568916", "15192634", "206374422", "17454298", "2064111", "12288021", "51871257", "10544417", "6729339", "13953196", "16446050", "7671652", "56434940", "10444741", "43993038", "17693690", "7847313", "201885", "17314948", "67258544", "15977970", "3092580", "8876341", "17726181", "13911716", "31747017", "7295606", "4543608", "5328073", "2380132", "15651779", "64268758", "79451294", "26469923", "12736777", "10650315", "7245910", "6290698", "10095943", "14126766", "16885120" ]

true

https://api.semanticscholar.org/CorpusID:9372718

0

1

0

[ [ "skeleton-controlled articulated object", "DATA" ], [ "intuitive user interface", "VISUALIZATION" ], [ "geometric detail", "DATA" ], [ "compelling result", "EVALUATION" ], [ "animation", "APPLICATION" ], [ "characteristic shape", "DATA" ], [ "object skin", "DATA" ] ]

Compact B-trees

14,943,088

A B-tree is compact if it is minimal in number of nodes, hence has optimal space utilization, among equally capacious B-trees of the same order. The space utilization of compact B-trees is analyzed and is compared with that of noncompact B-trees and of (node)-visit-optimal B-trees, which minimize the expected number of nodes visited per key access. Compact B-trees can be as much as a factor of 2.5 more space-efficient than visit-optimal B-trees; and the node-visit cost of a compact tree is never more than 1 + the node-visit cost of an optimal tree. Finally, an in-place compactification algorithm is presented which operates in linear time in the size of the file.

[ { "first": "Arnold", "middle": [ "L." ], "last": "Rosenberg", "suffix": "" }, { "first": "Lawrence", "middle": [], "last": "Snyder", "suffix": "" } ]

1,979

10.1145/582095.582102

SIGMOD '79

2003300921

[ "29859053" ]

[ "9971185", "31558750", "16876898", "208334833" ]

true

https://api.semanticscholar.org/CorpusID:14943088

0

1

0

[ [ "space utilization", "EVALUATION" ], [ "optimal tree", "METHOD" ], [ "optimal space utilization", "EVALUATION" ], [ "key access", "DATA" ], [ "node)-visit-optimal B-trees", "METHOD" ], [ "linear time", "METHOD" ], [ "B-tree", "DATA" ], [ "in-place compactification algorithm", "METHOD" ], [ "compact tree", "METHOD" ], [ "visit-optimal B-trees", "METHOD" ], [ "compact B-trees", "METHOD" ], [ "Compact B-trees", "METHOD" ], [ "node-visit", "EVALUATION" ], [ "-effi", "EVALUATION" ] ]

Investigating the Impact of 'Emphasis Frames' and Social Loafing on Player Motivation and Performance in a Crowdsourcing Game

9,917,278

With an increasing reliance on crowdsourcing games as data-gathering tools, it is imperative to understand how to motivate and sustain high levels of voluntary contribution. To this end, the present work directly compared the impact of various "emphasis frames," highlighting distinct intrinsic motivational factors, used to describe an online game in which players provide descriptive metadata "tags" for digitized images. An initial study showed that, compared to frames emphasizing personal enjoyment or altruistic motivations, a frame emphasizing a "growing community of players" solicited significantly fewer contributions. A second study tested the hypothesis that this lower level of contribution resulted from social loafing (the tendency to exert less effort in collective tasks in which contributions are anonymous and pooled). Results revealed that, compared to a no-frame control condition, a frame emphasizing the preponderance of other players reduced contribution levels and game replay likelihood, whereas a frame emphasizing the scarcity of fellow players increased contribution and replay levels. Various strategies for counteracting social loafing in crowdsourcing contexts are discussed.

[ { "first": "Geoff", "middle": [], "last": "Kaufman", "suffix": "" }, { "first": "Mary", "middle": [], "last": "Flanagan", "suffix": "" }, { "first": "Sukdith", "middle": [], "last": "Punjasthitkul", "suffix": "" } ]

2,016

10.1145/2858036.2858588

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

2395348601

[ "37535314", "11784974", "14576518", "7867498", "4389135", "61902782", "7237177", "9695952", "62602636", "17187531", "145557923", "143384892", "12694148", "16863664", "12999574", "40955384", "17485373", "43876594", "15279243", "18176830", "12381359", "12219985", "8117486", "6718133", "18619007", "53574707", "4467050", "147517926", "12543749", "145469511", "33849741", "62176518", "338469", "10486180", "14024990", "15596640", "3241708" ]

[ "46602370", "2700101", "13712195", "6325436", "199509099" ]

true

https://api.semanticscholar.org/CorpusID:9917278

0

1

0

[ [ "no-frame control condition", "METHOD" ], [ "data-gathering tool", "METHOD" ], [ "emphasis frame", "METHOD" ], [ "social loafing", "METHOD" ], [ "online game", "APPLICATION" ], [ "collective task", "APPLICATION" ], [ "contribution", "EVALUATION" ], [ "game replay likelihood", "EVALUATION" ], [ "descriptive metadata \"tags\"", "METHOD" ], [ "personal enjoy", "APPLICATION" ], [ "digitized image", "DATA" ], [ "replay level", "EVALUATION" ], [ "altruistic motivation", "DATA" ], [ "crowdsourcing game", "METHOD" ], [ "voluntary contribution", "EVALUATION" ], [ "motivational factor", "DATA" ], [ "crowdsourcing context", "APPLICATION" ] ]

Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes

44,064,817

Amazon Aurora is a high-throughput cloud-native relational database offered as part of Amazon Web Services (AWS). One of the more novel differences between Aurora and other relational databases is how it pushes redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. Doing so reduces networking traffic, avoids checkpoints and crash recovery, enables failovers to replicas without loss of data, and enables fault-tolerant storage that heals without database involvement. Traditional implementations that leverage distributed storage would use distributed consensus algorithms for commits, reads, replication, and membership changes and amplify cost of underlying storage. In this paper, we describe how Aurora avoids distributed consensus under most circumstances by establishing invariants and leveraging local transient state. Doing so improves performance, reduces variability, and lowers costs.

[ { "first": "Alexandre", "middle": [], "last": "Verbitski", "suffix": "" }, { "first": "Anurag", "middle": [], "last": "Gupta", "suffix": "" }, { "first": "Debanjan", "middle": [], "last": "Saha", "suffix": "" }, { "first": "James", "middle": [], "last": "Corey", "suffix": "" }, { "first": "Kamal", "middle": [], "last": "Gupta", "suffix": "" }, { "first": "Murali", "middle": [], "last": "Brahmadesam", "suffix": "" }, { "first": "Raman", "middle": [], "last": "Mittal", "suffix": "" }, { "first": "Sailesh", "middle": [], "last": "Krishnamurthy", "suffix": "" }, { "first": "Sandor", "middle": [], "last": "Maurice", "suffix": "" }, { "first": "Tengiz", "middle": [], "last": "Kharatishvilli", "suffix": "" }, { "first": "Xiaofeng", "middle": [], "last": "Bao", "suffix": "" } ]

2,018

10.1145/3183713.3196937

SIGMOD '18

2799174450

[ "3055672", "62054586", "8911383", "215404458", "1936192", "15014977", "37247738", "317107", "26268357", "15333301" ]

[ "203648257", "201651052", "202565599", "201636621", "203634663", "201609385" ]

true

https://api.semanticscholar.org/CorpusID:44064817

0

1

0

[ [ "redo processing", "METHOD" ], [ "distributed storage", "APPLICATION" ], [ "variability", "EVALUATION" ], [ "Aurora", "DATA" ], [ "relational database", "DATA" ], [ "cloud-native relational database", "DATA" ], [ "distributed consensus algorithm", "METHOD" ], [ "distributed consensus", "METHOD" ], [ "local transient state", "METHOD" ], [ "multi-tenant scale-out storage service", "METHOD" ], [ "replication", "APPLICATION" ], [ "fault-tolerant storage", "APPLICATION" ], [ "networking traffic", "EVALUATION" ], [ "membership change", "APPLICATION" ], [ "crash recovery", "METHOD" ] ]

ICEbox: Toward easy-to-use home networking

6,050,066

Home networking is becoming an essential part of everyday life. However, empirical studies and consumer reports indicate that the complexities of configuring and maintaining the home network impose a high barrier for most householders. In this paper, we explore the sources of the complexity of the home network, and describe a solution we have built to address this complexity. We have developed a prototype network appliance that acts as a centralized point of control for the home network, providing device provisioning and reprovisioning, security, discovery, and monitoring. Our solution provides a simple physical UI for network control, using pointing to introduce new devices onto the network, and a physical lock to secure network access. Results of our user studies indicate that users found this appliance both useful and usable as a network configuration and management tool.

[ { "first": "Jeonghwa", "middle": [], "last": "Yang", "suffix": "" }, { "first": "W. Keith", "middle": [], "last": "Edwards", "suffix": "" } ]

2,007

10.1007/978-3-540-74800-7_15

In Proceedings of IFIP Conference on HumanComputer Interaction, Rio de Janeiro

1537569888

[ "15789211", "11193929", "151870788", "9895971", "3056994", "59795829", "3923191", "18581816", "15560217", "1608699", "17424157", "16000909" ]

[ "14433157", "1102894", "18500083", "12636279", "12588887", "6903111", "18836664", "16120021", "11859411" ]

true

https://api.semanticscholar.org/CorpusID:6050066

1

[ [ "physical UI", "METHOD" ], [ "home network", "APPLICATION" ], [ "device provisioning and reprovisioning", "APPLICATION" ], [ "network configuration", "APPLICATION" ], [ "user study", "EVALUATION" ], [ "network access", "APPLICATION" ], [ "physical lock", "METHOD" ], [ "consumer report", "EVALUATION" ], [ "pointing", "METHOD" ], [ "security, discovery, and monitoring", "APPLICATION" ], [ "empirical study", "EVALUATION" ], [ "network control", "APPLICATION" ], [ "Home networking", "METHOD" ], [ "central", "METHOD" ], [ "prototype network appliance", "METHOD" ], [ "management tool", "APPLICATION" ] ]

Multi-view clustering using mixture models in subspace projections

11,567,333

Detecting multiple clustering solutions is an emerging research field. While data is often multi-faceted in its very nature, traditional clustering methods are restricted to find just a single grouping. To overcome this limitation, methods aiming at the detection of alternative and multiple clustering solutions have been proposed. In this work, we present a Bayesian framework to tackle the problem of multi-view clustering. We provide multiple generalizations of the data by using multiple mixture models. Each mixture describes a specific view on the data by using a mixture of Beta distributions in subspace projections. Since a mixture summarizes the clusters located in similar subspace projections, each view highlights specific aspects of the data. In addition, our model handles overlapping views, where the mixture components compete against each other in the data generation process. For efficiently learning the distributions, we propose the algorithm MVGen that exploits the ICM principle and uses Bayesian model selection to trade-off the cluster model's complexity against its goodness of fit. With experiments on various real-world data sets, we demonstrate the high potential of MVGen to detect multiple, overlapping clustering views in subspace projections of the data.

[ { "first": "Stephan", "middle": [], "last": "Günnemann", "suffix": "" }, { "first": "Ines", "middle": [], "last": "Färber", "suffix": "" }, { "first": "Thomas", "middle": [], "last": "Seidl", "suffix": "" } ]

2,012

10.1145/2339530.2339553

KDD

2059355863

[ "59849738", "14249421", "5391096", "15128952", "122501158", "1953096", "15484242", "8692838", "6427650", "2637998", "15940466", "34521547", "17363900", "122530182", "61085758", "14338901", "15926913", "6461690", "2817625", "10079637" ]

[ "10901064", "65091012", "4514819", "13524057", "50780393", "18994275", "46811529", "16278126", "199452882", "2478439", "54466541", "25707035", "11282545", "15533071", "17181263", "16450749" ]

true

https://api.semanticscholar.org/CorpusID:11567333

0

1

0

[ [ "mixture model", "METHOD" ], [ "data generation process", "METHOD" ], [ "Bayesian model selection", "METHOD" ], [ "multi-view clustering", "APPLICATION" ], [ "cluster", "DATA" ], [ "clustering solution", "APPLICATION" ], [ "subspace projection", "VISUALIZATION" ], [ "Beta distribution", "METHOD" ], [ "detection of alternative", "APPLICATION" ], [ "ICM principle", "METHOD" ], [ "cluster model", "METHOD" ], [ "mixture component", "DATA" ], [ "lapping view", "DATA" ], [ "Bayesian framework", "METHOD" ], [ "traditional clustering method", "METHOD" ], [ "real-world data set", "DATA" ], [ "MVGen", "METHOD" ] ]

Key lessons learned building recommender systems for large-scale social networks

2,700,260

By helping members to connect, discover and share relevant content or find a new career opportunity, recommender systems have become a critical component of user growth and engagement for social networks. The multidimensional nature of engagement and diversity of members on large-scale social networks have generated new infrastructure and modeling challenges and opportunities in the development, deployment and operation of recommender systems. This presentation will address some of these issues, focusing on the modeling side for which new research is much needed while describing a recommendation platform that enables real-time recommendation updates at scale as well as batch computations, and cross-leverage between different product recommendations. Topics covered on the modeling side will include optimizing for multiple competing objectives, solving contradicting business goals, modeling user intent and interest to maximize placement and timeliness of the recommendations, utility metrics beyond CTR that leverage both real-time tracking of explicit and implicit user feedback, gathering training data for new product recommendations, virality preserving online testing and virtual profiling.

[ { "first": "Christian", "middle": [], "last": "Posse", "suffix": "" } ]

2,012

10.1145/2339530.2339625

KDD

1986020933

[]

[ "15164918", "14075134" ]

false

true

https://api.semanticscholar.org/CorpusID:2700260

0

[ [ "recommender system", "METHOD" ], [ "multi", "DATA" ], [ "recommendation platform", "METHOD" ], [ "user growth", "APPLICATION" ], [ "explicit", "DATA" ], [ "utility metric", "METHOD" ], [ "virtual profiling", "APPLICATION" ], [ "batch computation", "METHOD" ], [ "training data", "DATA" ], [ "implicit user feedback", "DATA" ], [ "real-time tracking", "METHOD" ], [ "virality preserving online testing", "APPLICATION" ], [ "social network", "APPLICATION" ], [ "cross-lever", "METHOD" ], [ "real-time recommendation update", "APPLICATION" ], [ "side", "APPLICATION" ] ]

Generation of Kolam-Designs Based on Contextual Array P Systems

49,193,414

Kolam-designs are diagrams used to decorate the floor, especially in front of a house in South India. Methods of generation of the kolam diagrams were developed based on two-dimensional picture generating models, broadly known as array grammars, introduced for the description and analysis of picture patterns. Rewriting array P system, a membrane computing model based on array rewriting has been developed to evolve picture arrays, based on context-free array rewriting rules. In contrast to this array P system, another P system model called contextual array P system (CAP) using contextual array rules for the evolution or generation of picture arrays has been proposed and its power in generating picture arrays investigated. Here we develop an application of CAP for the generation of the kolam diagrams. The advantage of using CAP is that kolam diagrams that cannot be handled by array grammars can be generated by the CAP model.

[ { "first": "Ibrahim", "middle": [], "last": "Venkat", "suffix": "" }, { "first": "T.", "middle": [], "last": "Robinson", "suffix": "" }, { "first": "K.", "middle": [ "G." ], "last": "Subramanian", "suffix": "" }, { "first": "Philippe", "middle": [ "De" ], "last": "Wilde", "suffix": "" } ]

2,018

10.1007/978-3-319-91376-6_11

Diagrams

2803741759

[]

false

https://api.semanticscholar.org/CorpusID:49193414

null

[ [ "context-free array rewriting rule", "METHOD" ], [ "Rewriting array P system", "METHOD" ], [ "array rewriting", "METHOD" ], [ "P system model", "METHOD" ], [ "CAP", "METHOD" ], [ "picture pattern", "DATA" ], [ "two-dimensional picture generating model", "METHOD" ], [ "membrane computing model", "METHOD" ], [ "CAP model", "METHOD" ], [ "contextual array P system (CAP)", "METHOD" ], [ "array grammar", "METHOD" ], [ "contextual array rule", "METHOD" ], [ "Kolam-designs", "VISUALIZATION" ], [ "picture array", "DATA" ], [ "array P system", "METHOD" ], [ "kolam diagram", "VISUALIZATION" ] ]

Fast structure learning in generalized stochastic processes with latent factors

548,500

Understanding and quantifying the impact of unobserved processes is one of the major challenges of analyzing multivariate time series data. In this paper, we analyze a flexible stochastic process model, the generalized linear auto-regressive process (GLARP) and identify the conditions under which the impact of hidden variables appears as an additive term to the evolution matrix estimated with the maximum likelihood. In particular, we examine three examples, including two popular models for count data, i.e, Poisson and Conwey-Maxwell Poisson vector auto-regressive processes, and one powerful model for extreme value data, i.e., Gumbel vector auto-regressive processes. We demonstrate that the impact of hidden factors can be separated out via convex optimization in these three models. We also propose a fast greedy algorithm based on the selection of composite atoms in each iteration and provide a performance guarantee for it. Experiments on two synthetic datasets, one social network dataset and one climatology dataset demonstrate the the superior performance of our proposed models.

