ACM Transactions on

Intelligent Systems and Technology (TIST)

Latest Articles

Tensors for Data Mining and Data Fusion

Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we... (more)

Introduction to Intelligent Music Systems and Applications

Intelligent technologies have become an essential part of music systems and applications. This is evidenced by today's omnipresence of digital online... (more)

A Joyful Ode to Automatic Orchestration

Most works in automatic music generation have addressed so far specific tasks. Such a reductionist approach has been extremely successful and some of these tasks have been solved once and for all. However, few works have addressed the issue of generating automatically fully fledged music material, of human-level quality. In this article, we report... (more)

Getting Closer to the Essence of Music

This text offers a personal and very subjective view on the current situation of Music Information Research (MIR). Motivated by the desire to build systems with a somewhat deeper understanding of music than the ones we currently have, I try to sketch a number of challenges for the next decade of MIR research, grouped around six simple truths about... (more)

Harnessing Music-Related Visual Stereotypes for Music Information Retrieval

Over decades, music labels have shaped easily identifiable genres to improve recognition value and subsequently market sales of new music acts.... (more)

Sound and Music Recommendation with Knowledge Graphs

The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as... (more)

Tempo Driven Audio-to-Score Alignment Using Spectral Decomposition and Online Dynamic Time Warping

In this article, we present an online score following framework designed to deal with automatic... (more)

Towards Music Structural Segmentation across Genres

This article faces the problem of how different audio features and segmentation methods work with different music genres. A new annotated corpus of... (more)

Learning Contextualized Music Semantics from Tags Via a Siamese Neural Network

Music information retrieval faces a challenge in modeling contextualized musical concepts formulated by a set of co-occurring tags. In this article,... (more)


Recent TIST News: 

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 2.414 in 2015.  

Journal Metric

  • - Impact Factor: 2.4
  • - 5-year Impact Factor: 9.15

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
Exploring Communication Behaviors of Users to Target PotentialUsers in Mobile Social Networks

In mobile social networks, users can communicate with each other over different telecom carriers. Thus, for telecom operators, how to acquire and retain users is a significant issue. The work of churn prediction is to determine whether a customer would leave soon. Differing from churn prediction, our work is to find those users who are likely to join target services from the competitors in the near future, where these users are called potential users. To target potential users, we propose a framework including feature extraction, feature selection, and classifier learning to solve the problem. First, we construct a heterogeneous information network from the call detail records of users. Then, we extract the explicit features from potential users interaction behavior in the heterogeneous information network. Moreover, because users are influenced by their community, we extract implicit features of potential users. After feature extraction, we explore the Information Gain to select the effective features. We use the effective explicit and implicit features to learn potential user classifiers, and use the classifiers to determine the potential users. Finally, we conduct experiments on real datasets. The results of our experiments show that the features extracted by our proposed method can be effective for targeting potential users.

Large Sparse Cone Non-negative Matrix Factorization for Image Annotation

Image annotation assigns relevant tags to query images based on their semantic contents. Since non- negative matrix factorization (NMF) has the strong ability to learn parts-based representations, recently, a number of algorithms based on NMF have been proposed for image annotation and achieved good perfor- mance. However, most of the efforts have been focused upon the representations of images and annotations. The properties of the semantic parts have not been well studied. In this paper, we revisit the sparseness constrained NMF (sNMF) proposed by Hoyer [Hoyer 2004]. By endowing the sparseness constraint with a geometric interpretation and sNMF with theoretical analyses of the generalization ability, we show that NMF with such a sparseness constraint has three advantages for image annotation tasks. (1) The sparseness constraint is more l0-norm oriented than the l1-norm based sparseness, which significantly enhances the ability of NMF to robustly learn semantic parts. (2) The sparseness constraint has a large cone interpreta- tion and thus enables the reconstruction error of NMF to be smaller, which means that the learned semantic parts are more powerful to represent images for tagging. (3) The learned semantic parts are less correlated, which increases the discriminative ability for annotating images. Moreover, we present a new efficient large sparse cone NMF (LsCNMF) algorithm to optimize the sNMF problem by employing the Nesterovs opti- mal gradient method. We conducted experiments on the PASCAL VOC07 dataset and demonstrated the effectiveness of LsCNMF for image annotation.

Mobile Social Multimedia Analytics in the Big Data Era: An Introduction to the Special Issue

On Network Neutrality Measurements

Network level surveillance, censorship, and various man-in-the-middle attacks target only specific types of network traffic (e.g., HTTP, HTTPS, VoIP, or Email). Therefore packets of these types will likely receive special treatment by a man-in-the-middle attacker. An attacker may pass the targeted traffic through special software or equipment to gather data or perform an attack. This creates a measurable difference between the performance of the targeted traffic versus the general case. In networking terms, it violates the principle of network neutrality, which states that all traffic should be treated equally. Many techniques were designed to detect network neutrality violations, and some have naturally suggested using them to detect surveillance and censorship. In this paper, we show that the existing network neutrality measurement techniques can be easily detected and therefore circumvented. We then shortly propose a new approach to overcome the drawbacks of current measurement techniques.

