hotauthor: zhang_p* : Human-Computer Interaction Bibliography Search

[1] ID-Match: A Hybrid Computer Vision and RFID System for Recognizing Individuals in Groups Robot Personalities / Li, Hanchuan / Zhang, Peijin / Al Moubayed, Samer / Patel, Shwetak N. / Sample, Alanson P. Proceedings of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.1 p.4933-4944

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Summary: Technologies that allow autonomous robots and computer systems to quickly recognize and interact with individuals in a group setting has the potential to enable a wide range of personalized experiences. However, existing solutions fail to both identify and locate individuals with enough speed to enable seamless interactions in very dynamic environments that require fast, implicit, non-intrusive, and ubiquitous recognition of users. In this work, we present a hybrid computer vision and RFID system that uses a novel reverse synthetic aperture technique to recover the relative motion paths of an RFID tags worn by people and correlate that to physical motion paths of individuals as measured with a 3D depth camera. Results show that our real-time system is capable of simultaneously recognizing and correctly assigning IDs to individuals within 4 seconds with 96.6% accuracy and groups of five people in 7 seconds with 95% accuracy. In order to test the effectiveness of this approach in realistic scenarios, groups of five participants play an interactive quiz game with an autonomous robot, resulting in an ID assignment accuracy of 93.3%.

[2] ID-Match: A Hybrid Computer Vision and RFID System for Recognizing Individuals in Groups Video Showcase Presentations / Li, Hanchuan / Zhang, Peijin / Al Moubayed, Samer / Patel, Shwetak N. / Sample, Alanson P. Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.7

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Summary: Technologies that allow autonomous robots and computer systems to quickly recognize and interact with individuals in a group setting has the potential to enable a wide range of personalized experiences. We present ID-Match, a hybrid computer vision and RFID system that uses a novel reverse synthetic aperture technique to recover the relative motion paths of a RFID tags worn by people and correlate that to physical motion paths of individuals as measured with a 3D depth camera. Results show that our real-time system is capable of simultaneously recognizing and correctly assigning IDs to individuals within 4 seconds with 96.6% accuracy and groups of five people in 7 seconds with 95% accuracy. In order to test the effectiveness of this approach in realistic scenarios, groups of five participants play an interactive quiz game with an autonomous robot, resulting in an ID assignment accuracy of 93.3%.

[3] Online Object Tracking Based on CNN with Metropolis-Hasting Re-Sampling Poster Session 2 / Zhou, Xiangzeng / Xie, Lei / Zhang, Peng / Zhang, Yanning Proceedings of the 2015 ACM International Conference on Multimedia 2015-10-26 p.1163-1166

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Summary: Tracking-by-learning strategies have been effective in solving many challenging problems in visual tracking, in which the learning sample generation and labeling play important roles for final performance. Since the concern of deep learning based approaches has shown an impressive performance in different vision tasks, how to properly apply the learning model, such as CNN, to an online tracking framework is still challenging. In this paper, to overcome the overfitting problem caused by straight-forward incorporation, we propose an online tracking framework by constructing a CNN based adaptive appearance model to generate more reliable training data over time. With a reformative Metropolis-Hastings re-sampling scheme to reshape particles for a better state posterior representation during online learning, the proposed tracking outperforms most of the state-of-art trackers on challenging benchmark video sequences.

[4] Defragging Subgraph Features for Graph Classification Short Papers: Databases / Wang, Haishuai / Zhang, Peng / Tsang, Ivor / Chen, Ling / Zhang, Chengqi Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1687-1690

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Summary: Graph classification is an important tool for analysing structured and semi-structured data, where subgraphs are commonly used as the feature representation. However, the number and size of subgraph features crucially depend on the threshold parameters of frequent subgraph mining algorithms. Any improper setting of the parameters will generate many trivial short-pattern subgraph fragments which dominate the feature space, distort graph classifiers and bury interesting long-pattern subgraphs. In this paper, we propose a new Subgraph Join Feature Selection (SJFS) algorithm. The SJFS algorithm, by forcing graph classifiers to join short-pattern subgraph fragments, can defrag trivial subgraph features and deliver long-pattern interesting subgraphs. Experimental results on both synthetic and real-world social network graph data demonstrate the performance of the proposed method.

