ACM Transactions on

Intelligent Systems and Technology (TIST)

Latest Articles

P2P Lending Survey

P2P lending is an emerging Internet-based application where individuals can directly borrow money from each other. The past decade has witnessed the rapid development and prevalence of online P2P lending platforms, examples of which include Prosper, LendingClub, and Kiva. Meanwhile, extensive research has been done that mainly focuses on the... (more)

Social Incentives in Paid Collaborative Crowdsourcing

Paid microtask crowdsourcing has traditionally been approached as an individual activity, with units of work created and completed independently by... (more)

A Traffic Flow Approach to Early Detection of Gathering Events

Given a spatial field and the traffic flow between neighboring locations, the early detection of gathering events (edge) problem aims to discover and... (more)

Multi-Hypergraph Consistent Sparse Coding

Sparse representation has been a powerful technique for modeling high-dimensional data. As an unsupervised technique to extract sparse representations, sparse coding encodes the original data into a new sparse code space and simultaneously learns a dictionary representing high-level semantics. Existing methods have considered local manifold within... (more)

Introduction to Special Issue on Social Media Processing (TIST-SMP)

Rating Effects on Social News Posts and Comments

At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that... (more)


Recent TIST News: 

ACM Transactions on Intelligent Systems and Technology (TIST) is ranked No.1 in all ACM journals in terms of citations received per paper. Each paper published at TIST in the time span (from Jan. 2010 to Dec. 2014) has received 18 citations on average in ACM Digital Library in the past fiscal year (from July 1 2015 to June 30 2016).  

ACM Transactions on Intelligent Systems and Technology (TIST) has been a success story.  Submissions to the journal have increase 76 percent from 2013 to 2015, from 278 original papers and revisions to 488.  Despite this increase, the journal acceptance rate has remained at a steady rate of approximately 24 percent. Furthermore, the TIST Impact Factor increased from 1.251 in 2014 to 3.19 in 2016.  

Journal Metric (2016)

  • - Impact Factor: 3.19
  • - 5-year Impact Factor: 10.47
  • - Avg. Citations in ACM DL: 18 

About TIST

ACM Transactions on Intelligent Systems and Technology (ACM TIST) is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. READ MORE

Forthcoming Articles

ACM TIST Special Issue on Data-driven Intelligence for Wireless Networking

TensorBeat: Tensor Decomposition for Monitoring Multi-Person Breathing Beats with Commodity WiFi

Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation

As smartphone penetration increases, it has become pervasive for images to be associated with location information in the form of geotags. Geotags bridge the gap between the physical world and the cyber space, giving rise to new opportunities to gain further insights into user preferences and behaviors. In this paper, we seek to exploit geotagged photos from online photo-sharing sites for the purpose of personalized POI recommendation. Owing to the fact that most users have only very limited travel experiences, data sparseness poses a formidable challenge to personalized POI recommendation. To alleviate data sparseness, we propose to augment current collaborative filtering algorithms along from multiple perspectives. Specifically, hybrid preference cues comprising user-uploaded and user-favored photos are harvested to study users' tastes. Moreover, heterogeneous high-order relationship information is jointly captured from user social networks and POI multimodal contents with hypergraph models. We build upon the matrix factorization algorithm to integrate the disparate sources of preference and relationship information, and apply our approach to directly optimize user preference rankings. Extensive experiments on a large and publicly accessible dataset well verified the potential of our approach for addressing data sparseness and offering quality recommendations to users, especially for those who have only limited travel experiences.

Simulating Urban Pedestrian Crowds of Different Cultures

Accurate models of crowd dynamics are critically important for urban planning and management. The models generate synthetic behaviors for simulation, support analysis, and facilitate qualitative and quantitative predictions. One promising approach to crowd modeling relies on micro-level agent-based simulations, where the interactions of simulated individual agents in the crowd result in macro-level crowd dynamics which are the object of study. This paper reports on agent-based simulations of urban crowds, where \textit{culture is explicitly modeled}. We investigate cultural phenomena in pedestrians, and in building evacuations. In developing these simulations we take a step towards treating culture as a first-class object in models of physical crowds. In the pedestrians domain we relate to recorded pedestrian data in five different countries: Iraq, Israel, England, Canada and France and characterize these cultures based on cultural attributes at the individual level: personal spaces, speed, avoidance side and group formations. We use an agent-based simulation to investigate the impact on the resulting macro level behavior, such as pedestrian flow, number of collisions, etc. We quantitatively validate the simulation against data from movies of human crowds, in different countries. In the evacuation domain, we use an established simulation system to investigate cultural differences reported in the literature, and additionally explore the resulting macro level behavior.

UMCR: User Interaction-Driven Mobile Content Retrieval

Although mobile application ecosystems have experienced tremendous growth in recent years, retrieving content of mobile applications that serves a key to mobile content search engines still faces grand challenges. Compared to the web content retrieval, it is much more difficult to capture content in mobile applications due to the diversity of applications and the lack of URL indices. In this study, we propose a user interaction-driven mobile content retrieval (UMCR) scheme to address such issues, which is the first mobile content crawler in the current literature. UMCR combines the user interaction path traversing (UIPT) and Deep Package Inspection (DPI) together to obtain mobile content. UIPT determines the events of user interactions in various applications to capture the static content such as text and images, in which a traversal depth termination scheme and an optional cut-off component are adopted to balance the content coverage and traversing efficiency. Meanwhile, the analysis based on DPI is responsible for extracting the videos as well as digging the infrastructural information and performance metrics. In addition, a distributed traversal scheduling method is designed for UIPT tasks to improve the throughput and scalability in large-scale content retrieval. Experiments on retrieving content of 64 real mobile applications demonstrate that UMCR can handle diverse mobile applications efficiently. The scheduler can improve throughput by 3 times compared to the legacy arbitrary task assignment strategy.

