ACM Transactions on

Intelligent Systems and Technology (TIST)

Latest Articles

ACM TIST Special Issue on Urban Intelligence

Scalable Urban Mobile Crowdsourcing: Handling Uncertainty in Worker Movement

A Comfort-Based Approach to Smart Heating and Air Conditioning

Spotting Trip Purposes from Taxi Trajectories: A General Probabilistic Model

A Data Mining Approach to Assess Privacy Risk in Human Mobility Data

Social Bridges in Urban Purchase Behavior


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.

Dynamic Optimization of the Level of Operational Effectiveness of a CSOC under Adverse Conditions

The analysts at a cybersecurity operations center (CSOC) analyze the alerts that are generated by intrusion detection systems (IDSs). Under normal operating conditions, sufficient numbers of analysts are available to analyze the alert workload. For the purpose of this paper, this means that the cybersecurity analysts in each shift can fully investigate each and every alert that is generated by the IDSs in a reasonable amount of time, and perform their normal tasks in a shift. Normal tasks include analysis time, time to attend training programs, report writing time, personal break time, and time to update the signatures on new patterns in alerts as detected by the IDS. There are number of disruptive factors that occur randomly, and can adversely impact the normal operating condition of a CSOC such as 1) higher alert generation rates from a few IDSs, 2) new alert patterns that decreases the throughput of the alert analysis process, and 3) analyst absenteeism. The impact of all the above factors is that the alerts wait for a long duration before being analyzed, which impacts the Level of Operational Effectiveness (LOE) of the CSOC. In order to return the CSOC to normal operating conditions, the manager of a CSOC can take several actions such as increasing the alert analysis time spent by analysts in a shift by cancelling a training program, spending some of their own time to assist the analysts in alert investigation, and calling upon the on-call analyst workforce to boost the service rate of alerts. However, additional resources are limited in quantity over a 14-day work cycle, and the CSOC manager must determine when and how much action to take in the face of uncertainty, which arises from both the intensity and the random occurrences of the disruptive factors. The above decision by the CSOC manager is non-trivial and is often made in an ad-hoc manner using prior experiences. This paper develops a reinforcement learning (RL) model for optimizing the LOE throughout the entire 14-day work cycle of a CSOC in the face of uncertainties due to disruptive events. Results indicate that the RL model is able to assist the CSOC manager with a decision support tool to make better decisions than current practices in determining when and how much resource to allocate when the LOE of a CSOC deviates from the normal operating condition.

A Review of Co-saliency Detection Algorithms: Fundamentals, Applications, and Challenges

Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting eective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency, and designing eective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this paper, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifcally, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and fnally point out some unsolved challenges and promising future works. We expect this review to be benefcial to both fresh and senior researchers in this feld, and give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms.

A Bayesian Approach to Intervention-Based Clustering

It is important to be able to determine the varying effects of an intervention on patients' health. For new medical treatments, it is often the case that some patients do not respond, or worse yet, have adversary reactions. In this work, we are interested in identifying distinctive subpopulations that respond to the given intervention in particular ways, called the heterogeneity of the treatment effect (HTE) across subpopulations. For this purpose, we have developed a Bayesian mixture model. The novelty of our approach is that it combines the following features: complex decision boundaries, soft clustering, multivariate outcomes and prior knowledge. The last feature can be very useful for datasets with small sample sizes. We demonstrate how our method works by applying it to both simulated and real data. Results of our evaluation show that our model has strong predictive power and is capable of producing high quality clusters.

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.

Evolutionary Strategy to Perform Batch-Mode Active Learning on Multi-Label Data

Multi-label learning has become an important area of research, owing to the increasing number of real-world problems that contain multi-label data. Data labelling is an expensive process that requires expert handling. The annotation of multi-label data is laborious, since a human expert needs to consider the presence/absence of each possible label. Consequently, numerous modern multi-label problems may involve a small number of labelled examples and plentiful unlabelled examples simultaneously. Active learning methods allow to induce better classifiers by selecting the most useful unlabelled data, thus considerably reducing the labelling effort and the cost of training an accurate model. Batch-mode active learning methods focus on selecting a set of unlabelled examples in each iteration, in such a way that the selected examples are informative, and they are as diverse as possible. This paper presents a strategy to perform batch-mode active learning on multi-label data. The batch-mode active learning is formulated as a multi-objective problem, and it is solved by means of an evolutionary algorithm. Extensive experiments were conducted in a large collection of datasets, and the experimental results confirmed the effectiveness of our proposal for better batch-mode multi-label active learning.