[5] A Real-Time Eye Tracking Based Query Expansion Approach via Latent Topic Modeling Short Papers: Information Retrieval / Chen, Yongqiang / Zhang, Peng / Song, Dawei / Wang, Benyou Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1719-1722

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Summary: Formulating and reformulating reliable textual queries have been recognized as a challenging task in Information Retrieval (IR), even for experienced users. Most existing query expansion methods, especially those based on implicit relevance feedback, utilize the user's historical interaction data, such as clicks, scrolling and viewing time on documents, to derive a refined query model. It is further expected that the user's search experience would be largely improved if we could dig out user's latent query intention, in real-time, by capturing the user's current interaction at the term level directly. In this paper, we propose a real-time eye tracking based query expansion method, which is able to: (1) automatically capture the terms that the user is viewing by utilizing eye tracking techniques; (2) derive the user's latent intent based on the eye tracking terms and by using the Latent Dirichlet Allocation (LDA) approach. A systematic user study has been carried out and the experimental results demonstrate the effectiveness of our proposed methods.

[6] Modeling Infinite Topics on Social Behavior Data with Spatio-temporal Dependence Short Papers: Knowledge Management / Wang, Peng / Zhang, Peng / Zhou, Chuan / Li, Zhao / Li, Guo Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1919-1922

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Summary: The problem of modeling topics on user behavior data in social networks has been widely studied in social marketing and social emotion analysis, where latent topic models are commonly used as the solutions. The user behavior data are highly related in time and space, which demands new latent topic models that consider both temporal and spatial distances. However, existing topic models either fail to model these two factors simultaneously, or cannot handle the high order dependence among user behaviors. In this paper we present a new nonparametric Bayesian model Time and Space Dependent Chinese Restaurant Processes (TSD-CRP for short). TSD-CRP can auto-select the number of topics and model high-order temporal and spatial dependence behind user behavior data. Empirical results on real-world data sets demonstrate the effectiveness of the proposed method.

[7] MyoVibe: vibration based wearable muscle activation detection in high mobility exercises Novel sensing techniques / Mokaya, Frank / Lucas, Roland / Noh, Hae Young / Zhang, Pei Proceedings of the 2015 International Conference on Ubiquitous Computing 2015-09-07 p.27-38

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Summary: Skeletal muscles are activated to generate the force needed for movement in most high motion sports and exercises. However, incorrect skeletal muscle activation during these sports and exercises, can lead to sub-optimal performance and injury. Existing techniques are susceptible to motion artifacts, particularly when used in high motion sports (e.g. jumping, cycling, etc.). They require limited body movement, or experts to manually interpret results, making them unsuitable in sports scenarios.
This paper presents MyoVibe, a wearable system for determining muscle activation in high motion exercise scenarios. MyoVibe senses muscle vibration signals obtained from a wearable network of accelerometers to determine muscle activation. By modeling the characteristics of muscles and high motion noise using extreme value analysis, MyoVibe can reduce noise due to high mobility exercises. Our system can predict muscle activation with greater than 97% accuracy in isometric low motion exercise cases, up to 90% accuracy in high motion exercises.

[8] Modeling Multi-query Retrieval Tasks Using Density Matrix Transformation Short Papers / Li, Qiuchi / Li, Jingfei / Zhang, Peng / Song, Dawei Proceedings of the 2015 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2015-08-09 p.871-874

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Summary: The quantum probabilistic framework has recently been applied to Information Retrieval (IR). A representative is the Quantum Language Model (QLM), which is developed for the ad-hoc retrieval with single queries and has achieved significant improvements over traditional language models. In QLM, a density matrix, defined on the quantum probabilistic space, is estimated as a representation of user's search intention with respect to a specific query. However, QLM is unable to capture the dynamics of user's information need in query history. This limitation restricts its further application on the dynamic search tasks, e.g., session search. In this paper, we propose a Session-based Quantum Language Model (SQLM) that deals with multi-query session search task. In SQLM, a transformation model of density matrices is proposed to model the evolution of user's information need in response to the user's interaction with search engine, by incorporating features extracted from both positive feedback (clicked documents) and negative feedback (skipped documents). Extensive experiments conducted on TREC 2013 and 2014 session track data demonstrate the effectiveness of SQLM in comparison with the classic QLM.