[ { "first": "Mohammad Taha", "middle": [], "last": "Bahadori", "suffix": "" }, { "first": "Yan", "middle": [], "last": "Liu", "suffix": "" }, { "first": "Eric", "middle": [ "P." ], "last": "Xing", "suffix": "" } ]

2,013

10.1145/2487575.2487578

KDD '13

1982541060

[ "1124555", "128518090", "7128002", "88518843", "1522297", "123169956", "61718824", "60981168", "123605483", "120474741", "1175925", "1008724", "123107532", "88832", "2290047", "1004801", "14154576", "8249100", "88512332", "7411376", "122658260", "10218794", "122763955", "157259933", "9044762", "14217010", "121693923", "2450629", "544256", "27513679" ]

[ "14744071", "5032764", "26555094", "24388201", "5842463", "11684043", "1623439", "56486924", "18044306", "49557071", "27751015" ]

true

https://api.semanticscholar.org/CorpusID:548500

0

1

0

[ [ "composite atom", "DATA" ], [ "variable", "DATA" ], [ "social network dataset", "DATA" ], [ "Gumbel vector auto-regressive process", "METHOD" ], [ "stochastic process model", "METHOD" ], [ "performance guarantee", "EVALUATION" ], [ "climatology dataset", "DATA" ], [ "addit", "DATA" ], [ "extreme value data", "DATA" ], [ "multivariate time series data", "DATA" ], [ "maximum", "DATA" ], [ "greedy algorithm", "METHOD" ], [ "superior performance", "EVALUATION" ], [ "served process", "METHOD" ], [ "convex optimization", "METHOD" ], [ "count data", "DATA" ], [ "evolution matrix", "DATA" ], [ "synthetic dataset", "DATA" ], [ "Poisson and Conwey-Maxwell Poisson vector auto-regressive process", "METHOD" ], [ "linear auto-regressive process (GLARP)", "METHOD" ] ]

The Practitioner's Viewpoint.

35,288,310

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

1,961

PMC2612901

The Journal of the College of General Practitioners

2180791941

[]

false

https://api.semanticscholar.org/CorpusID:35288310

null

[]

ChakraSuit: experimental directed meditation wearable

202,159,476

ChakraSuit is an experimental wearable which aids in finding the best location for meditation, enabling one to learn about the interaction between the natural environment, sound, and the body. The prototype continuously listens to ambient sounds, and translates the identified audio frequencies to vibration on several points along the spine which correspond to chakra points. The project consists of a jumpsuit, pocket, and harness with integrated electronics running on a Raspberry Pi W Zero.

[ { "first": "Kristine", "middle": [], "last": "Kuprijanova", "suffix": "" }, { "first": "Luis", "middle": [ "Edgardo" ], "last": "Fraguada", "suffix": "" }, { "first": "Elizabeth", "middle": [ "Esther" ], "last": "Bigger", "suffix": "" } ]

2,019

10.1145/3341163.3346944

ISWC '19

2978035545,2972259133

[]

false

true

https://api.semanticscholar.org/CorpusID:202159476

0

1

0

[ [ "ChakraSuit", "METHOD" ], [ "integrated electronic", "METHOD" ], [ "experimental wearable", "METHOD" ], [ "ambient sound", "DATA" ], [ "audio frequency", "DATA" ], [ "chakra point", "DATA" ] ]

The use of information visualization to support software configuration management

18,564,515

This paper addresses the visualization of the collaboration history in the development of software items using a simple interactive representation called Revision Tree. The visualization presents detailed information on a single software item with the intention of supporting the awareness of the project managers and developers about the item evolution and the collaboration taking place on its development. We considered that repositories of Software Configuration Management tools are the best information source to extract relevant information dealing with the relationships between the programmers and software items, as well as information regarding the creation of baselines, branches and revisions, and useful date and time details for the arrangement of the development timeline and collaboration representation.

[ { "first": "Roberto", "middle": [], "last": "Therón", "suffix": "" }, { "first": "Antonio", "middle": [], "last": "Gonzalez", "suffix": "" }, { "first": "Francisco", "middle": [ "J." ], "last": "Garcia", "suffix": "" }, { "first": "Pablo", "middle": [], "last": "Santos", "suffix": "" } ]

2,007

10.1007/978-3-540-74800-7_26

INTERACT

1635539922

[ "8061414", "5804659", "59732190", "1910597", "15581238", "8455498", "13180593", "2028779", "15867304", "5887833", "8176455", "36490227", "195904394", "18437305", "57243626", "623664", "7912864" ]

[ "67116356", "6276495", "38196447", "15522947", "17980894" ]

true

https://api.semanticscholar.org/CorpusID:18564515

1

[ [ "project manager", "APPLICATION" ], [ "interactive representation", "VISUALIZATION" ], [ "collaboration history", "DATA" ], [ "software item", "APPLICATION" ], [ "development timeline", "APPLICATION" ], [ "collaboration representation", "APPLICATION" ], [ "item evolution", "DATA" ], [ "date and time detail", "DATA" ], [ "Revision Tree", "VISUALIZATION" ], [ "Software Configuration Management tool", "METHOD" ] ]

Managing Non-Volatile Memory in Database Systems

19,160,550

Non-volatile memory (NVM) is a new storage technology that combines the performance and byte addressability of DRAM with the persistence of traditional storage devices like flash (SSD). While these properties make NVM highly promising, it is not yet clear how to best integrate NVM into the storage layer of modern database systems. Two system designs have been proposed. The first is to use NVM exclusively, i.e., to store all data and index structures on it. However, because NVM has a higher latency than DRAM, this design can be less efficient than main-memory database systems. For this reason, the second approach uses a page-based DRAM cache in front of NVM. This approach, however, does not utilize the byte addressability of NVM and, as a result, accessing an uncached tuple on NVM requires retrieving an entire page. In this work, we evaluate these two approaches and compare them with in-memory databases as well as more traditional buffer managers that use main memory as a cache in front of SSDs. This allows us to determine how much performance gain can be expected from NVM. We also propose a lightweight storage manager that simultaneously supports DRAM, NVM, and flash. Our design utilizes the byte addressability of NVM and uses it as an additional caching layer that improves performance without losing the benefits from the even faster DRAM and the large capacities of SSDs.

[ { "first": "Alexander", "middle": [], "last": "van Renen", "suffix": "" }, { "first": "Viktor", "middle": [], "last": "Leis", "suffix": "" }, { "first": "Alfons", "middle": [], "last": "Kemper", "suffix": "" }, { "first": "Thomas", "middle": [], "last": "Neumann", "suffix": "" }, { "first": "Takushi", "middle": [], "last": "Hashida", "suffix": "" }, { "first": "Kazuichi", "middle": [], "last": "Oe", "suffix": "" }, { "first": "Yoshiyasu", "middle": [], "last": "Doi", "suffix": "" }, { "first": "Lilian", "middle": [], "last": "Harada", "suffix": "" }, { "first": "Mitsuru", "middle": [], "last": "Sato", "suffix": "" } ]

2,018

10.1145/3183713.3196897

SIGMOD '18

2798607736

[ "6745879", "492531", "3041027", "11839141", "26930249", "18937793", "9559094", "947717", "15069759", "2589691", "8135092", "8681081", "207212295", "16444695", "15569540", "3000665", "5374830", "18139314", "6946314", "17899330", "17537308", "206710977", "7087729", "2126358", "3542091", "9568561", "22671809", "30678283", "479301", "3732670", "70161", "302221", "206771165", "15712598", "1638781", "5404585", "14364696", "10076139", "2923071", "11566546", "12995406", "35946590", "4125677" ]

[ "46979201", "102487109", "212643346", "119184095", "59413873", "209392113", "195298486", "212725601", "210908711", "195873369", "211015952", "209379447", "155100364", "52923481", "212688706", "209696560", "214691290", "211478785" ]

true

https://api.semanticscholar.org/CorpusID:19160550

0

1

0

[ [ "NVM", "METHOD" ], [ "traditional buffer manager", "METHOD" ], [ "flash", "METHOD" ], [ "modern database system", "APPLICATION" ], [ "index structure", "DATA" ], [ "Non-volatile memory (NVM)", "METHOD" ], [ "uncached tuple", "DATA" ], [ "system design", "METHOD" ], [ "main-memory database system", "METHOD" ], [ "storage", "METHOD" ], [ "DRAM", "METHOD" ], [ "main memory", "DATA" ], [ "byte addressability", "EVALUATION" ], [ "in-memory database", "METHOD" ], [ "page-based DRAM cache", "METHOD" ], [ "lightweight storage manager", "METHOD" ], [ "traditional storage device", "METHOD" ], [ "storage layer", "METHOD" ], [ "performance gain", "EVALUATION" ], [ "caching layer", "METHOD" ] ]

Stalker, A Multilingual Text Mining Search Engine for Open Source Intelligence

32,963,501

The revolution in information technology is making open sources more accessible, ubiquitous, and valuable. The international Intelligence Communities have seen open sources grow increasingly easier and cheaper to acquire in recent years. But up to 80% of electronic data is textual and most valuable information is often hidden and encoded in pages which are neither structured, nor classified. The process of accessing all these raw data, heterogeneous in terms of source and language, and transforming them into information is therefore strongly linked to automatic textual analysis and synthesis, which are greatly related to the ability to master the problems of multilinguality. This paper describes a content enabling system that provides deep semantic search and information access to large quantities of distributed multimedia data for both experts and general public. STALKER provides with a language independent search and dynamic classification features for a broad range of data collected from several sources in a number of culturally diverse languages.

[ { "first": "Federico", "middle": [], "last": "Neri", "suffix": "" }, { "first": "Massimo", "middle": [], "last": "Pettoni", "suffix": "" } ]

2,008

10.1109/IV.2008.9

IV

2153674808

[]

[ "63553397" ]

false

true

false

https://api.semanticscholar.org/CorpusID:32963501

null

[ [ "dynamic classification feature", "METHOD" ], [ "automatic textual analysis and synthesis", "APPLICATION" ], [ "raw data", "DATA" ], [ "open source", "DATA" ], [ "content enabling system", "METHOD" ], [ "multilinguality", "APPLICATION" ], [ "distributed multimedia data", "DATA" ], [ "STALKER", "METHOD" ], [ "language independent search", "METHOD" ], [ "information access", "APPLICATION" ], [ "electronic data", "DATA" ], [ "deep semantic search", "APPLICATION" ], [ "information technology", "APPLICATION" ], [ "international Intelligence Communities", "APPLICATION" ] ]

Exploring trajectory-driven local geographic topics in foursquare

1,286,731

The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users' trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users' trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.

[ { "first": "Xuelian", "middle": [], "last": "Long", "suffix": "" }, { "first": "Lei", "middle": [], "last": "Jin", "suffix": "" }, { "first": "James", "middle": [], "last": "Joshi", "suffix": "" } ]

2,012

10.1145/2370216.2370423

UbiComp '12

2101180523

[ "2976890", "3177797", "20036452", "329441", "14264512", "3329660", "1669962", "1384614", "11783871", "14141544", "772748" ]

[ "28503894", "43964645", "16277981", "5349338", "575347", "142788332", "17293421", "14699438", "9201127", "39506209", "167601163", "67866245", "53092821", "49677457", "52933127", "86545821", "32166953", "1044859", "149841081", "189760777", "54646014", "210696109", "6608504", "2421236", "2754697", "14865771", "35829935", "22075884", "13364278", "14430677", "16088510", "14791153", "2385967", "5321445", "67871807", "11942175", "27436156", "11918036", "201811517" ]

true

https://api.semanticscholar.org/CorpusID:1286731

0

1

0

[ [ "Foursquare", "METHOD" ], [ "mining and analysis", "APPLICATION" ], [ "location based social networking service (LBSNSs)", "METHOD" ], [ "human mobility pattern", "DATA" ], [ "Latent Dirichlet Allocation (LDA) model", "METHOD" ], [ "LBSNSs", "METHOD" ], [ "local geographic topic", "APPLICATION" ], [ "geographic topic", "DATA" ], [ "LDA", "METHOD" ], [ "intrinsic relation", "DATA" ], [ "friend recommendation", "APPLICATION" ] ]

Prism: A Primal-Encoding Approach for Frequent Sequence Mining

9,366,907

Sequence mining is one of the fundamental data mining tasks. In this paper we present a novel approach called Prism, for mining frequent sequences. Prism utilizes a vertical approach for enumeration and support counting, based on the novel notion o/prime block encoding, which in turn is based on prime factorization theory. Via an extensive evaluation on both synthetic and real datasets, we show that Prism outperforms popular sequence mining methods like SPADE [10], PrefixSpan [6] and SPAM [2], by an order of magnitude or more.

[ { "first": "K.", "middle": [], "last": "Gouda", "suffix": "" }, { "first": "M.", "middle": [], "last": "Hassaan", "suffix": "" }, { "first": "M.J.", "middle": [], "last": "Zaki", "suffix": "" } ]

2,007

10.1109/ICDM.2007.33

Seventh IEEE International Conference on Data Mining (ICDM 2007)

2098514508

[ "12242182", "59848750", "54102649", "10975598", "3200149", "206782042", "14763104", "1053401", "124098574", "5387869" ]

[ "4733829", "51892061", "14999247", "15509924", "1898748", "16814005", "201622374", "12935788", "16965218", "6996112", "14351054", "33919456", "18998174", "42299237", "126266644" ]

true

https://api.semanticscholar.org/CorpusID:9366907

0

1

0

[ [ "fundamental data mining task", "APPLICATION" ], [ "tensive evaluation", "EVALUATION" ], [ "prime factorization theory", "METHOD" ], [ "SPADE", "METHOD" ], [ "SPAM", "METHOD" ], [ "vertical approach", "METHOD" ], [ "Prism", "METHOD" ], [ "Sequence mining", "APPLICATION" ], [ "PrefixSpan", "METHOD" ], [ "enumeration and support counting", "APPLICATION" ], [ "sequence mining method", "METHOD" ], [ "Pris", "METHOD" ], [ "mining frequent sequence", "DATA" ], [ "synthetic and real datasets", "DATA" ], [ "/prime block encoding", "METHOD" ] ]

Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination

196,172,882

Sequential recommendation and information dissemination are two traditional problems for sequential information retrieval. The common goal of the two problems is to predict future user-item interactions based on past observed interactions. The difference is that the former deals with users' histories of clicked items, while the latter focuses on items' histories of infected users.In this paper, we take a fresh view and propose dual sequential prediction models that unify these two thinking paradigms. One user-centered model takes a user's historical sequence of interactions as input, captures the user's dynamic states, and approximates the conditional probability of the next interaction for a given item based on the user's past clicking logs. By contrast, one item-centered model leverages an item's history, captures the item's dynamic states, and approximates the conditional probability of the next interaction for a given user based on the item's past infection records. To take advantage of the dual information, we design a new training mechanism which lets the two models play a game with each other and use the predicted score from the opponent to design a feedback signal to guide the training. We show that the dual models can better distinguish false negative samples and true negative samples compared with single sequential recommendation or information dissemination models. Experiments on four real-world datasets demonstrate the superiority of proposed model over some strong baselines as well as the effectiveness of dual training mechanism between two models.

[ { "first": "Qitian", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Yirui", "middle": [], "last": "Gao", "suffix": "" }, { "first": "Xiaofeng", "middle": [], "last": "Gao", "suffix": "" }, { "first": "Paul", "middle": [], "last": "Weng", "suffix": "" }, { "first": "Guihai", "middle": [], "last": "Chen", "suffix": "" } ]

2,019

10.1145/3292500.3330959

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

2950416221

[ "11212020", "15392131", "7849677", "207562781", "14910205", "3347254", "3347254", "1002614", "14873341", "14873341", "52180375", "5758868", "29150834", "10246046", "1964279", "11810482", "53514961", "16876738", "18012968", "52127932", "52343211", "52897747", "53046512", "7147249", "8183913", "1012652", "6194301", "207178809", "3383974", "467086", "8705842", "6917394", "1234838", "2552056", "39847715", "3331356", "14011056", "14011056", "4002880", "5362246", "53038169", "51868177", "2023817", "6181286", "1637394", "4899764" ]

[ "207852800" ]

true

https://api.semanticscholar.org/CorpusID:196172882

0

1

0

[ [ "Sequential recommendation", "APPLICATION" ], [ "sequential information retrieval", "APPLICATION" ], [ "dual training mechanism", "METHOD" ], [ "information dissemination model", "METHOD" ], [ "training mechanism", "METHOD" ], [ "true negative sample", "DATA" ], [ "past infection record", "DATA" ], [ "infected user", "DATA" ], [ "dual information", "DATA" ], [ "conditional probability", "EVALUATION" ], [ "dual model", "METHOD" ], [ "single sequential recommendation", "METHOD" ], [ "past clicking log", "DATA" ], [ "clicked item", "DATA" ], [ "real-world dataset", "DATA" ], [ "historical sequence", "DATA" ], [ "dynamic state", "DATA" ], [ "feedback signal", "METHOD" ], [ "dual sequential prediction model", "METHOD" ], [ "false negative sample", "DATA" ], [ "user-centered model", "METHOD" ], [ "item-centered model", "METHOD" ], [ "information dissemination", "APPLICATION" ] ]

Sets2Sets: Learning from Sequential Sets with Neural Networks

196,177,434

Given past sequential sets of elements, predicting the subsequent sets of elements is an important problem in different domains. With the past orders of customers given, predicting the items that are likely to be bought in their following orders can provide information about the future purchase intentions. With the past clinical records of patients at each visit to the hospitals given, predicting the future clinical records in the subsequent visits can provide information about the future disease progression. These useful information can help to make better decisions in different domains. However, existing methods have not studied this problem well. In this paper, we formulate this problem as a sequential sets to sequential sets learning problem. We propose an end-to-end learning approach based on an encoder-decoder framework to solve the problem. In the encoder, our approach maps the set of elements at each past time step into a vector. In the decoder, our method decodes the set of elements at each subsequent time step from the vectors with a set-based attention mechanism. The repeated elements pattern is also considered in our method to further improve the performance. In addition, our objective function addresses the imbalance and correlation existing among the predicted elements. The experimental results on three real-world data sets showthat our method outperforms the best performance of the compared methods with respect to recall and person-wise hit ratio by 2.7-20.6% and 2.1-26.3%, respectively. Our analysis also shows that our decoder has good generalization to output sequential sets that are even longer than the output of training instances.

