ACM Transactions on

Intelligent Systems and Technology (TIST)

Latest Articles


Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social-network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe the Stanford... (more)

Topic-Aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network

Modeling physical activity propagation, such as activity level and intensity, is a key to preventing obesity from cascading through communities, and... (more)

Multiagent Resource Allocation for Dynamic Task Arrivals with Preemption

In this article, we present a distributed algorithm for allocating resources to tasks in multiagent systems, one that adapts well to dynamic task... (more)

Dynamic Scheduling of Cybersecurity Analysts for Minimizing Risk Using Reinforcement Learning

An important component of the cyber-defense mechanism is the adequate staffing levels of its cybersecurity analyst workforce and their optimal... (more)

Using Scalable Data Mining for Predicting Flight Delays

Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15min late) and they are estimated to have an... (more)

Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features

Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In... (more)


The cloud service marketplace (CSM) is an exploratory project aiming to provide “an AppStore for Services.” It is an intelligent online marketplace that facilitates service discovery and acquisition for enterprise customers. Traditional service discovery and acquisition are time-consuming. In the era of OneClick Checkout and... (more)

Enhanced Knowledge-Leverage-Based TSK Fuzzy System Modeling for Inductive Transfer Learning

The knowledge-leverage-based Takagi--Sugeno--Kang fuzzy system (KL-TSK-FS) modeling method has shown promising performance for fuzzy modeling tasks... (more)

A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs

Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most... (more)


Emotion recognition in text has become an important research objective. It involves building classifiers capable of detecting human emotions for a specific application, for example, analyzing reactions to product launches, monitoring emotions at sports events, or discerning opinions in political debates. Most successful approaches rely heavily on... (more)

Driving Profiles Computation and Monitoring for Car Insurance CRM

Customer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the... (more)


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About TIST

ACM Transactions on Intelligent Systems and Technology (ACM TIST, Impact Factor: 2.4) 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
Soft Confidence-Weighted Learning

Online learning plays a very important role in many big data mining problems, because it enjoys very high efficiency. In literature, many first order online learning algorithms by using first order information such as, gradient, are proposed to solve online classification tasks. Recently, some second order online learning algorithms are proposed for classification tasks, which can uses some second order information, such as, the correlation between the features to improve the learning efficiency. Among them, Confidence-Weighted (CW) learning algorithms are very effective, which assume that the classification model satisfies a Gaussian distribution, so that the model can be effectively updated using the second order information of the data stream. Despite being studied actively, these CW algorithms cannot handle non-separable datasets and nosy datasets very well. Although, other methods, such as Adaptive Regularization of Weight (AROW), are proposed to alleviate this problem, they do not have the adaptive margin property of CW. In this paper, we propose a family of Soft Confidence Weighted (SCW) learning algorithms for both binary classification and multi-class classification tasks, which is the first family of online classification algorithms that enjoys four salient properties simultaneously: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the-art algorithms (including AROW, NAROW and NHERD), we found that SCW in general achieves better or at least comparable predictive performance, but enjoys considerably better efficiency advantage (i.e., using smaller number of updates and lower time cost). To facilitate future research, we release all the datasets and source code to public at~\url{}.

Learning k for kNN Classification

A new kNN classification is proposed by learning different k for different test data. To do so, the correlation matrix between training examples and test data is reconstructed, called CM-kNN classification. In the reconstruction process, we first employ a least square loss function to minimize the reconstruction error. The generation of different k values for different test data is carried out with the l1-norm. And then, assuming that training examples may have some noisy values, we apply l(2,1)-norm to generate the row sparsity for removing those noisy training examples. Finally, in an applied view, we extend CM-kNN classification to regression and missing data imputation. We conduct sets of experiments for illustrating the efficiency, and show that the proposed approach is of high accuracy, efficient and promising in data mining applications, such as classification, regression and missing data imputation.

Learning User Attributes via Mobile Social Multimedia Analytics

Learning user attributes from mobile social media is a fundamental basis for many applications, such as the personalized and targeting services. A large and growing body of literatures has investigated the user attributes learning problem. However, far too little attention has been paid to jointly consider the dual-heterogeneities of user attributes learning by harvesting multiple social media sources. In particular, user attributes are complementarily and comprehensively characterized by multiple social media sources, including footprints from Foursqare, daily updates from Twitter, professional careers from Linkedin and photo posts from Instagram; On the other hand, attributes are inter-correlated in a complex way rather than independent to each other, and highly related attributes may share similar feature sets. Towards this end, we proposed a unified model to jointly regularize the source consistency and graph-constrained relatedness among tasks. As a byproduct, it is able to learn the attribute-specific and attribute-sharing features via graph-guided fused lasso penalty. Besides, we have theoretically demonstrated its optimization. Extensive evaluations on real-world dataset thoroughly demonstrated the effectiveness of our proposed model.

Directly Optimize Diversity Evaluation Measures: a New Approach to Search Result Diversification

The queries issued to search engines are often ambiguous or multi-faceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible, called search result diversification. Recently, the relational learning to rank model, which designs the learnable ranking function following the criterion of maximal marginal relevance, has showed its effectiveness in search result diversification. The goodness of a diverse ranking model is usually evaluated with diversity evaluation measures such as $\alpha$-NDCG and ERR-IA. Ideally the learning algorithm would train a ranking model that could directly optimize the diversity evaluation measures with respect to the training data. Existing relational learning to rank algorithms, however, only train the ranking models by optimizing loss functions that loosely related to the evaluation measures. To deal with the problem, we propose a general framework for learning relational ranking models via directly optimizing any diversity evaluation measure. In learning, the loss function upper bounding the basic loss function defined on a diverse ranking measure are minimized. We can derive new diverse ranking algorithms under the framework and several diverse ranking algorithms are created, based on different upper bounds over the basic loss function. We have conducted comparisons between the proposed algorithms with conventional diverse ranking methods, using the TREC benchmark datasets. Experimental results show that the algorithms derived under the diverse learning to rank framework can always significantly outperform the state-of-the-art baselines.

Joint Structured Sparsity Regularized Multi-view Dimension Reduction for Video-based Facial Expression Recognition

Video-based facial expression recognition (FER) has recently received increased attention as a result of its widespread application. Using only one type of feature to describe facial expression in video sequences is often inadequate, because the available information is very complex. With the emergence of different features to represent different properties of facial expressions in videos, an appropriate combination of these features becomes an important yet challenging problem. Considering that the dimensionality of these features is usually high, we thus introduce multi-view dimension reduction (MVDR) to video-based FER. In MVDR, it is critical to explore the relationships between and within different feature views. To achieve this goal, we propose a novel framework for MVDR by enforcing joint structured sparsity at both inter- and intra-view levels. In this way, correlations on and between the feature spaces of different views tends to be well-exploited. In addition, a transformation matrix is learned for each view to discover the patterns contained in the original features, so that the different views are comparable in finding a common representation. The model can not only be performed in an unsupervised manner, but also easily extended to semi-supervised setting by incorporating some domain knowledge. An alternating algorithm is developed for problem optimization, and each sub-problem can be efficiently solved. Experiments on two challenging video-based FER datasets demonstrate the effectiveness of the proposed framework.