Tracking Illicit Drug Dealing and Abuse on Instagram using Multimodal Analysis

Illicit drug trade via social media sites, especially photo-oriented Instagram, has become a severe problem in recent years. As a result, tracking drug dealing and abuse on Instagram is of interest to law enforcement agencies and public health agencies. However, traditional approaches are based on manual search and browsing by trained domain experts, which suffer from the problem of poor scalability and reproducibility. In this paper, we propose a novel approach to detecting drug abuse and dealing automatically by utilizing multimodal data on social media. This approach also enables us to identify drug-related posts and analyze the behavior patterns of drug-related user accounts. To better utilize multimodal data on social media, multimodal analysis methods including multi-task learning and decision-level fusion are employed in our framework. We collect three datasets using Instagram and web search engine for training and testing our models. Experiment results on expertly labeled data have demonstrated the effectiveness of our approach, as well as its scalability and reproducibility over labor-intensive conventional approaches.

As-You-Type Social Aware Search

Modern search applications feature real-time as-you-type query search. In its elementary form, the problem consists in retrieving a set of k search results, i.e., performing a search with a given prefix, and showing the top ranked results. In this paper we focus on as-you-type keyword search over social media, that is data published by users who are interconnected through a social network. We adopt a network-aware interpretation for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness and efficiency in online search, even when the intent of the user is fully specified, as a complete query given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and traditional IR indexes such as inverted lists. We describe a memory efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing to output the most likely answer within any chosen running-time limit. We evaluate our approach through extensive experiments for several applications and search scenarios. We consider searching for posts in micro-blogging (Twitter and Tumblr), for businesses (Yelp), as well as for movies (Amazon) based on reviews. We also conduct a series of experiments comparing our algorithm with baselines using state of-the-art techniques and measuring the improvements brought by several key optimizations. They show that our solution is effective in answering real-time as-you-type searches over social media.

Securely Computing a Ground Speed Model

Consider a server offering risk assessment services, and potential clients of these services. The risk assessment model that is run by the server is based on current and historical data of the clients. However, the clients might prefer not sharing such sensitive data with external parties such as the server, and the server itself might consider the possession of this data as a liability rather than an asset. Secure multi-party computation (MPC) enables in principle to compute any function while hiding the inputs to the function, and would thus enable the computation of the risk assessment model while hiding the clients data from the server. However, a direct application of a generic MPC solution to this problem is rather inefficient, due to the large scale of the data and the complexity of the function. We describe a very efficient secure computation solution that is tailored for this problem. This solution demonstrates that a risk model can be applied over encrypted data fast enough to fit the requirements of commercial systems.

Automatic Construction of Statechart-Based Anomaly Detection Models for Multi-Threaded Industrial Control Systems

Traffic of Industrial Control System (ICS) between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is known to be highly periodic. However, it is sometimes multiplexed, due to asynchronous scheduling. Modeling the network traffic patterns of multiplexed ICS streams using Deterministic Finite Automata (DFA) for anomaly detection typically produces a very large DFA, and a high false-alarm rate. In this paper we introduce a new modeling approach that addresses this gap. Our Statechart DFA modeling includes multiple DFAs, one per cyclic pattern, together with a DFA-selector that de-multiplexes the incoming traffic into sub-channels and sends them to their respective DFAs. We demonstrate how to automatically construct the statechart from a captured traffic stream. Our learning algorithms first build a Discrete-Time Markov Chain (DTMC) from the stream. Next we split the symbols into sets, one per multiplexed cycle, based on symbol frequencies and node degrees in the DTMC graph. Then we create a sub-graph for each cycle, and extract Euler cycles for each sub-graph. The final statechart is comprised of one DFA per Euler cycle. The algorithms allow for non-unique symbols, that appear in more than one cycle, and also for symbols that appear more than once in a cycle. We evaluated our solution on traces from a production ICS using the Siemens S7-0x72 protocol. We also stress-tested our algorithms on a collection of synthetically-generated traces that simulated multiplexed ICS traces with varying levels of symbol uniqueness and time overlap. The algorithms were able to split the symbols into sets with 99.6% accuracy. The resulting statechart modeled the traces with a false-alarm rate as low as 2.27% in all but the more severe cases and 4.3% overall. In all but the most extreme scenarios the {\em Statechart} model drastically reduced both the false-alarm rate and the learned model size in comparison with the naive single-DFA model.

Daehr: a Discriminant Analysis Framework for Electronic Health Record Data and an Application to Early Detection of Mental Health Disorders

Electronic Health Records (EHR) in health care settings provide a rich source of medical data which present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data mining tools have been adopted for EHR-based disease early detection, Linear Discriminant Analysis (LDA) is one of the most commonly used statistical methods. However, it is difficult to train an accurate LDA model for early disease diagnosis when too few patients are known to have the target disease and the EHR data are heterogenous with significant noise. In such cases, the covariance matrices used in LDA are usually singular and estimated with a large variance. This paper presents Daehr, an extension of the LDA framework using Electronic Health Record data to address these issues. Beyond existing LDA analyzers, we propose Daehr to 1) eliminate the data noise caused by the manual encoding of EHR data, and 2) lower the decision risk of LDA model with finely-estimated parameters when only a few patients EHR are given for training. To achieve these two goals, we designed an iterative algorithm to improve the covariance matrix estimation with embedded data-noise/decision-risk reduction for LDA. We evaluated Daehr extensively using a large-scale real-world EHR dataset, CHSN. Specifically, our experiments compared the performance of LDA to three baselines (i.e., LDA and its derivatives) in terms of identifying college students at high risk for mental health disorders from 23 US universities. Experimental results show Daehr significantly outperforms the three baselines by achieving 1.4%19.4% higher accuracy, and a 7.5%43.5% higher F1-score.