Understanding and Identifying Rhetorical Questions in Social Media

Social media provides a platform for seeking information from a large user base. Information seeking in social media, however, occurs simultaneously with users expressing their viewpoints by making statements. Rhetorical questions, an important tool employed by users to express their viewpoints, have the form of a question but serve the function of a statement. Rhetorical questions might, therefore, mislead platforms assisting information seeking in social media. It becomes difficult to identify rhetorical questions are they not syntactically different from other questions. In this paper, we develop a framework to identify rhetorical questions by modeling the possible motivations of the users to post them. We focus on two possible motivations of the users drawing from linguistic theories, to implicitly convey a message and to modify the strength of a statement previously made. We develop a quantitative framework from these motivations to identify rhetorical questions in social media. We evaluate the framework using two datasets of questions posted on a social media platform Twitter and demonstrate its effectiveness in identifying rhetorical questions. This is the first framework, to the best of our knowledge, to model the possible motivations for posting rhetorical questions to identify them on social media platforms.

A data mining approach to assess privacy risk in human mobility data

Human mobility data are an important proxy for understanding human mobility dynamics and developing useful analytical services. Unfortunately these data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment in human mobility data provide the data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed every time new data records become available. In this paper we propose novel re-identification attacks and a fast and flexible data mining approach for privacy risk assessment in human mobility data. The idea is to learn classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experimentation on a real-world GPS data in two urban areas, and investigate the relations between human mobility patterns and the privacy risk of individuals.

RCMC: Recognizing Crowd Mobility Patterns in Cities based on Location Based Social Networks Data

During the past few years, the analysis of data generated from Location-Based Social Networks (LBSNs) have aided in the identification of urban patterns, understanding activity behaviours in urban areas, as well as producing novel recommender systems that facilitate users choices. Recognizing crowd mobility patterns in cities is very important for public safety, traffic managment, disaster management, and urban planning. In this paper we propose a framework for Recognizing the Crowd Mobility Patterns in Cities (RCMC) using Location Based Social Networks (LBSN) data. Our proposed framework comprises of four main components: data gathering, recurrent crowd mobility patterns extraction, temporal functional regions detection and visualization component. More specifically, we employ a novel approach based on Non-negative Matrix Factorization (NMF) and gaussian Kernel Density Estimation (KDE) for extracting the recurrent crowd mobility patterns in cities illustrating how crowd shifts from one area to another during each day across various time-slots. Moreover, the framework employs a hierarchical clustering based algorithm for identifying what we refer to as temporal functional regions by modeling functional areas taking into account temporal variation by means of check-ins categories. We build the framework using spatial-temporal dataset crawled from Twitter for two entire years 2013 and 2014 for the area of Manhattan in New York City (NYC). We perform a detailed analysis of the extracted crowd patterns with an exploratory visualization showing that our proposed approach can identify clearly obvious mobility patterns that recur over time and location in the urban scenario. Using same time interval, we show that correlating the temporal functional regions with the recognized recurrent crowd mobility patterns can yield to a deeper understanding of city dynamics and the motivation behind the crowd mobility. We are confident that our proposed framework can not only help in managing complex city environments and better allocation of resources based on the expected crowd mobility and temporal functional regions but can have a direct implication on a variety of applications such as personalized recommender systems, anomalous event detection, disaster resilience management systems, and others.

Stopping Criterion for Active Learning with Model Stability

Active learning selectively labels the most informative instances, aiming to reduce the cost of data annotation. While much effort has been devoted to active sampling functions, relatively limited attention has been paid to when the learning process should stop. In this paper, we focus on the stopping criterion of active learning and propose a model stability based criterion, i.e. when model does not change with inclusion of additional training instances. The challenge lies in how to measure the model change without labeling additional instances and training new models. Inspired by the stochastic gradient update rule, we use the gradient of the loss function at each candidate example to measure its effect on model change. We propose to stop active learning when the model change brought by any of the remaining unlabeled examples is lower than a given threshold. We apply the proposed stopping criterion to two popular classifiers: logistic regression (LR) and support vector machines (SVMs). In addition, we theoretically analyze the stability and generalization ability of the model obtained by our stopping criterion. Substantial experiments on various UCI benchmark data sets and ImageNet data sets have demonstrated that the proposed approach is highly effective.

Iteratively Divide-and-Conquer Learning for Nonlinear Classification and Ranking

Nonlinear classifiers (i.e., Kernel support vector machines (SVMs)) are effective for nonlinear data classification. However, nonlinear classifiers are usually prohibitively expensive when dealing with large nonlinear data. Ensembles of linear classifiers have been proposed to address this inefficiency, which is called the ensemble linear classifiers for nonlinear data problem. In this paper, a new iterative learning approach is introduced, which involves two steps at each iteration: partitioning the data into clusters according to Gaussian mixture models with local consistency and then training basic classifiers (i.e., linear SVMs) for each cluster. The two divide-and-conquer steps are combined into a graphical model. Meanwhile, with training each classifier is regarded as a task, clustered multi-task learning is employed to capture the relatedness among different tasks and avoid overfitting in each task. In addition, two novel extensions are introduced for the proposed approach. First, the approach is extended for quality-aware web data classification. In this problem, the types of web data vary in terms of information quality. The ignorance of the variations of information quality of web data leads to poor classification models. The proposed approach can effectively integrate quality-aware factors into web data classification. Secondly, the approach is extended for listwise learning to rank to construct an ensemble of linear ranking models, whereas most existing listwise ranking methods construct a solely linear ranking model. Experimental results on benchmark datasets show that our approach outperforms state-of-the-art algorithms. During prediction for nonlinear classification, it also obtains comparable classification performance to kernel SVMs, with much higher efficiency.

i2tag: RFID Mobility and Activity Identification through Intelligent Profiling

Many Radio Frequency Identification (RFID) applications, e.g., virtual shopping-cart and tag-assisted gaming, involve sensing and recognizing tag mobility. Existing RFID localization techniques however are mostly designed for static or slowly moving targets (less than 0.3 m/s). More importantly, we observe that prior schemes suffer from serious performance degradation for detecting realworld moving tags in typical indoor environments with multipath interference. In this paper, we present i2tag, an intelligent mobility-aware activity identification system for RFID tags in multipath-rich environments, e.g., indoors. i2tag employs a supervised learning framework based on our novel fine-grained mobility profile, which can quantify different levels of mobility. Unlike previous methods that mostly rely on phase measurement, i2tag takes into account various measurements, including RSSI variance, packet loss rate, and our novel relative-phase-based fingerprint. Additionally, we design a multiple dimensional dynamic time warping based algorithm to robustly detect mobility and the associated activities. We show that i2tag is readily deployable using off-the-shelf RFID devices. A prototype has been implemented using a Thingmagic reader and standard-compatible tags. Experimental results demonstrate its superiority in mobility detection and activity identification in various indoor environments.