SMARTS: Scalable Microscopic Adaptive Road Traffic Simulator

Microscopic traffic simulators are important tools for studying transportation systems as they describe the evolution of traffic to the highest level of detail. A major challenge to microscopic simulators is the slow simulation speed due to the complexity of traffic models. We develop SMARTS, a distributed microscopic traffic simulator that can achieve a significant improvement in simulation speed by utilizing network-connected computing nodes in parallel. The simulator implements an innovative spatial workload balancing strategy that helps to minimize computation workload and communication cost at the same time. SMARTS can perform fast large-scale simulations. For example, a simulation performed by 30 computing nodes runs 1.14 times faster than real time when there are one million vehicles in an area the size of Melbourne. SMARTS supports a number of driver models and traffic rules, such as car-following model and lane-changing model, which can be driver dependent. It can simulate multiple vehicle types, including bus and tram. The simulator is equipped with a wide range of features that help to customize, calibrate and monitor simulations. Users can build traffic scenarios such as simulating the spike of traffic towards a specified area. Simulations are accurate and confirm with real traffic behaviours. For example, it achieves 79.1% accuracy in predicting traffic on a 10-kilometre freeway 90 minutes into the future. The simulator can be used for predictive traffic advisories as well as traffic management decisions as simulations complete well ahead of real time. SMARTS can be easily deployed to different operating systems as it is developed with the standard Java libraries.

Getting Closer to the Essence of Music: The Con Espressione Manifesto

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 music that are probably generally agreed on, but often ignored in everyday research.

Rating Effects on Social News Posts and Comments

At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale in-vivo experiments in social media. We find that small, random rating manipulations on social media posts and comments created significant changes in downstream ratings resulting in significantly different final outcomes. We found positive herding effects for positive treatments on posts, increasing the final rating by 11.02% on average, but not for positive treatments on comments. Contrary to the results of related work, we found negative herding effects for negative treatments on posts and comments, decreasing the final ratings on average, of posts by 5.15% and of comments by 37.4%. Compared to the control group, the probability of reaching a high rating (>=2000) for posts is increased by 24.6% when posts receive the positive treatment and for comments is decreased by 46.6% when comments receive the negative treatment.

Measuring Similarity Similarly: LDA and Human Perception

Several intelligent technologies designed to improve navigability in and digestibility of text corpora use topic modeling such as the state-of-the-art Latent Dirichlet Allocation (LDA). This model and variants on it provide lower-dimensional document representations used in visualizations and in computing similarity between documents. This paper contributes a method for validating such algorithms against human perceptions of similarity, especially applicable to contexts where the algorithm is intended to support navigability between similar documents via dynamically generated hyperlinks. Such validation enables researchers to ground their methods in context of intended use instead of relying on assumptions of fit. In addition to the methodology, this paper presents the results of an evaluation using a corpus of short documents and the LDA algorithm. We also present some analysis of potential causes of differences between cases where this model matches human perceptions of similarity more or less well.

Towards Music Structural Segmentation Across Genres: Features, Structural Hypotheses and Annotation Principles

This article provides insights into how different audio features and segmentation methods embrace different music genres. By incorporating a newly composed corpus with Chinese traditional Jingju music into the music structural segmentation study, we propose to extend this research topic outside a Western context across genres. The investigated harmonic-percussive separation technique in the feature extraction process has introduced significant improvement for the segmentation. A systematic evaluation is carried out to examine the effectiveness of commonly used audio features and segmentation methods for several music categories. Results show that investigated features capture the structural patterns of different music genres in different ways. Well acknowledged feature types and structural hypotheses for Western pop music may have weak assumptions for the Chinese traditional genre. To summarise, in the scenario of music structural analysis, the selection of audio features, the design of segmentation paradigms as well as the associated signal processing techniques, the music types and the annotation principles should not be isolated in order to achieve successful segmentation.

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.

Privacy-Preserving Verifiable Incentive Mechanisms for Crowd Sensing Applications

Understanding the Relationship between Human Behavior and Susceptibility to Cyber-Attacks: A Data-Driven Approach

While human users are often considered to be the weakest link in security systems, the risks associated with typical day-to-day computing habits are not well understood. Using Symantec's WINE platform, we conduct a detailed study of 1.6 million machines over an 8-month period in order to learn the relationship between user behavior and cyber attacks against their personal computers.We classify users into 4 categories (gamers, professionals, software developers, others plus a fifth category comprising everyone) and identify a total of 7 independent variables to study: (i) number of binaries (executables) on a machine, (ii) fraction of low-prevalence binaries on a machine, (iii) fraction of high-prevalence binaries on a machine, (iv) fraction of unique binaries on a machine, (v) fraction of downloaded binaries on a machine, (vi) fraction of unsigned binaries on a machine and (vii) travel history of the machine based on number of ISPs from whom the machine connected to the Internet. For each of the 35 possible combinations (5 categories times 7 independent variables), we studied the relationship between each of these 7 independent variables and one dependent variable, namely the number of attempted malware attacks detected by Symantec on the machine. Our results show that the first variable is closely linked to number of attacks for software developers, while the next 5 are linked to the number of attacks for all user categories. Surprisingly, our results show that software developers are more at risk of engaging in risky cyber-behavior than other categories.

Prediction and Simulation of Human Mobility Following Natural Disasters

The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, understanding, predicting and simulating of human emergency mobility following disasters will become the critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, due to the uniqueness of various disasters and the unavailability of reliable and large scale human mobility data, such kind of research is very difficult to be performed. In this paper, we collect big and heterogeneous data (e.g. 1.6 million users' GPS records in three years, 17520 times of Japan earthquake data in four years, news reporting data, transportation network data and etc.) to capture and analyze human emergency mobility following natural disasters. By mining these big data, we aim to understand what basic laws govern human mobility following disasters, and develop an effective human mobility model for predicting and simulating population movements in the future events. The experimental results and validations demonstrate the efficiency of our model, and suggest that human mobility following disasters may be significantly more predictable and can be easier simulated than previously thought.

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.Referring to print magazines and later to music television as important distribution channels, the visual representation thus played and still plays a significant role in music marketing. Visual stereotypes developed over decades which enable us to quickly identify referenced music only by sight without listening. Despite of the richness of music related visual information provided by daily life such as music videos, album covers as well as T-shirts, advertisements and magazines, research towards harnessing this information to advance existing or approach new problems of music retrieval or recommendation is scarce or missing. In this paper we presents our research on visual music computing which aims to extract stereotypical music related visual information from music videos. To provide comprehensive and reproducible results we present the Music Video Dataset - a thoroughly assembled suite of datasets where each sub-set is dedicates to a certain Music Information Retrieval (MIR) task or evaluation scenario. Based on this dataset we provide evaluations of conventional low-level image processing features as well as affect-related features to provide an overview of the expressiveness of fundamental visual properties such as color, illumination and contrasts. Based on this we introduce a high-level approach to facilitate visual stereotypes based on visual concept detection. This approach decomposes the semantic content of music video frames into concrete concepts such as Guitars, Cars, Tools, etc. defined in a wide visual vocabulary. Concepts of this vocabulary are detected using convolutional neural networks and their frequency distributions are analyzed towards their performance in predicting the music genre of a video.

Learning Contextualized Music Semantics from Tags via a Siamese Neural Network

Music information retrievals (MIR) face a challenge of modelling contextualized musical concepts. In this paper, we propose a novel Siamese neural network to fight off this challenge. By means of tag features and probabilistic topic models, our Siamese network captures contextualized music semantics from tags via unsupervised learning, which leads to a distributed semantics space and a potential solution to the out of vocabulary (OOV) problem which has yet to be sufficiently addressed so far. We have conducted simulations on three public music tag collections, CAL500, MagTag5K and Million Song Dataset, and have compared our approach to a number of the state-of-the-art semantics learning approaches. Comparative results suggest that ours outperforms previous approaches in terms of the semantic priming and the tag completion tasks.