[ { "first": "Haoji", "middle": [], "last": "Hu", "suffix": "" }, { "first": "Xiangnan", "middle": [], "last": "He", "suffix": "" } ]

2,019

10.1145/3292500.3330979

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

2951227353

[ "59378336", "61556494", "50766964", "1820089", "46939182", "20970043", "5842463", "50773451", "29151031", "7392978", "12199225", "5241746", "1368427", "2023519", "206744664", "6628106", "5027797", "17195923", "742515", "207178809", "11258378", "8991522", "7961699", "14516773", "13756489", "1169492", "4002880", "150380651", "2023817", "97681", "1008003" ]

[ "211205085", "214795246" ]

true

https://api.semanticscholar.org/CorpusID:196177434

0

1

0

[ [ "encoder-decoder framework", "METHOD" ], [ "sequential set", "DATA" ], [ "objective function", "METHOD" ], [ "recall", "EVALUATION" ], [ "experimental result", "EVALUATION" ], [ "vector", "DATA" ], [ "end-to-end learning approach", "METHOD" ], [ "predicted element", "DATA" ], [ "clinical record", "DATA" ], [ "repeated element pattern", "METHOD" ], [ "set-based attention mechanism", "METHOD" ], [ "deco", "METHOD" ], [ "imbalance", "DATA" ], [ "progression", "DATA" ], [ "sequential set learning", "APPLICATION" ], [ "person-wise hit ratio", "EVALUATION" ], [ "real-world data set", "DATA" ], [ "correlation", "DATA" ] ]

Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization

2,270,829

We propose a fast Newton hard thresholding pursuit algorithm for sparsity constrained nonconvex optimization. Our proposed algorithm reduces the per-iteration time complexity to linear in the data dimension d compared with cubic time complexity in Newton's method, while preserving faster computational and statistical convergence rates. In particular, we prove that the proposed algorithm converges to the unknown sparse model parameter at a composite rate, namely quadratic at first and linear when it gets close to the true parameter, up to the minimax optimal statistical precision of the underlying model. Thorough experiments on both synthetic and real datasets demonstrate that our algorithm outperforms the state-of-the-art optimization algorithms for sparsity constrained optimization.

[ { "first": "Jinghui", "middle": [], "last": "Chen", "suffix": "" }, { "first": "Quanquan", "middle": [], "last": "Gu", "suffix": "" } ]

2,017

10.1145/3097983.3098165

KDD '17

2744964614

[ "16141163", "10572875", "9315983", "18580709", "15160034", "8706384", "15607153", "14116328", "8006871", "14112046", "4741563", "1162245", "2021016", "6587578", "600412", "17797070", "11761086", "11027141", "5007028", "14165823", "11593547", "1642637", "1004801", "62288331", "7964929", "11058559", "9040520", "7125812", "14784580", "1084204", "1876650", "7144363", "16162039", "1859832", "17149565", "17540387", "10910835", "17055542", "14369467", "18584871", "88519565", "5743097", "16007798" ]

[ "119645135", "207757673", "119634357" ]

true

https://api.semanticscholar.org/CorpusID:2270829

0

1

0

[ [ "data dimension", "DATA" ], [ "optimization algorithm", "METHOD" ], [ "sparsity constrained optimization", "APPLICATION" ], [ "cubic time complexity", "EVALUATION" ], [ "Newton hard thresholding pursuit algorithm", "METHOD" ], [ "per-iteration time complexity", "EVALUATION" ], [ "statistical convergence rate", "EVALUATION" ], [ "composite rate", "METHOD" ], [ "sparsity constrained nonconvex optimization", "APPLICATION" ], [ "synthetic and real datasets", "DATA" ], [ "sparse model parameter", "DATA" ], [ "'s method", "METHOD" ], [ "statistical precision", "EVALUATION" ] ]

Cooperative Behavior in Multicultural Settings: The Contribution of Altruistic Punishment

2,278,616

[ { "first": "Daniela", "middle": [], "last": "Rabellino", "suffix": "" }, { "first": "Rosalba", "middle": [], "last": "Morese", "suffix": "" }, { "first": "Angela", "middle": [], "last": "Ciaramidaro", "suffix": "" }, { "first": "Bruno", "middle": [ "G." ], "last": "Bara", "suffix": "" }, { "first": "Rosalba", "middle": [], "last": "Rosato", "suffix": "" }, { "first": "Francesca", "middle": [], "last": "Bosco", "suffix": "" } ]

2,012

CogSci

2399482401

[]

false

https://api.semanticscholar.org/CorpusID:2278616

null

[ [ "cultural Settings", "APPLICATION" ], [ "Cooperative Be", "APPLICATION" ], [ "Altruistic Punishment", "METHOD" ] ]

A rule-based object/task modelling approach

18,951,641

A rule-based object/task modelling approach is proposed which is characterized by specifying object behaviors and domain rules in terms of object-oriented logic programming, and specifying tasks and meta-rules in terms of network-oriented formalism. In addition the concepts of associations, virtual objects, multiple level integrity control and net expressions are introduced. The object-oriented logic programming system is extended for supporting the semantic modelling, and an explicit control knowledge representation mechanism is developed. This approach may be viewed as a step to the integration of object-oriented programming, logic programming, semantic modelling and event modelling, and to the combination of forward chaining and backward chaining techniques. Therefore, it can provide complementary benefits in deductive query support, integrity control, explicit control knowledge representation and intelligent user interface, and enhance the flexibility and extendibility of knowledge based systems to accommodate applications in multiple domains, towards a generalized, rule-based management of data, action and operational schemes. This approach is being designed and partially implemented on top of System C [Chen 85b] on a VAX computer.

[ { "first": "Qiming", "middle": [], "last": "Chen", "suffix": "" } ]

1,986

10.1145/16894.16882

SIGMOD '86

2068845509

[]

[ "17619028", "32151488", "15342155" ]

false

true

https://api.semanticscholar.org/CorpusID:18951641

0

[ [ "event modelling", "METHOD" ], [ "integrity control", "APPLICATION" ], [ "virtual object", "METHOD" ], [ "rule-based object/task modelling approach", "METHOD" ], [ "explicit control knowledge representation mechanism", "METHOD" ], [ "logic programming", "METHOD" ], [ "backward chaining technique", "METHOD" ], [ "object-oriented programming", "METHOD" ], [ "rule-based management", "APPLICATION" ], [ "object-oriented logic programming", "METHOD" ], [ "explicit control knowledge representation", "APPLICATION" ], [ "multiple level integrity control", "METHOD" ], [ "deductive query support", "APPLICATION" ], [ "net expression", "METHOD" ], [ "operational scheme", "APPLICATION" ], [ "object-oriented logic programming system", "METHOD" ], [ "knowledge based system", "METHOD" ], [ "data, action", "APPLICATION" ], [ "semantic modelling", "APPLICATION" ], [ "network-oriented formalism", "METHOD" ], [ "intelligent user interface", "APPLICATION" ], [ "forward chaining", "METHOD" ], [ "object", "DATA" ], [ "domain", "DATA" ] ]

It does not Fitts my data! analysing large amounts of mobile touch data

13,116,120

Touchscreens are the dominant input device for smartphones and learning about smartphone users' touch behaviour became even more important. We developed a game for Android phones to collect a truly large amount of touch data from diverse devices and players. A part of the game is designed as what we expected to be a Fitts' law task. By publishing the game in the Android Market we collected 5,359,650 micro tasks from 63,154 installations of the game. Using Fitts' law to find a model for these tasks we found a very weak correlation and an implausible high index of performance across different devices. Further analysis shows a similar correlation between time and distance as with Fitts' law but only a very weak correlation with the targets' width.

[ { "first": "Niels", "middle": [], "last": "Henze", "suffix": "" }, { "first": "Susanne", "middle": [], "last": "Boll", "suffix": "" } ]

2,011

10.1007/978-3-642-23768-3_83

INTERACT

44898837

[ "10595902", "501599", "14313454", "18069300", "1714600" ]

[ "3482350", "15069407", "3272287", "13970746", "13898955", "46068444", "35974079" ]

true

https://api.semanticscholar.org/CorpusID:13116120

1

[ [ "touch behaviour", "DATA" ], [ "device", "DATA" ], [ "weak corre", "EVALUATION" ], [ "plausible high index of performance", "EVALUATION" ], [ "Touchscreen", "METHOD" ], [ "touch data", "DATA" ], [ "weak correlation", "EVALUATION" ], [ "Fitts' law task", "APPLICATION" ], [ "width", "EVALUATION" ], [ "Fitts' law", "METHOD" ] ]

Accounting for Privacy in Citizen Science: Ethical Research in a Context of Openness

43,051,390

In citizen science, volunteers collect and share data with researchers, other volunteers, and the public at large. Data shared in citizen science includes information on volunteer location or other sensitive personal information; yet, volunteers do not typically express privacy concerns. This study uses the framework of contextual integrity to understand privacy accounting in the context of citizen science, by analyzing contextual variables including roles; information types; data flows and transmission principles; and, uses, norms, and values. Findings show that uses, norms, and values-including core values shared by researchers and public volunteers, and the motivations of individual volunteers' have a significant impact on privacy accounting. Overall, citizen science volunteers and practitioners share and promote openness and data sharing over protecting privacy. Studying the context of citizen science offers an example of contextually-appropriate data sharing that can inform broader questions about research ethics in an age of pervasive data. Based on these findings, this paper offers implications for designing data and information flows and supporting technologies in public and voluntary data sharing projects.

[ { "first": "Anne", "middle": [], "last": "Bowser", "suffix": "" }, { "first": "Katie", "middle": [], "last": "Shilton", "suffix": "" }, { "first": "Jenny", "middle": [], "last": "Preece", "suffix": "" }, { "first": "Elizabeth", "middle": [], "last": "Warrick", "suffix": "" } ]

2,017

10.1145/2998181.2998305

CSCW '17

2588913682

[ "15098526", "86258546", "26665089", "154537317", "22242448", "12058553", "3189720", "108624889", "11455786", "20825566", "18558685", "40726975", "18122201", "7031647", "122578905", "2894245", "14521025", "28158430", "43601682", "32423206", "60935820", "1952921", "17784765", "27209693", "27863868", "152813310", "19636969", "8126024", "12349102", "40436507", "15874637", "1883575", "361794", "12077882", "13999858", "5705197" ]

[ "145979652", "92980433", "134162493", "40377933", "208210224", "26373972", "116445931", "57760392", "49393415", "11692735", "52067221", "170035258" ]

true

https://api.semanticscholar.org/CorpusID:43051390

1

[ [ "personal information", "DATA" ], [ "privacy accounting", "APPLICATION" ], [ "volunteer location", "DATA" ], [ "pervasive data", "DATA" ], [ "type", "DATA" ], [ "public and voluntary data sharing project", "APPLICATION" ], [ "research ethic", "APPLICATION" ], [ "princip", "DATA" ], [ "contextually-appropriate data sharing", "METHOD" ], [ "contextual variable", "DATA" ], [ "citizen science", "APPLICATION" ], [ "contextual integrity", "METHOD" ], [ "information flow", "DATA" ], [ "volunteer", "EVALUATION" ], [ "data sharing", "METHOD" ] ]

See what i'm saying?: using Dyadic Mobile Eye tracking to study collaborative reference

15,972,996

To create intelligent collaborative systems able to anticipate and react appropriately to users' needs and actions, it is crucial to develop a detailed understanding of the process of collaborative reference. We developed a dyadic eye tracking methodology and metrics for studying the multimodal process of reference, and applied these techniques in an experiment using a naturalistic conversation elicitation task. We found systematic differences in linguistic and visual coordination between pairs of mobile and seated participants. Our results detail measurable interactions between referential form, gaze, and spatial context and can be used to enable the development of more natural collaborative user interfaces.

[ { "first": "Darren", "middle": [], "last": "Gergle", "suffix": "" }, { "first": "Alan", "middle": [ "T." ], "last": "Clark", "suffix": "" } ]

2,011

10.1145/1958824.1958892

CSCW '11

2030717901

[ "150084732", "144123735", "16075628", "3064660", "18177177", "11240199", "2926261", "14491937", "9493253", "10611951", "5578779", "5055492", "33344182", "1156914", "315089", "144365113", "144262728", "16366638", "14221543", "18092365", "2968889", "16088799", "1279108", "11522712", "17013982", "1942773", "3670", "7956206", "2918832", "15674294" ]

[ "54139561", "13384014", "15850172", "18760367", "18242002", "215555167", "14667453", "10843155", "17385310", "985400", "11632953", "39786646", "28408093", "11334724", "3160206", "140213332", "6461728", "53088324", "14358419", "15717193", "6887645", "16314024", "9484808", "15669202", "6144383", "18948342", "202121885" ]

true

https://api.semanticscholar.org/CorpusID:15972996

0

1

0

[ [ "collaborative user interface", "APPLICATION" ], [ "collaborative reference", "METHOD" ], [ "visual coordination", "EVALUATION" ], [ "spatial context", "DATA" ], [ "systematic difference", "EVALUATION" ], [ "linguistic", "EVALUATION" ], [ "multimodal process", "DATA" ], [ "naturalistic conversation elicitation task", "APPLICATION" ], [ "collaborative system", "APPLICATION" ], [ "dyadic eye tracking methodology", "METHOD" ], [ "referential form", "DATA" ], [ "measurable interaction", "DATA" ] ]

Query execution in DIRECT

411,415

In this paper query organization, execution, and optimization in the database machine DIRECT are discussed. We demonstrate that the use of a monitor for each relation referenced by a query along with the use of the NEXT PAGE construct permits the DIRECT back-end controller to assign a query to any number of processors for execution. Furthermore, these constructs also permit the controller to balance the load in the back-end by dynamically adjusting how many processors are assigned to each executing query.We also identify the problem of relation fragmentation which occurs when a query is executed by several processors in parallel and develop a technique for estimating the optimal number of processors to compress a relation so that the execution time of the entire query is minimized. These results appear to be applicable to all database machines which employ parallel processing techniques to enhance query execution.

[ { "first": "David", "middle": [ "J." ], "last": "DeWitt", "suffix": "" } ]

1,979

10.1145/582095.582098

SIGMOD '79

2021078717

[]

[ "17154711", "3117435", "17525594", "17815224", "16745275", "14753416", "1411738" ]

false

true

https://api.semanticscholar.org/CorpusID:411415

0

[ [ "database machine", "APPLICATION" ], [ "optimal", "EVALUATION" ], [ "database", "APPLICATION" ], [ "query execution", "APPLICATION" ], [ "DIRECT back-end controller", "METHOD" ], [ "NEXT PAGE construct", "METHOD" ], [ "monitor", "METHOD" ], [ "parallel processing technique", "METHOD" ], [ "execution time", "EVALUATION" ], [ "query organization", "METHOD" ], [ "relation fragmentation", "APPLICATION" ] ]

Eye to I: Males Recognize Own Eye Movements, Females Inhibit Recognition.

4,694,646

Studies show that people can recognize their own movements, such as their own walking (presented in silhouette using point lights), their own drawing (presented as a moving point light), own clapping, and their own piano playing. We extend this result to proprioceptive control, showing that people can recognize their own eye movements, when presented as just a point moving against a black background. Eye movements were recorded using a wearable eye tracking glass, while participants executed four tasks. A week later, participants were shown these videos, alongside another person's videos, for each task, and asked to recognize their own movements. Males recognized their own eye movements significantly above chance, but only for tasks with large and familiar body movements. Females performed below chance in these tasks. We argue that the standard common coding/motor simulation model does not account for this result, and propose an extension where eye movements and body movements are strongly coupled. In this model, eye movements automatically trigger covert motor activation, and thus participate directly in motor planning, simulations and the sense of agency.

