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)

Intelligent Process Adaptation in the SmartPM System

The increasing application of process-oriented approaches in new challenging dynamic domains beyond business computing (e.g., healthcare, emergency... (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
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.

A Distribution Separation Method Using Irrelevance Feedback Data for Information Retrieval

Relevance feedback is a widely used technique in Information Retrieval (IR), to build a refined query model based on a set of feedback documents. However, in practice (e.g., in pseudo relevance feedback where the top ranked documents returned by the system are assumed as relevant), the feedback document set is mixed by actually relevant and irrelevant documents. Therefore, the resultant query model (typically a term distribution) is often a mixture rather than a pure relevance term distribution, leading to a negative impact on the retrieval performance. To tackle this problem, a Distribution Separation Method (DSM) was recently proposed, which aims to approximate the true relevance distribution by separating a seed irrelevance distribution from the mixture one. While it achieved a promising performance in an empirical evaluation with simulated explicit irrelevance feedback data, it has not been deployed in the scenario where one should automatically obtain the irrelevance feedback data. In this article, we propose a substantial extension of the basic DSM from two perspectives: developing a further regularization framework and deploying DSM in the automatic irrelevance feedback scenario. Specifically, in order to better approximate the true relevance distribution, we propose a DSM regularization framework, which includes three algorithms, each corresponding to a regularization strategy. In addition, we exploit DSM in automatic (i.e., pseudo) irrelevance feedback, by automatically detecting the seed irrelevant documents via three different document re-ranking methods. We have carried out extensive experiments based on various TREC data sets, in order to systematically evaluate the proposed methods in the scenarios of both explicit and automatic relevance feedback. The experimental results demonstrate the effectiveness of our proposed approaches in comparison with various strong baselines.

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.

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.

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.

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.

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.

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.

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

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.

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

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

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.

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.

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.

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).

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.

Exploiting Social-Mobile Information for Location Visualization

With a smart phone at hand, it becomes easy now to snap pictures and publish them online with few lines of texts. The GPS coordinates and UGC (user generated content) data embedded in the shared photos provide opportunities to exploit important knowledge to tackle interesting tasks like geographically organizing photos and location visualization. In this work, we propose to organize photos both geographically and semantically, and investigate the problem of location visualization from multiple semantic themes. The novel visualization scheme provides a rich display landscape for geographical exploration from versatile views. A two-level solution is presented, where we first identify the highly photographed places of interests (POI) and discover their focused themes, and then aggregate the lower-level POI themes to generate the higher-level city themes for location visualization. We have conducted experiments on crawled Flickr and Instagram data and exhibited the visualization for the Singapore and Sydney cities. The experimental results have validated the proposed method and demonstrated the potentials of location visualization from multiple themes.

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.