A Supervised Learning Model for High-Dimensional and Large-Scale Data

We introduce a new supervised learning model using a discriminative regression approach. This new model estimates a regression vector to represent the similarity between a test example and training examples while seamlessly incorporating the class information in the similarity estimation. This distinguishes our model from common regression models and locally linear embedding approaches, rendering our method suitable for supervised learning problems in high-dimensional settings. Our model is easily extensible to account for nonlinear relationship, and applicable to general data including both high- and low-dimensional data. The objective function of the model is convex, for which two optimization algorithms are provided. These two optimization approaches induce two scalable solvers that mathematically provable linear time complexity. Experimental results verify the effectiveness of the proposed method on various kinds of data. For example, our method shows comparable performance on low-dimensional data and superior performance on high-dimensional data to several widely used classifiers; also, the linear solvers obtain promising performance on large-scale classification.

Nonnegative Matrix Factorization with Integrated Graph and Feature Learning

Matrix factorization is a useful technique for data representation in many data mining and machine learning tasks. Particularly, for data sets with all nonnegative entries, matrix factorization often requires that factor matrices be also nonnegative, leading to nonnegative matrix factorization (NMF). One important application of NMF is for clustering with reduced dimensions of the data represented in the new feature space. In this paper, we propose a new graph regularized NMF method capable of feature learning, and apply it to clustering. Unlike existing NMF methods that treat all features in the original feature space equally, our method distinguishes features by incorporating a feature-sparse approximation error matrix in the formulation. It enables important features to be more closely approximated by the factor matrices. Comprehensive experimental results demonstrate the effectiveness of the proposed method, which outperforms state-of-the-art algorithms.

Mining Search and Browse Logs for Web Search: A Survey

Tensors for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms

Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multi-aspect data. As a result, tensor decompositions, which extract useful latent information out of multi-aspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioners point of view. We then overview a very broad spectrum of applications where tensors have been instrumental in achieving state of the art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to todays big data scale, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.

A Joyful Ode to Automatic Orchestration

After long decades of research in computer music, music generation is now finally becoming a reality. The ability to produce automatically music of human-level quality, whatever this means, by algorithms able to understand sufficiently well the various problems underlying music generation. In this paper I report on a specific experiment in music generation resulting from a commission of the European Commision: the re-orchestration of Ode to Joy, the European anthem, in seven styles, using algorithms developped in the Flow Machines project and in a very limited time frame. I stress out the benefits of having had such a unifying goal, and the interesting new problems it raised in the way, both concerning symbolic and audio music generation.

Sound and Music Recommendation with Knowledge Graphs

The Web has moved, slowly but steady, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, e.g., to explain search results, to explore knowledge spaces, to semantically enrich textual documents or to feed knowledge intensive applications such as recommender systems. In this work we describe how to create a knowledge graph to supply a hybrid recommendation engine with information that builds on top of the collection of sounds freely available in Freesound, a popular site for sharing sound samples which count more than 4 million registered users and about 250,000 uploaded sounds. Tags and textual sound descriptions, added by contributors at the time of the upload of a sound, are exploited to extract and link entities to external graphs such as WordNet and DBpedia which are in turn used to semantically enrich the initial data. The extracted data are eventually merged with a domain specific tagging ontology to form a richer knowledge graph of entities and labelled relationships. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data shows significant improvements with respect to state of the art collaborative algorithms. In addition, we show how the semantic expansion of the initial sound descriptions help in achieving much better recommendation quality in terms of aggregated diversity and novelty.

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.

Intelligent Process Adaptation in the SmartPM System

Introduction to Intelligent Music Systems and Applications

Community Detection with Topological Structure and Attributes in Information Networks

Detecting community for information networks, which generally contain objects connected by multiple links and described by rich attributes, is a challenging research problem. There is a scarcity of effective approaches that balance the features of the network structure and the characteristics of the nodes. Some methods detect communities by considering topological structures while ignoring the contributions of attributes. Other methods that have considered both topological structure and attributes pay a high price in time complexity. In this paper, we establish a new community detection algorithm which explores both topological Structure and Attributes using Global structure and Local neighborhood features (SAGL) that also has low computational complexity. The initial step of SAGL is to evaluate the global importance of every node and calculate the similarity of every pair of nodes by combining edge strength and node attribute similarity. The final step is a clustering algorithm that identifies communities by measuring the similarity of two nodes which is calculated by the distance of their neighbors. Experimental results on two real-world datasets show the effectiveness of SAGL and its fast convergence when compared with the state-of-the-art attributed graph clustering methods.

Implicit Visual Learning: Image Recognition via Dissipative Learning Model

According to consciousness involvement, humans learning can be roughly classified into explicit learning and implicit learning. Contrasting strongly to explicit learning with clear targets and rules, such as our school study of mathematics, learning is implicit when we acquire new information without intending to do so. Research from psychology indicates that implicit learning is ubiquitous in our daily life. Moreover, implicit learning plays an important role in human visual perception. But in the past sixty years, most of the well-known machine learning models aimed to simulate explicit learning while the work of modeling implicit learning was relatively limited, especially for computer vision applications. This paper proposes a novel unsupervised computational model for implicit visual learning by exploring dissipative theoretical system, which provides a unifying macroscopic theory to connect biology with physics. We test the proposed Dissipative Implicit Learning Model (DILM) on various datasets. The experiments show that DILM not only provides a good match to human behavior, but also improves the explicit machine learning performance obviously on image classification tasks.

Personalized Microtopic Recommendation on Microblogs

Microblogging services such as Twitter and Sina Weibo allow users to create tags enclosed in a pair of # which are called microtopics. Each microtopic has a designate page, and can be directly visited or commented on. Microtopic recommendation can facilitate users to efciently acquire information by summarizing trending online topics and feeding comments with high quality. However, it is non-trivial to recommend microtopics to the users of microblogs to satisfy their information needs. In this paper, we investigate the task of personalized microtopic recommendation which exhibits two characteristics. First, the users tend not to give explicit ratings to the microtopics. Second, there exists rich information about users and microtopics, for example, users published content and biographical information. To address the above two characteristics, we propose a joint probabilistic latent factor model to integrate rich information into user adoption matrix factorization. Our model benets in collaborative ltering, content analysis and feature regression. Using two real-world datasets, we evaluate our model with different kinds of content and contextual information. Experimental results show that our model signicantly outperforms a few competitive baseline methods, especially in the circumstance that users have few adoption behaviors.

A Semantic Framework for Intelligent Match-making for Clinical Trial Eligibility Criteria

Differential Flattening: A Novel Framework for Community Detection in Multi-Layer Graphs

A multi-layer graph consists of multiple layers of weighted graphs, where the multiple layers represent the different aspects of relationships. Considering multiple aspects (i.e., layers) together is essential to achieve a comprehensive and consolidated view. In this paper, we propose a novel framework of differential flattening, which facilitates the analysis of multi-layer graphs, and apply this framework to community detection. Differential flattening merges multiple graphs into a single graph such that the graph structure with the maximum clustering coefficient is obtained from the single graph. It has two distinct features compared with existing approaches. First, dealing with multiple layers is done independently of a specific community detection algorithm whereas previous approaches rely on a specific algorithm. Thus, any algorithm for a single graph becomes applicable to multi-layer graphs. Second, the contribution of each layer to the single graph is determined automatically for the maximum clustering coefficient. Since differential flattening is formulated by an optimization problem, the optimal solution is easily obtained by well-known algorithms such as interior point methods. Extensive experiments were conducted using the LFR benchmark networks as well as the DBLP, 20 Newsgroups, and MIT Reality Mining networks. The results show that our approach of differential flattening leads to discovery of higher-quality communities than baseline approaches and the state-of-the-art algorithms.