[ { "first": "Sanjay", "middle": [], "last": "Chandrasekharan", "suffix": "" }, { "first": "Geetanjali", "middle": [], "last": "Date", "suffix": "" }, { "first": "Prajakt", "middle": [], "last": "Pande", "suffix": "" }, { "first": "Jeenath", "middle": [], "last": "Rahaman", "suffix": "" }, { "first": "Rafikh", "middle": [], "last": "Shaikh", "suffix": "" }, { "first": "Anveshna", "middle": [], "last": "Srivastava", "suffix": "" }, { "first": "Nisheeth", "middle": [], "last": "Srivastava", "suffix": "" }, { "first": "Harshit", "middle": [], "last": "Agrawal", "suffix": "" } ]

2,015

CogSci

2404408829

[ "53597697", "1831428", "9821852", "17652113", "54046837", "14113831", "12575727", "1404851", "19422070", "142209638", "11599742", "2270006", "10066635", "145258208", "144055093", "142868764", "20494000", "8724034", "30616017", "19130129", "14156836", "11421577", "5055086" ]

[ "3928558" ]

true

https://api.semanticscholar.org/CorpusID:4694646

0

1

0

[ [ "motor planning", "APPLICATION" ], [ "moving point light", "VISUALIZATION" ], [ "coding/motor simulation model", "METHOD" ], [ "familiar body movement", "DATA" ], [ "Eye movement", "DATA" ], [ "piano playing", "APPLICATION" ], [ "proprioceptive control", "APPLICATION" ], [ "black background", "VISUALIZATION" ], [ "wearable eye tracking glass", "METHOD" ], [ "eye movement", "DATA" ], [ "point light", "VISUALIZATION" ], [ "body movement", "DATA" ], [ "simulation", "APPLICATION" ], [ "covert motor activation", "METHOD" ] ]

Simple and Efficient Approximate Nearest Neighbor Search Using Spatial Sorting

24,338,717

Finding the nearest neighbors of a point is a highly used operation in many graphics applications. Recently, the neighborhood grid has been proposed as a new approach for this task, focused on low-dimensional spaces. In 2D, for instance, we would organize a set of points in a matrix in such a way that their x and y coordinates are at the same time sorted along rows and columns, respectively. Then, the problem of finding closest points reduces to only examining the nearby elements around a given element in the matrix. Based on this idea, we propose and evaluate novel spatial sorting strategies for the bidimensional case, providing significant performance and precision gains over previous works. We also experimentally analyze different scenarios, to establish the robustness of searching for nearest neighbors. The experiments show that for many dense point distributions, by using some of the devised algorithms, spatial sorting beats more complex and current techniques, like k-d trees and index sorting. Our main contribution is to show that spatial sorting, albeit a still scarcely researched topic, can be turned into a competitive approximate technique for the low-dimensional k-NN problem, still being simple to implement, memory efficient, robust on common cases, and highly parallelizable.

[ { "first": "Marcelo", "middle": [], "last": "De Gomensoro Malheiros", "suffix": "" }, { "first": "Marcelo", "middle": [], "last": "Walter", "suffix": "" } ]

2,015

10.1109/SIBGRAPI.2015.37

2015 28th SIBGRAPI Conference on Graphics, Patterns and Images

1897706930

[ "5636976", "56691", "62371709", "7645519", "5072469", "16791999", "5207906" ]

[ "7798828" ]

true

https://api.semanticscholar.org/CorpusID:24338717

0

1

0

[ [ "bidimensional case", "APPLICATION" ], [ "spatial sorting", "METHOD" ], [ "x and y coordinate", "DATA" ], [ "neighborhood grid", "VISUALIZATION" ], [ "memory efficient", "EVALUATION" ], [ "dense point distribution", "DATA" ], [ "spatial sorting strategy", "METHOD" ], [ "element", "DATA" ], [ "robust", "EVALUATION" ], [ "k-d tree", "METHOD" ], [ "competitive approximate technique", "METHOD" ], [ "graphic application", "APPLICATION" ], [ "nearest neighbor", "DATA" ], [ "low-dimensional k-NN problem", "APPLICATION" ], [ "low-dimensional space", "DATA" ], [ "index sorting", "METHOD" ], [ "closest point", "DATA" ], [ "significant performance and precision gain", "EVALUATION" ] ]

Enhancing Evaluation of Potential Dates Online Through Paired Collaborative Activities

13,529,170

Online dating systems are the most common way people meet their marriage partners online. Nevertheless, online daters struggle to evaluate personality traits of potential partners using profile pages and private messaging in these systems. Meanwhile, Multiplayer Online Games (MOGs) have emerged as a popular way young people find romantic partners for relationships in the physical world. We conducted two interview studies -- one concerning evaluation behavior in online dating systems (n=41) and the other concerning collaborative activities in MOGs (n=35). Insights from these studies reveal the weaknesses in evaluation tools native to online dating and suggest that collaborative activities could potentially address evaluation challenges in online dating. The paper concludes with a discussion of a series of design concepts for online dating in order to improve users' abilities to evaluate their potential romantic partners for in-person meetings.

[ { "first": "Doug", "middle": [], "last": "Zytko", "suffix": "" }, { "first": "Guo", "middle": [], "last": "Freeman", "suffix": "" }, { "first": "Sukeshini", "middle": [ "A." ], "last": "Grandhi", "suffix": "" }, { "first": "Susan", "middle": [ "C." ], "last": "Herring", "suffix": "" }, { "first": "Quentin (Gad)", "middle": [], "last": "Jones", "suffix": "" } ]

2,015

10.1145/2675133.2675184

CSCW '15

2086641625

[ "27915244", "323774", "345005", "7859959", "145414691", "9208060", "14691737", "14949735", "31064341", "181547", "197872365", "14502235", "24988297", "207162937", "7448008", "15620191", "206022503", "146617366", "142611072", "144464313", "13984982", "16332253", "128909158", "33263476", "6550631", "18672684", "146669854", "28953108", "14796765", "146490045", "15392342", "1753915", "31652865" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:13529170

0

1

0

[ [ "online daters", "APPLICATION" ], [ "evaluation challenge", "APPLICATION" ], [ "profile page", "DATA" ], [ "interview study", "EVALUATION" ], [ "private messaging", "METHOD" ], [ "design concept", "METHOD" ], [ "online dating", "APPLICATION" ], [ "online dating system", "APPLICATION" ], [ "Online dating system", "METHOD" ], [ "Multiplayer Online Games (MOGs)", "METHOD" ], [ "collaborative activity", "APPLICATION" ], [ "evaluation behavior", "EVALUATION" ], [ "MOGs", "APPLICATION" ], [ "romantic partner", "APPLICATION" ], [ "physical world", "APPLICATION" ], [ "evaluation tool", "METHOD" ] ]

Mining massively incomplete data sets by conceptual reconstruction

15,071,565

Incomplete data sets have become almost ubiquitous in a wide variety of application domains. Common examples can be found in climate and image data sets, sensor data sets and medical data sets. The incompleteness in these data sets may arise from a number of factors: in some cases it may simply be a reflection of certain measurements not being available at the time; in others the information may be lost due to partial system failure; or it may simply be a result of users being unwilling to specify attributes due to privacy concerns. When a significant fraction of the entries are missing in all of the attributes, it becomes very difficult to perform any kind of reasonable extrapolation on the original data. For such cases, we introduce the novel idea of conceptual reconstruction, in which we create effective conceptual representations on which the data mining algorithms can be directly applied. The attraction behind the idea of conceptual reconstruction is to use the correlation structure of the data in order to express it in terms of concepts rather the original dimensions. As a result, the reconstruction procedure estimates only those conceptual aspects of the data which can be mined from the incomplete data set, rather than force errors created by extrapolation. We demonstrate the effectiveness of the approach on a variety of real data sets.

[ { "first": "Charu", "middle": [ "C." ], "last": "Aggarwal", "suffix": "" }, { "first": "Srinivasan", "middle": [], "last": "Parthasarathy", "suffix": "" } ]

2,001

10.1145/502512.502543

KDD '01

2028020328

[ "15071565", "207676583", "122725444", "53303687", "2801908" ]

[ "38411285", "51609046", "11041379", "20986958", "9202078", "1863568", "18052887", "8052107", "21283505", "7743521", "2144648", "209459005", "912673", "16868432", "1839515", "15071565", "18041871", "12231176", "7316344" ]

true

https://api.semanticscholar.org/CorpusID:15071565

0

1

0

[ [ "real data set", "DATA" ], [ "partial system failure", "METHOD" ], [ "privacy concern", "APPLICATION" ], [ "Incomplete data set", "DATA" ], [ "correlation structure", "DATA" ], [ "incomplete data set", "DATA" ], [ "medical data set", "DATA" ], [ "climate and image data set", "DATA" ], [ "data mining algorithm", "METHOD" ], [ "conceptual representation", "METHOD" ], [ "reasonable extrapolation", "METHOD" ], [ "conceptual reconstruction", "METHOD" ], [ "reconstruction procedure", "METHOD" ], [ "sensor data set", "DATA" ], [ "data", "METHOD" ] ]

Computing for biologists: lessons from some successful case studies

7,474,822

My presentation will be online at the address http://cs.nyu.edu/cs/faculty/shasha/papers/sigmodtut05.ppt in addition to at the SIGMOD site. The presentation discusses computational techniques that have helped biologists, including combinatorial design to support a disciplined experimental design, visualization techniques to display the interaction among multiple inputs, and the discovery of gene function through the search through related species, and others.In this writeup, I confine myself to informal remarks describing both social and technical lessons I have learned while working with biologists. I intersperse these comments with references to relevant papers when appropriate.The tutorial is meant to appeal to researchers and practitioners in databases, data mining, and combinatorial algorithms as well as to natural scientists, especially biologists.

[ { "first": "Dennis", "middle": [], "last": "Shasha", "suffix": "" } ]

2,005

10.1145/1066157.1066309

SIGMOD '05

2049183267

[ "43560543", "28467466", "830", "37732855", "15313788", "13978660", "2940771" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:7474822

0

1

0

[ [ "function", "METHOD" ], [ "combinatorial algorithm", "APPLICATION" ], [ "natural scientist", "APPLICATION" ], [ "experimental design", "APPLICATION" ], [ "visualization technique", "METHOD" ], [ "combinatorial design", "METHOD" ], [ "databases, data mining", "APPLICATION" ], [ "computational technique", "METHOD" ], [ "technical lesson", "EVALUATION" ] ]

Queueing performance analysis of co-scheduling in a pool of processors environment

10,481,605

We consider a connected set of workstations as a “pool of processors” and develop a queueing model to analyze the performance of optimal co-scheduling algorithms. The pool of processors model was originally developed for the Amoeba operating system. It was also used in the design of the recent IBM supercomputer model 9076 SP1. Recently, co-scheduling has been suggested as an approach for scheduling computationally intensive tasks in the pool of processors model. Co-scheduling algorithms select the best possible subset of workstations for a task to minimize its completion time. We develop a queueing model which allows us to investigate the dynamic performance of co-scheduling algorithms from the system point of view under several queueing strategies. We use six different queueing strategies in combination with co-scheduling and compare the results to the M/M/m system where arriving tasks would be assigned to workstations as whole computations, and no co-scheduling would take place. The results show that the co-scheduling approach is viable under a wide range of system parameters. Moreover, performance differences of queueing strategies tend to diminish as the number of workstations grows. This suggests that co-scheduling is universally applicable across the queueing disciplines considered here when there are a large number of workstations.

[ { "first": "Margaret", "middle": [ "A." ], "last": "Schaar", "suffix": "" }, { "first": "Kemal", "middle": [], "last": "Efe", "suffix": "" }, { "first": "Weijia", "middle": [], "last": "Shang", "suffix": "" } ]

1,994

10.1145/181181.181547

ICS '94

2087181287

[ "4997952", "13482636", "59678997", "31490523", "17656737" ]

[ "16698131" ]

true

https://api.semanticscholar.org/CorpusID:10481605

0

1

0

[ [ "dynamic performance", "EVALUATION" ], [ "pool of processor model", "METHOD" ], [ "performance difference", "EVALUATION" ], [ "computationally intensive task", "APPLICATION" ], [ "Amoeba operating system", "APPLICATION" ], [ "co-scheduling approach", "METHOD" ], [ "queueing strategy", "METHOD" ], [ "queueing discipline", "APPLICATION" ], [ "complet", "METHOD" ], [ "co-scheduling", "METHOD" ], [ "queueing model", "METHOD" ], [ "co-scheduling algorithm", "METHOD" ], [ "system parameter", "DATA" ], [ "optimal co-scheduling algorithm", "METHOD" ], [ "M/M/m system", "METHOD" ], [ "supercomputer model", "METHOD" ], [ "Co-scheduling algorithm", "METHOD" ] ]

Sensing Kirigami

195,259,307

This pictorial presents our material-driven inquiry into carbon-coated paper and kirigami structures. We investigated two variations of this paper and their affordances for tangible interaction; particularly their electrical, haptic, and visual aspects when shaped into three-dimensional forms through cutting, folding, and bending. Through this exploration, we uncovered distinct affordances between the two paper types for sensing folds and bends, due to differences in their material compositions. From these insights, we propose three applications that showcase the possibilities of this material for tangible interaction design. In addition, we leverage the pictorial format to expose working design schematics for others to take up their own explorations.

[ { "first": "Clement", "middle": [], "last": "Zheng", "suffix": "" }, { "first": "HyunJoo", "middle": [], "last": "Oh", "suffix": "" }, { "first": "Laura", "middle": [], "last": "Devendorf", "suffix": "" }, { "first": "Ellen Yi-Luen", "middle": [], "last": "Do", "suffix": "" } ]

2,019

10.1145/3322276.3323689

DIS '19

2969102351

[]

false

https://api.semanticscholar.org/CorpusID:195259307

null

[ [ "kirigami structure", "METHOD" ], [ "working design schematic", "DATA" ], [ "material-driven inqui", "APPLICATION" ], [ "pictorial format", "VISUALIZATION" ], [ "tangible interaction", "APPLICATION" ], [ "carbon-coated paper", "METHOD" ], [ "three-dimensional form", "DATA" ], [ "material composition", "DATA" ], [ "distinct affordances", "EVALUATION" ], [ "aspect", "DATA" ], [ "fold and", "EVALUATION" ], [ "tangible interaction design", "APPLICATION" ] ]

Motives and Concerns of Dashcam Video Sharing

6,243,713

Dashcams support continuous recording of external views that provide evidence in case of unexpected traffic-related accidents and incidents. Recently, sharing of dashcam videos has gained significant traction for accident investigation and entertainment purposes. Furthermore, there is a growing awareness that dashcam video sharing will greatly extend urban surveillance. Our work aims to identify the major motives and concerns behind the sharing of dashcam videos for urban surveillance. We conducted two survey studies (n=108, n=373) in Korea. Our results show that reciprocal altruism/social justice and monetary reward were the major motives and that participants were strongly motivated by altruism and social justice. Our studies have also identified major privacy concerns and found that groups with greater privacy concerns had lower altruism and justice motive, but had higher monetary motive. Our main findings have significant implications on the design of a dashcam video-sharing service.

[ { "first": "Sangkeun", "middle": [], "last": "Park", "suffix": "" }, { "first": "Joohyun", "middle": [], "last": "Kim", "suffix": "" }, { "first": "Rabeb", "middle": [], "last": "Mizouni", "suffix": "" }, { "first": "Uichin", "middle": [], "last": "Lee", "suffix": "" } ]

2,016

10.1145/2858036.2858581

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

2399811568

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[ [ "survey study", "EVALUATION" ], [ "urban surveillance", "APPLICATION" ], [ "social justice", "APPLICATION" ], [ "reciprocal altruism/social justice", "APPLICATION" ], [ "altruism and justice motive", "DATA" ], [ "dashcam video-sharing service", "APPLICATION" ], [ "monetary motive", "EVALUATION" ], [ "accident investigation", "APPLICATION" ], [ "dashcam video sharing", "METHOD" ], [ "entertainment purpose", "APPLICATION" ], [ "Das", "METHOD" ], [ "monetary reward", "EVALUATION" ], [ "traffic-related accident", "APPLICATION" ], [ "dashcam video", "DATA" ] ]

Knowing where and when to look in a time-critical multimodal dual task

13,896,618

Human-computer systems intended for time-critical multitasking need to be designed with an understanding of how humans can coordinate and interleave perceptual, memory, and motor processes. This paper presents human performance data for a highly-practiced time-critical dual task. In the first of the two interleaved tasks, participants tracked a target with a joystick. In the second, participants keyed-in responses to objects moving across a radar display. Task manipulations include the peripheral visibility of the secondary display (visible or not) and the presence or absence of auditory cues to assist with the radar task. Eye movement analyses reveal extensive coordination and overlapping of human information processes and the extent to which task manipulations helped or hindered dual task performance. For example, auditory cues helped only a little when the secondary display was peripherally visible, but they helped a lot when it was not peripherally visible.