Energy Efficient Mobile Video Streaming: A Location-aware Approach

Video streaming is one of the most widely used mobile applications today, and it also accounts for a large fraction of mobile battery usage. Much of the energy consumption is for wireless data transmission, and is highly correlated to network bandwidth conditions. In periods of poor connectivity, up to 90\% of streaming energy can be used for wireless data transfer. In this paper, we study the problem of energy efficient mobile video streaming. We make use of the observed correlation between bandwidth and user \emph{location}, and also observe that a user's location is \emph{predictable} in many situations, such as when commuting to a known destination. Based on the user's predicted locations and bandwidth conditions, we optimize wireless transmission times to achieve high quality video playback while minimizing energy use. We propose an optimal offline algorithm for this problem which runs in $O(Tk)$ time, where $T$ is the duration of the video and $k$ is the size of the video buffer. We also propose LAWS, a Location AWare Streaming algorithm. LAWS learns from historical location-aware bandwidth conditions and predicts future bandwidths along a planned route to make online wireless download decisions. We evaluate LAWS using real bandwidth traces, and show that LAWS closely approximates the performance of the optimal offline algorithm, achieving 90.6\% of the optimal performance on average, and 97\% in certain cases. LAWS also outperforms three popular strategies used in practice by on average 69\%, 63\%, and 38\% respectively. Lastly, we show that LAWS is able to deal with noisy data, and can attain the stated performance after sampling bandwidth conditions only 5 times.

Knowledge Representations and Inference Techniques for Medical Question Answering

Answering medical question related to complex medical cases, as required in modern Clinical Decision Support (CDS) systems, imposes (1) access to vast medical knowledge and (2) sophisticated inference techniques. In this paper, we examine the representation and role of combining medical knowledge automatically derived from (a) clinical practice and (b) research findings for inferring answers to medical questions. Knowledge from medical practice was distilled from a vast Electronic Medical Record (EMR) system, while research knowledge was processed from biomedical articles available in PubMed Central. The knowledge automatically acquired from the EMR system took into account the clinical picture and therapy recognized from each medical record to generate a probabilistic Markov network denoted as a Clinical Picture and Therapy Graph (CPTG). Moreover, we represented the background of medical questions available from the description of each complex medical case as a medical knowledge sketch. We considered three possible representations of medical knowledge sketches that were used by four different probabilistic inference methods to pinpoint the answers from the CPTG. In addition, several answer-informed relevance models were developed to provide a ranked list of biomedical articles containing the answers. Evaluations on the TREC-CDS data show which of the medical knowledge representations and inference methods perform optimally. The experiments indicate an improvement of biomedical article ranking by 85% over state-of-the-art results.

Energy Usage Behavior Modeling in Energy Disaggregation via Hawkes Processes

Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household's energy consumption is user's daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances by householders across different time slots. The major challenge in modeling such relationship in that, with ambiguous appliance usage membership of householders, we find it difficult to appropriately model the influence between appliances, since such influence is determined by human behaviors in energy usage. To address this problem, we propose to model the influence between householders' energy usage behaviors directly through a novel probabilistic model, which combines topic models with the Hawkes processes. The proposed model simultaneously disaggregates the whole home electricity signal into each component appliance and infers the appliance usage membership of household members, and enables those two tasks mutually benefit each other. Experimental results on both synthetic data and four real world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in not only decomposing the entire consumed energy to each appliance in houses, but also the inference of household structures. We further analyze the inferred appliance-householder assignment and the corresponding influence within the appliance usage of each householder and across different householders, which provides insight into appealing human behavior patterns in appliance usage

SocialWave: Visual Analysis of Spatio-temporal Diffusion of Information on Social Media

Rapid advancement of social media tremendously facilitates and accelerates the information diffusion among users around the world. How and to what extent will the information on social media achieve widespread diffusion across the world? How can we quantify the interaction between users from different geolocations in the diffusion process? How will the spatial patterns of information diffusion change over time? To address these questions, a dynamic social gravity model (SGM) is proposed to quantify the dynamic spatial interaction behavior among social media users in information diffusion. The dynamic SGM includes three factors that are theoretically significant to the spatial diffusion of information: geographic distance, cultural proximity, and linguistic similarity. Temporal dimension is also taken into account to help detect recency effect, and ground-truth data is integrated into the model to help measure the diffusion power. Furthermore, SocialWave, a visual analytic system, is developed to support both spatial and temporal investigative tasks. SocialWave provides a temporal visualization that allows users to quickly identify the overall temporal diffusion patterns, which reflect the spatial characteristics of the diffusion network. When a meaningful temporal pattern is identified, SocialWave utilizes a new occlusion-free spatial visualization, which integrates a node-link diagram into a circular cartogram for further analysis. Moreover, we propose a set of rich user interactions that enable in-depth, multi-faceted analysis of the diffusion on social media. The effectiveness and efficiency of the mathematical model and visualization system are evaluated with two datasets on social media, namely, Ebola Epidemics and Ferguson Unrest.

