ACM Transactions on

Intelligent Systems and Technology (TIST)

Latest Articles

Refined-Graph Regularization-Based Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is one of the most popular data representation methods in the field of computer vision and pattern recognition.... (more)

Multifeature Anisotropic Orthogonal Gaussian Process for Automatic Age Estimation

Automatic age estimation is an important yet challenging problem. It has many promising applications in social media. Of the existing age estimation... (more)

Finding Semantically Valid and Relevant Topics by Association-Based Topic Selection Model

Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can... (more)

i2tag: RFID Mobility and Activity Identification Through Intelligent Profiling

Many radio frequency identification (RFID) applications, such as virtual shopping cart and tag-assisted gaming, involve sensing and recognizing tag mobility. However, existing RFID localization methods are mostly designed for static or slowly moving targets (less than 0.3m/sec). More importantly, we observe that prior methods suffer from serious... (more)

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 mobile energy can be used for wireless... (more)

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 web content retrieval, it is much more difficult to capture content in mobile applications due to the diversity of... (more)

TensorBeat: Tensor Decomposition for Monitoring Multiperson Breathing Beats with Commodity WiFi

Breathing signal monitoring can provide important clues for health problems. Compared to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this article, we propose TensorBeat, a system to employ... (more)

Exploring Indoor White Spaces in Metropolises

It is a promising vision to exploit white spaces, that is, vacant VHF and UHF TV channels, to meet the rapidly growing demand for wireless data services in both outdoor and indoor scenarios. While most prior works have focused on outdoor white space, the indoor story is largely open for investigation. Motivated by this observation and discovering... (more)

Secure IoT-Based, Incentive-Aware Emergency Personnel Dispatching Scheme with Weighted Fine-Grained Access Control

Emergency response times following a traffic accident are extremely crucial in reducing the number... (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
A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis

The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced framework for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage subsequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach. We find that the proposed multi-label multi-view framework can help overcome the pain of mixed-usage subsequences and can be generalized to latent activity analysis in sequential data, beyond in-App usage analytics.

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.

SPACE-TA: Cost-Effective Task Allocation Exploiting Intra- and Inter-Data Correlations in Sparse Crowdsensing

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric, rather than the overall sensed data error in the target sensing area. In this paper, we propose to leverage spatio-temporal correlations among the sensed data in the target sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intra-data correlations within the same type of sensed data and inter-data correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning and transfer learning, to dynamically select a small set of sub-areas for sensing in each timeslot (cycle), while inferring the data of unsensed sub-areas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature monitoring task leveraging intra-data correlations, SPACE-TA requires data from only 15.5% of the sub-areas while keeping the inference error below 0.25 C in 95% of the cycles, reducing the number of sensed sub-areas by 18.0-26.5% compared to baselines. When multiple tasks run simultaneously, e.g., for temperature and humidity monitoring, SPACE-TA can further reduce <10% of the sensed sub-areas by exploiting inter-data correlations.

Virtual Metering: An Efficient Water Disaggregation Algorithm via Non-Intrusive Load Monitoring

The scarcity of potable water is a critical challenge in many regions around the world. Previous studies have shown that knowledge of device level water usage can lead to significant conservation. Although there is considerable interest in determining discriminative features via sparse coding for water disaggregation to separate whole house consumption into its component appliances, existing methods lack a mechanism for fitting coefficient distributions and are thus unable to accurately discriminate parallel devices' consumption. This paper proposes a Bayesian discriminative sparse coding model, referred to as Virtual Metering (VM), for this disaggregation task. Mixture-of-Gammas is employed for the prior distribution of coefficients, contributing two benefits: (1) guaranteeing the coefficients' sparseness and non-negativeness; and (2) capturing the distribution of active coefficients. The resulting method effectively adapts the bases to aggregated consumption to facilitate discriminative learning in the proposed model, and devices' shape features are formalized and incorporated into Bayesian sparse coding to direct the learning of basis functions. Compact Gibbs Sampling (CGS) is developed to accelerate the inference process by utilizing the sparse structure of coefficients. The empirical results obtained from applying the new model to large scale real and synthetic datasets revealed that VM significantly outperformed the benchmark methods.

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.

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.