[ { "first": "Anthony", "middle": [ "J." ], "last": "Hornof", "suffix": "" }, { "first": "Yunfeng", "middle": [], "last": "Zhang", "suffix": "" }, { "first": "Tim", "middle": [], "last": "Halverson", "suffix": "" } ]

2,010

10.1145/1753326.1753647

CHI

2064919653

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[ "34063178", "83461326", "7681064", "13234726", "215209388", "15326565", "205981556", "15549067", "11638535", "46219110", "13171373", "13756428", "15545940", "7400557" ]

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[ [ "time-critical multitasking", "APPLICATION" ], [ "radar display", "VISUALIZATION" ], [ "secondary display", "VISUALIZATION" ], [ "auditory cue", "METHOD" ], [ "radar task", "APPLICATION" ], [ "perceptual, memory, and motor process", "METHOD" ], [ "peripheral visibility", "VISUALIZATION" ], [ "dual task performance", "APPLICATION" ], [ "time-critical dual task", "APPLICATION" ], [ "human performance data", "DATA" ], [ "human information process", "METHOD" ], [ "joystick", "METHOD" ], [ "Human-computer system", "METHOD" ], [ "Eye movement analysis", "EVALUATION" ], [ "task manipulation", "METHOD" ] ]

A hierarchical Bayesian model for improving wisdom of the crowd aggregation of quantities with large between-informant variability

16,793,394

A hierarchical Bayesian model for improving wisdom of the crowd aggregation of quantities with large between-informant variability Saiwing Yeung ([email protected]) Institute of Education, Beijing Institute of Technology, China Abstract The wisdom of the crowd technique has been shown to be very effective in producing judgments more accurate than those of individuals. However, its performance in situations in which the intended estimates would involve responses of greatly dif- fering magnitudes is less well understood. We first carried out an experiment to elicit people’s estimates in one such domain, populations of U.S. metropolitan areas. Results indicated that there were indeed vast between-subjects differences in magni- tudes of responses. We then proposed a hierarchical Bayesian model that incorporates different respondents’ biases in terms of the overall magnitudes of their answers and the amount of individual uncertainties. We implemented three variations of this model with different ways of instantiating the individual differences in overall magnitude. Estimates produced by the variation that accounts for the stochasticities in response mag- nitude outperformed those based on standard wisdom of the crowd aggregation methods and other variations. Keywords: wisdom of the crowd; graphical model; hierarchi- cal Bayesian model; human judgments; individual differences. Introduction The wisdom of the crowd (WoC) technique involves aggre- gating decisions or estimates made by a group of people. Much research has found that the crowd as a whole can produce estimates that are much more accurate than those by a random informant (Surowiecki, 2005). However, most research focused on types of quantities that are naturally bounded. For example, if the targets of estimation were prob- abilities of events, all responses would need to be between 0 and 1. This restriction constrains the plausible range of re- sponses and could, as a result, potentially help produce more accurate estimates. Other similarly naturally bounded quanti- ties, some to a lesser degree, include year of events, tempera- ture of cities, etc. In contrast, many real life estimation prob- lems involve values that are not naturally bounded, such that estimates given by different informants could vary by multi- ple orders of magnitude. Previous studies have found that when applied to quantities that are not naturally bounded, traditional WoC aggregation methods, such as the mean or median of a crowd’s estimates, yield relatively smaller improvement, compared to those that are naturally bounded. For example, Yeung (2013) reported that neither mean nor median improved confidence interval estimates for questions without natural bounds, while they did improve those with natural bounds. Rauhut and Lorenz (2011) also reported that averaging an individual’s multiple responses to the same questions did not improve estimates for general numerical questions, in contrast to similar previ- ous research using questions about percentage values (Vul & Pashler, 2008). Estimates about quantities without natural bounds are com- monly encountered because many naturally occurring quanti- ties can be described by distributions without a natural maxi- mum and are severely right skewed. For example, Gibrat’s law suggested that the distribution of populations of cities follows a log-normal distribution (Eeckhout, 2004). Other distributions with similar characteristics include power-law, Pareto, and exponential distributions. They naturally occur in many different contexts, including income and wealth, num- ber of friends, waiting time, time till failure, etc. (Barabasi, 2005). How to best aggregate these quantities in a WoC con- text is not very well understood. In the present research we demonstrate a hierarchical Bayesian approach to the problem. Hierarchical Bayesian models formally express the rela- tionships between psychological constructs, stimuli, and ob- servations. They produce quantitative predictions that can be compared with empirical data, providing a way to test psy- chological theories encapsulated in the models (Lee, 2011). In this paper our main objectives are to improve the WoC estimates for quantities without natural bounds using such models, and to investigate the psychological assumptions on which these models rely. We first carried out an empiri- cal experiment to obtain the data on which we will base our analyses. Standard WoC procedures will be applied and their performances will be evaluated. We will then pro- pose and implement a family of computational models that is based on assumptions made about the structure of people’s responses. We will then compare the performance of these models against those of the standard WoC methods. Finally we will discuss the implication of our findings. The experiment We recruited 101 participants from Amazon Mechanical Turk. Workers were required to be 18 years or older, be resid- ing in the U.S., and have a lifetime acceptance rate on MTurk of over 95%. Each participant was paid US$0.40. We first reminded participants to not use any external re- sources during the experiment. We then asked participants to rate the level of their knowledge about geography and popu- lation on a 7-point scale (from “Very Good” to “Very Poor”). The participants then completed a set of trivia questions on U.S. geography taken from the experiment in Moore and Healy (2008). We used all nine geography questions there that were about the U.S. Out of those, three were classified by Moore and Healy as easy, four medium, and one hard. Two of the questions were changed slightly to make them more difficult in order to increase the discriminatory power about the participants’ knowledge. The participants would then proceed to the main part of the experiment. Here they were asked to make estimates about the population of 20 U.S. metropolitan areas (the full list can

[ { "first": "Saiwing", "middle": [], "last": "Yeung", "suffix": "" } ]

2,014

CogSci

2400360137

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[ [ "between-subjects difference", "EVALUATION" ], [ "individual uncertainty", "EVALUATION" ], [ "prob- lem", "APPLICATION" ], [ "hierarchical Bayesian model", "METHOD" ], [ "waiting time", "APPLICATION" ], [ "exponential distribution", "METHOD" ], [ "computational model", "METHOD" ], [ "external re- source", "DATA" ], [ "percentage value", "DATA" ], [ "Pareto", "METHOD" ], [ "numerical question", "APPLICATION" ], [ "power-law", "METHOD" ], [ "empirical data", "DATA" ], [ "lifetime acceptance rate", "EVALUATION" ], [ "psychological construct", "DATA" ], [ "psychological assumption", "METHOD" ], [ "discriminatory power", "EVALUATION" ], [ "metropolitan", "APPLICATION" ], [ "7-point scale", "EVALUATION" ], [ "MTur", "METHOD" ], [ "empiri- cal experiment", "EVALUATION" ], [ "random informant", "METHOD" ], [ "difference", "DATA" ], [ "accurate estimate", "EVALUATION" ], [ "natural bound", "DATA" ], [ "Gibrat’s law", "METHOD" ], [ "judgments; individual difference", "EVALUATION" ], [ "hierarchical Bayesian approach", "METHOD" ], [ "overall magnitude", "DATA" ], [ "Hierarchical Bayesian model", "METHOD" ], [ "crowd technique", "METHOD" ], [ "confidence interval estimate", "EVALUATION" ], [ "plausible range", "EVALUATION" ], [ "log-normal distribution", "METHOD" ], [ "geography question", "EVALUATION" ], [ "WoC con- text", "METHOD" ], [ "geography", "DATA" ], [ "wisdom of the crowd (WoC) technique", "METHOD" ], [ "psy- chological theory", "METHOD" ], [ "crowd aggregation method", "METHOD" ], [ "Standard WoC procedure", "METHOD" ], [ "crowd aggregat", "METHOD" ], [ "quantitative prediction", "EVALUATION" ], [ "WoC estimate", "EVALUATION" ], [ "mean", "METHOD" ], [ "u- lation", "DATA" ], [ "WoC aggregation methods,", "METHOD" ], [ "between-informant variabil", "DATA" ], [ "trivia question", "EVALUATION" ], [ "standard WoC method", "METHOD" ] ]

Collaborative translation by monolinguals with machine translators

16,795,920

In this paper, we present the concept for collaborative translation, where two non-bilingual people who use different languages collaborate to perform the task of translation using machine translation (MT) services, whose quality is imperfect in many cases. The key idea of this model is that one person, who handles the source language (source lan-guage side) and another person, who handles the target language (target language side), play different roles: the target language side modifies the translated sentence to improve its fluency, and the source language side evaluates its adequacy. We demonstrated the effectiveness and the practicality of this model in a tangible way.

[ { "first": "Daisuke", "middle": [], "last": "Morita", "suffix": "" }, { "first": "Toru", "middle": [], "last": "Ishida", "suffix": "" } ]

2,009

10.1145/1502650.1502701

IUI '09

2132214281

[ "14302557", "55375229", "11819644", "41028851", "144668382", "14222043", "14269229" ]

[ "14468633", "27102027", "17061021", "52050003", "52050003", "17493482", "9368697", "2768484", "21041885", "2705354", "13954643", "17299748", "6919475", "9660209", "62039005", "119828", "12095488" ]

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[ [ "collaborative translation", "METHOD" ], [ "machine translation (MT) service", "METHOD" ], [ "translation", "APPLICATION" ], [ "tangible way", "EVALUATION" ] ]

Stochastic Blockmodel with Cluster Overlap, Relevance Selection, and Similarity-Based Smoothing

10,693,000

Stochastic block models provide a rich, probabilistic framework for modeling relational data by expressing the objects being modeled in terms of a latent vector representation. This representation can be a latent indicator vector denoting the cluster membership (hard clustering), a vector of cluster membership probabilities (soft clustering), or more generally a real-valued vector (latent space representation). Recently, a new class of overlapping stochastic block models has been proposed where the idea is to allow the objects to have hard memberships in multiple clusters (in form of a latent binary vector). This aspect captures the properties of many real-world networks in domains such as biology and social networks where objects can simultaneously have memberships in multiple clusters owing to the multiple roles they may have. In this paper, we improve upon this model in three key ways: (1) we extend the overlapping stochastic block model to the bipartite graph case which enables us to simultaneously learn the overlapping clustering of two different sets of objects in the graph, the unipartite graph is just a special case of our model, (2) we allow objects (in either set) to not have membership in any cluster by using a relevant object selection mechanism, and (3) we make use of additionally available object features (or a kernel matrix of pair wise object similarities) to further improve the overlapping clustering performance. We do this by explicitly encouraging similar objects to have similar cluster membership vectors. Moreover, using nonparametric Bayesian prior distributions on the key model parameters, we side-step the model selection issues such as selecting the number of clusters a priori. Our model is quite general and can be applied for both overlapping clustering and link prediction tasks in unipartite and bipartite networks (directed/undirected), or for overlapping co-clustering of general binary-valued data. Experiments on synthetic and real-world datasets from biology and social networks demonstrate that our model outperforms several state-of-the-art methods.

[ { "first": "Joyce", "middle": [ "Jiyoung" ], "last": "Whang", "suffix": "" }, { "first": "Piyush", "middle": [], "last": "Rai", "suffix": "" }, { "first": "Inderjit", "middle": [ "S." ], "last": "Dhillon", "suffix": "" } ]

2,013

10.1109/ICDM.2013.156

2013 IEEE 13th International Conference on Data Mining

2057728196

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[ "15359088", "919703", "11204752", "29831431" ]

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[ [ "overlapping clustering performance", "EVALUATION" ], [ "graph", "DATA" ], [ "bipartite graph", "DATA" ], [ "unipartite graph", "DATA" ], [ "real-world network", "DATA" ], [ "clustering", "METHOD" ], [ "nonparametric Bayesian prior distribution", "METHOD" ], [ "relevant object selection mechanism", "METHOD" ], [ "membership (hard clustering", "METHOD" ], [ "binary-valued data", "DATA" ], [ "social network", "APPLICATION" ], [ "model parameter", "DATA" ], [ "unipartite and bipartite network", "APPLICATION" ], [ "relational data", "DATA" ], [ "link prediction task", "APPLICATION" ], [ "latent binary vector", "METHOD" ], [ "ctor (latent space representation", "VISUALIZATION" ], [ "ities (soft clustering", "METHOD" ], [ "object feature", "DATA" ], [ "stochastic block model", "METHOD" ], [ "overlapping clustering", "APPLICATION" ], [ "biology", "APPLICATION" ], [ "probabilistic framework", "METHOD" ], [ "latent vector representation", "VISUALIZATION" ], [ "Stochastic block model", "METHOD" ], [ "synthetic and real-world datasets", "DATA" ], [ "latent indicator vector", "DATA" ], [ "overlapping co-clustering", "APPLICATION" ], [ "pair wise object similarity", "DATA" ], [ "cluster membership vector", "DATA" ], [ "model selection issue", "EVALUATION" ] ]

Sign language experience affects comprehension and attention to gesture.

116,863,301

[ { "first": "Jenny", "middle": [], "last": "Lu", "suffix": "" }, { "first": "Nicole", "middle": [], "last": "Burke", "suffix": "" }, { "first": "Susan", "middle": [], "last": "Goldin-Meadow", "suffix": "" }, { "first": "Amanda", "middle": [ "L." ], "last": "Woodward", "suffix": "" } ]

2,018

CogSci

2941212330

[]

false

https://api.semanticscholar.org/CorpusID:116863301

null

[ [ "Sign language", "APPLICATION" ] ]

Low-level Visual Statistics in Infant-Perspective Scenes Change with Development.

116,863,699

[ { "first": "Christina", "middle": [], "last": "DeSerio", "suffix": "" }, { "first": "T.", "middle": [ "Rowan" ], "last": "Candy", "suffix": "" }, { "first": "Jason", "middle": [ "M." ], "last": "Gold", "suffix": "" }, { "first": "Linda", "middle": [ "B." ], "last": "Smith", "suffix": "" } ]

2,018

CogSci

2940734829

[]

false

https://api.semanticscholar.org/CorpusID:116863699

null

[ [ "Infant-Perspective Scene", "APPLICATION" ], [ "Visual Statistics", "VISUALIZATION" ] ]

Multinomial Processing Models for Syllogistic Reasoning: A Comparison.

116,863,771

[ { "first": "Hannah", "middle": [], "last": "Dames", "suffix": "" }, { "first": "Jan", "middle": [ "Ole", "von" ], "last": "Hartz", "suffix": "" }, { "first": "Mario", "middle": [], "last": "Kantz", "suffix": "" }, { "first": "Nicolas", "middle": [], "last": "Riesterer", "suffix": "" }, { "first": "Marco", "middle": [], "last": "Ragni", "suffix": "" } ]

2,018

CogSci

2942016116

[ "122830292", "15103886", "147274549", "45977419", "12663101", "9785530", "16112915", "10571398", "23251686", "17995032", "14994393", "21510690" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:116863771

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1

0

[ [ "Syllogistic Reasoning", "APPLICATION" ], [ "Multinomial Processing Models", "METHOD" ] ]

When Boys Are More Generous Than Girls: Effects of Gender and Coordination Level on Prosocial Behavior in 4-year-old Chinese Children.

116,864,742

[ { "first": "Yingjia", "middle": [], "last": "Wan", "suffix": "" }, { "first": "Hong", "middle": [], "last": "Fu", "suffix": "" }, { "first": "Michael", "middle": [ "K." ], "last": "Tanenhaus", "suffix": "" } ]

2,018

CogSci

2940859526

[]

false

https://api.semanticscholar.org/CorpusID:116864742

null

[ [ "Prosocial Behavi", "APPLICATION" ] ]

Child-guided math practice: The role of regulatory emotional self-efficacy for children experiencing homelessness.

116,864,818

[ { "first": "Macey", "middle": [], "last": "Cartwright", "suffix": "" }, { "first": "Heidi", "middle": [], "last": "Kloos", "suffix": "" }, { "first": "Quintino", "middle": [ "R." ], "last": "Mano", "suffix": "" }, { "first": "Casey", "middle": [], "last": "Hord", "suffix": "" } ]

2,018

CogSci

2942202136

[ "145391498", "142746089", "143717122", "145183163", "7061729", "16273808", "144195837", "143471831", "151806166", "145420210", "145505800", "3156627", "12098609", "28601560", "144415437", "14688874" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:116864818

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1

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[ [ "regulatory emotional self-efficacy", "METHOD" ], [ "Child-guided math practice", "METHOD" ] ]

REPRISE: A Retrospective and Prospective Inference Scheme.

116,865,243

[ { "first": "Martin", "middle": [ "V." ], "last": "Butz", "suffix": "" }, { "first": "David", "middle": [ "K." ], "last": "Bilkey", "suffix": "" }, { "first": "Alistair", "middle": [], "last": "Knott", "suffix": "" }, { "first": "Sebastian", "middle": [], "last": "Otte", "suffix": "" } ]

2,018

CogSci

2940539867

[]

[ "67856452" ]

false

true

false

https://api.semanticscholar.org/CorpusID:116865243

null

[ [ "Retrospective and Prospective Inference Scheme", "METHOD" ] ]

Interaction, cognitive diversity and abstraction.