Supervised Representation Learning with Double Encoding-layer Autoencoder for Transfer Learning

Transfer learning has gained a lot of attention and interest in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep learning has been proposed to learn more robust or higher-level features for transfer learning. However, to the best of our knowledge, most of the previous approaches neither minimize the difference between domains explicitly nor encode label information in learning the representation. In this paper, we adapt the autoencoder technique to transfer learning and propose a supervised representation learning method based on double encoding-layer autoencoder. The proposed framework consists of two encoding layers: one for embedding and the other one for label encoding. In the embedding layer, the distribution distance of the embedded instances between the source and target domains is minimized in terms of KL-Divergence. In the label encoding layer, label information of the source domain is encoded using a softmax regression model. Moreover, to empirically explore why the proposed framework can work well for transfer learning, we propose a new effective measure based on autoencoder to compute the distribution distance between different domains. Experimental results show that the proposed new measure can better reflect the degree of transfer difficulty and has stronger correlation with the performance from supervised learning algorithms (e.g., Logistic Regression), compared with previous ones, such as KL-Divergence and Maximum Mean Discrepancy (MMD). Therefore, actually in our model, we have incorporated two distribution distance measures to minimize the difference between source and target domains in the embedding representations. Extensive experiments conducted on three real-world image data sets and one text data demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.


Publication Years 2010-2017
Publication Count 475
Citation Count 5776
Available for Download 475
Downloads (6 weeks) 4988
Downloads (12 Months) 46019
Downloads (cumulative) 216782
Average downloads per article 456
Average citations per article 12
First Name Last Name Award
Rakesh Agrawal ACM Fellows (2003)
Benjamin B Bederson ACM Distinguished Member (2011)
Andrei Broder ACM Paris Kanellakis Theory and Practice Award (2012)
ACM Fellows (2007)
Carlos A. Castillo ACM Senior Member (2014)
Charles L A Clarke ACM Distinguished Member (2015)
Ingemar J. Cox ACM Fellows (2013)
ACM Distinguished Member (2011)
Umeshwar Dayal ACM Fellows (2008)
Alberto Del Bimbo ACM Distinguished Member (2016)
Inderjit Dhillon ACM Fellows (2014)
Deborah Estrin ACM Athena Lecturer Award (2006)
ACM Fellows (2000)
Christos Faloutsos ACM Fellows (2010)
Wen Gao ACM Fellows (2013)
Maria L Gini ACM Distinguished Member (2006)
Jiawei Han ACM Fellows (2003)
James Hendler ACM Fellows (2016)
Xian-Sheng Hua ACM Distinguished Member (2015)
ACM Senior Member (2009)
Ramesh C Jain ACM Fellows (2003)
Sarit Kraus ACM Fellows (2014)
Vipin Kumar ACM Fellows (2005)
Chih-Jen Lin ACM Fellows (2015)
ACM Distinguished Member (2011)
ACM Senior Member (2010)
C.L. Liu ACM Karl V. Karlstrom Outstanding Educator Award (1989)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Dana Nau ACM Fellows (2013)
Jeffrey Nichols ACM Senior Member (2013)
Judea Pearl ACM Fellows (2015)
ACM A. M. Turing Award (2011)
ACM AAAI Allen Newell Award (2003)
Jian Pei ACM Fellows (2015)
ACM Senior Member (2007)
Yong Rui ACM Distinguished Member (2009)
ACM Senior Member (2006)
Michael Rung-Tsong Lyu ACM Fellows (2015)
Stefan Savage ACM Prize in Computing (2015)
ACM Fellows (2010)
Stuart Shieber ACM Fellows (2014)
Yoav Shoham ACM AAAI Allen Newell Award (2012)
ACM Fellows (2012)
Padhraic Smyth ACM Fellows (2013)
Gita Reese Sukthankar ACM Senior Member (2013)
Jie Tang ACM Senior Member (2017)
Jaime Teevan ACM Senior Member (2012)
Moshe Tennenholtz ACM AAAI Allen Newell Award (2012)
Feiyue Wang ACM Distinguished Member (2007)
Ouri Wolfson ACM Fellows (2001)
Michael Wooldridge ACM Fellows (2015)
Xing Xie ACM Senior Member (2010)
Hui Xiong ACM Distinguished Member (2014)
ACM Senior Member (2010)
Shuicheng Yan ACM Distinguished Member (2016)
Qiang Yang ACM Distinguished Member (2011)
Philip S Yu ACM Fellows (1997)
Franco Zambonelli ACM Distinguished Member (2012)
ACM Senior Member (2009)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)