Data-Driven Frequency-Based Airline Profit Maximization

Though there are numerous traditional models to predict market share and demand along airline routes, the prediction of existing models is not precise enough and, to the best of our knowledge, there is no use of data-mining based forecasting techniques to improve airline profitability. We propose the MAP (Maximizing Airline Profits) architecture designed to help airlines and make two key contributions in airline market share and route demand prediction and prediction-based airline profit optimization. Compared with past methods to forecast market share and demand along airline routes, we introduce a novel Ensemble Forecasting (MAP-EF) approach considering two new classes of features: (i) features derived from clusters of similar routes, and (ii) features based on equilibrium pricing. We show that MAP-EF achieves much better Pearson Correlation Coefficients (over 0.95 vs. 0.82 for market share, 0.98 vs. 0.77 for demand) and R2-values compared with three state-of-the-art works for forecasting market share and demand, while showing much lower variance. Using the results of MAP-EF, we develop MAP-Bilevel Branch and Bound (MAP-BBB) and MAP-Greedy (MAP-G) algorithms to optimally allocate flight frequencies over multiple routes, to maximize an airlines profit. We also study two extensions of the profit maximization problem considering frequency constraints and long term profits. Furthermore, we develop algorithms for computing Nash equilibrium frequencies when there are multiple strategic airlines. Experimental results show that airlines can increase profits by a significant margin. All experiments were conducted with data aggregated from four sources: US Bureau of Transportation Statistics (BTS), US Bureau of Economic Analysis (BEA), the National Transportation Safety Board (NTSB), and the US Census Bureau (CB).

Advanced Economic Control of Electricity-based Space Heating Systems in Domestic Coalitions with Shared Intermittent Energy Resources

Over the past few years, domestic heating automation systems (DHASs) that optimize the domestic space heating control process with minimum user-input, utilizing appropriate occupancy prediction technology, have emerged as commercial products (e.g, the smart thermostats from Nest and Honeywell). At the same time, many houses are being equipped with, potentially grid-connected, intermittent energy resources (IERs), such as rooftop photovoltaic systems and/or small wind turbine generators. Now, in many regions of the world, such houses can sell energy to the grid but at a lower price than the price of buying it. In this context, and given the anticipated increase in electrification of heating, the next generation DHASs need to incorporate advanced economic control (AEC). Such AEC can exploit the energy buffer that heating loads provide, in order to shift the consumption of electricity-based heating systems to follow the intermittent energy generation of the house. By so doing, the energy imported from the grid can be minimized and considerable monetary gains for the household can be achieved, without affecting the occupants' schedule. These benefits can be amplified still further in domestic coalitions, where a number of houses come together and share their IER generation to minimize their cumulative grid energy import. Given the above, in this work we extend a state-of-the-art DHAS, to propose AdaHeat+, a practical DHAS, that, for the first time, incorporates AEC. Our work is applicable to both individual houses and domestic coalitions and comes complete with a cost allocation mechanism to share the gains of the coalition. Importantly, we propose an effective heuristic heating schedule planning approach for collective AEC which: (i) has a complexity that scales in a linear and parallelizable manner with the size of the coalition, and (ii) enables AdaHeat+ to handle different preferences, in balancing heating cost and thermal discomfort of the individual households. Our approach relies on stochastic IER power output predictions. To achieve this, we propose a new adaptive site-specific calibration technique to improve such predictions, utilizing Gaussian process modeling. Finally, we demonstrate the effectiveness of AdaHeat+ through real data evaluation, to show that collective AEC can improve heating cost-efficiency by up to 60%, compared to independent AEC (and even more when compared to no-AEC).

Modeling Topics and Behaviors of Microbloggers: An Integrated Approach

Microblogging encompasses both user generated content and behaviors. Microblogging users' behaviors include adoption specific hashtags, retweeting specific incoming tweets, etc.. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behaviors. In this paper, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and her dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content data only. By associating user behaviors with the latent background and personal topics, GBT helps to model the user behaviors by the two types of topics. GBT also distinguishes itself from other earlier ones by modeling multiple types of behaviors together. Our experiments on two Twitter datasets show that GBT can effectively mine the representative topics for each realm. We also demonstrate that GBT significantly outperforms other state-of-the-art models in modeling content topics and user profiling.