Social Bridges in Urban Purchase Behavior

The understanding and modeling of human purchase behavior in city environment can have important implications in the study of urban economy and in the design and organization of cities. In this paper, we study human purchase behavior at community level and argue that, people who live in different communities but work at close-by locations could act as ``social bridges'' that link their respective communities and make the community purchase behavior similar. We provide empirical evidence to our conjecture by studying millions of credit card transaction records for tens of thousands of individuals in city environment during a period of three months. More specifically, we show that the number of social bridges between communities is a much stronger indicator of similarity in their purchase behavior than traditionally considered factors such as income and socio-demographic variables. Our findings also suggest that such an effect varies across different merchant categories, that presence of female customers in social bridges is a stronger indicator compared to that of their male counterparts, and that there seems to be a geographical constraint for this effect, all of which may have implications in the studies of urban economy and data-driven urban planning.

Concept and Attention Based CNN for Question Retrieval in Multi-View Learning

Question retrieval, which aims to find similar questions of a given question, is playing a pivotal role in various question answering (QA) systems. This task is quite challenging mainly in five aspects: lexical gap, polysemy, word order, question length, and data sparsity. In this paper, we propose a unified framework to simultaneously handle these five problems. We use the word combined with corresponding concept information to handle the lexical gap problem and the polysemous problem. The concept embedding and word embedding are learned at the same time from both context-dependent and context-independent view. To handle the word order problem, we propose a high-level feature embedded convolutional semantic model to learn the question embedding by inputting the concept embedding and word embedding. Due to the fact that the lengths of some questions are long, we propose a value-based convolutional attentional method to enhance the proposed high-level feature embedded convolutional semantic model in learning the key parts of the question and the answer. The proposed high-level feature embedded convolutional semantic model nicely represents the hierarchical structures of word information and concept information in sentences with their layer-by-layer convolution and pooling. Finally, to resolve the data sparsity, we propose to use the multi-view learning method to train the attention based convolutional semantic model on question answer pairs. To the best of our knowledge, we are the first who propose to simultaneously handle the above five problems in question retrieval using one framework. Experiments on two real question answering datasets show that the proposed framework significantly outperforms the state-of-the-art solutions.

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.

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.

A Novel Image-centric Approach Towards Direct Volume Rendering

Transfer Function (TF) generation is a fundamental problem in Direct Volume Rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF tools are complex and unintuitive for the users who are more likely to be medical professionals than computer scientists. In this paper, we propose a novel image-centric method for TF generation where instead of complex tools, the user directly manipulates volume data to generate DVR. The user's work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, the voxels are classified using our novel Sparse Nonparametric Support Vector Machine classifier, which combines both local and near-global distributional information of the training data. The voxel classes are mapped to aesthetically pleasing and distinguishable color and opacity values using harmonic colors. Experimental results on several benchmark datasets and a detailed user survey show the effectiveness of the proposed method.

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.

Vertical Ensemble Co-Training for Text Classification

High quality, labeled data is essential for successfully applying machine learning methods to real-world text classification problems. However, in many cases, the amount of labeled data is very small compared to that of the unlabeled, and labeling additional samples could be expensive and time consuming. Co-training algorithms, which make use of unlabeled data in order to improve classification, have proven to be very effective in such cases. Generally, co-training algorithms work by using two classifiers, trained on two different views of the data, to label large amounts of unlabeled data. Doing so can help minimize the human effort required for labeling new data, as well as improve classification performance. In this paper, we propose an ensemble based co-training approach that uses an ensemble of classifiers from different training iterations to improve labeling accuracy. This approach, which we call \textit{vertical ensemble} incurs almost no additional computational cost. Experiments conducted on six textual datasets show a significant improvement of over 45% in AUC compared with the original co-training algorithm.