Optimal Scheduling of Cybersecurity Analysts for Minimizing Risk

Cybersecurity threats are on the rise with evermore digitization of the nformation that many day-to-day systems depend upon. The demand for cybersecurity analysts outpaces supply, which calls for optimal management of the analyst resource. Therefore, a key component of the cybersecurity defense system is the optimal scheduling of its analysts. Sensor data is analyzed by automatic processing systems, and alerts are generated. A portion of these alerts is considered to be {\it significant}, which requires thorough examination by a cybersecurity analyst. Risk, in this paper, is defined as the percentage of unanalyzed or not thoroughly analyzed alerts among the {\it significant} alerts by analysts. The paper presents a generalized optimization model for scheduling cybersecurity analysts to minimize risk (a.k.a maximize {\it significant} alert coverage by analysts) and maintain risk under a pre-determined upper bound. The paper tests the optimization model and its scalability on a set of given sensors with varying analyst experiences, alert generation rates, system constraints, and system requirements. Results indicate that the optimization model is scalable, and is capable of identifying both the right mix of analyst expertise in an organization and the sensor-to-analyst allocation in order to maintain risk below a given upper bound. Several meta-principles are presented, which are derived from the optimization model, and they further serve as guiding principles for hiring and scheduling cybersecurity analysts. The simulation studies (validation) of the optimization model outputs indicate that risk varies non-linearly with analyst/sensor ratio, and for a given analyst/sensor ratio, the risk is independent of the number of sensors in the system.

A Survey of Appearance Models in Visual Object Tracking

When Location Meets Social Multimedia: A Survey on Vision-based Recognition and Mining for Geo-Social Multimedia Analytics

Event Classification in Microblog via Social Tracking

Social media websites have become important information sharing platforms. The rapid development of social media platforms has led to increasingly large scale data, which has shown remarkable societal and marketing values. There are needs to extract the focus in live social media streams given a list of concerned entities, such as events, brands, or known names. In this paper, we take brand tracking as the example, and the objective is to detect brands for live data. It is noted that this is a hard task due to the short and conversational natural of microblogs and the incompatible meanings between the text and the corresponding image in microblog. To overcome these limitations, we propose a novel deep learning architecture, named Multi-modal Multi-instance Deep Network (M2DN), for microblog detection, which is able to handle the weakly labeled microblog data oriented from the incompatible meanings inside microblogs. Besides predicting each microblog as predefined categories, we propose a social path learning method to extract social related auxiliary information to enrich the testing sample. We extract a set of candidate relevant microblogs in a short time window with its social path and the text using dense subgraph extraction. These selected microblogs and the testing sample are formulated in a Markov Random Field model, which finally generates the detection results. This method is evaluated on the Brand-Social-Net dataset for tracking 100 brands. Experimental results and comparison with state-of-the-art show that the proposed method can achieve a high data coverage for the microblog classification task.

Analyzing User Behavior across Social Sharing Environments

Bridging the Air Gap between Isolated Networks and Mobile Phones in a Practical Cyber-Attack

Information is the most critical asset of modern organizations, and accordingly it is one of the resources most coveted by adversaries. When highly sensitive data is involved, an organization may resort to air gap isolation in which there is no networking connection between the inner network and the external world. While infiltrating an air gapped network has been proven feasible in recent years (e.g., Stuxnet), data exfiltration from an air gapped network is still considered to be one of the most challenging phases of an advanced cyber-attack. In this paper we present "AirHopper", a bifurcated malware that bridges the air gap between an isolated network and nearby infected mobile phones using FM signals. While it is known that software can intentionally create radio emissions from a video display unit, this is the first time that mobile phones are considered in an attack model as the intended receivers of maliciously crafted radio signals. We examine the attack model and its limitations and discuss implementation considerations such as stealth and modulation methods. We test AirHopper on an existing workplace at a typical office building and demonstrate how textual and binary data can be exfiltrated from physically isolated computers to mobile phones at a distance of 1-7 meters, with an effective bandwidth of 13-60 BPS (Bytes per second).

SPRank: Semantic Path-based Ranking for Top-N Recommendations using Linked Open Data

In most real world scenarios the ultimate goal of recommender system (RS) applications is to suggest a short ranked list of items, namely Top-N recommendations, supposed to be the most appealing for the end user. Often, the problem of computing Top-N recommendations is mainly tackled with a two steps approach. The system focuses first on predicting the unknown ratings which are eventually used to generated a ranked recommendation list. Actually, the Top-N recommendation task can be directly seen as a ranking problem where the main goal is not to accurately predict ratings but directly find the best ranked list of items to recommend. In this paper, we present SPRank, a novel hybrid recommendation algorithm able to compute Top-N recommendations exploiting freely available knowledge in the Web of Data. In particular we employ DBpedia, a well-known encyclopedic knowledge base in the Linked Open Data cloud, to extract semantic path-based features and to eventually compute Top-N recommendations in a Learning to Rank fashion. Experiments with three datasets related to different domains (books, music and movies) prove the effectiveness of our approach compared to state-of-the-art recommendation algorithms.

A Unified Point-of-interest Recommendation Framework in Location-based Social Networks

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks since it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task since it can capture users preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a users check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top-k recommendation than directly using matrix matrix factorization that aims to minimize the point- wise rating error. To consider users preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real world LBSNs datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.

CIM: Community-based Influence Maximization in Social Networks

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

In this paper, we present an online score following framework designed to deal with automatic accompaniment. The proposed framework is based on spectral factorization and online Dynamic Time Warping (DTW) and has two separated stages: preprocessing and alignment. In the first one, we convert the score into a reference audio signal using a MIDI synthesizer software and we analyze the provided information in order to obtain the spectral patterns (i.e. basis functions) associated to each combination of concurrent notes in the score. These spectral patterns are learned from the synthetic MIDI signal using a method based on Non-negative Matrix Factorization (NMF) with Beta-divergence where the gains are initialized as the ground-truth transcription inferred from the MIDI. On the second stage, a non-iterative signal decomposition method with fixed spectral patterns per combination of notes is used over the magnitude spectrogram of the input signal resulting in a distortion matrix that can be interpreted as the cost of the matching for each combination of notes at each frame. Finally, the relation between the performance and the musical score times is obtained using a strategy based on online DTW, where the optimal path is biased by the speed of interpretation. Our system has been evaluated and compared to other systems, yielding reliable results and performance.

A Machine Learning Approach to College Drinking Prediction and Risk Factor Identification

A Risk-Scoring Feedback Model for Webpages and Web Users based on Browsing Behavior

It has been claimed that many security breaches are often caused by vulnerable (naïve) employees within the organization [1]. Thus, the weakest link in security is often not the technology itself but rather the people who use it [2]. In this paper, we propose a machine learning scheme for detecting risky webpages and risky browsing behavior, performed by naïve users in the organization. The scheme analyzes the interaction between two modules: one represents naïve users, while the other represents risky webpages. It implements a feedback loop between these modules such that if a webpage is exposed to a lot of traffic from risky users, its "risk score" increases, while in a similar manner, as the user is exposed to risky webpages (with a high "risk score"), his own "risk score" increases. The proposed scheme is tested on a real-world dataset of HTTP logs provided by a large American toolbar company. The results suggest that a feedback learning process involving webpages and users can improve the scoring accuracy and lead to the detection of unknown malicious webpages.