First Name Last Name Paper Counts
Dacheng Tao 8
Tatseng Chua 7
Xing Xie 7
Shuicheng Yan 6
Enhong Chen 6
Nicholasjing Yuan 5
Yu Zheng 5
Xiansheng HUA 5
Jinhui Tang 5
Xuan Song 4
Steven Hoi 4
Ryosuke Shibasaki 4
Yuval Elovici 4
Changsheng Xu 4
Qiang Yang 4
Michelle Zhou 4
Ya'akov Gal 3
Quanshi Zhang 3
Martha Larson 3
Philip YU 3
Christopherchuen Yang 3
Wen Gao 3
Xue Li 3
Alex Rogers 3
Richang Hong 3
Hui Xiong 3
Rongrong Ji 3
Xiaowei Shao 3
Huanhuan Cao 3
Francesco Bonchi 3
Meng Wang 3
Rebecca Castaño 3
Irwin King 3
VS Subrahmanian 3
Qi Tian 3
Wenchih Peng 3
Tao Li 3
Iván Cantador 2
Ido Guy 2
Eran Toch 2
Liqiang Nie 2
Bohao Chen 2
Yixin Chen 2
Fuzheng Zhang 2
Nathan Eagle 2
Manish Marwah 2
Nicholas Jennings 2
Alan Said 2
Daqing Zhang 2
Jitao Sang 2
Li Chen 2
Xavier Serra 2
Shihchia Huang 2
Bernhard Schölkopf 2
Ramesh Jain 2
Huijing Zhao 2
Xindong Wu 2
Shuaiqiang Wang 2
Qingzhong Liu 2
Jiawei Han 2
Luan Tang 2
JiLei Tian 2
Mahdi Jalili 2
Claudio Biancalana 2
Giuseppe Sansonetti 2
Robin Cohen 2
Luca Cagliero 2
Chong Peng 2
Boi Faltings 2
Yue Shi 2
Alan Hanjalic 2
Charles Ling 2
Daqing Zhang 2
Hasan Cam 2
Anlei Dong 2
Mohan Kankanhalli 2
Zhengjun Zha 2
Yue Gao 2
Yoshinobu Kawahara 2
Chihjen Lin 2
Diane Cook 2
Defu Lian 2
Elena Baralis 2
Tania Cerquitelli 2
Robin Cohen 2
Vincent Tseng 2
Jie Cheng 2
Hongxun Yao 2
Zhiwen Yu 2
Paulo Shakarian 2
Hongyuan Zha 2
Sihong Xie 2
Haggai Roitman 2
SungWook Yoon 2
Oded Maimon 2
Mahmud Hossain 2
Quan Fang 2
Shazia Sadiq 2
Jure Leskovec 2
Liyan Zhang 2
Alberto Del Bimbo 2
Yongdong Zhang 2
Amin Javari 2
Jian Pei 2
Amit Chopra 2
Alexander Artikis 2
Jiaching Ying 2
Venkatramanan Subrahmanian 2
Maria Sapino 2
Guirong Xue 2
Xueqi Cheng 2
Matteo Venanzi 2
Jinshi Cui 2
Katia Sycara 2
Jia Zeng 2
Dana Nau 2
Xuning Tang 2
Shoude Lin 2
Hongzhi Yin 2
Ling Guan 2
Shulamit Reches 2
Jamal Bentahar 2
Kyumin Lee 2
James Caverlee 2
Eugenio Sciascio 2
Wangchien Lee 2
Thomas Dietterich 2
Subbarao Kambhampati 2
Jalal Mahmud 2
Ron Hirschprung 2
Hanqing Lu 2
Tao Mei 2
Pablo Castells 2
Meir Kalech 2
Daxin Jiang 2
Rino Falcone 2
Michael Fire 2
Neil Yorke-Smith 2
Laiwan Chan 2
Jaegil Lee 2
Ratnesh Sharma 2
Fabio Gasparetti 2
Alessandro Micarelli 2
Munindar Singh 2
Gita Sukthankar 2
Zhiyuan Cheng 2
John Dickerson 2
Alvin Chin 2
David Carmel 2
Sushil Jajodia 2
Xiaofang Zhou 2
Jun Ma 2
Jiuyong Li 2
Yuichi Motai 2
Masaki Aono 2
Evangelos Papalexakis 2
Vito Ostuni 2
David Thompson 2
Yihsuan Yang 2
Benno Stein 2
Alejandro Bellogín 2
Bingbing Ni 2
Michael Lyu 2
Jeffrey Nichols 2
Rajesh Ganesan 2
Zhi Geng 2
Kun Zhang 2
Naren Ramakrishnan 2
John Doucette 2
Lior Rokach 2
Sarit Kraus 2
Tommaso Noia 2
Rui Zhang 2
Kiri Wagstaff 2
Martin Potthast 2
Michelle Zhou 1
Gerd Stumme 1
Liangliang Cao 1
José Cortizo 1
Yong Yu 1
Janardhan Doppa 1
Victor Lesser 1
Bhavesh Shrestha 1
Daniel McFarlane 1
Yosi Mass 1
Hal Daumé 1
Richong Zhang 1
Wenjun Zhou 1
Chihchung Chang 1
Dana Nau 1
Bernardo Huberman 1
Kyumin Lee 1
Hongtai Li 1
Wangsheng Zhang 1
Brent Longstaff 1
Joshua Selsky 1
Atesmachew Hailegiorgis 1
Aris Anagnostopoulos 1
B Prakash 1
Yuval Shavitt 1
Amit Kleinmann 1
Benny Pinkas 1
Guillermo Jiménez-Díaz 1
Fatih Gedikli 1
Hongbin Zha 1
Yuchun Shen 1
Furu Wei 1
Ya Zhang 1
Marjan Momtazpour 1
Jason Hong 1
Licia Capra 1
Ouri Wolfson 1
Eoghan Furey 1
Aonghus Lawlor 1
Dan Lin 1
Juan Cao 1
Byron Gao 1
Theodoros Semertzidis 1
Martin Bockle 1
Yubin Kim 1
Jaime Teevan 1
Alina Huldtgren 1
Eepeng Lim 1
Xibin Zhao 1
Lingjing Hu 1
Rong Yan 1
Jiang Bian 1
W Towne 1
Changshing Perng 1
Jing Lv 1
Joan Serrà 1
Ranieri Baraglia 1
Bowei Chen 1
Jianfei Cai 1
Yang Yang 1
Bruce Elder 1
Wenbin Chen 1
Chunyan Miao 1
Fan Liu 1
Zhen Hai 1
Miaojing Shi 1
Paul McKevitt 1
Marc Cavazza 1
Fred Charles 1
Éric Beaudry 1
Elias Bareinboim 1
Hua Chen 1
Xiaohua Zhou 1
Jixue Liu 1
Gem Stapleton 1
Bernadette Bouchon-Meunier 1
Kyle Feuz 1
Chidansh Bhatt 1
Jie Yu 1
Guojun Qi 1
Yimin Zhang 1
Zhenhui Li 1
Fusun Yaman 1
Debprakash Patnaik 1
Sarvapali Ramchurn 1
Melinda Gervasio 1
Ari Jónsson 1
Ashish Garg 1
Lourenço Bandeira 1
Ricardo Ricardo 1
Tianyu Cao 1
Raju Balakrishnan 1
Sudhakar Reddy 1
Michael Iatauro 1
Azin Ashkan 1
Paweł Woźniak 1
Mohammad Obaid 1
Jiashi Feng 1
Teng Li 1
Haoyi Xiong 1
Long Xia 1
Yuexian Hou 1
Yizhou Sun 1
Tuananh Hoang 1
Xiaofeng Zhu 1
Xuelong Li 1
Aleksandr Farseev 1
Waitat Fu 1
Patrick De Boer 1
Yicheng Chen 1
Omar Alonso 1
Jinpeng Wang 1
Matthijs Leeuwen 1
Neilzhenqiang Gong 1
Dawn Song 1
Yi Chang 1
James Herbsleb 1
Yexi Jiang 1
Javid Ebrahimi 1
Graham Pinhey 1
Hongbo Ni 1
Fabrizio Marozzo 1
Domenico Talia 1
Rok Sosič 1
Tieke He 1
Zhenmin Tang 1
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Katrin Erk 1
David Newman 1
Padhraic Smyth 1
Trevor Cohn 1
Steven Burrows 1
Kostas Kolomvatsos 1
Stathes Hadjiefthymiades 1
Albert Bifet 1
Weinan Zhang 1
Fabiano Belém 1
Jintao Ye 1
Dihong Gong 1
Hweepink Tan 1
Domonkos Tikk 1
Marco Baroni 1
Benno Stein 1
Steffen Becker 1
Denis Helic 1
Roman Kern 1
Charles Parker 1
Ugur Kuter 1
Daniel Corkill 1
Kurt Rothermel 1
Zhengxiang Wang 1
Wil Van Der Aalst 1
Argimiro Arratia 1
Tao Li 1
Haiyin Shen 1
Yi Wang 1
Zhenlong Sun 1
Patricia Serrano-Alvarado 1
Xiaoping Chen 1
Pramod Anantharam 1
Chris Nugent 1
Osmar Zaïane, 1
Eunju Kim 1
John Champaign 1
Rakesh Agrawal 1
Han Yu 1
Damian Martínez-Muñoz 1
Alexander Schindler 1
Zhonggang Wu 1
Haikun Wei 1
Jiming Liu 1
Tara Estlin 1
Steve Chien 1
Bernd Freisleben 1
Ning Zhang 1
Lingyu Duan 1
Steffen Rendle 1
Zhixian Yan 1
Dipanjan Chakraborty 1
Zhengzheng Pan 1
Bill Dolan 1
Idan Szpektor 1
Philip Resnik 1
Benjamin Bederson 1
Brynjar Gretarsson 1
Shixia Liu 1
Huadong Ma 1
Wei Peng 1
Tong Sun 1
Weiwei Cui 1
Pierre Rouille 1
Geoffrey Holmes 1
Yuhang Zhao 1
Bingqing Qu 1
David Glass 1
Toon De Pessemier 1
Daniel Tran 1
Robert Pappalardo 1
Vasant Dhar 1
Yuzhou Zhang 1
Edward Chang 1
Gilles Gasso 1
Bo Liu 1
Huibo Wang 1
Erik Edrosa 1
Guande Qi 1
Nithya Ramanathan 1
D George 1
Marek Lipczak 1
Vishvas Vasuki 1
Berkant Savas 1
Lei Tang 1
Yingying Jiang 1
Michele Gelfand 1
Juan Rogers 1
Jingdong Wang 1
Sheng Li 1
Evgeniy Gabrilovich 1
Haipeng Chen 1
Mordechai Guri 1
Guiguang Ding 1
Belén Díaz-Agudo 1
Dietmar Jannach 1
Yushi Lin 1
Hitoshi Yamamoto 1
Xiaohua Liu 1
Ming Zhou 1
Ruiqiang Zhang 1
Keyi Shen 1
Yiping Han 1
Xiangyu Wang 1
Oukhellou Latifa 1
Chang Tan 1
Sashi Gurung 1
Goran Radanovic 1
Peng Dai 1
Chen Chen 1
Guy De Pauw 1
Orphée De Clercq 1
Walter Daelemans 1
Yashar Moshfeghi 1
Jinghe Zhang 1
Zhao Kang 1
Jingfei Li 1