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 497
Citation Count 5982
Available for Download 497
Downloads (6 weeks) 5529
Downloads (12 Months) 46930
Downloads (cumulative) 226899
Average downloads per article 457
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)
Keith Ross ACM Fellows (2012)
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 9
Enhong Chen 8
Xing Xie 8
Tatseng Chua 7
Shuicheng Yan 6
Nicholasjing Yuan 5
Steven Hoi 5
Yu Zheng 5
Xiansheng HUA 5
Jinhui Tang 5
Xuan Song 4
Ryosuke Shibasaki 4
Yuval Elovici 4
Richang Hong 4
Changsheng Xu 4
Wenchih Peng 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
Hui Xiong 3
Liqiang Nie 3
Rongrong Ji 3
Xiaowei Shao 3
Francesco Bonchi 3
Huanhuan Cao 3
Meng Wang 3
Rebecca Castaño 3
Irwin King 3
VS Subrahmanian 3
Qi Tian 3
Tao Li 3
Jure Leskovec 2
Hao Fu 2
Liyan Zhang 2
Yonggang Wen 2
Alberto Del Bimbo 2
Yongdong Zhang 2
Jian Pei 2
Amin Javari 2
Amit Chopra 2
Alexander Artikis 2
Jiaching Ying 2
Venkatramanan Subrahmanian 2
Maria Sapino 2
Guirong Xue 2
Xueqi Cheng 2
Iván Cantador 2
Ido Guy 2
Eran Toch 2
Zhiyuan Liu 2
Bohao Chen 2
David Thompson 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
Bernhard Schölkopf 2
Tommaso Noia 2
Rui Zhang 2
Jianke Zhu 2
Ramesh Jain 2
Naren Ramakrishnan 2
Sarit Kraus 2
John Doucette 2
Lior Rokach 2
Kiri Wagstaff 2
Neilzhenqiang Gong 2
Wenjun Zhou 2
Shuaiqiang Wang 2
Chong Peng 2
Qingzhong Liu 2
Jiawei Han 2
Luan Tang 2
JiLei Tian 2
Claudio Biancalana 2
Giuseppe Sansonetti 2
Mahdi Jalili 2
Gita Sukthankar 2
Robin Cohen 2
Luca Cagliero 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
Jie Cheng 2
Chihjen Lin 2
Diane Cook 2
Defu Lian 2
Elena Baralis 2
Tania Cerquitelli 2
Robin Cohen 2
Vincent Tseng 2
Jintao Li 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
Martin Potthast 2
Alan Said 2
Daqing Zhang 2
Jitao Sang 2
Li Chen 2
Qi Liu 2
Xavier Serra 2
Shihchia Huang 2
Huijing Zhao 2
Eugenio Sciascio 2
Xindong Wu 2
Shulamit Reches 2
Jamal Bentahar 2
Kyumin Lee 2
James Caverlee 2
Wangchien Lee 2
Thomas Dietterich 2
Subbarao Kambhampati 2
Jalal Mahmud 2
Ron Hirschprung 2
Yixin Chen 2
Fuzheng Zhang 2
Zhifeng Li 2
Manish Marwah 2
Nicholas Jennings 2
Nathan Eagle 2
Hanqing Lu 2
Tao Mei 2
Pablo Castells 2
Daxin Jiang 2
Meir Kalech 2
Rino Falcone 2
Matteo Venanzi 2
Katia Sycara 2
Jinshi Cui 2
Jia Zeng 2
Dana Nau 2
Xuning Tang 2
Shoude Lin 2
Hongzhi Yin 2
Ling Guan 2
Michael Fire 2
Neil Yorke-Smith 2
Laiwan Chan 2
Jaegil Lee 2
Dihong Gong 2
Ratnesh Sharma 2
Fabio Gasparetti 2
Alessandro Micarelli 2
Munindar Singh 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
Evangelos Papalexakis 2
Vito Ostuni 2
Yihsuan Yang 2
Yuichi Motai 2
Xingyu Gao 2
Masaki Aono 2
Lester Mackey 1
Nhathai Phan 1
Chen Cheng 1
Mirco Nanni 1
Jialei Wang 1
Martin Sotir 1
Jing Jiang 1
Xiaofei Sun 1
Chao Huang 1
Elena Simperl 1
Hefu Zhang 1
Xiubo Geng 1
Xudong Zhang 1
Yan Song 1
Maria Gini 1
Yongsheng Dong 1
Yong Rui 1
Jun Wang 1
Jianmin Zheng 1
Guangming Shi 1
Ao Tang 1
Jie Huang 1
Yi Zhen 1
Wen Ji 1
Shanshan Huang 1
Peizhe Cheng 1
George Baciu 1
Seungchan Kim 1
Subbarao Kambhampati 1
Julie Porteous 1
Songchun Zhu 1
Pingfeng Xu 1
Einat Minkov 1
Luis Leiva 1
Daniel Martín-Albo 1
Peng Luo 1
Gerhard Widmer 1
Andreas Rauber 1
Markus Schedl 1
Ke Chen 1
Giuseppe Manco 1
Qiang Cheng 1
Xuyu Wang 1
Areej Malibari 1
Xiaoyi Fan 1
Jiangchuan Liu 1
Yongsheng Dong 1
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Yiqiang Chen 1
Chao Sun 1
J Benton 1
Seth Flaxman 1
Lin Liu 1
Bingyu Sun 1
Réjean Plamondon 1
Furui Liu 1
Judea Pearl 1
Zhikun Wang 1
Yuriy Pepyolyshev 1