Efficient methods for Influence-Based Network-oblivious Community Detection

Community detection is an important analysis tool which can provide important insights on the structure of a social network, and support many applications. Given a social graph representing ``friendship'' or ``who-follows-whom'' relations, the task is to extract groups of users which are highly connected inside the group, and loosely connected outside it. This article tackles the problem of detecting social communities when the social graph in not available, but instead we have access to a log of user activity, that is a dataset of tuples $(u,i,t)$ recording the fact that user $u$ ``adopted'' item $i$ at time $t$. This is the only input to our problem. The key idea is to model communities through the lenses of \emph{social contagion}, which is the phenomenon of diffusion of ideas, beliefs, innovations, and information through the links of a social network, driven by social influence. More in details, we propose a stochastic framework which assumes that item adoptions are governed by an underlying diffusion process over the unobserved social network, and that such diffusion model is based on \emph{community-level influence}. By fitting the model parameters to the user activity log, we learn the community membership and the level of influence of each user in each community. This allows us to identify for each community the ``key'' users, i.e., the leaders which are most likely to influence the rest of the community to adopt a certain item. The general framework can be instantiated with different diffusion models, which respond to different assumptions. In particular we consider two models: the extension to the community level of the classic (discrete time) \emph{Independent Cascade} model, and a model that focuses on the time delay between adoptions. We also show that the computational complexity of both approaches is linear in the number of users and in the size of the propagation log. Experiments on synthetic data with planted community structure, show that our methods outperform three non-trivial baselines. The effectiveness of the proposed techniques is further validated on real-word data, on which our methods are able to detect high quality communities.


