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Yu Zheng, Huichu Zhang, and Yong Yu

The collective anomaly denotes a collection of nearby locations that are anomalous during a few consecutive time intervals in terms of phenomena collectively witnessed by multiple datasets. The collective anomalies suggest there are underlying problems that may not be identified based on a single data source or in a single location. It also associates individual locations and time intervals, formulating a panoramic view of an event. To detect a collective anomaly is very challenging, however, as...

Publication details
Date: 1 November 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Bimal Viswanath, Muhammad Ahmad Bashir, Muhammad Bilal Zafar, Simon Bouget, Saikat Guha, Krishna Gummadi, Aniket Kate, and Alan Mislove
Publication details
Date: 1 November 2015
Type: Inproceeding
Publication details
Date: 1 November 2015
Type: Technical report
Publisher: USENIX – Advanced Computing Systems Association
Number: MSR-TR-2015-59
Alistair Moffat, Falk Scholer, Paul Thomas, and Peter Bailey

Evaluation of information retrieval systems with test collections makes use of a suite of fixed resources: a document corpus; a set of topics; and associated judgments of the relevance of each document to each topic. With large modern collections, exhaustive judging is not feasible. Therefore an approach called pooling is typically used where, for example, the documents to be judged can be determined by taking the union of all documents returned in the top positions of the answer lists returned...

Publication details
Date: 1 October 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
Meredith Ringel Morris, Andrew Begel, and Ben Wiedermann

Technology workers are often stereotyped as being socially awkward or having difficulty communicating, often with humorous intent; however, for many technology workers with atypical cognitive profiles, such issues are no laughing matter. In this paper, we explore the hidden lives of neurodiverse technology workers, e.g., those with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and/or other learning disabilities, such as dyslexia. We present findings from interviews...

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Zhongyuan Wang, Haixun Wang, Ji-Rong Wen, and Yanghua Xiao

Humans understand the world by classifying objects into an appropriate level of categories. This process is often automatic and subconscious. Psychologists and linguists call it as Basic-level Categorization (BLC). BLC can benefit lots of applications such as knowledge panel, advertising and recommendation. However, how to quantify basic-level concepts is still an open problem. Recently, much work focuses on constructing knowledge bases or semantic networks from web scale text corpora, which makes it...

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
J. Burggraaff, J. Dorn, M. D'Souza, C. P. Kamm, P. Tewarie, P. Kontschieder, C. Morrison, A. Sellen, A. Criminisi, F. Dahlke, L. Kappos, and B. M. J. Uitdehaag
Publication details
Date: 1 October 2015
Type: Inproceeding
C. Morrison, K. Huckvale, A. Sakar, P. Kontschieder, J. Dorn, S. Steinheimer, C. P. Kamm, J. Burggraaff, M. D'Souza, F. Dahlke, L. Kappos, B. Uitdehaag, A. Criminisi, and A. Sellen
Publication details
Date: 1 October 2015
Type: Inproceeding
Jianpeng Cheng, Zhongyuan Wang, Ji-Rong Wen, Jun Yan, and Zheng Chen

Representing discrete words in a continuous vector space turns out to be useful for natural language applications related to text understanding. Meanwhile, it poses extensive challenges, one of which is due to the polysemous nature of human language. A common solution (a.k.a word sense induction) is to separate each word into multiple senses and create a representation for each sense respectively. However, this approach is usually computationally expensive and prone to data sparsity, since each sense...

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
M. D'Souza, J. Burggraaff, P. Kontschieder, J. Dorn, C.P.Kamm, S. Seinheimer, P. Tewarie, C. Morrison, A. Sellen, A. Criminisi, F. Dahlke, B Uitdehaag, and L. Kappos
Publication details
Date: 1 October 2015
Type: Inproceeding
Bhaskar Mitra and Nick Craswell

Query auto-completion (QAC) systems typically suggest queries that have previously been observed in search logs. Given a partial user query, the system looks up this query prefix against a precomputed set of candidates, then orders them using ranking signals such as popularity. Such systems can only recommend queries for prefixes that have been previously seen by the search engine with adequate frequency. They fail to recommend if the prefix is sufficiently rare such that it has no matches in the...