116,865,398

[ { "first": "Kristian", "middle": [], "last": "Tylén", "suffix": "" }, { "first": "Johanne", "middle": [ "Stege" ], "last": "Philipsen", "suffix": "" }, { "first": "Svend", "middle": [], "last": "Østergaard", "suffix": "" }, { "first": "Joanna", "middle": [], "last": "Raczaszek-Leonardi", "suffix": "" }, { "first": "Frederik", "middle": [], "last": "Stjernfelt", "suffix": "" }, { "first": "Riccardo", "middle": [], "last": "Fusaroli", "suffix": "" } ]

2,018

CogSci

2942157886

[]

false

https://api.semanticscholar.org/CorpusID:116865398

null

[ [ "cognitive diversity", "APPLICATION" ] ]

Accessing the web: from search to integration

25,897,413

We have witnessed the rapid growth of the Web-- It has not only "broadened" but also "deepened": While the "surface Web" has expanded from the 1999 estimate of 800 million to the recent 19.2 billion pages reported by Yahoo index, an equally or even more significant amount of information is hidden on the "deep Web," behind query forms, recently estimated at over 1.2 million, of online databases. Accessing the information on the Web thus requires not only search to locate pages of interests, from the surface Web, but also integration to aggregate data from alternative or complementary sources, from the deep Web. Although the opportunities are unprecedented, the challenges are also immense: On the one hand, for the surface Web, while search seems to have evolved into a standard technology, its maturity and pervasiveness have also invited the attack of spam and the demand of personalization. On the other hand, for the deep Web, while the proliferation of structured sources has promised unlimited possibilities for more precise and aggregated access, it has also presented new challenges for realizing large scale and dynamic information integration. These issues are in essence related to data management, in a large scale, and thus present novel problems and interesting opportunities for our research community. This tutorial will discuss the new access scenarios and research problems in Web information access: from search of the surface Web to integration of the deep Web.

[ { "first": "Kevin", "middle": [ "Chen-Chuan" ], "last": "Chang", "suffix": "" }, { "first": "Junghoo", "middle": [], "last": "Cho", "suffix": "" } ]

2,006

10.1145/1142473.1142601

SIGMOD '06

2015432483

[ "4347646", "827735", "4658514" ]

[ "14323428", "334120", "1836345", "17471169", "9156262", "10860580", "15473023", "60708351", "10961", "17479318", "48358579", "6494383", "6463101", "2710859", "15320241", "7371166", "293799", "16361075", "16346746", "5897913" ]

true

https://api.semanticscholar.org/CorpusID:25897413

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0

[ [ "data management", "APPLICATION" ], [ "Web information access", "APPLICATION" ], [ "online database", "DATA" ], [ "surface Web", "METHOD" ], [ "d source", "DATA" ], [ "query form", "DATA" ], [ "personaliza", "APPLICATION" ], [ "dynamic information integration", "APPLICATION" ], [ "spam", "APPLICATION" ], [ "deep Web", "METHOD" ] ]

The Structure of Tube - A Tool for Implementing Advanced User Interfaces

59,986,872

[ { "first": "Ralph", "middle": [ "D." ], "last": "Hill", "suffix": "" }, { "first": "Marc", "middle": [], "last": "Herrmann", "suffix": "" } ]

1,989

10.2312/egtp.19891001

198285135

[]

[ "932896", "62382737" ]

false

true

false

https://api.semanticscholar.org/CorpusID:59986872

null

[ [ "Advanced User Interfaces", "APPLICATION" ] ]

MARK: a boosting algorithm for heterogeneous kernel models

1,320,116

Support Vector Machines and other kernel methods have proven to be very effective for nonlinear inference. Practical issues are how to select the type of kernel including any parameters and how to deal with the computational issues caused by the fact that the kernel matrix grows quadratically with the data. Inspired by ensemble and boosting methods like MART, we propose the Multiple Additive Regression Kernels (MARK) algorithm to address these issues. MARK considers a large (potentially infinite) library of kernel matrices formed by different kernel functions and parameters. Using gradient boosting/column generation, MARK constructs columns of the heterogeneous kernel matrix (the base hypotheses) on the fly and then adds them into the kernel ensemble. Regularization methods such as used in SVM, kernel ridge regression, and MART, are used to prevent overfitting. We investigate how MARK is applied to heterogeneous kernel ridge regression. The resulting algorithm is simple to implement and efficient. Kernel parameter selection is handled within MARK. Sampling and "weak" kernels are used to further enhance the computational efficiency of the resulting additive algorithm. The user can incorporate and potentially extract domain knowledge by restricting the kernel library to interpretable kernels. MARK compares very favorably with SVM and kernel ridge regression on several benchmark datasets.

[ { "first": "Kristin", "middle": [ "P." ], "last": "Bennett", "suffix": "" }, { "first": "Michinari", "middle": [], "last": "Momma", "suffix": "" }, { "first": "Mark", "middle": [ "J." ], "last": "Embrechts", "suffix": "" } ]

2,002

10.1145/775047.775051

KDD '02

1979840843

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https://api.semanticscholar.org/CorpusID:1320116

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0

[ [ "MARK", "METHOD" ], [ "Support Vector Machines", "METHOD" ], [ "Kernel parameter selection", "METHOD" ], [ "computational efficiency", "EVALUATION" ], [ "benchmark dataset", "DATA" ], [ "kernel matri", "DATA" ], [ "ensemble and boosting method", "METHOD" ], [ "kernel matrix", "DATA" ], [ "computational issue", "EVALUATION" ], [ "SVM", "METHOD" ], [ "heterogeneous kernel ridge regression", "APPLICATION" ], [ "kernel ensemble", "METHOD" ], [ "kernel ridge regression", "METHOD" ], [ "MART", "METHOD" ], [ "heterogeneous kernel matrix", "DATA" ], [ "Multiple Additive Regression Kernels (MARK) algorithm", "METHOD" ], [ "additive algorithm", "METHOD" ], [ "nonlinear inference", "APPLICATION" ], [ "interpretable kernel", "METHOD" ], [ "Regularization method", "METHOD" ], [ "Sampling and \"weak\" kernel", "METHOD" ], [ "gradient boosting/column generation", "METHOD" ], [ "kernel library", "METHOD" ] ]

Automatic prediction of misconceptions in multilingual computer-mediated communication

5,669,343

Multilingual communities using machine translation to overcome language barriers are showing up with increasing frequency. However, when a large number of translation errors get mixed into conversations, users have difficulty completely understanding each other. In this paper, we focus on misconceptions found in high volume in actual online conversations using machine translation. We first examine the response patterns in machine translation-mediated communication and associate them with misconceptions. Analysis results indicate that response messages to include misconceptions posted via machine translation tend to be incoherent, often focusing on short phrases of the original message. Next, based on the analysis results, we propose a method that automatically predicts the occurrence of misconceptions in each dialogue. The proposed method assesses the tendency of each dialogue including misconceptions by calculating the gaps between the regular discussion thread (syntactic thread) and the discussion thread based on lexical cohesion (semantic thread). Verification results show significant positive correlation between actual misconception frequency and gaps between syntactic and semantic threads, which indicate the validity of the method.

[ { "first": "Naomi", "middle": [], "last": "Yamashita", "suffix": "" }, { "first": "Toru", "middle": [], "last": "Ishida", "suffix": "" } ]

2,006

10.1145/1111449.1111469

IUI '06

2103450086

[ "55375229", "14302557", "11618140", "150698200", "31445836", "41047906", "60879123", "673280", "200065340", "3370752", "5070516", "41028851", "3337898", "45699093", "9143525", "707969", "8517727", "144668382", "14222043", "7730969", "147274549" ]

[ "13956315", "46026884", "2768484", "18627624", "2705354", "14331348", "14866382", "14269229", "21697648", "6919475", "11476073", "5075050", "14901938", "12095488" ]

true

https://api.semanticscholar.org/CorpusID:5669343

0

1

0

[ [ "analysis result", "EVALUATION" ], [ "discussion thread", "DATA" ], [ "Multi", "APPLICATION" ], [ "actual online conversation", "APPLICATION" ], [ "Analysis result", "EVALUATION" ], [ "sion (semantic thread", "DATA" ], [ "thread (syntactic thread", "DATA" ], [ "machine translation", "METHOD" ], [ "actual misconception frequency", "DATA" ], [ "machine translation-mediated communication", "APPLICATION" ], [ "syntactic and semantic thread", "DATA" ], [ "translation error", "DATA" ], [ "positive correlation", "EVALUATION" ] ]

Dependency networks for relational data

9,292,630

Instance independence is a critical assumption of traditional machine learning methods contradicted by many relational datasets. For example, in scientific literature datasets, there are dependencies among the references of a paper. Recent work on graphical models for relational data has demonstrated significant performance gains for models that exploit the dependencies among instances. In this paper, we present relational dependency networks (RDNs), a new form of graphical model capable of reasoning with such dependencies in a relational setting. We describe the details of RDN models and outline their strengths, most notably the ability to learn and reason with cyclic relational dependencies. We present RDN models learned on a number of real-world datasets, and evaluate the models in a classification context, showing significant performance improvements. In addition, we use synthetic data to evaluate the quality of model learning and inference procedures.

[ { "first": "J.", "middle": [], "last": "Neville", "suffix": "" }, { "first": "D.", "middle": [], "last": "Jensen", "suffix": "" } ]

2,004

10.1109/ICDM.2004.10101

Fourth IEEE International Conference on Data Mining (ICDM'04)

2124965089

[ "116757950", "18446817", "207226010", "2312137", "743435", "11581349", "980412", "10748648", "277918", "270398", "11015651", "7321438", "4745154", "2282762" ]

[ "1640332", "9586753", "59916681", "59916681", "7014143", "38649778", "15799755", "8488829", "3134993", "6903891", "1527334", "2804817", "16140565", "3888429", "209455398", "9920811", "2929149", "12590148", "11942376", "14247395", "2271819", "947628", "1219249", "2715893", "15680117", "9018448", "3403798", "15141819", "14938908", "2473806", "8078589", "16124102", "121853560", "685560", "16744468", "272573", "7196775", "16686760", "14130954", "8659730", "14195636", "8526215", "13145941", "10590921", "16240103", "7476468", "19025861", "14642127", "5663235", "16951375", "24199641", "141066532", "307055", "17027529", "10171536", "8829552", "1748141", "9794009", "45095560", "3866581", "6565558", "16773487", "2364120" ]

true

https://api.semanticscholar.org/CorpusID:9292630

1

[ [ "RDN model", "METHOD" ], [ "significant performance gain", "EVALUATION" ], [ "machine learning method", "METHOD" ], [ "relational dataset", "DATA" ], [ "real-world datasets", "DATA" ], [ "relational dependency network (RDNs", "METHOD" ], [ "significant performance improvement", "EVALUATION" ], [ "synthetic data", "DATA" ], [ "graphical model", "METHOD" ], [ "inference procedure", "METHOD" ], [ "cyclic relational dependency", "METHOD" ], [ "scientific literature datasets", "DATA" ], [ "model learning", "METHOD" ], [ "setting", "METHOD" ], [ "relational data", "DATA" ], [ "classification context", "APPLICATION" ] ]

CoCoST: A Computational Cost Efficient Classifier

15,344,749

Computational cost of classification is as important as accuracy in on-line classification systems. The computational cost is usually dominated by the cost of computing implicit features of the raw input data. Very few efforts have been made to design classifiers which perform effectively with limited computational power; instead, feature selection is usually employed as a pre-processing step to reduce the cost of running traditional classifiers. We present CoCoST, a novel and effective approach for building classifiers which achieve state-of-the-art classification accuracy, while keeping the expected computational cost of classification low, even without feature selection. CoCost employs a wide range of novel cost-aware decision trees, each of which is tuned to specialize in classifying instances from a subset of the input space, and judiciously consults them depending on the input instance in accordance with a cost-aware meta-classifier. Experimental results on a network flow detection application show that, our approach can achieve better accuracy than classifiers such as SVM and random forests, while achieving 75%-90% reduction in the computational costs.

[ { "first": "Liyun", "middle": [], "last": "Li", "suffix": "" }, { "first": "Umut", "middle": [], "last": "Topkara", "suffix": "" }, { "first": "Baris", "middle": [], "last": "Coskun", "suffix": "" }, { "first": "Nasir", "middle": [], "last": "Memon", "suffix": "" } ]

2,009

10.1109/ICDM.2009.46

2009 Ninth IEEE International Conference on Data Mining

2011608056

[ "11064726", "5107531", "6681228", "482140", "10156891", "15010442", "2616594", "206742345", "7720725", "1471352", "16149383", "199806", "14544135", "25893958", "937841", "6644398", "89141", "32628419", "15904777", "6644398", "15828106", "2443126", "13216311" ]

[ "201070002", "49527701", "215225673", "14425112", "10204104" ]

true

https://api.semanticscholar.org/CorpusID:15344749

0

1

0

[ [ "classifier", "METHOD" ], [ "computational cost", "EVALUATION" ], [ "random forest", "METHOD" ], [ "classification accuracy", "EVALUATION" ], [ "on-line classification system", "APPLICATION" ], [ "input instance", "DATA" ], [ "implicit feature", "DATA" ], [ "SVM", "METHOD" ], [ "traditional classifier", "METHOD" ], [ "cost-aware meta-classifier", "METHOD" ], [ "cost-aware decision tree", "METHOD" ], [ "feature selection", "METHOD" ], [ "input", "DATA" ], [ "class", "METHOD" ], [ "network flow detection application", "APPLICATION" ], [ "CoCoST", "METHOD" ], [ "computational power", "EVALUATION" ], [ "raw input data", "DATA" ] ]

Forms of collaboration in high performance computing: exploring implications for learning

1,575,693

Successful collaboration is not only an occasion for the accomplishment of shared goals, but also provides opportunities for individual collaborators to learn from each other. Extended interaction allows for participants to resolve personal and professional differences and thus create a foundation for successful collaboration. This paper contrasts opportunities for learning in short-term and long-term collaboration in the context of scientists working with High Performance Computing (HPC) system experts. It explores how factors conducive to successful collaboration in longer, more tightly organized collaboration might be adapted in more transient collaboration between scientists and HPC consultants.

[ { "first": "Catalina", "middle": [], "last": "Danis", "suffix": "" } ]

2,006

10.1145/1180875.1180952

CSCW '06

1980749785

[ "153811205", "59260732", "15042722", "13432377", "41772235" ]

[ "6581912", "15540709" ]

true

https://api.semanticscholar.org/CorpusID:1575693

0

1

0

[ [ "Extended interaction", "METHOD" ], [ "personal and professional difference", "DATA" ], [ "transient collaboration", "APPLICATION" ], [ "HPC consultant", "APPLICATION" ], [ "High Performance Computing (HPC) system expert", "APPLICATION" ] ]

Volume Conserving Simulation of Deformable Bodies

16,168,294

[ { "first": "Raphael", "middle": [], "last": "Diziol", "suffix": "" }, { "first": "Jan", "middle": [], "last": "Bender", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Bayer", "suffix": "" } ]

2,009

10.2312/egs.20091043

Eurographics

2282238460

[ "10230896", "25439654", "18229419", "12555605", "7773119", "13915377", "3401588", "812380", "10976958", "11645503", "2052936", "52800005" ]

[ "28559386", "34249804", "1195075", "17267166", "312041", "4424818", "31712989", "34634391", "6965973", "9453784", "6160580", "18067371" ]

true

https://api.semanticscholar.org/CorpusID:16168294

0

1

0

[ [ "Volume Conserving Simulation", "METHOD" ], [ "Deformable Bodies", "DATA" ] ]

Strategy-Based Instruction: Lessons Learned in Teaching the Effective and Efficient Use of Computer Applications

14,198,666

Numerous studies have shown that many users do not acquire the knowledge necessary for the effective and efficient use of computer applications such as spreadsheets and Web-authoring tools. While many cognitive, cultural, and social reasons have been offered to explain this phenomenon, there have been few systematic attempts to address it. This article describes how we identified a framework to organize effective and efficient strategies to use computer applications and used an approach called strategy-based instruction to teach those strategies over five years to almost 400 students. Controlled experiments demonstrated that the instructional approach (1) enables students to learn strategies without harming command knowledge, (2) benefits students from technical and nontechnical majors, and (3) is robust across different instructional contexts and new applications. Real-world classroom experience of teaching strategy-based instruction over several instantiations has enabled the approach to be disseminated to other universities. The lessons learned throughout the process of design, implementation, evaluation, and dissemination should allow teaching a large number of users in many organizations to rapidly acquire the strategic knowledge to make more effective and efficient use of computer applications.