Publication Years 2010-2016
Publication Count 411
Citation Count 4229
Available for Download 411
Downloads (6 weeks) 4551
Downloads (12 Months) 44400
Downloads (cumulative) 176350
Average downloads per article 429
Average citations per article 10
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)
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 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-Infosys Foundation Award in the Computing Sciences (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)
Qiang Yang ACM Distinguished Member (2011)
Franco Zambonelli ACM Distinguished Member (2012)
ACM Senior Member (2009)
Yu Zheng 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 6
Enhong CHEN 6
Xing Xie 6
Tatseng Chua 5
Yu Zheng 5
Xiansheng Hua 5
Jinhui Tang 5
Shuicheng Yan 5
Nicholasjing Yuan 4
Changsheng Xu 4
Qiang Yang 4
Michelle Zhou 4
Xuan Song 3
Steven Hoi 3
Philip Yu 3
Martha Larson 3
Ryosuke Shibasaki 3
Christopherchuen Yang 3
Wen Gao 3
Xue Li 3
Hui Xiong 3
Rongrong Ji 3
Xiaowei Shao 3
Huanhuan Cao 3
Rebecca Castano 3
Qi Tian 3
Wenchih Peng 3
Tao Li 3
Alan Hanjalic 2
Charles Ling 2
Daqing Zhang 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
Mahmud Hossain 2
SungWook Yoon 2
Jure Leskovec 2
Vincent Tseng 2
Hongxun Yao 2
Zhiwen Yu 2
Paulo Shakarian 2
Hongyuan Zha 2
Sihong Xie 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
Maria Sapino 2
Venkatramanan Subrahmanian 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
Tao Mei 2
Hanqing Lu 2
Meir Kalech 2
Daxin Jiang 2
Xuning Tang 2
Francesco Bonchi 2
Katia Sycara 2
Rino Falcone 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
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
Irwin King 2
Jun Ma 2
Jiuyong Li 2
Yuichi Motai 2
Masaki Aono 2
David Thompson 2
Benno Stein 2
Alejandro Bellogín 2
Bingbing Ni 2
Jeffrey Nichols 2
Bernhard Schölkopf 2
Zhi Geng 2
Kun Zhang 2
Ramesh Jain 2
Naren Ramakrishnan 2
Sarit Kraus 2
John Doucette 2
Lior Rokach 2
Kiri Wagstaff 2
Martin Potthast 2
Alan Said 2
Li Chen 2
Shihchia Huang 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
Ya'akov Gal 2
Quanshi Zhang 2
Shuaiqiang Wang 2
Qingzhong Liu 2
Jiawei Han 2
Luan Tang 2
Jilei Tian 2
Mahdi Jalili 2
Giuseppe Sansonetti 2
Claudio Biancalana 2
Anlei Dong 2
Robin Cohen 2
Luca Cagliero 2
Yue Shi 2
Luigi Di Caro 1
Chengbin Zeng 1
Huamin Qu 1
Eibe Frank 1
Ming Hao 1
Changxing Ding 1
Jing Liu 1
Qiang Li 1
Zechao Li 1
Yantao Jia 1
Xiaolong Jin 1
Azhar Ibrahim 1
Ibrahim Venkat 1
Michael Hardegger 1
Chiachun Lian 1
Wanrong Jih 1
James Michaelis 1
Xiaoqinshelley Zhang 1
James Hendler 1
Geoffrey Levine 1
Zhexuan Song 1
Lukas Mandrake 1
Kristina Lerman 1
Walter Daelemans 1
Lieve Macken 1
Shihwen Huang 1
Yao Zhao 1
Arpad Berta 1
Nirwan Sharma 1
Chen Luo 1
Mingxuan Yuan 1
Michael Strintzis 1
Kuanta Chen 1
Irwin King 1
Christina Katsimerou 1
Shengdong Zhao 1
Bin Li 1
Yong Ge 1
Maosong Sun 1
Aristides Gionis 1
Michael Lyu 1
Léon Bottou 1
Patrick Roos 1
Yudong Guang 1
Mohamed Bouguessa 1
Rajesh Ganesan 1
George Karypis 1
Anca Sailer 1
Ignacio Silva-Lepe 1
Zhaohong Deng 1
Hisao Ishibuchi 1
Shitong Wang 1
Bo Zhang 1
Gang Pan 1
Hua Lu 1
David Wilkie 1
Jinha Kang 1
Deborah Estrin 1
Bin Guo 1
Nagarajan Natarajan 1
Kevin Mcnally 1
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Thuc Vu 1
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Carles Sierra 1
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Lifeng Wang 1
Clement Leung 1
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David Thompson 1
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Natalie Fridman 1
Kazumi Saito 1
Nitin Madnani 1
Majid Ahmadabadi 1
Svetlin Bostandjiev 1
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Lars Haug 1
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David Ben Shimon 1
Marcos Gonçalves 1
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Guy Shani 1
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Alena Neviarouskaya 1
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Tatjen Cham 1
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Ke Lu 1
Scott Spurlock 1
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Marco Gavanelli 1
Denilson Barbosa 1
Richang Hong 1
Carlos Guestrin 1
Elif Kürklü 1
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Youxi Wu 1
Kamer Kaya 1
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Claudia Goldman 1
Lin Lin 1
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Amip Shah 1
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Maria Gini 1
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Guangming Shi 1
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Ao Tang 1
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George Baciu 1
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Seungchan Kim 1
Julie Porteous 1
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Einat Minkov 1
Luis Leiva 1
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Yiyang Yang 1
Charles Clarke 1
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Lena Tenenboim-Chekina 1
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Mario Cataldi 1
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Deming Zhai 1
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Ronald Greeley 1
Norbert Schorghofer 1
Hao Wang 1
Hao Wang 1
Alberto Rosi 1
Markus Endler 1
Hadi Moradi 1
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Ke Zhou 1
Dingquan Wang 1
Xueming Wang 1
Xueqi Cheng 1
K Subramanian 1
Ahamad Khader 1
Alberto Calatroni 1
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Myungcheol Doo 1
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Prithviraj Dasgupta 1
Anshul Sawant 1
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Hong Zhou 1
Steven Skiena 1
Bernhard Pfahringer 1
Jie Tang 1
Rodrygo Santos 1
Zhifeng Li 1
Philippe De Wilde 1
Jiaul Paik 1
Gerhard Tröster, 1
Qi Liu 1
Daniel Gatica-Perez 1
Markus Strohmaier 1
Dominik Benz 1
Elad Yom-Tov 1
Ethan Trewhitt 1
Chongjie Zhang 1
Phillip DiBona 1
Martin Hofmann 1
Haofen Wang 1
Mohammad Hossain 1
Ghulam Muhammad 1
Sarah Schulz 1
Huaming Rao 1
Richard Comont 1
Yunchao Wei 1
Advaith Siddharthan 1
Elaine O'mahony 1
Guangchan Liu 1
Robert Morris 1
Xiaoming Zhang 1
Mark Melenhorst 1
Jasminko Novak 1
Dimitrios Michalopoulos 1
Michael Borish 1
Krzysztof Grudzien 1
Vivekanand Gopalkrishnan 1
Szuhao Huang 1
Shanghong Lai 1
Thomas Tran 1
Zhiyuan Liu 1
Lawrence Saul 1
Marina Spivak 1
Yizhang Jiang 1
Kupsze Choi 1
Hongzhi Yin 1
Evangelos Milios 1
Inderjit Dhillon 1
Hang Li 1
Vanja Josifovski 1
Lance Riedel 1
Weishi Zhang 1
Pablo Castells 1
Yi Chang 1
Li Song 1
Yiliang Zhao 1
Wei Jiang 1
Ali Hurson 1
William Groves 1
Frederic Font 1
Fanchieh Cheng 1
Fabrizio Silvestri 1
Tao Guan 1
Liya Duan 1
Yinting Wang 1
Jiadong Zhang 1
Chongyu Chen 1
Meng Wang 1
Haiyan Li 1
Nan Dong 1
Guodong Guo 1
Haiwei Dong 1
Hong Liu 1
Kartik Talamadupula 1
Alessandro Saffiotti 1
Daniel Neill 1
Steven Reece 1
Vincentwenchen Zheng 1
Andrew Sung 1
Mengyu Qiao 1
Hoda Sepehri Rad 1
Jiebo Luo 1
Karen Haigh 1
Jaegil Lee 1
Roland Kays 1
Joshua Plotkin 1
Shyam Boriah 1
Christopher Potter 1
Jinfeng Zhuang 1
Zhenyu Lu 1
Si Liu 1
Qiang Chen 1
Itamar Hata 1
Rómer Rosales 1
Qiusha Zhu 1