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Bhanu Vattikonda, Vacha Dave, Saikat Guha, and Alex C. Scoeren
Publication details
Date: 1 October 2015
Type: Inproceeding
Abdullah-Al Mamun, Iyswarya Narayanan, Di Wang, Anand Sivasubramaniam, and Hosam K. Fathy
Publication details
Date: 1 October 2015
Type: Article
Publisher: ASME
Dongwook Yoon, Nicholas Chen, François Guimbretière, and Abigail Sellen

This paper introduces a novel document annotation system that aims to enable the kinds of rich communication that usually only occur in face-to-face meetings. Our system, RichReview, lets users create annotations on top of digital documents using three main modalities: freeform inking, voice for narration, and deictic gestures in support of voice. RichReview uses novel visual representations and timesynchronization between modalities to simplify annotation access and navigation. Moreover, RichReview’s...

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
H. Lombaert, A. Criminisi, and N. Ayache

This paper presents a new method for classifying surface data via spectral representations of shapes. Our approach benefits classification problems that involve data living on surfaces, such as in cortical parcellation. For instance, current methods for labeling cortical points into surface parcels often involve a slow mesh deformation toward pre-labeled atlases, requiring as much as 4 hours with the established FreeSurfer. This may burden neuroscience studies involving region-specific measurements....

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: Springer
Yi Yang, Wen-tau Yih, and Christopher Meek

We describe the WikiQA dataset, a new publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. Most previous work on answer sentence selection focuses on a dataset created using the TREC-QA data, which includes editor-generated questions and candidate answer sentences selected by matching content words in the question. WikiQA is constructed using a more natural process and is more than an order of magnitude larger than the previous...

Publication details
Date: 21 September 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon

Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al. 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that...

Publication details
Date: 17 September 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Vasileios Lampos, Elad Yom-Tov, Richard Pebody, and Ingemar J. Cox

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of usergenerated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the...

Publication details
Date: 7 September 2015
Type: Article
Publisher: Springer
Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: Springer
Dilek Hakkani-Tur, Yun-Cheng Ju, Geoffrey Zweig, and Gokhan Tur

Spoken language understanding (SLU) in today’s conversational systems focuses on recognizing a set of domains, intents, and associated arguments, that are determined by application developers. User requests that are not covered by these are usually directed to search engines, and may remain unhandled. We propose a method that aims to find common user intents amongst these uncovered, out-of-domain utterances, with the goal of supporting future phases of dialog system design. Our approach relies on...

Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: Interspeech 2015 Conference
Fiana Raiber, Oren Kurland, Filip Radlinski, and Milad Shokouhi

Several applications in information retrieval rely on asymmetric co-relevance estimation; that is, estimating the relevance of a document to a query under the assumption that another document is relevant. We present a supervised model for learning an asymmetric co-relevance estimate. The model uses different types of similarities with the assumed relevant document and the query, as well as document-quality measures. Empirical evaluation demonstrates the merits of using the co-relevance estimate in...

Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Ahmed Kharrufa, James Nicholson, Paul Dunphy, Steve Hodges, Pam Briggs, and Patrick Olivier

In addition to their popularity as personal devices, tablets, are becoming increasingly prevalent in work and public settings. In many of these newly-established application domains a supervisor user – such as the teacher in a classroom – oversees the function of one or more devices. Access to supervisory functions is typically controlled through the use of a passcode, but experience shows that keeping this passcode secret can be problematic. In this paper we introduce SwipeID, a method of identifying...

Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: IFIP
Suman Ravuri and Andreas Stolcke

Utterance classification is a critical pre-processing step for many speech understanding and dialog systems. In multi-user settings, one needs to first identify if an utterance is even directed at the system, followed by another level of classification to determine the intent of the user’s input. In this work, we propose RNN and LSTM models for both these tasks. We show how both models outperform baselines based on ngram-based language models (LMs), feedforward neural network LMs, and boosting...

Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: ISCA - International Speech Communication Association
Rui Lin, Shujie Liu, Muyun Yang, Mu Li, Ming Zhou, and Sheng Li

This paper proposes a novel hierarchical recurrent neural network language model (HRNNLM) for document modeling. After establishing a RNN to capture the coherence between sentences in a document, HRNNLM integrates it as the sentence history information into the word level RNN to predict the word sequence with cross-sentence contextual information. A two-step training approach is designed, in which sentence-level and word-level language models are approximated for the convergence in a pipeline style....

Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: EMNLP
Liwen Xu, Xiaohong Hao, Nicholas D. Lane, Xin Liu, and Thomas Moscibroda

Mobile crowdsourcing is a powerful tool for collecting data of various types. The primary bottleneck in such systems is the high burden placed on the user who must manually collect sensor data or respond in-situ to simple queries (e.g., experience sampling studies). In this work, we present Compressive CrowdSensing (CCS) – a framework that enables compressive sensing techniques to be applied to mobile crowdsourcing scenarios. CCS enables each user to provide significantly reduced amounts of manually...

Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
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