Modeling Queries with Contextual Snippets for Information Retrieval

Query expansion under the pseudo relevance feedback (PRF) framework has been extensively studied in information retrieval. However, most expansion methods are mainly based on the statistics of single terms, which can generate plenty of irrelevant query terms and decrease the retrieval performance. To alleviate this problem, we propose an approach that adapts the PRF-based contextual snippets into a context-aware topic model to enhance query representations. Specifically, instead of selecting a series of independent terms, we make full use of the query contextual information and focus on the snippets with the length of n in the PRF documents. Furthermore, we propose a context-aware topic (CAT) model to mine the topic distributions of the query relevant snippets, namely fine contextual snippets. Different from the traditional topic models that infer the topics from the whole corpus, we establish a bridge between the snippets and the corresponding PRF documents which can be used for modeling the topics more precisely and efficiently. Finally, the topic distributions of the fine snippets are used for query representations, which are both context-aware and topic-sensitive. To evaluate the performance of our approach, we integrate the obtained queries into a topic-based hybrid retrieval model, and conduct extensive experiments on various TREC collections. The experimental results show that our query modeling approach is more effective in boosting the retrieval performance compared with the state-of-the-art methods.

Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach

With the rapid growth of social media, massive misinformation is also spreading widely on social media, such as Weibo and Twitter, and brings negative effects to human life. Nowadays, automatic misinformation identification has drawn attention from academic and industrial communities. For an event on social media usually consists of multiple microblogs, current methods are mainly constructed based on global statistical features. However, information on social media is full of noisy, which should be alleviated. Moreover, most of microblogs about an event have little contribution to the identification of misinformation, where useful information can be easily overwhelmed by useless information. Thus, it is important to mine significant microblogs for constructing a reliable misinformation identification method. In this paper, we propose an Attention-based approach for Identification of Misinformation (AIM). Based on the attention mechanism, AIM can select microblogs with largest attention values for misinformation identification. The attention mechanism in AIM contains two parts: content attention and dynamic attention. Content attention is calculated based textual features of each microblog. Dynamic attention is related to the time interval between the posting time of a microblog and the beginning of the event. To evaluate AIM, we conduct a series of experiments on the Weibo dataset and the Twitter dataset, and the experimental results show that the proposed AIM model outperforms the state-of-the-art methods.

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.

Quick Bootstrapping of a Personalized Gaze Model from Real-Use Interactions

Understanding human visual attention is essential for understanding human cognition, which in turn benefits human-computer interaction. Recent work has demonstrated a Personalized, Auto-Calibrating Eye-tracking system (PACE), which makes it possible to achieve accurate gaze estimation using only an off-the-shelf webcam by identifying and collecting data implicitly from user interaction events. However, this method is constrained by the need for large amounts of well-annotated data. We thus present fast-PACE, an adaptation to PACE that adapts knowledge from existing source data to accelerate the learning speed of the personalized model. The result is an adaptive, data-driven approach that continuously recalibrates, adapts and improves with additional use. Experimental evaluations of fast-PACE demonstrate its competitive accuracy of iris localization, validity of alignment identification between gaze and interactions, and effectiveness of gaze transfer. In general, fast-PACE achieves an initial visual error of 3.98º, and then steadily improves to 2.52º given incremental interaction-informed data. Our performance is comparable to state-of-the-art, but without the need for explicit training or calibration. Our technique addresses the data quality and quantity problems. It therefore has the potential to enable comprehensive gaze-aware applications in the wild.