Zhongfei Zhang 1
Xi Li 1
Anton Hengel 1
Brandon Gozick 1
Nitesh Chawla 1
Chewlim Tan 1
Elisabeth Weiss 1
Hannes Wolf 1
Yugyung Lee 1
Deendayal Dinakarpandian 1
Klaus Herrmann 1
Rong Yan 1
Viviana Patti 1
Jiang Bian 1
Nicoletta Fornara 1
Michael Wooldridge 1
Cristiano Castelfranchi 1
Yiqun Hu 1
Liu Wenyin 1
Jiqing Han 1
Tieran Zheng 1
Ofrit Lesser 1
Luigi Caro 1
Jiawei Han 1
John Debenham 1
Liubin Wang 1
Michele Judd 1
Dingkun Ma 1
Alen Docef 1
Md Seddiqui 1
Alice Chan 1
Benjamin Bornstein 1
Aijun Bai 1
Ying Xu 1
Randy Goebel 1
Mark Beattie 1
Ana Martínez-García 1
Homer Chen 1
Yohan Jin 1
Xilin Chen 1
Junming Xu 1
Xin Sun 1
Pietro Pala 1
Pavel Serdyukov 1
Christine Parent 1
Kouzou Ohara 1
Trevor Cohn 1
Chang Hu 1
Katrin Erk 1
Yuval Marton 1
Steven Burrows 1
David Newman 1
Padhraic Smyth 1
Kostas Kolomvatsos 1
Stathes Hadjiefthymiades 1
Albert Bifet 1
Weinan Zhang 1
Fabiano BeléM 1
Dihong Gong 1
Jintao Ye 1
Hweepink Tan 1
Domonkos Tikk 1
Steffen Becker 1
Benno Stein 1
Marco Baroni 1
Denis Helic 1
Roman Kern 1
Charles Parker 1
Ugur Kuter 1
Daniel Corkill 1
Robert Pappalardo 1
Daniel Tran 1
Guy De Pauw 1
Orphée De Clercq 1
Yashar Moshfeghi 1
Lingjing Hu 1
Goran Radanovic 1
Peng Dai 1
Chen Chen 1
Vasant Dhar 1
Yuzhou Zhang 1
Edward Chang 1
Gilles Gasso 1
Huibo Wang 1
Erik Edrosa 1
Bo Liu 1
Sushil Jajodia 1
Changshing Perng 1
Xiaofang Zhou 1
Guande Qi 1
Nithya Ramanathan 1
D George 1
Vishvas Vasuki 1
Lei Tang 1
Marek Lipczak 1
Berkant Savas 1
Juan Rogers 1
Yingying Jiang 1
Jingdong Wang 1
Michele Gelfand 1
Sheng Li 1
Evgeniy Gabrilovich 1
Yushi Lin 1
Guiguang Ding 1
Dietmar Jannach 1
Hitoshi Yamamoto 1
Xiaohua Liu 1
Ruiqiang Zhang 1
Keyi Shen 1
Yiping Han 1
Ming Zhou 1
Xiangyu Wang 1
Oukhellou Latifa 1
Sashi Gurung 1
Chang Tan 1
Joan Serrà 1
Ranieri Baraglia 1
Bowei Chen 1
Jianfei Cai 1
Yang Yang 1
Bruce Elder 1
Chunyan Miao 1
Wenbin Chen 1
Fan Liu 1
Zhen Hai 1
Miaojing Shi 1
Paul McKevitt 1
Éric Beaudry 1
Marc Cavazza 1
Fred Charles 1
Elias Bareinboim 1
Hua Chen 1
Xiaohua Zhou 1
Jixue Liu 1
Kyle Feuz 1
Gem Stapleton 1
Beryl Plimmer 1
Chidansh Bhatt 1
Bernadette Bouchon-Meunier 1
Jie Yu 1
Guojun Qi 1
Yimin Zhang 1
Fusun Yaman 1
Zhenhui Li 1
Debprakash Patnaik 1
Sarvapali Ramchurn 1
Melinda Gervasio 1
Ari Jónsson 1
Sudhakar Reddy 1
Michael Iatauro 1
Ashish Garg 1
Lourenço Bandeira 1
Ricardo Ricardo 1
Tianyu Cao 1
Raju Balakrishnan 1
Rushi Bhatt 1
Leong U 1
Azin Ashkan 1
Jiangwen Sun 1
Anthony Dick 1
Parisa Rashidi 1
Joydeep Ghosh 1
John Yen 1
William Cushing 1
Philip Hendrix 1
Ling Huang 1
David Norton 1
Ameet Talwalkar 1
Jaewon Yang 1
Frank Dignum 1
Scott Gerard 1
Jie Zhang 1
Elisabetta Erriquez 1
Chris Burnett 1
Sae Schatz 1
Hedi Tabia 1
Takashi Ninomiya 1
Qing Li 1
Vien Tran 1
You Xu 1
Weixiong Zhang 1
Chingyung Lin 1
Claudio Schifanella 1
Josh Ying 1
Wenchih Peng 1
Nardine Osman 1
Daniel Sui 1
Zhihui Jin 1
Yang Gao 1
Giulia Bruno 1
Silvia Chiusano 1
VS Subrahmanian 1
Haodong Yang 1
Alfredo Milani 1
Yihsuan Yang 1
Ralph Ewerth 1
Lingfang Li 1
Hong Chang 1
Ashok Ramadass 1
Timothy Rogers 1
Bin Xu 1
Diane Cook 1
Marco Mamei 1
Stefano Spaccapietra 1
Achla Marathe 1
Masahiro Kimura 1
Olivia Buzek 1
Shimei Pan 1
Boi Faltings 1
Alina Huldtgren 1
Ingrid Heynderickx 1
Paweł Woźniak 1
Mohammad Obaid 1
Wenjun Zhou 1
Richong Zhang 1
Chihchung Chang 1
Dana Nau 1
Bernardo Huberman 1
Hongtai Li 1
Oded Maimon 1
Graham Pinhey 1
Hasan Cam 1
Kyumin Lee 1
Fabrizio Marozzo 1
Domenico Talia 1
Yexi Jiang 1
Rok Sosič 1
Tieke He 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
Juan Cao 1
Byron Gao 1
Licia Capra 1
Ouri Wolfson 1
Eoghan Furey 1
Aonghus Lawlor 1
Marjan Momtazpour 1
Jason Hong 1
Dan Lin 1
Zhenmin Tang 1
Stevende Jong 1
Franco Nardini 1
Yuesong Wang 1
Mingli Song 1
Jiajun Bu 1
Ah Tsoi 1
Matthew Kyan 1
Guoyu Sun 1
Paisarn Muneesawang 1
Yufei Wang 1
Nadia Figueroa 1
Tianzhu Zhang 1
Kuiyu Chang 1
Chao Xu 1
Paul Schermerhorn 1
Matthias Scheutz 1
Daniel Bryce 1
Michael Verdicchio 1
Abder Benaskeur 1
Na Shan 1
Alex Smola 1
Marina Demeshko 1
Hadrien Hours 1
Ernst Biersack 1
Patrick Loiseau 1
Saisai Ma 1
Yuchin Juan 1
Siddhartha Ghosh 1
Michela Milano 1
Carla Gomes 1
Ming Ji 1
Yintao Yu 1
Matthew Boyce 1
Michael Steinbach 1
Yang Mu 1
Tieyan Liu 1
Anísio Lacerda 1
Marco Ribeiro 1
Adriano Veloso 1
Hengshu Zhu 1
Ümit Çatalyürek 1
Jinbo Bi 1
Yu Wu 1
Stephen Armeli 1
Amos Azaria 1
Weiming Hu 1
Bin Chen 1
Thomas Hoens 1
Wenyuan Zhu 1
Waynexin Zhao 1
Bin Wu 1
Elisa Marengo 1
Bo Long 1
Lihong Li 1
Timothy Norman 1
Olivier Colot 1
Qun Jin 1
Huijing Zhao 1
Enrico Pontelli 1
Xiangfeng Luo 1
Lora Aroyo 1
Wangchien Lee 1
Alice Leung 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
Payam Barnaghi 1
Amit Sheth 1
Feng Wu 1
Miyoung Kim 1
José García-Macías 1
David Hayden 1
Markus Mühling 1
Yujin Zhang 1
Xianming Liu 1
Shiguang Shan 1
Myunghoon Suk 1
Shaohui Liu 1
Mary Pendleton Hoffer 1
Daniel Schuster 1
Benjamin Hung 1
Stephan Kolitz 1
Yakov Kronrod 1
Hossein Hajimirsadeghi 1
Aurélien Max 1
Anne Vilnat 1
Tobias Höllerer 1
Yi Zhang 1
Siegfried Handschuh 1
Jing Bai 1
Carolina Batista 1
Yuanzhuo Wang 1
Jiankang Deng 1
Dingqi Yang 1
Tie Luo 1
Guangming Guo 1
Luc Martens 1
Paolo Cremonesi 1
Yue Zhou 1
Tanzeem Choudhury 1
Jiawei Han 1
Guan Wang 1
Francisco Carrero 1
Wengkeen Wong 1
Huzaifa Zafar 1
Kenneth Whitebread 1
Linyun Fu 1
Scott DuVall 1
Quan Fang 1
Joemon Jose 1
Gianmario Motta 1
Zhenxing Wang 1
Zhenfeng Zhu 1
Yanhui Xiao 1
Márk Jelasity 1
Anne Robinson 1
Yizhou Wang 1
Chris Mellish 1
Rene Van Der Wal 1
Matthew Johnson 1
Joris Albeda 1
Tomasz Jaworski 1
Aristidis Pappaioannou 1
Michal Feldman 1
Wangchien Lee 1
Ankit Shah 1
Tao Li 1
Shazia Sadiq 1
Zheng Song 1
Jian Ma 1
Zhaohui Wu 1
Leye Wang 1
J Gibson 1
Chengkang Hsieh 1
John Jenkins 1
Zhengdong Lu 1
Michael O’Mahony 1
Claudio Cioffi-Revilla 1
Zhen Liao 1
Hongan Wang 1
Peter Prettenhofer 1
Hilal Khashan 1
Shiwan Zhao 1
Ingemar Cox 1
Ping Tan 1
Chiyin Chow 1
Houqiang Li 1
Meiyu Huang 1
Yu Zhu 1
Jonathan Doherty 1
Takashi Washio 1
Zhou Jin 1
Peter Rodgers 1
Paolo Cagnoli 1
Massimiliano Cattafi 1
Sahar Changuel 1
Nicolas Labroche 1
Yuan Zhou 1
Lei Wu 1
Jia Liu 1
Bart Peintner 1
Tomasz Stepinski 1
Onur Küçüktunç 1
Zhiguo Gong 1
Chunhua Shen 1
Ram Dantu 1
Gregory Cooper 1
Vincenzo D'Elia 1
Kurt Rothermel 1
Yicheng Chen 1
Matthijs Leeuwen 1
Jinpeng Wang 1
Di Fu 1
Dawn Song 1
Neilzhenqiang Gong 1
Matteo Baldoni 1
Yi Chang 1
Jeremiah Folsom-Kovarik 1
Zhengxiang Wang 1
Qi Guo 1
Shunxuan Wang 1
Fabian Abel 1
Wil Van Der Aalst 1
Argimiro Arratia 1
Tao Li 1
Haiyin Shen 1
Yi Wang 1
Zhenlong Sun 1
John Champaign 1
Jiming Liu 1
Tara Estlin 1
Pramod Anantharam 1
Chris Nugent 1
Xiaoping Chen 1
Osmar Zaïane 1
Eunju Kim 1
Steve Chien 1
Patricia Serrano-Alvarado 1
Bernd Freisleben 1
Ning Zhang 1
Lingyu Duan 1
Steffen Rendle 1
Zhixian Yan 1
Dipanjan Chakraborty 1
Zhengzheng Pan 1
Bill Dolan 1
Idan Szpektor 1
Philip Resnik 1
Benjamin Bederson 1
Brynjar Gretarsson 1
Shixia Liu 1
Huadong Ma 1
Wei Peng 1
Tong Sun 1
Weiwei Cui 1
Geoffrey Holmes 1
Yuhang Zhao 1
Pierre Rouille 1
Gerd Stumme 1
Bingqing Qu 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
Waitat Fu 1
Jiashi Feng 1
Teng Li 1
Theodoros Semertzidis 1
Martin Bockle 1
Yubin Kim 1
Jaime Teevan 1
Patrick De Boer 1