CRADLE: An Online Plan Recognition Algorithm for Exploratory Domains

activities, extraneous actions, and mistakes. Such settings are prevalent in real world applications such as interaction with open-ended software, collaborative office assistants, and integrated development environments. Despite the prevalence of such settings in the real world, there is scarce work in formalizing the connection between high-level goals and low-level behavior and inferring the former from the latter in these settings. We present a formal grammar for describing users activities in such domains. We describe a new top-down plan recognition algorithm called CRADLE that uses this grammar to recognize agents interactions in exploratory domains. We compare the performance of CRADLE with state-of-the-art plan recognition algorithms in several experimental settings consisting of real and simulated data. Our results show that CRADLE was able to output plans exponentially more quickly than the state-of-the-art without compromising its correctness, as determined by domain experts. Our approach can form the basis of future systems that use plan recognition to provide real-time support to users in a growing class of interesting and challenging domains.

i2tag: RFID Mobility and Activity Identification through Intelligent Profiling

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

Location-Based Parallel Tag Completion for Geo-tagged Social Image Retrieval

Benefit from tremendous growth of user-generated content, social annotated tags get higher importance in organization and retrieval of large scale image database on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, existing studies only utilize the relations between visual content and tags to construct global information completion models. In this paper, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship, to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image-user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework which learns the image-tag relation within each POI. To solve the proposed model, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large scale online image database. To fit the response time requirement and storage limitations of tag-based image retrieval (TBIR) on mobile devices, we introduce Asymmetric Locality Sensitive Hashing (ALSH) to reduce the time cost and meanwhile improve the efficiency of retrieval.

ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place as most of the items visited by a user are usually located within a short distance from the user's home. Moreover, user interests and behavior patterns may vary dramatically across different time and different geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this paper. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns of spatial items and the content of spatial items. To further alleviate the data sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments and the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency and online recommendation efficiency.

Cost-Optimized Microblog Distribution over Geo-Distributed Data Centers: Insights from Cross-Media Analysis

The unprecedent growth of microblog services poses significant challenges on network traffic and service latency to the underlay infrastructure (i.e., geo-distributed data centers). Furthermore, the dynamic evolution in microblog status generates a huge workload on data consistence maintenance. In this paper, motivated by insights of cross media analysis based propagation patterns, we propose a novel cache strategy for microblog service systems to reduce the inter data center traffic and consistence maintenance cost, while achieve low service latency. Specifically, we first present a microblog classification method, which utilizes the external knowledge from correlated domains, to categorize microblogs. Then we conduct a large-scale measurement on a representative online social network system to study the category based propagation diversity on region and time scales. These insights illustrate social common habits on creating and consuming microblogs, and further motivate our architecture design. Finally, we formulate the content cache problem as a constrained optimization problem. By jointly using the Lyapunov optimization framework and simplex gradient method, we find the optimal online control strategy. Extensive trace driven experiments further demonstrate that our algorithm reduces the system cost by 24.5\% against traditional approaches with the same service latency.

An Unsupervised Approach to Inferring the Localness of People Using Incomplete Geo-Temporal Online Check-in Data

Inferring the localness of people is to identify whether a person is a local resident in a city or not by analyzing online check-in points that are contributed by users consisting of both local and non-local people (e.g., tourists). This information is critical for the targeted ads of local business, urban planning, and localized news recommendations. While there are prior work on geo-locating people in a city using supervised learning approaches, the accuracy of those techniques largely depends on the training datasets with complete geo-temporal information, which are difficult and expensive to obtain in practice. In this paper, we propose an unsupervised approach to infer the localness of people in a city by using the incomplete crowdsourcing data (i.e., online check-in points) that are publicly available. In particular, we develop an Incomplete-Geo-Temporal Expectation Maximization (IGT-EM) scheme, which incorporates a set of hidden variables to represent the localness of people and a set of estimation parameters to represent the likelihood of venues to attract local and non-local people respectively. Our solution can jointly estimate 1) the localness of a person and 2) the probability of a venue to attract local people without requiring any training data. We also implement a parallel IGT-EM algorithm by leveraging the computing power of a Graphic Processing Unit (GPU) that consists of 2496 cores. We evaluate our new approach on four real-world datasets collected from the city of New York, Chicago, Boston and Washington D.C. The results showed that our approach can accurately estimate the localness of people and significantly outperform other state-of-the-art baselines in terms of both estimation accuracy and execution time.