[9] Fast Search for Distance Dependent Chinese Restaurant Processes Posters / Feng, Weiwei / Wang, Peng / Zhou, Chuan / Guo, Li / Zhang, Peng Companion Proceedings of the 2015 International Conference on the World Wide Web 2015-05-18 v.2 p.33-34

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Summary: The distance dependent Chinese Restaurant Processes (dd-CRP), a nonparametric Bayesian model, can model distance sensitive data. Existing inference algorithms for dd-CRP, such as Markov Chain Monte Carlo (MCMC) and variational algorithms, are inefficient and unable to handle massive online data, because posterior distributions of dd-CRP are not marginal invariant. To solve this problem, we present a fast inference algorithm for dd-CRP based on the A-star search. Experimental results show that the new search algorithm is faster than existing dd-CRP inference algorithms with comparable results.

[10] Online Event Recommendation for Event-based Social Networks Posters / Ji, Xiancai / Qiao, Zhi / Xu, Mingze / Zhang, Peng / Zhou, Chuan / Guo, Li Companion Proceedings of the 2015 International Conference on the World Wide Web 2015-05-18 v.2 p.45-46

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Summary: With the rapid growth of event-based social networks, the demand of event recommendation becomes increasingly important. While, the existing event recommendation approaches are batch learning fashion. Such approaches are impractical for real-world recommender systems where training data often arrive sequentially. Hence, we present an online event recommendation method. Experimental results on several real-world datasets demonstrate the utility of our method.

[11] On Topology of Baidu's Association Graph Based on General Recommendation Engine and Users' Behavior Posters / Men, Cong / Tang, Wanwan / Zhang, Po / Hou, Junqi Companion Proceedings of the 2015 International Conference on the World Wide Web 2015-05-18 v.2 p.75-76

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Summary: To better meet users' underlying navigational requirement, search engines like Baidu has developed general recommendation engine and provided related entities on the right side of the search engine results page (SERP). However, users' behavior have not been well investigated after the association of individual queries in search engine. To better understand users' navigational activities, we propose a new method to map users' behavior to an association graph and make graph analysis. Interesting properties like clustering and assortativity are found in this association graph. This study provides a new perspective on research of semantic network and users' navigational behavior on SERP.

[12] Topic-aware Source Locating in Social Networks Posters / Zang, Wenyu / Zhou, Chuan / Guo, Li / Zhang, Peng Companion Proceedings of the 2015 International Conference on the World Wide Web 2015-05-18 v.2 p.141-142

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Summary: In this paper we address the problem of source locating in social networks from a topic modeling perspective. From the observation that the topic factor can help infer the propagation paths, we propose a topic-aware source locating method based on topic analysis of propagation items and participants. We evaluate our algorithm on both generated and real-world datasets. The experimental results show significant improvement over existing popular methods.

[13] LApp: A Speech Loudness Application for People with Parkinson's on Google Glass Making & Sharing Assistive Technologies / McNaney, Roisin / Poliakov, Ivan / Vines, John / Balaam, Madeline / Zhang, Pengfei / Olivier, Patrick Proceedings of the ACM CHI'15 Conference on Human Factors in Computing Systems 2015-04-18 v.1 p.497-500

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Summary: Reduced vocal volume in Parkinson's is extremely common and can have significant social and emotional impact. We describe the development and evaluation of LApp -- an application for Google Glass to help people with Parkinson's (PwP) monitor their speech volume and cue themselves to speak louder when necessary. Our findings highlight enthusiasm for using the application both at home as a volume training tool and in public social settings as a situated cueing device. We contribute insights to the literature on how eyewear technologies can provide assistance to people with health conditions and offer insights for the design of future self-monitoring and management applications on Google Glass.