Affiliation Paper Counts
Ryukoku University 1
University of Connecticut Health Center 1
University of Lausanne 1
Federal University of Amazonas 1
University of Macedonia 1
Demokritos National Centre for Scientific Research 1
University of Michigan 1
Anhui University 1
National Taitung University Taiwan 1
University of Sheffield 1
Ehime University 1
University of Haifa 1
University of Perugia 1
Joint Institute for Nuclear Research, Dubna 1
Instituto Superior Tecnico 1
University of Auckland 1
Bogazici University 1
University of Houston 1
University of Pennsylvania 1
University of Koblenz-Landau 1
Guangdong University of Technology 1
Northwestern University 1
Smithsonian National Museum of Natural History 1
Hebrew University of Jerusalem 1
Osaka Prefecture University 1
Duke University 1
Vrije Universiteit Amsterdam 1
Birkbeck University of London 1
Educational Testing Service 1
IBM Almaden Research Center 1
Wayne State University 1
Northeast Normal University China 1
Central European University 1
University of Arizona 1
Rissho University 1
Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende 1
The University of British Columbia 1
Dartmouth College 1
Hohai University 1
The University of Western Ontario 1
Citigroup 1
Lingnan University 1
King's College London 1
Center for Mathematics and Computer Science - Amsterdam 1
University of Messina 1
University of Shizuoka 1
Aoyama Gakuin University 1
United States National Science Foundation 1
Ionian University 1
University of Passau 1
Eastman Kodak Company 1
Imperial College London 1
University of Saskatchewan 1
General Electric Company 1
New York State Museum 1
Beijing Institute of Technology 1
Charles Stark Draper Lab Inc 1
University of Sussex 1
Defence Research and Development Canada 1
Nankai University 1
Vienna University of Technology 1
Washington State University Pullman 1
Office of Naval Research 1
Polytechnic School of Montreal 1
Macquarie University 1
Netherlands Organisation for Applied Scientific Research - TNO 1
Binghamton University State University of New York 1
American University 1
Massachusetts General Hospital and Harvard Medical School 1
University of Surrey 1
Nanjing University of Aeronautics and Astronautics 1
Aalborg University 1
Naresuan University 1
Politecnico di Milano 1
Shanghai University 1
Soka University 1
Ecole Centrale Paris 1
National University of Defense Technology China 1
University of Fribourg 1
National Central University Taiwan 1
Dublin City University 1
Catholic University of Leuven, Leuven 1
The University of North Carolina at Chapel Hill 1
University of Cincinnati 1
University of Sydney 1
INSA Rouen 1
University of Udine 1
Institute of Intelligent Machines Chinese Academy of Sciences 1
Capital Medical University China 1
United States Military Academy 1
University of Quebec in Montreal 1
Yunnan University 1
Lanzhou University 1
Berlin University of Applied Sciences 1
Northeastern University 1
Fairleigh Dickinson University 1
Research Organization of Information and Systems National Institute of Informatics 1
University of Hawaii System 1
University of Chicago 1
University of Southern California 1
European Space Agency - ESA 1
University of California, Riverside 1
Institute of Applied Physics and Computational Mathematics 1
Columbia University 1
Ecole des Mines de Paris 1
Hosei University 1
Rutgers, The State University of New Jersey 1
Boeing Corporation 1
Santa Fe Institute 1
Michigan State University 1
University of Western Australia 1
Indian Institute of Technology Roorkee 1
North Dakota State University 1
University of Electro-Communications 1
Pontifical Catholic University of Rio de Janeiro 1
University of Jyvaskyla 1
University of Chittagong 1
University of Seville 1
Mehran University of Engineering & Technology 1
University of Sousse 1
Nanjing University of Information Science and Technology 1
Know-Center, Graz 1
Institute for Cancer Research and Treatment, Candiolo 1
Reykjavik University 1
Macau University of Science and Technology 1
SONY Computer Science Laboratory, Paris 1
Google Switzerland GmbH 1
Intel Research Laboratories 1, Inc. 1
Nanyang Technological University School of Computer Engineering 1
Florida Institute for Human & Machine Cognition 1
Fujitsu America, Inc. 1
Shandong University of Finance 1
Shandong Academy of Sciences 1
Austrian Institute of Technology 1
Laboratoire d'Informatique de Nantes-Atlantique 1
Yuncheng University 1
Liverpool Hope University 1
Qatar Foundation 1
CSIRO Data61 1
University of Texas at Arlington 2
University of Manchester 2
King Abdulaziz University 2
Southeast University China, Nanjing 2
University of Lugano 2
University of Missouri-Kansas City 2
University of Wolverhampton 2
University of Texas at El Paso 2
University of Brighton 2
Utrecht University 2
National University of Ireland, Galway 2
University of Massachusetts Dartmouth 2
Universite des Sciences et Technologies de Lille 2
Harvard University 2
University of Kent 2
RMIT University 2
Technical University of Berlin 2
Open University 2
University of Zurich 2
University of Antwerp 2
King Saud University 2
Telecom Bretagne 2
Aston University 2
University of Hawaii at Hilo 2
Aristotle University of Thessaloniki 2
University of Southern California, Information Sciences Institute 2
Dalhousie University 2
Academia Sinica Taiwan 2
National Tsing Hua University 2
Xiamen University 2
Technical University of Dresden 2
Tamkang University 2
University of Nebraska at Omaha 2
University of Bristol 2
East China Normal University 2
Communication University of China 2
Ecole d' Ingenieurs Telecom Lille 1 2
Johannes Kepler University Linz 2
Queen Mary, University of London 2
University of Central Florida 2
Waseda University 2
Lancaster University 2
University of Massachusetts Boston 2
University of Ferrara 2
Sam Houston State University 2
University of Rochester 2
University of Edinburgh 2
Jerusalem College of Technology 2
Hungarian Academy of Sciences 2
New Mexico Institute of Mining and Technology 2
University of Athens 2
Texas State University-San Marcos 2
Universite de Rennes 1 2
University of California System 2