Exploiting Multi-Label Information for Noise Resilient Feature Selection

In conventional supervised learning paradigm, each data instance is associated with one single class label. Multi-label learning differs in the way that data instances may belong to multiple concepts simultaneously, which naturally appear in a variety of high impact domains, ranging from bioinformatics, information retrieval to multimedia analysis. It targets to leverage the multiple label information of data instances to build a predictive learning model which can classify unlabeled instances into one or multiple predefined target classes. In multi-label learning, even though each instance is associated with a rich set of class labels, the label information could be noisy and incomplete as the labeling process is both time consuming and labor expensive, leading potential missing annotations or even erroneous annotations. The existence of noisy and missing labels could negatively affect the performance of underlying learning algorithms. More often than not, multi-labeled data often has noisy, irrelevant and redundant features of high dimensionality. The existence of these uninformative features may also deteriorate the predictive power of the learning model due to the curse of dimensionality. Feature selection, as an effective dimensionality reduction technique, has shown to be powerful in preparing high-dimensional data for numerous data mining and machine learning tasks. However, a vast majority of existing multi-label feature selection algorithms either boil down to solving multiple single-labeled feature selection problems or directly make use of the imperfect labels to guide the selection of representative features. As a result, they may not be able to obtain discriminative features shared across multiple labels. In this paper, to bridge the gap between rich source of multi-label information and its blemish in practical usage, we propose a novel noise resilient multi-label informed feature selection framework - MIFS by exploiting the correlations among different labels. In particular, to reduce the negative effects of imperfect label information in obtaining label correlations, we decompose the multi-label information of data instances into a low-dimensional space and then employ the reduced label representation to guide the feature selection phase via a joint sparse regression framework. Empirical studies on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of the proposed MIFS framework.

Sparse Passive Aggressive Learning for Bounded Online Kernel Methods

One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on scalable online kernel learning have attempted to overcome this shortcoming, e.g., by imposing a constant budget on the number of support vectors. Although they attempt to bound the number of support vectors at each online learning iteration, most of them fail to bound the number of support vectors for the final output hypothesis which is often obtained by averaging the series of hypotheses over all the iterations. In this paper, we propose a novel framework for bounded online kernel methods, named ``Sparse Passive Aggressive (SPA)" learning, which is able to yield a final output kernel-based hypothesis with a bounded number of support vectors. Unlike the common budget maintenance strategy used by many existing budget online kernel learning approaches, the idea of our approach is to attain the bounded number of support vectors using an efficient stochastic sampling strategy which samples an incoming training example as a new support vector with a probability proportional to its loss suffered. We theoretically prove that SPA achieves an optimal mistake bound in expectation, and empirically show that it outperforms various budget online kernel learning algorithms. Finally, in addition to general online kernel learning tasks, we also apply SPA to derive bounded online multiple kernel learning algorithms, which can significantly improve the scalability of traditional Online Multiple Kernel Classification (OMKC) algorithms while achieving satisfactory learning accuracy as compared with the existing unbounded OMKC algorithms.

Fuzzy Cognitive Diagnosis for Modelling Examinee Performance

Recent decades has witnessed the rapid growth of educational data mining (EDM), which aims to automatically extract valuable information from large repositories of data generated by or related to people's learning activities in educational settings. One of the key EDM tasks is cognitive modelling based on examination data, which profiles examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills) in a psychometrical way. However, the problem of extracting information from both objective and subjective exam problems to get more precise and interpretable cognitive analysis is still underexplored. To this end, in this paper, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees' cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then, we combine fuzzy set theory and educational hypotheses to model the examinees' mastery on the problems. Finally we simulate the generation of examination scores by considering both slip and guess factors to build the whole framework. For further comprehensive verification, we design effective solutions based on our FuzzyCDF for three classical cognitive assessment tasks, i.e. predicting examinee performance, slip & guess detection and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for the three cognitive assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.