Publication Years 2010-2017
Publication Count 438
Citation Count 4949
Available for Download 438
Downloads (6 weeks) 4446
Downloads (12 Months) 45085
Downloads (cumulative) 194673
Average downloads per article 444
Average citations per article 11
First Name Last Name Award
Benjamin B Bederson ACM Distinguished Member (2011)
Andrei Broder ACM Paris Kanellakis Theory and Practice Award (2012)
Carlos A. Castillo ACM Senior Member (2014)
Charles L A Clarke ACM Distinguished Member (2015)
Ingemar J. Cox ACM Distinguished Member (2011)
Alberto Del Bimbo ACM Distinguished Member (2016)
Deborah Estrin ACM Athena Lecturer Award (2006)
Maria L Gini ACM Distinguished Member (2006)
Xian-Sheng Hua ACM Distinguished Member (2015)
ACM Senior Member (2009)
Chih-Jen Lin 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)
Jeffrey Nichols ACM Senior Member (2013)
Judea Pearl ACM A. M. Turing Award (2011)
ACM AAAI Allen Newell Award (2003)
Jian Pei ACM Senior Member (2007)
Yong Rui ACM Distinguished Member (2009)
ACM Senior Member (2006)
Stefan Savage ACM Prize in Computing (2015)
Yoav Shoham ACM AAAI Allen Newell Award (2012)
Gita Reese Sukthankar ACM Senior Member (2013)
Jaime Teevan ACM Senior Member (2012)
Moshe Tennenholtz ACM AAAI Allen Newell Award (2012)
Feiyue Wang ACM Distinguished Member (2007)
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)
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 7
Xing Xie 7
Enhong Chen 6
Tatseng Chua 5
Nicholasjing Yuan 5
Yu Zheng 5
Xiansheng HUA 5
Jinhui Tang 5
Shuicheng Yan 5
Steven Hoi 4
Xuan Song 4
Ryosuke Shibasaki 4
Changsheng Xu 4
Qiang Yang 4
Michelle Zhou 4
Quanshi Zhang 3
Philip YU 3
Martha Larson 3
Christopherchuen Yang 3
Wen Gao 3
Xue Li 3
Hui Xiong 3
Rongrong Ji 3
Xiaowei Shao 3
Huanhuan Cao 3
Francesco Bonchi 3
Irwin King 3
Rebecca Castaño 3
Qi Tian 3
Wenchih Peng 3
Tao Li 3
Mahdi Jalili 2
Claudio Biancalana 2
Giuseppe Sansonetti 2
Anlei Dong 2
Luca Cagliero 2
Yue Shi 2
Alan Hanjalic 2
Charles Ling 2
Daqing Zhang 2
Jure Leskovec 2
Mohan Kankanhalli 2
Zhengjun Zha 2
Yue Gao 2
Yuval Elovici 2
Yoshinobu Kawahara 2
Chihjen Lin 2
Diane Cook 2
Defu Lian 2
Elena Baralis 2
Tania Cerquitelli 2
Robin Cohen 2
SungWook Yoon 2
Mahmud Hossain 2
Vincent Tseng 2
Sihong Xie 2
Hongxun Yao 2
Zhiwen Yu 2
Paulo Shakarian 2
Hongyuan Zha 2
Haggai Roitman 2
Liyan Zhang 2
Alex Rogers 2
Alberto Del Bimbo 2
Yongdong Zhang 2
Amin Javari 2
Jian Pei 2
Alexander Artikis 2
Venkatramanan Subrahmanian 2
Maria Sapino 2
Guirong Xue 2
Iván Cantador 2
Ido Guy 2
Bohao Chen 2
Yixin Chen 2
Fuzheng Zhang 2
Nathan Eagle 2
Manish Marwah 2
Hanqing Lu 2
Tao Mei 2
Pablo Castells 2
Meir Kalech 2
Daxin Jiang 2
Xuning Tang 2
Rino Falcone 2
Katia Sycara 2
Jinshi Cui 2
Jia Zeng 2
Dana Nau 2
Shoude Lin 2
Ling Guan 2
Michael Fire 2
Neil Yorke-Smith 2
Laiwan Chan 2
Meng Wang 2
Jaegil Lee 2
Ratnesh Sharma 2
Fabio Gasparetti 2
Alessandro Micarelli 2
Munindar Singh 2
Gita Sukthankar 2
Zhiyuan Cheng 2
John Dickerson 2
Alvin Chin 2
David Carmel 2
Michael Lyu 2
Vito Ostuni 2
Jun Ma 2
Jiuyong Li 2
Yuichi Motai 2
Masaki Aono 2