NEC Corporation 2
University of Verona 2
TU Dortmund University 2
Intel Corporation 2
U.S. Army Research Laboratory 2
American University of Beirut 2
Universite Paris-Est 2
Telecom & Management SudParis 2
SRI International 3
David R. Cheriton School of Computer Science 3
Universiti Sains Malaysia 3
Chalmers University of Technology 3
Universite Pierre et Marie Curie 3
University of Glasgow 3
University of Texas at San Antonio 3
University College Dublin 3
Brigham Young University 3
Jiangnan University 3
Universitat Politecnica de Catalunya 3
Orebro University 3
University of Wyoming 3
University of Texas at Dallas 3
Washington State University 3
Free University of Bozen-Bolzano 3
Universidad Politecnica de Valencia 3
Changchun University of Technology 3
BBN Technologies 3
University of Oregon 3
Philipps-Universitat Marburg 3
Beihang University 3
Universidad de Jaen 3
Georgia Tech Research Institute 3
University of Stuttgart 3
Kassel University 3
Wright State University 3
Simon Fraser University 3
Singapore Management University 3
Xerox Corporation 3
Graz University of Technology 3
New York University 3
University of Macau 3
University of Connecticut 3
University of North Texas 3
Universite Paris-Sud XI 3
University of Utah 3
University of Konstanz 3
Xidian University 3
University of Teesside 3
Huazhong University of Science and Technology 3
Center For Research And Technology - Hellas 3
Stony Brook University 3
University of Oxford 3
University of Szeged 3
University of Bologna 3
University of Pisa 3
EURECOM Ecole d'Ingenieurs & Centre de Recherche en Systemes de Communication 3
University of California, San Diego 3
Utah State University 3
University of Wisconsin Madison 3
University of Roma La Sapienza 3
Intel Corporation, China 3
Rutgers University-Newark Campus 4
New Mexico State University Las Cruces 4
Toyohashi University of Technology 4
Ohio State University 4
University of Waikato 4
The University of North Carolina at Charlotte 4
University of Vermont 4
University of Adelaide 4
University of Florida 4
University of Pavia 4
University of Washington, Seattle 4
University of Trento 4
Technical University of Lodz 4
Tianjin University 4
University of Calabria 4
Guangxi Normal University 4
University of Liverpool 4
Indiana University 4
West Virginia University 4
University of Notre Dame 4
University of Florence 4
University of Virginia 4
Massachusetts Institute of Technology 4
University of Tehran 4
Swiss Federal Institute of Technology, Zurich 4
National University of Ireland, Maynooth 4
Complutense University of Madrid 4
Sharif University of Technology 4
University of Ottawa, Canada 4
University of California, Santa Barbara 4
University of Miami 4
Korea Advanced Institute of Science & Technology 4
Eindhoven University of Technology 4
Nanjing University 4
Cornell Tech 4
Shenzhen University 5
Institute for Infocomm Research, A-Star, Singapore 5
Bar-Ilan University 5
North Carolina State University 5
Google Inc. 5
University of California, Irvine 5
Soochow University 5
Istituto Di Scienze E Tecnologie Della Cognizione, Rome 5
City University of Hong Kong 5
University of Massachusetts Amherst 5
National Cheng Kung University 5
University of Pittsburgh 5
CSIC - Instituto de Investigacion en Inteligencia Artificial 5
University College London 5
Beijing Jiaotong University 5
Osaka University 5
Rensselaer Polytechnic Institute 5
Microsoft Corporation 5
Southern Illinois University at Carbondale 5
Max Planck Institute for Intelligent Systems 5
TECH Lab 5
Yahoo Research Barcelona 5
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 5
Pennsylvania State University 6
Virginia Commonwealth University 6
Hong Kong Polytechnic University 6
Washington University in St. Louis 6
National Taipei University of Technology 6
Hong Kong Baptist University 6
IBM Thomas J. Watson Research Center 6
NEC Laboratories America, Inc. 6
Texas A and M University 6
Missouri University of Science and Technology 6
Biblioteca CICESE 6
TELECOM ParisTech 6
University of Alberta 6
Ryerson University 6
Shandong University 6
University of South Australia 6
Universitat Pompeu Fabra 6
Stanford University 7
Oregon State University 7
George Mason University 7
Institute of Automation Chinese Academy of Sciences 7
University of Ulster 7
University of Bari 7
Universidad Autonoma de Madrid 7
HP Labs 8
National Chiao Tung University Taiwan 8
Roma Tre University 8
Drexel University 8
University of Aberdeen 8
University of Southampton 8
University of Queensland 8
Ghent University 8
Northwestern Polytechnical University China 9
NASA Ames Research Center 9
University of Texas at Austin 9
University of Waterloo 9
University of California, Berkeley 9
University of Illinois at Chicago 9
Bauhaus University Weimar 9
Hefei University of Technology 10
University of Turin 10
Beijing University of Posts and Telecommunications 10
Virginia Tech 10
Swiss Federal Institute of Technology, Lausanne 10
University of Minnesota Twin Cities 10
Federal University of Minas Gerais 10
Hong Kong University of Science and Technology 11
Nanjing University of Science and Technology 11
Georgia Institute of Technology 11
University of Melbourne 11
Nokia Corporation 11
Zhejiang University 12
Shanghai Jiaotong University 12
Delft University of Technology 13
Polytechnic Institute of Turin 13
University of Technology Sydney 13
Chinese University of Hong Kong 14
University of California, Los Angeles 14
Florida International University 15
Yahoo Research Labs 15
Arizona State University 16
Tel Aviv University 16
Tsinghua University 16
Carnegie Mellon University 16
Harbin Institute of Technology 17
University of Tokyo 18
Nanyang Technological University 19
University of Illinois at Urbana-Champaign 20
Microsoft Research 20
National Taiwan University 21
Institute of Computing Technology Chinese Academy of Sciences 21
Microsoft Research Asia 21
Peking University 22
Ben-Gurion University of the Negev 23
Jet Propulsion Laboratory 24
IBM Research 24
University of Maryland 29
University of Science and Technology of China 32
Chinese Academy of Sciences 35
National University of Singapore 38