[14] Document Boltzmann Machines for Information Retrieval Topic and Document Models / Yu, Qian / Zhang, Peng / Hou, Yuexian / Song, Dawei / Wang, Jun Proceedings of ECIR 2015, the 2015 European Conference on Information Retrieval 2015-03-29 p.666-671

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Summary: Probabilistic language modelling has been widely used in information retrieval. It estimates document models under the multinomial distribution assumption, and uses query likelihood to rank documents. In this paper, we aim to generalize this distribution assumption by exploring the use of fully-observable Boltzmann Machines (BMs) for document modelling. BM is a stochastic recurrent network and is able to model the distribution of multi-dimensional variables. It yields a kind of Boltzmann distribution which is more general than multinomial distribution. We propose a Document Boltzmann Machine (DBM) that can naturally capture the intrinsic connections among terms and estimate query likelihood efficiently. We formally prove that under certain conditions (with 1-order parameters learnt only), DBM subsumes the traditional document language model. Its relations to other graphical models in IR, e.g., MRF model, are also discussed. Our experiments on the document reranking demonstrate the potential of the proposed DBM.

[15] Object Tracking using Reformative Transductive Learning with Sample Variational Correspondence Posters 2 / Zhuo, Tao / Zhang, Peng / Zhang, Yanning / Huang, Wei / Sahli, Hichem Proceedings of the 2014 ACM International Conference on Multimedia 2014-11-03 p.941-944

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Summary: Tracking-by-learning strategies have effectively solved many challenging problems for visual tracking. When labeled samples are limited, the learning performance can be improved by exploiting unlabeled ones. Thus, a key issue for semi-supervised learning is the label assignment of the unlabeled samples, which is the principal focus of transductive learning. Unfortunately, the optimization scheme employed by the transductive learning is hard to be applied to online tracking because of its large amount of computation for sample labeling. In this paper, a reformative transductive learning was proposed with the variational correspondence between the learning samples, which are utilized to build an effective matching cost function for more efficient label assignment during the learning of representative separators. By using a weighted accumulative average to update the coefficients via a fixed budget of support vectors, the proposed tracking has been demonstrated to outperform most of the state-of-art trackers.

[16] Instructive Video Retrieval Based on Hybrid Ranking and Attribute Learning: A Case Study on Surgical Skill Training Posters 2 / Chen, Lin / Zhang, Peng / Li, Baoxin Proceedings of the 2014 ACM International Conference on Multimedia 2014-11-03 p.1045-1048

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Summary: Video-based systems have been increasingly used in various training tasks in applications like sports, dancing, and surgery. One key task to add automation to such systems is to automatically select reference videos for a given training video of a trainee. In this paper, we formulate a new problem of instructive video retrieval and propose a solution using both attribute learning and learning to rank. The method first evaluates a user's skill attributes by relative attribute learning. Then, the most critical skill attribute in need of improvement is selected and reported to the user. Finally, a hybrid ranking learning to rank method is employed to retrieve instructive videos from a dataset, which serve as reference for the user. Two main technical problems are solved in this method. First, we combine both skill and visual feature to characterize skill superiority and context similarity. Second, we propose a hybrid ranking approach that works with both pair-wise and point-wise labels of the data. The benefit of the proposed method over other heuristic methods is demonstrated by both objective and subjective experiments, using surgical training videos as a case study.

[17] GPQ: Directly Optimizing Q-measure based on Genetic Programming IR Track Posters / Lin, Yuan / Lin, Hongfei / Zhang, Ping / Xu, Bo Proceedings of the 2014 ACM Conference on Information and Knowledge Management 2014-11-03 p.1859-1862

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Summary: Ranking plays an important role in information retrieval system. In recent years, a kind of research named 'learning to rank' becomes more and more popular, which applies machine learning technology to solve ranking problems. Lots of ranking models belonged to learning to rank have been proposed, such as Regression, RankNet, and ListNet. Inspired by this, we proposed a novel learning to rank algorithm named GPQ in this paper, in which genetic programming was employed to directly optimize Q-measure evaluation metric. Experimental results on OHSUMED benchmark dataset indicated that our method GPQ could be competitive with Ranking SVM, SVMMAP and ListNet, and improve the ranking accuracies.