Bingbing Ni 2
David Thompson 2
Yihsuan Yang 2
Benno Stein 2
Alejandro Bellogín 2
Jeffrey Nichols 2
John Doucette 2
Daqing Zhang 2
Tommaso Noia 2
Zhi Geng 2
Kun Zhang 2
Bernhard Schölkopf 2
Ramesh Jain 2
Naren Ramakrishnan 2
Sarit Kraus 2
Lior Rokach 2
Kiri Wagstaff 2
Martin Potthast 2
Alan Said 2
Li Chen 2
Rui Zhang 2
Eugenio Sciascio 2
Shihchia Huang 2
Xavier Serra 2
Huijing Zhao 2
Xindong Wu 2
Shulamit Reches 2
Wangchien Lee 2
Subbarao Kambhampati 2
Jamal Bentahar 2
Kyumin Lee 2
James Caverlee 2
Thomas Dietterich 2
Jalal Mahmud 2
Robin Cohen 2
Ya'akov Gal 2
Shuaiqiang Wang 2
Qingzhong Liu 2
Jiawei Han 2
Luan Tang 2
Jilei Tian 2
Bingyu Sun 1
Réjean Plamondon 1
Aidan Delaney 1
Dhaval Patel 1
Mingbo Zhao 1
Deng Cai 1
Jianke Zhu 1
Xiaofeng Tong 1
Tao Wang 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
Suhyin Lee 1
Qi He 1
Nan Li 1
Haizheng Zhang 1
Xiaoming Li 1
Xiangnan Kong 1
Lester Mackey 1
Marco Colombetti 1
Pınar Yolum 1
Wiebe Hoek 1
Michele Piunti 1
Cristina Conati 1
Qiang Lu 1
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Shixia Liu 1
Bill Dolan 1
Idan Szpektor 1
Brynjar Gretarsson 1
Huadong Ma 1
Wei Peng 1
Tong Sun 1
Weiwei Cui 1
Pierre Rouille 1
Geoffrey Holmes 1
Yuhang Zhao 1
Bingqing Qu 1
Gerd Stumme 1
David Glass 1
Toon De Pessemier 1
Michelle Zhou 1
Liangliang Cao 1
José Cortizo 1
Janardhan Doppa 1
Bhavesh Shrestha 1
Victor Lesser 1
Daniel McFarlane 1
Yong Yu 1
Yosi Mass 1
Hal Daumé 1
Richong Zhang 1
Wenjun Zhou 1
Chihchung Chang 1
Dana Nau 1
Bernardo Huberman 1
Kyumin Lee 1
Hongtai Li 1
Oded Maimon 1
Wangsheng Zhang 1
Brent Longstaff 1
Joshua Selsky 1
Atesmachew Hailegiorgis 1
Aris Anagnostopoulos 1
Yuchun Shen 1
Fatih Gedikli 1
Guillermo Jiménez-Díaz 1
Hongbin Zha 1
Furu Wei 1
Ya Zhang 1
Marjan Momtazpour 1
Jason Hong 1
Licia Capra 1
Ouri Wolfson 1
Eoghan Furey 1
Aonghus Lawlor 1
Dan Lin 1
Juan Cao 1
Byron Gao 1
Damian Martínez-Muñoz 1
Alexander Schindler 1
Zhonggang Wu 1
Haikun Wei 1
Chong Peng 1
Tao Li 1
Ankit Shah 1
Tao Gu 1
Jiangbo Jia 1
Xingshe Zhou 1
Shazia Sadiq 1
Anna Monreale 1
Zhenmin Tang 1
Franco Nardini 1
Mingli Song 1
Jiajun Bu 1
Ah Tsoi 1
Yuesong Wang 1
Matthew Kyan 1
Guoyu Sun 1
Paisarn Muneesawang 1
Stevende Jong 1
Yufei Wang 1
Tianzhu Zhang 1
Nadia Figueroa 1
Kuiyu Chang 1
Paul Schermerhorn 1
Matthias Scheutz 1
Daniel Bryce 1
Michael Verdicchio 1
Abder Benaskeur 1
Chao Xu 1
Alex Smola 1
Na Shan 1
Hadrien Hours 1
Ernst Biersack 1
Patrick Loiseau 1
Marina Demeshko 1
Saisai Ma 1
Siddhartha Ghosh 1
Yuchin Juan 1
Ming Ji 1
Yintao Yu 1
Carla Gomes 1
Michela Milano 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
Ümit Çatalyürek 1
Amos Azaria 1
Weiming Hu 1
Bin Chen 1
Jinbo Bi 1
Yu Wu 1
Stephen Armeli 1
Thomas Hoens 1
Chandan Reddy 1
Wenyuan Zhu 1
Waynexin Zhao 1
Bin Wu 1
Bo Long 1
Lihong Li 1
Elisa Marengo 1
Olivier Colot 1
Qun Jin 1
Timothy Norman 1
Huijing Zhao 1
Xiangfeng Luo 1
Enrico Pontelli 1
Lora Aroyo 1
Alice Leung 1
Wangchien Lee 1
Chenghua Lin 1
Paola Mello 1
Marta Arias 1
Ramon Xuriguera 1
Janyl Jumadinova 1
Ching Law 1
Paolo Garza 1
Xing Xie 1
Zhenfeng Zhu 1
Yanhui Xiao 1