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


Publication Years 2010-2017
Publication Count 513
Citation Count 6078
Available for Download 513
Downloads (6 weeks) 6875
Downloads (12 Months) 46173
Downloads (cumulative) 235246
Average downloads per article 459
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)
Carla Gomes ACM Fellows (2017)
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 Fellows (2017)
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)
Paolo Trunfio ACM Senior Member (2017)
Geoffrey M Voelker ACM Fellows (2017)
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 Fellows (2017)
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
Daqing Zhang 3
Qi Tian 3
Tao Li 3
Haggai Roitman 2
SungWook Yoon 2
Oded Maimon 2
Mahmud Hossain 2
Quan Fang 2
Shazia Sadiq 2
Jure Leskovec 2
Liyan Zhang 2
Yonggang Wen 2
Alberto Del Bimbo 2
Yongdong Zhang 2
Amin Javari 2
Jian Pei 2
Amit Chopra 2
Alexander Artikis 2
Jiaching Ying 2
Hao Fu 2
Maria Sapino 2
Venkatramanan Subrahmanian 2
Guirong Xue 2
Xueqi Cheng 2
Iván Cantador 2
Ido Guy 2
Jiuyong Li 2
Yihsuan Yang 2
Evangelos Papalexakis 2
Vito Ostuni 2
Yuichi Motai 2
Xingyu Gao 2
Masaki Aono 2
David Thompson 2
Benno Stein 2
Alejandro Bellogín 2
Bingbing Ni 2
Michael Lyu 2
Jeffrey Nichols 2
Rajesh Ganesan 2
Kun Zhang 2
Bernhard Schölkopf 2
Zhi Geng 2
Tommaso Noia 2
Rui Zhang 2
Jianke Zhu 2
Ramesh Jain 2
Naren Ramakrishnan 2
Sarit Kraus 2
Shuaiqiang Wang 2
Chong Peng 2
Jiawei Han 2
Qingzhong Liu 2
Luan Tang 2
JiLei Tian 2
Mahdi Jalili 2
Claudio Biancalana 2
Giuseppe Sansonetti 2
Boi Faltings 2
Robin Cohen 2
Luca Cagliero 2
Yue Shi 2
Alan Hanjalic 2
Charles Ling 2
Daqing Zhang 2
Wenjun Zhou 2
Neilzhenqiang Gong 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
Leye Wang 2
Dingqi Yang 2
Jie Cheng 2
Weiming Hu 2
Elena Baralis 2
Tania Cerquitelli 2
Robin Cohen 2
Jintao Li 2
Vincent Tseng 2
Hongxun Yao 2
Zhiwen Yu 2
Paulo Shakarian 2
Hongyuan Zha 2
Sihong Xie 2
John Doucette 2
Lior Rokach 2
Kiri Wagstaff 2
Martin Potthast 2
Alan Said 2
Jitao Sang 2
Li Chen 2
Xavier Serra 2
Shihchia Huang 2
Huijing Zhao 2
Haoyi Xiong 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
Qi Liu 2
Ron Hirschprung 2
Eran Toch 2
Bohao Chen 2
Yixin Chen 2
Fuzheng Zhang 2
Zhifeng Li 2
Manish Marwah 2
Nathan Eagle 2
Nicholas Jennings 2
Hanqing Lu 2
Tao Mei 2
Pablo Castells 2
Meir Kalech 2
Daxin Jiang 2
Rino Falcone 2
Matteo Venanzi 2
Katia Sycara 2
Jinshi Cui 2
Jia Zeng 2
Dana Nau 2
Xuning Tang 2
Zhiyuan Liu 2
Jalal Mahmud 2
Shoude Lin 2
Hongzhi Yin 2
Ling Guan 2
Neil Yorke-Smith 2
Michael Fire 2
Laiwan Chan 2
Jaegil Lee 2
Dihong Gong 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
Sungsu Lim 1
Yuefeng Li 1
Xuhang Ying 1
Jiaxing Shen 1
Loyao Yeh 1
Keith Ross 1
Zhao Zhang 1
Chenglin Liu 1
Sinnojialin Pan 1
Ronghua Liang 1
Travis Goodwin 1
Gilad Katz 1
Rong Jin 1
Andreas Krause 1
Jameson Toole 1
Perukrishnen Vytelingum 1
Pauline Berry 1
Mitchell Ai-Chang 1
Juan Castilla-Rubio 1
Wei Ding 1
Edleno Moura 1
Wei Chen 1
RubéN Lara 1
Dell Zhang 1
Erik Saule 1
Howard Tennen 1
Aaron Steele 1
Sukjin Lee 1
Saranya Krishnamoorthy 1
Yubin Park 1
Boualem Benattalah 1
Teodora Buda 1
Ji Wan 1
Munindar Singh 1
Mohamed Daoudi 1
Liangtien Chia 1
Timothy Shih 1
Yo Ehara 1
Khoi Nguyen 1
Feiyue Wang 1
Pakkin Wong 1
Ruoyun Huang 1
Zhihua Zhou 1
Emilio Ferrara 1
Geert Houben 1
Neil Rubens 1
Thomas Porta 1
Myungcheol Doo 1
Ling Liu 1
Wei Gao 1
François Poulet 1
Federico Chesani 1
Jianhui Ye 1
Luigi Grimaudo 1
Prithviraj Dasgupta 1
Anshul Sawant 1
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Alessandro Farinelli 1
Jesús Cerquides 1
Tudor Dumitraş 1
Avishai Wool 1
Isaac Ben-Israel 1
Eyal Kolman 1
Michal Neria 1
Fernando Díez 1
Yongdong Zhang 1
Marco Colombetti 1
Pınar Yolum 1
Wiebe Hoek 1
Michele Piunti 1
Cristina Conati 1
Qiang Lu 1
Jyhren Shieh 1
Pasquale De Meo 1
Lora Aroyo 1
Kamfai Wong 1
Juan Cruz 1
Cécile Bothorel 1
Carles Sierra 1
Fabrizio Maggi 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