[18] Generalized Bias-Variance Evaluation of TREC Participated Systems IR Track Posters / Zhang, Peng / Hao, Linxue / Song, Dawei / Wang, Jun / Hou, Yuexian / Hu, Bin Proceedings of the 2014 ACM Conference on Information and Knowledge Management 2014-11-03 p.1911-1914

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Summary: Recent research has shown that the improvement of mean retrieval effectiveness (e.g., MAP) may sacrifice the retrieval stability across queries, implying a tradeoff between effectiveness and stability. The evaluation of both effectiveness and stability are often based on a baseline model, which could be weak or biased. In addition, the effectiveness-stability tradeoff has not been systematically or quantitatively evaluated over TREC participated systems. The above two problems, to some extent, limit our awareness of such tradeoff and its impact on developing future IR models. In this paper, motivated by a recently proposed bias-variance based evaluation, we adopt a strong and unbiased "baseline", which is a virtual target model constructed by the best performance (for each query) among all the participated systems in a retrieval task. We also propose generalized bias-variance metrics, based on which a systematic and quantitative evaluation of the effectiveness-stability tradeoff is carried out over the participated systems in the TREC Ad-hoc Track (1993-1999) and Web Track (2010-2012). We observe a clear effectiveness-stability tradeoff, with a trend of becoming more obvious in more recent years. This implies that when we pursue more effective IR systems over years, the stability has become problematic and could have been largely overlooked.

[19] Adding directional context to gestures using Doppler effect Posters / Bannis, Adeola / Pan, Shijia / Zhang, Pei Adjunct Proceedings of the 2014 International Joint Conference on Pervasive and Ubiquitous Computing 2014-09-13 v.2 p.5-8

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Summary: Human beings often give non-verbal instructions through motions of the hand and arm, such as pointing or waving. These motions convey not just actions, but the direction or target of those actions. In this paper, we integrate direction into gesture definitions by detecting frequency shifts created by relative motion between a receiver and transmitter and combining this with inertial motion data captured by a smartphone. With the combined data we are able separate similar gestures with 71.7% accuracy in a typical home use environment.

[20] Supervised hashing with latent factor models Session 2c: hashing and efficiency / Zhang, Peichao / Zhang, Wei / Li, Wu-Jun / Guo, Minyi Proceedings of the 2014 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2014-07-06 p.173-182

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Summary: Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing (LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time.

[21] Exploring the acceptability of google glass as an everyday assistive device for people with Parkinson's Interactive technologies for rehabilitation / McNaney, Rísin / Vines, John / Roggen, Daniel / Balaam, Madeline / Zhang, Pengfei / Poliakov, Ivan / Olivier, Patrick Proceedings of ACM CHI 2014 Conference on Human Factors in Computing Systems 2014-04-26 v.1 p.2551-2554

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Summary: We describe a qualitative study investigating the acceptability of the Google Glass eyewear computer to people with Parkinson's disease (PD). We held a workshop with 5 PD patients and 2 carers exploring perceptions of Glass. This was followed by 5-day field trials of Glass with 4 PD patients, where participants wore the device during everyday activities at home and in public. We report generally positive responses to Glass as a device to instill confidence and safety for this potentially vulnerable group. We also raise concerns related to the potential for Glass to reaffirm dependency on others and stigmatise wearers.