Julio Carabias-Orti 1
François Pachet 1
Songtao Wu 1
Yuichi Kawamoto 1
Aidan Delaney 1
Dhaval Patel 1
Xiaofeng Tong 1
Mingbo Zhao 1
Tao Wang 1
Jungeun Kim 1
Hairuo Xie 1
Yoshihide Sekimoto 1
Xu Zhang 1
Deng Cai 1
Jeremy Frank 1
Olivier Chapelle 1
Eren Manavoglu 1
Rushi Bhatt 1
Zhongxue Chen 1
Chao Chen 1
Meiling Shyu 1
Hang Li 1
Jian Su 1
Hamed Valizadegan 1
Davide Susta 1
Federica Cena 1
Pasquale Lops 1
Marco Colombetti 1
Pınar Yolum 1
Wiebe Hoek 1
Michele Piunti 1
Cristina Conati 1
Qiang Lu 1
Guangzhong Sun 1
Jyhren Shieh 1
Pasquale De Meo 1
Jiashi Feng 1
Teng Li 1
Yuexian Hou 1
Yizhou Sun 1
Xuelong Li 1
Haoyi Xiong 1
Long Xia 1
Tuananh Hoang 1
Xiaofeng Zhu 1
Aleksandr Farseev 1
Yicheng Chen 1
Matthijs Leeuwen 1
Yi Chang 1
James Herbsleb 1
Yexi Jiang 1
Jinpeng Wang 1
Dawn Song 1
Fabrizio Marozzo 1
Domenico Talia 1
Graham Pinhey 1
Javid Ebrahimi 1
Hongbo Ni 1
Rok Sosič 1
Tieke He 1
Mauricio Chiazzaro 1
Yang Li 1
Maria Glenski 1
Amin Khezerlou 1
Zhenmin Tang 1
Franco Nardini 1
Mingli Song 1
Jiajun Bu 1
Ah Tsoi 1
Stevende Jong 1
Yuesong Wang 1
Matthew Kyan 1
Guoyu Sun 1
Paisarn Muneesawang 1
Yufei Wang 1
Tianzhu Zhang 1
Nadia Figueroa 1
Kuiyu Chang 1
Chao Xu 1
Daniel Bryce 1
Michael Verdicchio 1
Paul Schermerhorn 1
Matthias Scheutz 1
Abder Benaskeur 1
Kamfai Wong 1
Juan Cruz 1
Cécile Bothorel 1
Carles Sierra 1
Fabrizio Maggi 1
Zhenhen Hu 1
Meng Wang 1
Zhong Ming 1
Brammert Ottens 1
Yongdong Zhang 1
Yanfang Ye 1
Lifeng Wang 1
Edgar Chávez 1
Clement Leung 1
Yuanxi Li 1
David Thompson 1
Qingming Huang 1
Dityan Yeung 1
Balakrishnan Prabhakaran 1
Lijun Zhu 1
Franco Zambonelli 1
Kazumi Saito 1
Nitin Madnani 1
Natalie Fridman 1
Svetlin Bostandjiev 1
Xiaoxiao Lian 1
Majid Ahmadabadi 1
Lars Haug 1
Jussara Almeida 1
Marcos Gonçalves 1
Tongliang Liu 1
Jinhui Tang 1
Daniel Roggen 1
Robert Jäschke 1
David Ben-Shimon 1
Le Wu 1
Simon Dooms 1
Guy Shani 1
Bracha Shapira 1
Thomas Huang 1
Wei Jin 1
Hala Mostafa 1
Steve Chien 1
Georgios Paltoglou 1
Lora Aroyo 1
Vasileios Lampos 1
Alex Smola 1
Hadrien Hours 1
Ernst Biersack 1
Patrick Loiseau 1
Saisai Ma 1
Na Shan 1
Marina Demeshko 1
Sergio Oramas 1
Massimo Mecella 1
Ruide Zhang 1
Changlai Du 1
Wenjing Lou 1
Chao Yang 1
Siddhartha Ghosh 1
Yuchin Juan 1
Yanqiu Wu 1
Liping Xie 1
Hao Yin 1
Geyong Min 1
Dongchao Guo 1
Carla Gomes 1
Michela Milano 1
Ming Ji 1
Yintao Yu 1
Matthew Boyce 1
Michael Steinbach 1
Yang Mu 1
Hengshu Zhu 1
Tieyan Liu 1
Marco Ribeiro 1
Anísio Lacerda 1
Adriano Veloso 1
Weiming Hu 1
Bin Chen 1
Jinbo Bi 1
Yu Wu 1
Stephen Armeli 1
Chandan Reddy 1
Ümit Çatalyürek 1
Thomas Hoens 1
Amos Azaria 1
Elisa Marengo 1
Timothy Norman 1
Olivier Colot 1
Qun Jin 1
Huijing Zhao 1
Xiangfeng Luo 1
Wangchien Lee 1
Alejandro Jaimes 1
Fang Wu 1
William Bainbridge 1
Chiharold Liu 1
Wendong Wang 1
Jie Zhu 1
Hongsuda Tangmunarunkit 1
J Ooms 1
Faisal Alquaddoomi 1
Runhe Huang 1
Peter Briggs 1
Haifeng Wang 1
Quan Yuan 1
Hadas Schwartz-Chassidim 1
Tamir Mendel 1
Iradben Gal 1
Yuchih Chen 1
Juan Recio-García 1
Nathannan Liu 1
Pranam Kolari 1
Yan Liu 1
Jianmin Wu 1
Xiaokang Yang 1
Kenneth Joseph 1
Kaixu Liu 1
Isabel Micheel 1
Michalis Lazaridis 1
Kevyn Collins-Thompson 1
Shuguang Han 1
Judith Redi 1
James Lindsey 1
Bart Desmet 1
Yue Gao 1
Dapeng Tao 1
Jiafeng Guo 1
Qingming Huang 1
Luming Zhang 1
Stuart Shieber 1
Xiangnan Kong 1
Di Fu 1
Suhyin Lee 1
Nan Li 1
Yang Zhou 1
Xiaoming Li 1
Ramendra Sahoo 1
Qi He 1
Haizheng Zhang 1
Alice Leung 1
Enrico Pontelli 1
Chenghua Lin 1
Paola Mello 1
Marta Arias 1
Ramon Xuriguera 1
Hefei Ling 1
Yonggang Wen 1
Yuchao Duan 1
Jialie Shen 1
Jianshe Zhou 1
Xue Li 1