Natalie Fridman 1
Nitin Madnani 1
Majid Ahmadabadi 1
Svetlin Bostandjiev 1
Xiaoxiao Lian 1
Lars Haug 1
Jinhui Tang 1
Jussara Almeida 1
Marcos Gonçalves 1
Tongliang Liu 1
Daniel Roggen 1
Robert Jäschke 1
Le Wu 1
Simon Dooms 1
David Ben-Shimon 1
Guy Shani 1
Bracha Shapira 1
Thomas Huang 1
Hala Mostafa 1
Yoshiyuki Inagaki 1
Wenning Kuo 1
Alexei Pozdnoukhov 1
Alena Neviarouskaya 1
Jiankai Sun 1
Gianmario Motta 1
Anne Robinson 1
Chris Mellish 1
Rene Van Der Wal 1
Joris Albeda 1
Tomasz Jaworski 1
Zhenfeng Zhu 1
Yanhui Xiao 1
Márk Jelasity 1
Yizhou Wang 1
Matthew Johnson 1
Joemon Jose 1
Dawei Song 1
Xinbo Gao 1
Wenyuan Zhu 1
Bo Long 1
Lihong Li 1
Bin Wu 1
Waynexin Zhao 1
Tao Li 1
Ankit Shah 1
Tao Gu 1
Jiangbo Jia 1
Xingshe Zhou 1
Anna Monreale 1
Fan Liu 1
Cristina Muntean 1
Karl Tuyls 1
Tatjen Cham 1
Ke Lu 1
Qionghai Dai 1
Scott Spurlock 1
Ioannis Refanidis 1
Xinghai Sun 1
Perfecto Herrera-Boyer 1
Andrea Marrella 1
Stephen Roberts 1
Mi Tian 1
Kai Zhu 1
Jincheng Zhang 1
Ranveer Chandra 1
Chisheng Zhang 1
Hsinhan Huang 1
Wei Liu 1
Denilson Barbosa 1
Qing He 1
Yexun Zhang 1
Cornelia Caragea 1
Marco Gavanelli 1
Carlos Guestrin 1
Elif Kürklü 1
Steven Klooster 1
Youxi Wu 1
Kamer Kaya 1
Panagiotis Adamopoulos 1
Alexander Tuzhilin 1
Lin Lin 1
Zinovi Rabinovich 1
Claudia Goldman 1
Kalyan Subbu 1
Iyad Batal 1
Riccardo Molinari 1
Cristopher Yang 1
Lina Yao 1
Jianguo Jiang 1
Christos Dimitrakakis 1
Xinyu Ou 1
Frank Dignum 1
Munindar Singh 1
Cristina Baroglio 1
Neil Yen 1
Nobuyuki Shimizu 1
Hiroshi Nakagawa 1
Ziqiang Shi 1
Tianshi Chen 1
Lena Tenenboim-Chekina 1
Rami Puzis 1
Mario Cataldi 1
Mehdi Elahi 1
Juan Pane 1
Alessandro Fiori 1
Aston Zhang 1
Xuemin Zhao 1
Naeem Mahoto 1
Sumi Helal 1
Daniel Gaines 1
Robert Anderson 1
Michael Burl 1
Yantao Zheng 1
Deming Zhai 1
Ronald Greeley 1
Stefano Berretti 1
Julian Panetta 1
Norbert Schorghofer 1
Hao Wang 1
Hao Wang 1
Alberto Rosi 1
Markus Endler 1
John O’Donovan 1
Weihong Qian 1
Xueying Li 1
Shriram Revankar 1
Vinhtuan Thai 1
Lixin Shi 1
Ke Zhou 1
Dingquan Wang 1
Xueming Wang 1
Alberto Calatroni 1
Lirong Xia 1
Jennifer Moody 1
Ahamad Khader 1
Jamie Ward 1
Hans Gellersen 1
Danny Wyatt 1
James Kitts 1
Bing Liu 1
Quanquan Gu 1
Fabian Loose 1
Paolo Rosso 1
Darren Appling 1
Elizabeth Whitaker 1
Deborah McGuinness 1
Antons Rebguns 1
Gerald Dejong 1
Reid MacTavish 1
Jinhong Guo 1
Anusua Trivedi 1
Piyush Rai 1
Tad Hogg 1
Sergej Sizov 1
Nello Cristianini 1
Carlos Castillo 1
Chunnan Hsu 1
Hao Ma 1
Geoffrey Voelker 1
K Subramanian 1
Justin Ma 1
Moshe Tennenholtz 1
Shengrui Wang 1
Rebecca Goolsby 1
Dong Wang 1
Jun Tao 1
Fan Zhang 1
Guifeng Wang 1
Jilin Chen 1
Yun Lu 1
Chang Liu 1
Naphtali Rishe 1
Asmaa Elbadrawy 1
S Nolen 1
Guozhong Dai 1
Elizabeth Salmon 1
Xiatian Zhang 1
Rongyao Fu 1
Yoav Shoham 1
Athanasios Panagopoulos 1
Matan Monitz 1
Chunping Li 1
Lara Quijano-Sánchez 1
Shlomo Berkovsky 1
Paul Cook 1
Timothy Baldwin 1
Hongyuan Zha 1
Xiao Gu 1
Clemens Drews 1
Patrick Butler 1
Helmut Prendinger 1
Mitsuru Ishizuka 1
Come Etienne 1
Xiang Wu 1
Yicheng Song 1
Linlin You 1
Tianyi Ma 1
Benjamin Lok 1
Petros Daras 1
Andrzej Romanowski 1
Praveen Paritosh 1
Ingrid Heynderickx 1
Morten Fjeld 1
Bin Cheng 1
András Benczúr 1
Véronique Hoste 1
István Hegedűs 1
Levente Kocsis 1
Bo Xin 1
Kristen Venable 1
Wenyuan Dai 1
Yueying He 1
Alvaro Rosero 1
Qiang Cheng 1
Bin Hu 1
Laura Barnes 1
Jun Xu 1
Debo Cheng 1
Changsheng Xu 1
Xuelong Li 1
Amip Shah 1
Naren Ramakrishnan 1
Chuan Shi 1
Eui Shin 1
Derrall Heath 1
Paolo Trunfio 1
Valentina Sintsova 1
Paolo Tomeo 1
Wenkui Ding 1
Daniel Hennes 1
You Yang 1
Richard Souvenir 1
Gao Cong 1
Abdulmotaleb Saddik 1
Kevin Curran 1
Froduald Kabanza 1
Jianhua Guo 1
Jiji Zhang 1
Marcello Cirillo 1
Lars Karlsson 1
Xiaogang Dong 1
Amy Fire 1
Peng Ding 1
Thucduy Le 1
Yan Liu 1
Shenghua Zhong 1
Nicholas Jennings 1
Weisheng Chin 1
Yong Zhuang 1
Ubai Sandouk 1
Mark Sandler 1
Francisco Rodríguez-Serrano 1
Nicola Barbieri 1
Shaojie Zhuo 1
Ling Zhong 1