[ { "first": "Suresh K.", "middle": [], "last": "Bhavnani", "suffix": "" }, { "first": "Frederick A.", "middle": [], "last": "Peck", "suffix": "" }, { "first": "Frederick", "middle": [], "last": "Reif", "suffix": "" } ]

2,008

10.1145/1352782.1352784

TCHI

2062422788

[ "141669015", "143497933", "22377178", "107290182", "14498785", "13403298", "62635033", "2516613", "5410539", "830375", "110170345", "1907015", "15793474", "145280199", "204178691", "60058400", "156550202", "32735751", "3113915", "1536184", "45586327", "8182379", "143256836", "63647121", "62543244", "15878561", "37334395", "22505340", "10670028", "62758006", "143817191", "60461833", "13514458", "54258893", "59782916", "142087642", "16721666", "59907799", "18752335", "156316667", "2403594", "144350708", "52809884", "40335935", "1723145", "205480856", "142968719", "14843553", "122658466", "16555424", "60731940", "9403384", "143690992" ]

[ "18682511", "63736542", "63025989", "67866527", "114356028", "17358331", "55376636", "112422839", "67463194", "27951264", "5335221", "60532867", "1322159", "55268185" ]

true

https://api.semanticscholar.org/CorpusID:14198666

0

1

0

[ [ "strategic knowledge", "EVALUATION" ], [ "strategy-based instruction", "METHOD" ], [ "computer application", "APPLICATION" ], [ "Web-authoring tool", "APPLICATION" ], [ ", cultural,", "METHOD" ], [ "Controlled experiment", "EVALUATION" ], [ "spreadsheet", "APPLICATION" ], [ "instructional approach", "METHOD" ], [ "social reason", "METHOD" ], [ "command knowledge", "EVALUATION" ] ]

Social navigation of food recipes

6,016,107

The term Social Navigation captures every-day behaviour used to find information, people, and places - namely through watching, following, and talking to people. We discuss how to design information spaces to allow for social navigation. We applied our ideas in a recipe recommendation system. In a follow-up user study, subjects state that social navigation adds value to the service: it provides for social affordance, and it helps turning a space into a social place. The study also reveals some unresolved design issues, such as the snowball effect where more and more users follow each other down the wrong path, and privacy issues.

[ { "first": "Martin", "middle": [], "last": "Svensson", "suffix": "" }, { "first": "Kristina", "middle": [], "last": "Höök", "suffix": "" }, { "first": "Jarmo", "middle": [], "last": "Laaksolahti", "suffix": "" }, { "first": "Annika", "middle": [], "last": "Waern", "suffix": "" } ]

2,001

10.1145/365024.365130

CHI '01

2086018338

[ "63734786", "218706", "16766884", "18962254", "129515458", "6901130", "6897564", "709746", "53831076", "5546580", "15531432", "31897872", "15554723" ]

[ "29194249", "106760912", "18065345", "31961888", "15570386", "18175851", "6481687", "6517714", "1830838", "22949969", "17161072", "146803817", "4211154", "15405681", "5934619", "1133633", "6955335", "907787", "3018152", "10025718", "12903191", "15418977", "14524480", "8896680", "43085805", "9143256", "14757586", "59745584", "15272961", "16264667", "53619458", "116609597", "8176837", "15528442", "17671256", "8689176", "14965030", "15642543", "17012405", "53381654", "17822871", "40216299", "30773668", "9152981", "5175085", "2820405", "14114218", "174804224", "16324692", "17793239", "846653", "18906716", "16172509" ]

true

https://api.semanticscholar.org/CorpusID:6016107

1

[ [ "privacy issue", "APPLICATION" ], [ "d design issue", "EVALUATION" ], [ "follow-up user study", "EVALUATION" ], [ "social affordance", "APPLICATION" ], [ "snowball effect", "METHOD" ], [ "social navigation", "APPLICATION" ], [ "recipe recommendation system", "APPLICATION" ], [ "information space", "VISUALIZATION" ], [ "place", "APPLICATION" ], [ "Social Navigation", "METHOD" ] ]

Pressure-Based Gain Factor Control for Mobile 3D Interaction using Locally-Coupled Devices

21,585,032

We present the design and evaluation of pressure-based interactive control of 3D navigation precision. Specifically, we examine the control of gain factors in tangible 3D interactions using locally-coupled mobile devices. By focusing on pressure as a separate input channel we can adjust gain factors independently from other input modalities used in 3D navigation, in particular for the exploration of 3D visualisations. We present two experiments. First, we determined that people strongly preferred higher pressures to be mapped to higher gain factors. Using this mapping, we compared pressure with rate control, velocity control, and slider-based control in a second study. Our results show that pressure-based gain control allows people to be more precise in the same amount of time compared to established input modalities. Pressure-based control was also clearly preferred by our participants. In summary, we demonstrate that pressure facilitates effective and efficient precision control for mobile 3D navigation.

[ { "first": "Lonni", "middle": [], "last": "Besançon", "suffix": "" }, { "first": "Mehdi", "middle": [], "last": "Ammi", "suffix": "" }, { "first": "Tobias", "middle": [], "last": "Isenberg", "suffix": "" } ]

2,017

10.1145/3025453.3025890

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

2604192544

[ "9016452", "1210960", "18091550", "1788482", "2290931", "16626037", "15880565", "12221413", "9089695", "59922612", "9566177", "11890402", "620720", "49210213", "5705695", "64470036", "1687533", "16952246", "734761", "25807928", "6292200", "83668", "462228", "7350663", "6857584", "14747549", "2656403", "7264069", "13610888", "1362836", "15881107", "15394965", "13200178", "867879", "13064259", "6259790", "588690", "13968040", "10390195", "12793253", "1365177", "12211354", "1799282", "625699", "8453320", "17701361", "2708601" ]

[ "52895710", "115148344", "199440807", "214589443" ]

true

https://api.semanticscholar.org/CorpusID:21585032

1

[ [ "velocity control", "METHOD" ], [ "higher gain factor", "DATA" ], [ "mobile 3D navigation", "APPLICATION" ], [ "established input modality", "METHOD" ], [ "pressure-based gain control", "METHOD" ], [ "3D navigation precision", "EVALUATION" ], [ "gain factor", "METHOD" ], [ "tangible 3D interaction", "APPLICATION" ], [ "3D navigation", "APPLICATION" ], [ "pressure-based interactive control", "METHOD" ], [ "locally-coupled mobile device", "METHOD" ], [ "precision control", "METHOD" ], [ "Pressure-based control", "METHOD" ], [ "3D visualisation", "VISUALIZATION" ], [ "rate control", "METHOD" ], [ "slider-based control", "METHOD" ], [ "channel", "DATA" ] ]

Estimating Causal Power between Binary Cause and Continuous Outcome.

22,477,001

[ { "first": "Motoyuki", "middle": [], "last": "Saito", "suffix": "" }, { "first": "Patricia", "middle": [ "W." ], "last": "Cheng", "suffix": "" } ]

2,017

CogSci

2786874276

[ "6844972", "141512745", "3127333", "43579858", "43420667", "15335444", "38762223", "15097360", "10871785", "61503518", "2294399", "14885825", "4679605", "5214240", "27589859" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:22477001

0

1

0

[ [ "Estimating Causal Power", "EVALUATION" ], [ "Binary Cause", "METHOD" ], [ "Continuous Out", "METHOD" ] ]

Privacy-preserving distributed k-means clustering over arbitrarily partitioned data

16,219,652

Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Data mining can extract valuable knowledge from this data, and organizations have realized that they can often obtain better results by pooling their data together. However, the collected data may contain sensitive or private information about the organizations or their customers, and privacy concerns are exacerbated if data is shared between multiple organizations.Distributed data mining is concerned with the computation of models from data that is distributed among multiple participants. Privacy-preserving distributed data mining seeks to allow for the cooperative computation of such models without the cooperating parties revealing any of their individual data items. Our paper makes two contributions in privacy-preserving data mining. First, we introduce the concept of arbitrarily partitioned data, which is a generalization of both horizontally and vertically partitioned data. Second, we provide an efficient privacy-preserving protocol for k-means clustering in the setting of arbitrarily partitioned data.

[ { "first": "Geetha", "middle": [], "last": "Jagannathan", "suffix": "" }, { "first": "Rebecca", "middle": [ "N." ], "last": "Wright", "suffix": "" } ]

2,005

10.1145/1081870.1081942

KDD '05

2009733253

[ "207676583", "18422433", "59918722", "59641635", "657888", "57368876", "6278891", "60375743", "279432", "7753307", "10262915", "1091397", "296057" ]

[ "207759116", "11012491", "201932036", "53107939", "614620", "2939344", "14727719", "14947444", "8497143", "2353572", "778408", "207924467", "44160895", "7691627", "2054786", "104292559", "15840609", "15282463", "214692159", "10488675", "19819644", "15931729", "65176454", "61758153", "14860472", "6794903", "9807669", "14948081", "14948081", "49563021", "8973118", "15455415", "2061615", "34620325", "203655262", "153311245", "8019041", "52927031", "3197441", "7024154", "51970701", "16949768", "15893541", "15523717", "203603104", "4699102", "12330895", "17681395", "6818201", "6256606", "15805617", "213177838", "2850118", "207327697", "16466282", "211244493", "157115749", "208221152", "18238270", "2351063", "15268724", "14586574", "44182124", "9482658", "53368529", "67871624", "11819908", "15442795", "2122910", "7383105", "3140379", "17510330", "15995547", "6325561", "28451880", "12065835", "15857279", "46136773", "14418613", "56359330", "10215503", "54074006", "18091049", "5828923", "84186615", "810559", "589685", "7110276", "14961239", "16367477", "6443799", "5180604", "15119238", "9046984", "55208085", "7179578", "16888126", "57364858", "10982670", "195344969", "14749544", "26544572", "29836506", "33695457", "33695457", "8439943", "46984632", "17995257", "212564997", "1668609", "16436048", "1827989", "45425195", "10754782", "23798693", "20714", "196084320", "15880358", "2786269", "14070271", "10366285", "57373874", "210705751", "26940177", "14369806", "10139396", "6538187", "14702597", "5700076", "1660824", "15537367", "11909170", "9592674", "14400267", "18490360", "17405756", "13718492", "18996901", "10423039", "2990900", "15950818", "204949444", "11838393", "15565844", "18617672", "8761996", "12413059", "10587776", "14696100", "53481813", "56895584", "6581044", "15269980", "17242875", "14620644", "18268622", "210713364", "9684783", "4764874", "4433489", "202781896", "18308093", "17018624", "15279322", "48195062", "11929113", "12971662", "49481225", "49481225", "181989633", "1436070", "14351054", "8038653", "7791932", "2686271", "5686319", "17012976", "39048142", "52274565", "17950253", "17756910", "207891529", "3912351", "8154476", "10340829", "3428831", "18469251", "13888651", "17104854", "16140191", "46794978", "23778155", "13089419", "1586659", "11605311", "2191215", "876326", "7813293", "14595720", "30693961", "13055997", "7701205", "50351237", "6943997", "12905626", "18876598", "18390273", "53415363", "17715557", "6436603", "8312138", "204772074", "33044367", "8362750" ]

true

https://api.semanticscholar.org/CorpusID:16219652

0

1

0

[ [ "k-means clustering", "APPLICATION" ], [ "privacy-preserving data mining", "APPLICATION" ], [ "cooperative computation", "METHOD" ], [ "individual data item", "DATA" ], [ "privacy-preserving protocol", "METHOD" ], [ "horizontal", "DATA" ], [ "tributed data mining", "APPLICATION" ], [ "Privacy-preserving distributed data mining", "METHOD" ], [ "Data mining", "APPLICATION" ], [ "database technology", "APPLICATION" ], [ "private", "DATA" ], [ "arbitrarily partitioned data", "DATA" ], [ "rily partitioned data", "DATA" ], [ "vertically partitioned data", "DATA" ], [ "computer networking", "APPLICATION" ] ]

Accessibility and interactive TV: design recommendations for the Brazilian scenario

31,639,515

TV can be regarded as the most far-reaching media in Brazil. Its presence is noticed in 90% of Brazilian homes and it is the main source of information for a major part of the population. The moment of definition and consolidation of the digital TV technology provides us with a unique opportunity for analyzing and discussing this media accessibility. Making sure that TV contents and devices are flexible enough so that people are able to perceive, understand and interact with them is a main asset for its use and an essential requirement for the democratization of information via TV broadcasting. This paper analyzes interactive digital TV accessibility in informal, formal, and technical levels, considering the Brazilian context. In addition, it presents recommendations to design accessible interfaces by referring to the W3C guidelines 2.0 for Web accessibility and specific recommendations for iDTV.

[ { "first": "Lara", "middle": [ "S.", "G." ], "last": "Piccolo", "suffix": "" }, { "first": "Amanda", "middle": [ "Meincke" ], "last": "Melo", "suffix": "" }, { "first": "Maria", "middle": [ "Cecília", "Calani" ], "last": "Baranauskas", "suffix": "" } ]

2,007

10.1007/978-3-540-74796-3_34

INTERACT

2157917199

[ "14316407", "15223833", "58804295", "17056483", "108248893", "8937545", "3956113", "17703831" ]

[ "1552918", "1098641", "39938437", "18588708", "5107287", "146119296", "7791928", "12435252", "11948211", "73648106" ]

true

https://api.semanticscholar.org/CorpusID:31639515

1

[ [ "TV broadcasting", "APPLICATION" ], [ "digital TV technology", "METHOD" ], [ "iDTV", "APPLICATION" ], [ "democrat", "APPLICATION" ], [ "interactive digital TV accessibility", "APPLICATION" ], [ "Web accessibility", "APPLICATION" ], [ "accessible interface", "APPLICATION" ] ]

Neural bases of semantic-memory deficits for events.

16,039,764

[ { "first": "Chia-Ming", "middle": [], "last": "Lei", "suffix": "" }, { "first": "Haley", "middle": [ "C." ], "last": "Dresang", "suffix": "" }, { "first": "Michelle", "middle": [ "B." ], "last": "Holcomb", "suffix": "" }, { "first": "Tessa", "middle": [], "last": "Warren", "suffix": "" }, { "first": "Michael", "middle": [ "Walsh" ], "last": "Dickey", "suffix": "" } ]

2,016

CogSci

2786140772

[ "23181385", "5134480", "15307706", "2517460", "141035126", "6191389", "2796630", "14200060", "11300809", "5738729", "28761377", "38074530", "5537043", "23007505", "7491697", "3132645", "32070836", "2616234", "17117610", "144521964", "13892224", "2602346", "17464595", "12395704", "43895463", "145712907", "15874130", "1660596" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:16039764

0

1

0

[ [ "semantic-memory deficit", "DATA" ] ]

Communicating Centrality in Policy Network Drawings

1,189,315

We introduce a network visualization technique that supports an analytical method applied in the social sciences. Policy network analysis is an approach to study policy making structures, processes, and outcomes, thereby concentrating on relations between policy actors. An important operational concept for the analysis of policy networks is the notion of centrality, i.e., the distinction of actors according to their importance in a relational structure. We integrate this measure in a layout model for networks by mapping structural to geometric centrality. Thus, centrality values and network data can be presented simultaneously and explored interactively.

[ { "first": "Ulrik", "middle": [], "last": "Brandes", "suffix": "" }, { "first": "Patrick", "middle": [], "last": "Kenis", "suffix": "" }, { "first": "Dorothea", "middle": [], "last": "Wagner", "suffix": "" } ]

2,003

10.1109/TVCG.2003.1196010

IEEE Trans. Vis. Comput. Graph.

2141177212

[ "118421505", "43861471", "15119099", "59732190", "26017396", "119693509", "1492408", "657954", "63936426", "32035638", "22476033", "12643668", "751590", "9463029", "144889829", "31468174", "239537", "26372900", "152838545", "115689505", "196010928", "36049419", "206402244", "119981743", "8367756", "144742473" ]

[ "3400470", "18918665", "152739699", "123611172", "55706791", "1305951", "1506144", "2921338", "20087682", "9852297", "32147927", "18123296", "86828300", "14289612", "60503795", "6920543", "11790964", "15581033", "7736149", "23471356", "17773994", "73706628", "55520867", "18722099", "16781555", "6153604", "14926800", "885555", "7406966", "11237214", "10270257", "14205226", "2957724", "9907884", "55519178", "8518994", "9493652", "199517266", "30829833", "1182886", "17194280", "2065240", "2426197", "18310278", "18820410", "3077346", "15854983", "16737689", "18948171", "364692", "125011517", "12654270", "205129384", "18447736", "13575587", "158294780", "14237281", "14237281", "14386223", "90608498", "17717254" ]

true

https://api.semanticscholar.org/CorpusID:1189315

1

[ [ "network visualization technique", "METHOD" ], [ "policy making", "APPLICATION" ], [ "layout model", "METHOD" ], [ "policy network", "APPLICATION" ], [ "network data", "DATA" ], [ "centrality value", "DATA" ], [ "Policy network analysis", "METHOD" ], [ "analytical method", "METHOD" ], [ "geometric centrality", "METHOD" ], [ "policy actor", "APPLICATION" ], [ "social science", "APPLICATION" ] ]

Direct manipulation of parallel coordinates

14,620,092

This paper proposes the direct manipulation of parallel coordinates and introduces two novel techniques to manipulate them. The polyline averaging makes it possible to summarize dynamically a set of polylines, and the other technique visualizes correlation coefficients between polyline subsets. Both techniques were implemented on a Java-based parallel coordinate browser.

[ { "first": "Harri", "middle": [], "last": "Siirtola", "suffix": "" } ]

2,000

10.1145/633292.633361

CHI EA '00

1977607108

[ "59732190", "764211", "21046022" ]

[ "10102281", "15850822", "17280537", "6629981", "1070841", "28322876", "26977582", "16430101", "24984639", "110691997", "3141361", "15556562", "12386273", "6751449", "18898373", "14413538", "1786236", "14294028", "1066519", "198112910", "14449606", "6052187", "15365307", "4769348", "2648324", "9326139", "17691718", "53350789", "8665647", "5987728", "10239407", "9470105", "104834", "1798684" ]

true

https://api.semanticscholar.org/CorpusID:14620092

0

1

0

[ [ "manipula", "METHOD" ], [ "polyline subset", "DATA" ], [ "parallel coordinate browser", "METHOD" ], [ "parallel coordinate", "DATA" ], [ "polylines", "DATA" ], [ "polyline averaging", "METHOD" ], [ "correlation coefficient", "DATA" ] ]

Attractor Dynamics in Delay Discounting: A Call for Complexity.