Xing Xie 1
Ching Law 1
José García-Macías 1
Paolo Garza 1
Markus Mühling 1
Janyl Jumadinova 1
Feng Wu 1
Payam Barnaghi 1
Amit Sheth 1
Miyoung Kim 1
David Hayden 1
Yujin Zhang 1
Xianming Liu 1
Shiguang Shan 1
Myunghoon Suk 1
Shaohui Liu 1
Mary Pendleton Hoffer 1
Daniel Schuster 1
Benjamin Hung 1
Stephan Kolitz 1
Yakov Kronrod 1
Tobias Höllerer 1
Aurélien Max 1
Anne Vilnat 1
Yi Zhang 1
Hossein Hajimirsadeghi 1
Hadi Moradi 1
Siegfried Handschuh 1
Jing Bai 1
Carolina Batista 1
Jiankang Deng 1
Dingqi Yang 1
Yuanzhuo Wang 1
Tie Luo 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
Iowa State University 1
Northumbria University 1
Joint Institute for Nuclear Research, Dubna 1
Instituto Superior Tecnico 1
University of Electronic Science and Technology of China 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
Tohoku 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
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
Donghua University 1
Citigroup 1
Lingnan University, Hong Kong 1
University of Dublin, Trinity College 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
University of Tennessee, Knoxville 1
General Electric Company 1
New York State Museum 1
National Taipei University 1
China Electric Power Research Institute 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
University of Science and Technology Beijing 1
Office of Naval Research 1
Polytechnic School of Montreal 1
Macquarie University 1
Boston University 1
Netherlands Organisation for Applied Scientific Research - TNO 1
Capital Normal University China 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
Institute of Software Chinese Academy of Sciences 1
INSA Rouen 1
University of Udine 1
Institute of Intelligent Machines Chinese Academy of Sciences 1
University of Exeter 1
Capital Medical University China 1
National Chengchi University 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
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
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
Shenzhen Institute of Advanced Technology 1
Shandong Academy of Sciences 1
Austrian Institute of Technology 1
Laboratoire d'Informatique de Nantes-Atlantique 1
Communaute d'Universites et d'Etablissements Lille Nord de France 1
Yuncheng University 1
Liverpool Hope University 1
Qatar Foundation 1
CSIRO Data61 1
Facebook, Inc. 1
U.S. Army Research Laboratory 2
American University of Beirut 2
Universite Paris-Est 2
Shandong University of Finance 2
Telecom & Management SudParis 2
New York University Shanghai 2
IBM Ireland Limited 2
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 Arizona 2
University of Kent 2
RMIT University 2
Technical University of Berlin 2
University of Sherbrooke 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
Beijing Institute of Technology 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 Sydney 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
University of California, Riverside 2
Texas State University-San Marcos 2
Universite de Rennes 1 2
University of California System 2
NEC Corporation 2
Michigan State University 2
University of Verona 2
TU Dortmund University 2
Intel Corporation 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
Queensland University of Technology 3
Universidad Politecnica de Valencia 3
Changchun University of Technology 3
Auburn University 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
Xerox Corporation 3
Graz University of Technology 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
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