ACM Transactions on Intelligent Systems and Technology (TIST)

Volume 8 Issue 6, July 2017  Issue-in-Progress
Volume 8 Issue 4, July 2017 Special Issue: Cyber Security and Regular Papers
Volume 8 Issue 5, July 2017  Issue-in-Progress
Volume 8 Issue 3, April 2017 Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers
Volume 8 Issue 2, January 2017 Survey Paper, Special Issue: Intelligent Music Systems and Applications and Regular Papers

Volume 8 Issue 1, October 2016
Volume 7 Issue 4, July 2016 Special Issue on Crowd in Intelligent Systems, Research Note/Short Paper and Regular Papers
Volume 7 Issue 3, April 2016 Regular Papers, Survey Papers and Special Issue on Recommender System Benchmarks
Volume 7 Issue 2, January 2016 Special Issue on Causal Discovery and Inference

Volume 7 Issue 1, October 2015
Volume 6 Issue 4, August 2015 Regular Papers and Special Section on Intelligent Healthcare Informatics
Volume 6 Issue 3, May 2015 Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence
Volume 6 Issue 2, May 2015 Special Section on Visual Understanding with RGB-D Sensors
Volume 6 Issue 1, April 2015
Volume 5 Issue 4, January 2015 Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers

Volume 5 Issue 3, September 2014 Special Section on Urban Computing
Volume 5 Issue 2, April 2014 Special Issue on Linking Social Granularity and Functions

Volume 5 Issue 1, December 2013 Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Volume 4 Issue 4, September 2013 Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Volume 4 Issue 3, June 2013 Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Volume 4 Issue 2, March 2013 Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Volume 4 Issue 1, January 2013 Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context

Volume 3 Issue 4, September 2012
Volume 3 Issue 3, May 2012
Volume 3 Issue 2, February 2012

Volume 3 Issue 1, October 2011
Volume 2 Issue 4, July 2011
Volume 2 Issue 3, April 2011
Volume 2 Issue 2, February 2011
Volume 2 Issue 1, January 2011

Volume 1 Issue 2, November 2010
Volume 1 Issue 1, October 2010
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