Affiliation Paper Counts
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
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
Chongqing University 1
Shanghai University 1
Soka University 1
Ecole Centrale Paris 1
National University of Defense Technology China 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
Shanghai University of Finance and Economics 1
University of Chittagong 1
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
Intel Corporation 2
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 Fribourg 2
University of Ferrara 2
Sam Houston State University 2
University of Sydney 2
Zhejiang University of Technology 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
University of Verona 2
TU Dortmund University 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
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
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
Michigan 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
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
University of North Texas 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
University of Texas at Dallas 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
Tianjin University 5
University College London 5
Beijing Jiaotong University 5
Osaka University 5
Rensselaer Polytechnic Institute 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
Microsoft Corporation 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
Missouri University of Science and Technology 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
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
Hong Kong University of Science and Technology 12
Delft University of Technology 13
Polytechnic Institute of Turin 13
University of Technology Sydney 13
University of California, Los Angeles 14
Zhejiang University 15
Florida International University 15
Shanghai Jiaotong 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
Microsoft Research 23
Nanyang Technological University 24
Jet Propulsion Laboratory 24
IBM Research 24
Peking University 24
Tsinghua University 25
Ben-Gurion University of the Negev 25
Institute of Computing Technology Chinese Academy of Sciences 28
University of Maryland 29
National University of Singapore 38
University of Science and Technology of China 40
Chinese Academy of Sciences 45

ACM Transactions on Intelligent Systems and Technology (TIST)

Volume 9 Issue 3, December 2017  Issue-in-Progress
Volume 9 Issue 2, October 2017  Issue-in-Progress
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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