Affiliation Paper Counts
Ryukoku University 1
University of Connecticut Health Center 1
Universitat Lausanne Schweiz 1
Universidade Federal do Amazonas 1
Panepistimion Makedonias 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
Universita degli Studi di Perugia 1
Joint Institute for Nuclear Research, Dubna 1
Instituto Superior Tecnico 1
University of Auckland 1
Bogazici Universitesi 1
University of Houston 1
University of Pennsylvania 1
Universitat Koblenz-Landau 1
Guangdong University of Technology 1
Northwestern University 1
Smithsonian National Museum of Natural History 1
Hebrew University of Jerusalem 1
Osaka Prefecture University 1
Duke University 1
Vrije Universiteit Amsterdam 1
Birkbeck University of London 1
Educational Testing Service 1
IBM Almaden Research Center 1
Wayne State University 1
Northeast Normal University 1
Kozep-Europai Egyetem 1
Harvard University 1
University of Arizona 1
Rissho University 1
Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende 1
The University of British Columbia 1
Dartmouth College 1
Hohai University 1
The University of Western Ontario 1
Citigroup 1
Lingnan University 1
King's College London 1
Centrum voor Wiskunde en Informatica 1
Universita degli Studi di Messina 1
University of Shizuoka 1
Open University 1
Aoyama Gakuin University 1
National Science Foundation 1
Ionian Panepistimion 1
Universitat Passau 1
Eastman Kodak Company 1
University of Hawaii at Hilo 1
University of Saskatchewan 1
University of Washington 1
General Electric Company 1
New York State Museum 1
Beijing Institute of Technology 1
Charles Stark Draper Lab Inc 1
University of Sussex 1
Defence Research and Development Canada 1
Nankai University 1
Technische Universitat Wien 1
Washington State University Pullman 1
Office of Naval Research 1
Ecole Polytechnique de Montreal 1
Netherlands Organisation for Applied Scientific Research - TNO 1
Binghamton University State University of New York 1
American University 1
Massachusetts General Hospital and Harvard Medical School 1
University of Surrey 1
Nanjing University of Aeronautics and Astronautics 1
Aalborg Universitet 1
Naresuan University 1
Politecnico di Milano 1
Shanghai University 1
Soka University 1
Ecole Centrale Paris 1
National University of Defense Technology 1
Universite de Fribourg 1
National Central University Taiwan 1
Dublin City University 1
Katholieke Universiteit Leuven 1
The University of North Carolina at Chapel Hill 1
University of Cincinnati 1
Universita degli Studi di Udine 1
Institute of Intelligent Machines Chinese Academy of Sciences 1
Capital Medical University China 1
United States Military Academy at West Point 1
Universite du Quebec a Montreal 1
Fachhochschule fur Technik und Wirtschaft Berlin 1
Fairleigh Dickinson University 1
Research Organization of Information and Systems National Institute of Informatics 1
University of Hawaii System 1
University of Chicago 1
University of Southern California 1
European Space Agency - ESA 1
University of California, Riverside 1
Columbia University in the City of New York 1
Texas State 1
Ecole des Mines de Paris 1
Hosei University 1
Rutgers, The State University of New Jersey 1
Boeing Corporation 1
Santa Fe Institute 1
Michigan State University 1
University of Western Australia 1
Indian Institute of Technology Roorkee 1
North Dakota State University 1
University of Electro-Communications 1
Pontificia Universidade Catolica do Rio de Janeiro 1
Jyvaskylan Yliopisto 1
University of Chittagong 1
U.S. Army Research Laboratory 1
Universidad de Sevilla 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
Nanyang Technological University School of Computer Engineering 1
Florida Institute for Human & Machine Cognition 1
Fujitsu America, Inc. 1
Shandong University of Finance 1
Shandong Academy of Sciences 1
Austrian Institute of Technology 1
Laboratoire d'Informatique de Nantes-Atlantique 1
Liverpool Hope University 1
Qatar Foundation 1
Shenzhen University 2
University of Texas at Arlington 2
University of Manchester 2
King Abdulaziz University 2
Southeast University 2
Universita della Svizzera italiana 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
University of Kent 2
Royal Melbourne Institute of Technology University 2
Technische Universitat Berlin 2
Universitat Zurich 2
Universiteit Antwerpen 2
King Saud University 2
Telecom Bretagne 2
Aston University 2
Aristoteleion Panepistimion Thessalonikis 2
University of Southern California, Information Sciences Institute 2
Dalhousie University 2
Academia Sinica Taiwan 2
National Tsing Hua University 2
Xiamen University 2
Technische Universitat Dresden 2
Tamkang University 2
Singapore Management 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 Universitat Linz 2
Queen Mary, University of London 2
University of Central Florida 2
Waseda University 2
Lancaster University 2
University of Massachusetts Boston 2
Universita degli Studi di Ferrara 2
Sam Houston State University 2
Xidian University 2
University of Oxford 2
University of Edinburgh 2
Jerusalem College of Technology 2
Magyar Tudomanyos Akademia 2
New Mexico Institute of Mining and Technology 2
University of Athens 2
Southern Illinois University at Carbondale 2
Universite de Rennes 1 2
University of California System 2
NEC Corporation 2
Universitat Dortmund 2
Intel Corporation 2
American University of Beirut 2
Universite Paris-Est 2
Telecom & Management SudParis 2
SRI International 3
David R. Cheriton School of Computer Science 3
Universiti Sains Malaysia 3
Chalmers Tekniska Hogskola 3
Universite Pierre et Marie Curie 3
University of Glasgow 3
University of Texas at San Antonio 3
University College Dublin 3
Tel Aviv University 3
Brigham Young University 3
The University of North Carolina at Charlotte 3
Jiangnan University 3