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 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
New York University 4
University of Florence 4
University of Virginia 4
Massachusetts Institute of Technology 4
University of Tehran 4
University of Iowa 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
University of California, San Diego 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
University of Massachusetts Amherst 5
National Cheng Kung University 5
University of Washington, Seattle 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
Washington University in St. Louis 6
National Taipei University of Technology 6
City University of Hong Kong 6
Hong Kong Baptist University 6
IBM Thomas J. Watson Research Center 6
NEC Laboratories America, Inc. 6
Simon Fraser University 6
Singapore Management University 6
Texas A and M University 6
Missouri University of Science and Technology 6
TELECOM ParisTech 6
University of Alberta 6
Ryerson University 6
University of South Australia 6
Centro de Investigacion Cientifica y de Educacion Superior de Ensenada 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
Roma Tre University 8
Drexel University 8
University of Aberdeen 8
Huazhong University of Science and Technology 8
University of Queensland 8
Shandong University 8
Ghent University 8
Northwestern Polytechnical University China 9
NASA Ames Research Center 9
Hong Kong Polytechnic University 9
University of Texas at Austin 9
University of Waterloo 9
University of Notre Dame 9
University of California, Berkeley 9
University of Illinois at Chicago 9
Bauhaus University Weimar 9
University of Turin 10
Beijing University of Posts and Telecommunications 10
University of Southampton 10
University of Minnesota Twin Cities 10
Federal University of Minas Gerais 10
Hefei University of Technology 11
Hong Kong University of Science and Technology 11
Nanjing University of Science and Technology 11
National Chiao Tung University Taiwan 11
Georgia Institute of Technology 11
University of Melbourne 11
Nokia Corporation 11
Swiss Federal Institute of Technology, Lausanne 11
Shanghai Jiaotong University 12
Zhejiang University 13
Delft University of Technology 13
Polytechnic Institute of Turin 13
University of Technology Sydney 13
University of California, Los Angeles 14
Florida International University 15
Virginia Tech 15
Yahoo Research Labs 15
Arizona State University 16
Tel Aviv University 16
Carnegie Mellon University 16
Chinese University of Hong Kong 18
University of Tokyo 18
University of Illinois at Urbana-Champaign 20
Harbin Institute of Technology 20
National Taiwan University 21
Microsoft Research Asia 21
Peking University 22
Nanyang Technological University 23
Microsoft Research 23
Ben-Gurion University of the Negev 23
Jet Propulsion Laboratory 24
IBM Research 24
Institute of Computing Technology Chinese Academy of Sciences 24
Tsinghua University 25
University of Maryland 29
National University of Singapore 38
University of Science and Technology of China 40
Chinese Academy of Sciences 43

ACM Transactions on Intelligent Systems and Technology (TIST) - Regular Papers and Special Issue: Data-driven Intelligence for Wireless Networking

Volume 9 Issue 1, October 2017 Regular Papers and Special Issue: Data-driven Intelligence for Wireless Networking
Volume 8 Issue 5, September 2017
Volume 8 Issue 6, September 2017 Survey Paper, Regular Papers and Special Issue: Social Media Processing
Volume 8 Issue 4, July 2017 Special Issue: Cyber Security and Regular Papers
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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