Affiliation Paper Counts
Florida Institute for Human & Machine Cognition 1
Fujitsu America, Inc. 1
Shandong University of Finance 1
Shandong Academy of Sciences 1
Laboratoire d'Informatique de Nantes-Atlantique 1
Liverpool Hope University 1
Qatar Foundation 1
Singapore Management University 1
Polytechnic School of 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 University 1
Naresuan University 1
University of Bari 1
Politecnico di Milano 1
Shanghai University 1
Soka University 1
Ecole Centrale Paris 1
National University of Defense Technology China 1
University of Fribourg 1
National Central University Taiwan 1
Dublin City University 1
Catholic University of Leuven 1
The University of North Carolina at Chapel Hill 1
University of Cincinnati 1
University of Udine 1
Institute of Intelligent Machines Chinese Academy of Sciences 1
Capital Medical University China 1
United States Military Academy 1
University of Quebec in Montreal 1
Berlin University of Applied Sciences 1
Fairleigh Dickinson University 1
Research Organization of Information and Systems National Institute of Informatics 1
University of Hawaii System 1
University of Southern California 1
European Space Agency - ESA 1
Institute of Applied Physics and Computational Mathematics 1
Columbia University 1
Texas State University-San Marcos 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
Korea Advanced Institute of Science & Technology 1
North Dakota State University 1
University of Electro-Communications 1
University of Roma La Sapienza 1
Pontifical Catholic University of Rio de Janeiro 1
University of Jyvaskyla 1
University of Chittagong 1
U.S. Army Research Laboratory 1
University of Seville 1
Mehran University of Engineering & Technology 1
University of Sousse 1
Nanjing University of Information Science and Technology 1
Know-Center, Graz 1
Institute for Cancer Research and Treatment, Candiolo 1
Reykjavik University 1
Macau University of Science and Technology 1
Google Switzerland GmbH 1
Intel Research Laboratories 1
Nanyang Technological University School of Computer Engineering 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
Joint Institute for Nuclear Research, Dubna 1
Instituto Superior Tecnico 1
University of Auckland 1
Bogazici University 1
University of Houston 1
University of Pennsylvania 1
University of Koblenz-Landau 1
Guangdong University of Technology 1
Northwestern University 1
Smithsonian National Museum of Natural History 1
Hebrew University of Jerusalem 1
Osaka Prefecture University 1
Duke University 1
Vrije Universiteit Amsterdam 1
Birkbeck University of London 1
Educational Testing Service 1
IBM Almaden Research Center 1
Wayne State University 1
Northeast Normal University China 1
Central European University 1
Harvard University 1
University of Arizona 1
Rissho University 1
The University of British Columbia 1
Dartmouth College 1
Hohai University 1
The University of Western Ontario 1
Citigroup 1
Lingnan University 1
King's College London 1
Center for Mathematics and Computer Science - Amsterdam 1
University of Messina 1
University of Shizuoka 1
Open University 1
Aoyama Gakuin University 1
United States National Science Foundation 1
Ionian University 1
University of Passau 1
Eastman Kodak Company 1
University of Saskatchewan 1
University of Washington 1
General Electric Company 1
New York State Museum 1
Beijing Institute of Technology 1
Academia Sinica Taiwan 1
Charles Stark Draper Lab Inc 1
University of Sussex 1
Defence Research and Development Canada 1
Nankai University 1
Washington State University Pullman 1
Office of Naval Research 1
Shenzhen University 2
University of Texas at Arlington 2
King Abdulaziz University 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
Hong Kong Polytechnic University 2
University of Massachusetts Dartmouth 2
Universite des Sciences et Technologies de Lille 2
University of Kent 2
Technical University of Berlin 2
University of Zurich 2
University of Antwerp 2
King Saud University 2
Telecom Bretagne 2
Aston University 2
Aristotle University of Thessaloniki 2
University of Southern California, Information Sciences Institute 2
Dalhousie University 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
Communication University of China 2
Ecole d' Ingenieurs Telecom Lille 1 2
University of Central Florida 2
Waseda University 2
Lancaster University 2
University of Massachusetts Boston 2
University of Ferrara 2
Sam Houston State University 2
Xidian University 2
University of Oxford 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
Universite de Rennes 1 2
University of California System 2
NEC Corporation 2
Universitat Pompeu Fabra 2
University of 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 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
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 University 3
University of Wyoming 3
University of Texas at Dallas 3
Washington State University 3
Free University of Bozen-Bolzano 3
Universidad Politecnica de Valencia 3
Changchun University of Technology 3
BBN Technologies 3
Philipps-Universitat Marburg 3
Beihang University 3
Georgia Tech Research Institute 3
University of Washington Seattle 3
University of Stuttgart 3
Kassel University 3
Wright State University 3
Simon Fraser University 3
CSIC - Instituto de Investigacion en Inteligencia Artificial 3
Xerox Corporation 3
Graz University of Technology 3
New York University 3
Massachusetts Institute of Technology 3
University of Macau 3
University of Connecticut 3
University of North Texas 3
Universite Paris-Sud XI 3
University of Utah 3
University of Konstanz 3
University of Teesside 3
Huazhong University of Science and Technology 3
Center For Research And Technology - Hellas 3
Stony Brook University 3
University of Szeged 3
University of Bologna 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
Intel Corp., China 3
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 3
Rutgers University-Newark Campus 4
New Mexico State University Las Cruces 4
Institute for Infocomm Research, A-Star, Singapore 4
Bar-Ilan University 4
Hefei University of Technology 4
Toyohashi University of Technology 4
Ohio State University 4
Northwestern Polytechnical University China 4
University of Waikato 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
University of Liverpool 4
Indiana University 4
West Virginia University 4
University of Notre Dame 4
University of Florence 4
University of Tehran 4
Swiss Federal Institute of Technology, Zurich 4
National University of Ireland, Maynooth 4
University of Melbourne 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
Eindhoven University of Technology 4
Nanjing University 4
Cornell Tech 4
Lockheed Martin 5
North Carolina State University 5
Google Inc. 5
University of California, Irvine 5
Soochow University 5
Istituto Di Scienze E Tecnologie Della Cognizione, Rome 5
City University of Hong Kong 5
University of Massachusetts Amherst 5
National Cheng Kung University 5
University of Pittsburgh 5
George Mason University 5
University College London 5
Beijing Jiaotong University 5
Osaka University 5
Missouri University of Science and Technology 5
Rensselaer Polytechnic Institute 5
Microsoft 5
TELECOM ParisTech 5
Max Planck Institute for Intelligent Systems 5
Shandong University 5
Yahoo Research Barcelona 5
Pennsylvania State University 6
Virginia Commonwealth 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
University of Alberta 6
Ryerson University 6
University of South Australia 6
Oregon State University 7
Institute of Automation Chinese Academy of Sciences 7
University of Ulster 7
Universidad Autonoma de Madrid 7
HP Labs 8
National Chiao Tung University Taiwan 8
Roma Tre University 8
Drexel University 8
University of Aberdeen 8
University of California, Berkeley 8
Virginia Tech 8
Swiss Federal Institute of Technology, Lausanne 8
Ghent University 8
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 University Weimar 9
University of Minnesota Twin Cities 9
Chinese University of Hong Kong 10
University of Turin 10
Beijing University of Posts and Telecommunications 10
Federal University of Minas Gerais 10
Nanjing University of Science and Technology 11
Georgia Institute of Technology 11
University of Technology Sydney 11
Nokia 11
Zhejiang University 12
Shanghai Jiaotong University 12
Carnegie Mellon University 12
Delft University of Technology 13
Polytechnic Institute of Turin 13
University of Tokyo 14
Tsinghua University 14
University of California, Los Angeles 14
Yahoo Research Labs 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
Arizona State University 16
Harbin Institute of Technology 17
Microsoft Research 18
University of Illinois at Urbana-Champaign 20
National Taiwan University 21
Microsoft Research Asia 21
Peking University 22
Jet Propulsion Laboratory 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) Archive


Volume 8 Issue 1, August 2016  Issue-in-Progress
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
All ACM Journals | See Full Journal Index