Universitat Politecnica de Catalunya 3
Orebro Universitet 3
University of Wyoming 3
University of Texas at Dallas 3
Washington State University 3
Libera Universita di Bolzano 3
Universidad Politecnica de Valencia 3
Changchun University of Technology 3
BBN Technologies 3
University of Oregon 3
Universitat Marburg 3
Beihang University 3
Universidad de Jaen 3
Georgia Tech Research Institute 3
University of Washington Seattle 3
Universitat Stuttgart 3
Universitat Kassel 3
Wright State University 3
Universite Simon Fraser 3
CSIC - Instituto de Investigacion en Inteligencia Artificial 3
Xerox Corporation 3
Technische Universitat Graz 3
New York University 3
University of Macau 3
University of Connecticut 3
University of North Texas 3
Universite Paris-Sud XI 3
University of Utah 3
Universitat Konstanz 3
University of Teesside 3
Huazhong University of Science and Technology 3
Center For Research And Technology - Hellas 3
Stony Brook University 3
Szegedi Tudomanyegyetem 3
Alma Mater Studiorum Universita di Bologna 3
Universita di Pisa 3
EURECOM Ecole d'Ingenieurs & Centre de Recherche en Systemes de Communication 3
University of California, San Diego 3
University of Queensland 3
Utah State University 3
University of Wisconsin Madison 3
Universita degli Studi di Roma La Sapienza 3
Intel Corp., China 3
Rutgers University-Newark Campus 4
New Mexico State University Las Cruces 4
Bar-Ilan University 4
Hefei University of Technology 4
Toyohashi University of Technology 4
Ohio State University 4
University of Waikato 4
University of Vermont 4
University of Adelaide 4
University of Florida 4
Universita degli Studi di Pavia 4
Universita degli Studi di Trento 4
Politechnika Lodzka 4
Universita della Calabria 4
University of Liverpool 4
Indiana University 4
West Virginia University 4
University of Notre Dame 4
Universita degli Studi di Firenze 4
Massachusetts Institute of Technology 4
University of Tehran 4
Eidgenossische Technische Hochschule Zurich 4
National University of Ireland, Maynooth 4
Universidad Complutense de Madrid 4
Sharif University of Technology 4
University of Ottawa, Canada 4
University of California, Santa Barbara 4
University of Miami 4
Korea Advanced Institute of Science & Technology 4
Technische Universiteit Eindhoven 4
Nanjing University 4
Cornell Tech 4
Institute for Infocomm Research, A-Star, Singapore 5
North Carolina State University 5
Google Inc. 5
UC Irvine 5
Soochow University 5
Istituto Di Scienze E Tecnologie Della Cognizione, Rome 5
City University of Hong Kong 5
University of Massachusetts Amherst 5
National Cheng Kung University 5
University of Pittsburgh 5
George Mason University 5
Beijing Jiaotong Daxue 5
Osaka University 5
Missouri University of Science and Technology 5
Rensselaer Polytechnic Institute 5
Microsoft 5
Max Planck Institute for Intelligent Systems 5
TECH Lab 5
Shandong University 5
Yahoo Research Barcelona 5
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 5
Pennsylvania State University 6
Virginia Commonwealth University 6
Hong Kong Polytechnic University 6
Washington University in St. Louis 6
National Taipei University of Technology 6
Stanford University 6
Hong Kong Baptist University 6
IBM Thomas J. Watson Research Center 6
NEC Laboratories America, Inc. 6
Texas A and M University 6
University of Southampton 6
Biblioteca CICESE 6
TELECOM ParisTech 6
University of Alberta 6
Ryerson University 6
University of South Australia 6
Universitat Pompeu Fabra 6
Oregon State University 7
Institute of Automation Chinese Academy of Sciences 7
University of Ulster 7
Universita degli Studi di Bari 7
Universidad Autonoma de Madrid 7
Hewlett Packard Laboratories 8
National Chiao Tung University Taiwan 8
Universita degli Studi Roma Tre 8
Drexel University 8
University of Aberdeen 8
UC Berkeley 8
Virginia Polytechnic Institute and State University 8
Universiteit Gent 8
Northwestern Polytechnical University 9
NASA Ames Research Center 9
Hong Kong University of Science and Technology 9
University of Texas at Austin 9
University of Waterloo 9
University of Illinois at Chicago 9
Bauhaus-Universitat Weimar 9
Universita degli Studi di Torino 10
Beijing University of Posts and Telecommunications 10
Ecole Polytechnique Federale de Lausanne 10
University of Minnesota Twin Cities 10
Universidade Federal de Minas Gerais 10
Nanjing University of Science and Technology 11
Georgia Institute of Technology 11
University of Melbourne 11
Nokia 11
Zhejiang University 12
University of Technology Sydney 12
Shanghai Jiaotong University 12
Delft University of Technology 13
Politecnico di Torino 13
Chinese University of Hong Kong 14
Tsinghua University 14
University of California, Los Angeles 14
Nanyang Technological University 15
Florida International University 15
Ben-Gurion University of the Negev 15
Institute of Computing Technology Chinese Academy of Sciences 15
Yahoo Research Labs 15
Arizona State University 16
Carnegie Mellon University 16
Harbin Institute of Technology 17
University of Tokyo 18
University of Illinois at Urbana-Champaign 20
Microsoft Research 20
National Taiwan University 21
Microsoft Research Asia 21
Peking University 22
Jet Propulsion Laboratory, California Institute of Technology 24
IBM Research 24
University of Maryland 25
Chinese Academy of Sciences 27
National University of Singapore 29
University of Science and Technology of China 32

ACM Transactions on Intelligent Systems and Technology (TIST) - Survey Paper, Special Issue: Intelligent Music Systems and Applications and Regular Papers

Volume 8 Issue 2, January 2017 Survey Paper, Special Issue: Intelligent Music Systems and Applications and Regular Papers
Volume 8 Issue 3, January 2017  Issue-in-Progress

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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