[22] Combining geographical information of users and content of items for accurate rating prediction WWW 2014 posters / Qiao, Zhi / Zhang, Peng / He, Jing / Cao, Yanan / Zhou, Chuan / Guo, Li Companion Proceedings of the 2014 International Conference on the World Wide Web 2014-04-07 v.2 p.361-362

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Summary: Recommender systems have attracted attentions lately due to their wide and successful applications in online advertising. In this paper, we propose a Bayesian generative model to describe the generative process of rating, which combines geographical information of users and content of items. The generative model consists of two interacting LDA models, where one LDA model for location-based user groups (user dimension) and the other for the topics of content of items (item dimension). A Gibbs sampling algorithm is proposed for parameter estimation. Experiments have shown our proposed method outperforms baseline methods.

[23] An upper bound based greedy algorithm for mining top-k influential nodes in social networks WWW 2014 posters / Zhou, Chuan / Zhang, Peng / Guo, Jing / Guo, Li Companion Proceedings of the 2014 International Conference on the World Wide Web 2014-04-07 v.2 p.421-422

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Summary: Influence maximization [4] is NP-hard under the Linear Threshold (LT) model, where a line of greedy algorithms have been proposed. The simple greedy algorithm [4] guarantees accuracy rate of 1-1/e to the optimal solution; the advanced greedy algorithm, e.g., the CELF algorithm [6], runs 700 times faster by exploiting the submodular property of the spread function. However, both models lack efficiency due to heavy Monte-Carlo simulations during estimating the spread function. To this end, in this paper we derive an upper bound for the spread function under the LT model. Furthermore, we propose an efficient UBLF algorithm by incorporating the bound into CELF. Experimental results demonstrate that UBLF, compared with CELF, reduces about 98.9% Monte-Carlo simulations and achieves at least 5 times speed-raising when the size of seed set is small.

[24] Maximizing the long-term integral influence in social networks under the voter model WWW 2014 posters / Zhou, Chuan / Zhang, Peng / Zang, Wenyu / Guo, Li Companion Proceedings of the 2014 International Conference on the World Wide Web 2014-04-07 v.2 p.423-424

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Summary: We address the problem of discovering the influential nodes in social networks under the voter model, which allows multiple activations to the same node, by defining an integral influence maximization problem in a long term. We analyze the problem formulation and present an exact solution to the maximization problem. We also provide a sufficient condition for the convergence of the integral influence. We experimentally compare the exact solution with other heuristic algorithms in the aspects of quality and efficiency.

[25] EDITED BOOK Mobile Social Networking: An Innovative Approach Computational Social Sciences / Chin, Alvin / Zhang, Daqing 2014 p.243 Springer

ISBN: 978-1-4614-8578-0 (Print) 978-1-4614-8579-7 (Online)

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Introduction (1-7)
	+ Chin, Alvin
	+ Zhang, Daqing
Socially Aware Computing: Concepts, Technologies, and Practices (9-23)
	+ Yu, Zhiwen
	+ Zhou, Xingshe
Ephemeral Social Networks (25-64)
	+ Chin, Alvin
Social Behavior in Mobile Social Networks: Characterizing Links, Roles, and Communities (65-78)
	+ Atzmueller, Martin
Mobile Social Service Design for Special Context (79-107)
	+ Liu, Huanglingzi
	+ Wang, Wei
	+ Liu, Dong
	+ Wang, Hao
	+ Liu, Ying
Exploiting Personal and Community Context in Mobile Social Networks (109-138)
	+ Zhang, Daqing
	+ Yu, Zhiyong
	+ Guo, Bin
	+ Wang, Zhu
Enhancing Mobile Social Networks with Ambient Intelligence (139-163)
	+ Doolin, Kevin
	+ Taylor, Nick
	+ Crotty, Micheal
	+ Roddy, Mark
	+ Jennings, Edel
	+ Roussaki, Ioanna
	+ McKitterick, David
Data Analysis on Location-Based Social Networks (165-194)
	+ Gao, Huiji
	+ Liu, Huan
Towards Trustworthy Mobile Social Networking (195-235)
	+ Yan, Zheng
	+ Niemi, Valtteri
	+ Chen, Yu
	+ Zhang, Peng
	+ Kantola, Raimo
Conclusions (237-243)
	+ Chin, Alvin
	+ Zhang, Daqing