37,514,240

[ { "first": "Martin", "middle": [], "last": "Schoemann", "suffix": "" }, { "first": "Stefan", "middle": [], "last": "Scherbaum", "suffix": "" } ]

2,017

CogSci

2787648246

[ "27687210", "23691829", "18849469", "997691", "35717021", "13976282", "14690162", "31256541", "14405387", "34728894" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:37514240

0

1

0

[ [ "Delay Discounting", "APPLICATION" ], [ "Attractor Dynamics", "METHOD" ] ]

Investigating statistical machine learning as a tool for software development

1,971,561

As statistical machine learning algorithms and techniques continue to mature, many researchers and developers see statistical machine learning not only as a topic of expert study, but also as a tool for software development. Extensive prior work has studied software development, but little prior work has studied software developers applying statistical machine learning. This paper presents interviews of eleven researchers experienced in applying statistical machine learning algorithms and techniques to human-computer interaction problems, as well as a study of ten participants working during a five-hour study to apply statistical machine learning algorithms and techniques to a realistic problem. We distill three related categories of difficulties that arise in applying statistical machine learning as a tool for software development: (1) difficulty pursuing statistical machine learning as an iterative and exploratory process, (2) difficulty understanding relationships between data and the behavior of statistical machine learning algorithms, and (3) difficulty evaluating the performance of statistical machine learning algorithms and techniques in the context of applications. This paper provides important new insight into these difficulties and the need for development tools that better support the application of statistical machine learning.

[ { "first": "Kayur", "middle": [], "last": "Patel", "suffix": "" }, { "first": "James", "middle": [], "last": "Fogarty", "suffix": "" }, { "first": "James", "middle": [ "A." ], "last": "Landay", "suffix": "" }, { "first": "Beverly", "middle": [], "last": "Harrison", "suffix": "" } ]

2,008

10.1145/1357054.1357160

CHI

2108816886

[ "59648070", "1758223", "2232284", "8198139", "10954740", "1183716", "207160641", "6589581", "1894216", "8485237", "8350916", "3104757", "16995592", "14110921", "62153141" ]

[ "140213542", "204815860", "2991944", "207960475", "13029170", "6011375", "195848259", "215723053", "11647910", "202541329", "169040174", "140124326", "848734", "1394956", "190233499", "9363701", "88499204", "53083207", "1296086", "210154117", "7537204", "18145269", "5210", "14710451", "170079153", "1762680", "12429105", "7444687", "49293828", "2641518", "2174858", "14417389", "3366554", "53080227", "53079659", "21740766", "3892249", "9168766", "3571027", "8322689", "20318189", "202786294", "5060661", "18510392" ]

true

https://api.semanticscholar.org/CorpusID:1971561

0

1

0

[ [ "statistical machine learning algorithm", "METHOD" ], [ "human-computer interaction problem", "APPLICATION" ], [ "development tool", "METHOD" ], [ "realistic problem", "APPLICATION" ], [ "exploratory process", "METHOD" ], [ "software development", "APPLICATION" ], [ "statistical machine learning", "METHOD" ], [ "five-hour study", "EVALUATION" ] ]

Incorporating Expert Feedback into Active Anomaly Discovery

15,285,595

Unsupervised anomaly detection algorithms search for outliers and then predict that these outliers are the anomalies. When deployed, however, these algorithms are often criticized for high false positive and high false negative rates. One cause of poor performance is that not all outliers are anomalies and not all anomalies are outliers. In this paper, we describe an Active Anomaly Discovery (AAD) method for incorporating expert feedback to adjust the anomaly detector so that the outliers it discovers are more in tune with the expert user's semantic understanding of the anomalies. The AAD approach is designed to operate in an interactive data exploration loop. In each iteration of this loop, our algorithm first selects a data instance to present to the expert as a potential anomaly and then the expert labels the instance as an anomaly or as a nominal data point. Our algorithm updates its internal model with the instance label and the loop continues until a budget of B queries is spent. The goal of our approach is to maximize the total number of true anomalies in the B instances presented to the expert. We show that when compared to other state-of-the-art algorithms, AAD is consistently one of the best performers.

[ { "first": "Shubhomoy", "middle": [], "last": "Das", "suffix": "" }, { "first": "Weng-Keen", "middle": [], "last": "Wong", "suffix": "" }, { "first": "Thomas", "middle": [], "last": "Dietterich", "suffix": "" }, { "first": "Alan", "middle": [], "last": "Fern", "suffix": "" }, { "first": "Andrew", "middle": [], "last": "Emmott", "suffix": "" } ]

2,016

10.1109/ICDM.2016.0102

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

2584401436

[ "15209254", "6787631", "6505449", "17445696", "324600", "18080069", "9406699", "28826624", "207605508", "8763034", "9061266", "1842693", "21630172", "2054204", "126672539", "62604959", "14186475" ]

[ "209951873", "201065935", "52006724", "202558787", "211060064", "113397352", "208910953", "211990349", "41626281", "203654925", "44086511", "76663533", "57364852", "52912139", "118647644", "196200564", "204939503", "57365050", "59528439", "195776772", "53236735", "3723511", "54445467", "164463459", "59291893", "204828023", "43938706", "207758734", "49258388", "77393160", "53475766", "214665291", "145841787", "207832408", "52298207", "201070305", "210839676", "209078253" ]

true

https://api.semanticscholar.org/CorpusID:15285595

0

1

0

[ [ "data instance", "DATA" ], [ "Unsupervised anomaly detection algorithm", "METHOD" ], [ "instance label", "METHOD" ], [ "false negative rate", "EVALUATION" ], [ "AAD approach", "METHOD" ], [ "interactive data exploration loop", "METHOD" ], [ "nominal data point", "DATA" ], [ "high false positive", "EVALUATION" ], [ "user's semantic understanding", "VISUALIZATION" ], [ "anomaly detector", "METHOD" ], [ "B instance", "DATA" ], [ "true anomaly", "DATA" ], [ "B que", "APPLICATION" ], [ "AAD", "METHOD" ], [ "internal model", "METHOD" ], [ "Active Anomaly Discovery (AAD) method", "METHOD" ], [ "expert feedback", "EVALUATION" ] ]

Accessing a New Land: Designing for a Social Conceptualisation of Access

140,339,246

This paper presents a study of mobile phone use by people settling in a new land to access state provided digital services. It shows that digital literacy and access to technology are not the only resources and capabilities needed to successfully access digital services and do not guarantee a straightforward resettlement process. Using creative engagement methods, the research involved 132 "newcomers" seeking to settle in Sweden. Ribot and Peluso's theory of access (2003) was employed to examine the complex web of access experienced by our participants. We uncover that when communities are dealing with high levels of precarity, their primary concerns are related to accessing the benefits of a service, rather than controlling access. Broadening the HCI framework, the paper concludes that a sociotechnical model of access needs to connect access control and access benefit to facilitate the design of an effective digital service.

[ { "first": "Lizzie", "middle": [], "last": "Coles-Kemp", "suffix": "" }, { "first": "Rikke", "middle": [ "Bjerg" ], "last": "Jensen", "suffix": "" } ]

2,019

10.1145/3290605.3300411

CHI '19

2942320160

[ "46032954", "1677206", "660975", "191550865", "153235059", "142889571", "12633005", "151786021", "14978159", "10340931", "8156821", "1704924", "49351209", "38759893", "134647604", "197819301", "145737262", "16535981", "143315909", "158441352", "142992264", "10875971", "168695037", "131468676", "146579068", "17957367", "211296116", "144438691", "166629707", "144650453", "54658629", "205705584", "60777607", "1958270", "157799437", "23213093", "14266943", "153913717", "151613128", "158179206", "18910209", "33098830", "63928", "44470327", "23103377", "8279070" ]

[ "212644856" ]

true

https://api.semanticscholar.org/CorpusID:140339246

0

1

0

[ [ "HCI framework", "METHOD" ], [ "digital literacy", "APPLICATION" ], [ "mobile phone use", "APPLICATION" ], [ "sociotechnical model", "METHOD" ], [ "access control", "METHOD" ], [ "provided digital service", "APPLICATION" ], [ "straightforward resettlement process", "EVALUATION" ], [ "digital service", "APPLICATION" ], [ "creative engagement method", "METHOD" ], [ "Ribot and Peluso's theory of access", "METHOD" ], [ "access benefit", "METHOD" ] ]

Discovering Valuable items from Massive Data

5,908,435

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-SELECT, which utilizes prior knowledge about similarity between items, expressed as a kernel function. GP-SELECT uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-SELECT to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-SELECT and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine design task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users.

[ { "first": "Hastagiri", "middle": [ "P." ], "last": "Vanchinathan", "suffix": "" }, { "first": "Andreas", "middle": [], "last": "Marfurt", "suffix": "" }, { "first": "Charles-Antoine", "middle": [], "last": "Robelin", "suffix": "" }, { "first": "Donald", "middle": [], "last": "Kossmann", "suffix": "" }, { "first": "Andreas", "middle": [], "last": "Krause", "suffix": "" } ]

2,015

1506.00935

10.1145/2783258.2783360

KDD '15

1979152732,2952237617

[ "207609497", "3698068", "581697", "121230628", "18101293", "1028408", "52827488", "10660879", "992783", "8085527", "5768856", "1496901", "1304870", "15442140", "2763630", "51975610", "121166589", "12555103", "207178795", "320371", "119751462", "8306952", "59860283", "64543020", "207319017", "12665157", "645814", "16269200", "15921784", "14664386", "5908435", "1479633", "14136770", "10342491", "7388181", "6916016" ]

[ "202780709", "195347125", "54445413", "26764844", "196195538", "6424683", "52985278", "3680951", "208059110", "56895438", "5908435", "20035958", "53065861" ]

true

https://api.semanticscholar.org/CorpusID:5908435

1

[ [ "multi-arm bandit", "APPLICATION" ], [ "GP-SELECT", "METHOD" ], [ "Gaussian process prediction", "METHOD" ], [ "binding-affine peptide", "DATA" ], [ "kernel function", "METHOD" ], [ "model update", "METHOD" ], [ "arbitrary monotone submodular function", "METHOD" ], [ "web-scale recommender system", "APPLICATION" ], [ "active search", "APPLICATION" ], [ "Global Distribution System", "APPLICATION" ], [ "order of magnitud", "EVALUATION" ], [ "maximal value", "DATA" ], [ "knapsack problem", "APPLICATION" ], [ "diminishing return", "EVALUATION" ], [ "case study", "EVALUATION" ], [ "vaccine design task", "APPLICATION" ], [ "travel industry", "APPLICATION" ] ]

A Dynamic Model of Cracks Development Based on a 3D Discrete Shrinkage Volume Propagation

6,895,873

We attempt to model and visualize the main characteristics of cracks produced on the surface of a desiccating crusted soil: their patterns, their different widths and depths and their dynamics of creation and evolution. In this purpose we propose a method to dynamically produce three-dimensional (3D) quasi-static fractures, which takes into account the characteristics of the soil. The main originality of this method is the use of a 3D discrete propagation of ‘shrinkage volumes’ with respect to 2D precalculated paths. In order to get realistic cracks, we newly propose to take into account a possibly inhomogeneous thickness of the shrinking layer by using a watershed transformation to compute these paths. Moreover, we use the waterfall algorithm in order to introduce in our simulation a hierarchy notion in the cracks appearance, which is therefore linked with the initial structure of the surface. In this paper, this method is presented in detail and a validation of the cracks patterns by a comparison with real ones is given.

[ { "first": "Gilles", "middle": [], "last": "Valette", "suffix": "" }, { "first": "Stéphanie", "middle": [], "last": "Prévost", "suffix": "" }, { "first": "Laurent", "middle": [], "last": "Lucas", "suffix": "" }, { "first": "Joël", "middle": [], "last": "Léonard", "suffix": "" } ]

2,008

10.1111/j.1467-8659.2007.01042.x

Comput. Graph. Forum

2032686953

[]

[ "33887345", "40574634", "3033896", "3734052", "8336496", "16786488" ]

false

true

false

https://api.semanticscholar.org/CorpusID:6895873

null

[ [ "3D discrete propagation", "METHOD" ], [ "shrinking layer", "DATA" ], [ "crack pattern", "EVALUATION" ], [ "shrinkage volume", "DATA" ], [ "watershed transformation", "METHOD" ], [ "realistic crack", "EVALUATION" ], [ "crack", "DATA" ], [ "desiccating crusted soil", "DATA" ], [ "hierarchy notion", "VISUALIZATION" ], [ "initial", "DATA" ], [ "waterfall algorithm", "METHOD" ], [ "surface", "DATA" ], [ "inhomogeneous thickness", "DATA" ], [ "2D precalculated path", "DATA" ], [ "three-dimensional (3D) quasi-static fracture", "METHOD" ], [ "crack appearance", "VISUALIZATION" ] ]

Motivation-biased design

10,578,899

Motivation-biased design concerns how positive attitudes of designers can inhibit critical evaluation of their designs. Good intentions, admiration for certain design elements, or even concern to make a good impression on others can inhibit designers from being sufficiently critical of their designs. The result may be designs that are not as good as they would be otherwise. This article presents examples of motivationbiased design, explores cognitive mechanisms that might explain it, and considers how knowledge of the phenomenon might be useful in improving design practice.

[ { "first": "Cameron", "middle": [], "last": "Shelley", "suffix": "" } ]

2,011

CogSci

2577460839

[ "20512182", "34952329", "64498263", "145505538", "143452957", "5728562", "90927985", "142876062", "14214712" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:10578899

0

1

0

[ [ "Motivation-biased design", "METHOD" ], [ "motivationbiased design", "METHOD" ], [ "cognitive mechanism", "METHOD" ], [ "design practice", "APPLICATION" ], [ "critical evaluation", "EVALUATION" ] ]

Priming the production of implications.

33,908,044

[ { "first": "Alice", "middle": [], "last": "Rees", "suffix": "" }, { "first": "Lewis", "middle": [], "last": "Bott", "suffix": "" } ]

2,017

CogSci

2785331241

[ "43353279", "22292049", "29581886", "10246012", "39451965", "18540259", "145374163", "61583422", "207158734", "116929349", "21975212", "24809611", "2376271", "145819383", "6117358", "28830903", "206866165", "144712872", "5774443", "55454153", "62633084", "152230066", "34753974", "16048249", "19675817", "42596294", "53391051", "24718788", "144047467", "207651095", "56280196", "9676507" ]

[]

true

false

true

https://api.semanticscholar.org/CorpusID:33908044

0

1

0

[]

Brushables: Example-based Edge-aware Directional Texture Painting

11,399,771

In this paper we present Brushables-a novel approach to example-based painting that respects user-specified shapes at the global level and preserves textural details of the source image at the local level. We formulate the synthesis as a joint optimization problem that simultaneously synthesizes the interior and the boundaries of the region, transferring relevant content from the source to meaningful locations in the target. We also provide an intuitive interface to control both local and global direction of textural details in the synthesized image. A key advantage of our approach is that it enables a "combing" metaphor in which the user can incrementally modify the target direction field to achieve the desired look. Based on this, we implement an interactive texture painting tool capable of handling more complex textures than ever before, and demonstrate its versatility on difficult inputs including vegetation, textiles, hair and painting media.

[ { "first": "Michal", "middle": [], "last": "Lukáč", "suffix": "" }, { "first": "Jakub", "middle": [], "last": "Fišer", "suffix": "" }, { "first": "Paul", "middle": [], "last": "Asente", "suffix": "" }, { "first": "Jingwan", "middle": [], "last": "Lu", "suffix": "" }, { "first": "Eli", "middle": [], "last": "Shechtman", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Sýkora", "suffix": "" } ]

2,015

10.1111/cgf.12764

Comput. Graph. Forum

2100443219

[ "3346458", "1650656", "12650942", "129468", "207173916", "205396440", "2322307", "10500317", "13917123", "3320450", "14764369", "6276519", "13722545", "12992900", "2147097", "14811621", "400954", "1748436", "23703905", "11597751", "12335738", "15623941", "14292247", "3070134", "7226972", "11891609", "1243612", "11378842", "7322302", "11450020" ]

[ "139104189", "28707976", "10866737", "26659783", "53037756", "17473474", "38747832", "982903", "209862532", "29017560", "51929906" ]

true

https://api.semanticscholar.org/CorpusID:11399771

0

1

0

[ [ "example-based painting", "APPLICATION" ], [ "joint optimization problem", "APPLICATION" ], [ "textural detail", "DATA" ], [ "source image", "DATA" ], [ "user-specified shape", "DATA" ], [ "intuitive interface", "METHOD" ], [ "painting medium", "APPLICATION" ], [ "target direction field", "DATA" ], [ "interactive texture painting tool", "METHOD" ], [ "Brushables", "METHOD" ], [ "\"combing\" metaphor", "METHOD" ], [ "synthesized image", "DATA" ] ]