CIM: Community-based Influence Maximization in Social Networks

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 430
Citation Count 4593
Available for Download 430
Downloads (6 weeks) 5835
Downloads (12 Months) 43031
Downloads (cumulative) 189344
Average downloads per article 440
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
Nicholasjing Yuan 5
Tatseng Chua 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
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
Francesco Bonchi 2
Xuning Tang 2
Katia Sycara 2
Rino Falcone 2
Jinshi Cui 2
Jia Zeng 2
Dana Nau 2
Shoude Lin 2
Jaegil Lee 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
Michael Lyu 2
Vito Ostuni 2
Yihsuan Yang 2
Jun Ma 2
Jiuyong Li 2
Yuichi Motai 2
Masaki Aono 2
Bingbing Ni 2
David Thompson 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
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
Payam Barnaghi 1
Amit Sheth 1
José García-Macías 1
Zhenfeng Zhu 1
Yanhui Xiao 1
Joemon Jose 1
Matthew Johnson 1
Márk Jelasity 1
Gianmario Motta 1
Yizhou Wang 1
Quan Fang 1
Anne Robinson 1
Rene Van Der Wal 1
Chris Mellish 1
Joris Albeda 1
Tomasz Jaworski 1
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Yujin Zhang 1
Xianming Liu 1
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Myunghoon Suk 1
Shaohui Liu 1
Mary Pendleton Hoffer 1
Daniel Schuster 1
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Stephan Kolitz 1
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Yi Zhang 1
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Anne Vilnat 1
Tobias Höllerer 1
Hossein Hajimirsadeghi 1
Hadi Moradi 1
Siegfried Handschuh 1
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Wangchien Lee 1
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Jian Ma 1
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Leye Wang 1
J Gibson 1
Chengkang Hsieh 1
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Valentina Sintsova 1
Andrea Marrella 1
Fan Liu 1
Cristina Muntean 1
Tatjen Cham 1
Perfecto Herrera-Boyer 1
Mi Tian 1
Qionghai Dai 1
Ke Lu 1
Scott Spurlock 1
Ioannis Refanidis 1
Balakrishnan Prabhakaran 1
Lijun Zhu 1
Franco Zambonelli 1
Natalie Fridman 1
Kazumi Saito 1
Xiaoxiao Lian 1
Nitin Madnani 1
Svetlin Bostandjiev 1
Majid Ahmadabadi 1
Lars Haug 1
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Faisal Alquaddoomi 1
Runhe Huang 1
Jitao Sang 1
Peter Briggs 1
Haifeng Wang 1
Quan Yuan 1
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Juan Recio-García 1
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Steven Burrows 1
David Newman 1
Padhraic Smyth 1
Kostas Kolomvatsos 1
Stathes Hadjiefthymiades 1
Albert Bifet 1
Weinan Zhang 1
Jintao Ye 1
Fabiano BeléM 1
Dihong Gong 1
Hweepink Tan 1
Domonkos Tikk 1
Marco Baroni 1
Steffen Becker 1
Fabrizio Marozzo 1
Domenico Talia 1
James Herbsleb 1
Yexi Jiang 1
Javid Ebrahimi 1
Graham Pinhey 1
Hasan Cam 1
Hongbo Ni 1
Rok Sosič 1
Tieke He 1
Damian Martínez-Muñoz 1
Chong Peng 1
Ingemar Cox 1
Alexander Schindler 1
Zhonggang Wu 1
Haikun Wei 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
Lei Wu 1
Sahar Changuel 1
Nicolas Labroche 1
Yuan Zhou 1
Jia Liu 1
Paolo Cagnoli 1
Massimiliano Cattafi 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
Benno Stein 1
Denis Helic 1
Roman Kern 1
Charles Parker 1
Ugur Kuter 1
Daniel Corkill 1
Daniel Tran 1
Robert Pappalardo 1
Vasant Dhar 1
Yuzhou Zhang 1
Edward Chang 1
Gilles Gasso 1
Bo Liu 1
Huibo Wang 1
Erik Edrosa 1
Guande Qi 1
Nithya Ramanathan 1
D George 1
Marek Lipczak 1
Vishvas Vasuki 1
Berkant Savas 1
Lei Tang 1
Yingying Jiang 1
Michele Gelfand 1
Sheng Li 1
Juan Rogers 1
Jingdong Wang 1
Evgeniy Gabrilovich 1
Guiguang Ding 1
Yushi Lin 1
Dietmar Jannach 1
Hitoshi Yamamoto 1
Ming Zhou 1
Ruiqiang Zhang 1
Keyi Shen 1
Yiping Han 1
Xiangyu Wang 1
Xiaohua Liu 1
Chang Tan 1
Oukhellou Latifa 1
Sashi Gurung 1
Anca Sailer 1
Ignacio Silva-Lepe 1
Brigitte Piniewski 1
Rajesh Ganesan 1
Zhaohong Deng 1
Hisao Ishibuchi 1
Shitong Wang 1
Yicheng Chen 1
Matthijs Leeuwen 1
Jinpeng Wang 1
Di Fu 1
Neilzhenqiang Gong 1
Dawn Song 1
Yi Chang 1
Matteo Baldoni 1
Jeremiah Folsom-Kovarik 1
Zhengxiang Wang 1
Shunxuan Wang 1
Qi Guo 1
Fabian Abel 1
Wil Van Der Aalst 1
Tao Li 1
Haiyin Shen 1
Zhenlong Sun 1
Argimiro Arratia 1
Yi Wang 1
John Champaign 1
Xiaoping Chen 1
Osmar Zaïane, 1
Pramod Anantharam 1
Eunju Kim 1
Chris Nugent 1
Patricia Serrano-Alvarado 1
Jiashi Feng 1
Waitat Fu 1
Teng Li 1
Theodoros Semertzidis 1
Martin Bockle 1
Yubin Kim 1
Jaime Teevan 1
Patrick De Boer 1
Boi Faltings 1
Alina Huldtgren 1
Ingrid Heynderickx 1
Paweł Woźniak 1
Mohammad Obaid 1
Jiming Liu 1
Tara Estlin 1
Steve Chien 1
Bernd Freisleben 1
Ning Zhang 1
Lingyu Duan 1
Steffen Rendle 1
Zhixian Yan 1
Dino Pedreschi 1
Valerio Grossi 1
Pedro Vera-Candeas 1
Sebastian SardiñA 1
Bowei Chen 1
Joan Serrà 1
Ranieri Baraglia 1
Jianfei Cai 1
Nicholas Sidiropoulos 1
Evangelos Papalexakis 1
Yang Yang 1
Bruce Elder 1
Fan Liu 1
Chunyan Miao 1
Wenbin Chen 1
Zhen Hai 1
Paul McKevitt 1
Marc Cavazza 1
Fred Charles 1
Éric Beaudry 1
Miaojing Shi 1
Elias Bareinboim 1
Xiaohua Zhou 1
Hua Chen 1
Jixue Liu 1
Gem Stapleton 1
Beryl Plimmer 1
Chidansh Bhatt 1
Bernadette Bouchon-Meunier 1
Kyle Feuz 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
Sudhakar Reddy 1
Michael Iatauro 1
Ari Jónsson 1
Ashish Garg 1
Lourenço Bandeira 1
Ricardo Ricardo 1
Tianyu Cao 1
Raju Balakrishnan 1
Azin Ashkan 1
Dipanjan Chakraborty 1
Zhengzheng Pan 1
Idan Szpektor 1
Bill Dolan 1
Philip Resnik 1
Benjamin Bederson 1
Shixia Liu 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
Toon De Pessemier 1
Michelle Zhou 1
Gerd Stumme 1
David Glass 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
Bernardo Huberman 1
Dana Nau 1
Hongtai Li 1
Kyumin Lee 1
Oded Maimon 1
Wangsheng Zhang 1
Brent Longstaff 1
Joshua Selsky 1
Atesmachew Hailegiorgis 1
Aris Anagnostopoulos 1
Yuchun Shen 1
Guillermo Jiménez-Díaz 1
Fatih Gedikli 1
Hongbin Zha 1
Leong U 1
Anthony Dick 1
Jiangwen Sun 1
Parisa Rashidi 1
Joydeep Ghosh 1
Philip Hendrix 1
William Cushing 1
John Yen 1
Ling Huang 1
Ameet Talwalkar 1
Jaewon Yang 1
David Norton 1
Frank Dignum 1
Jie Zhang 1
Elisabetta Erriquez 1
Chris Burnett 1
Hedi Tabia 1
Scott Gerard 1
Sae Schatz 1
Takashi Ninomiya 1
Qing Li 1
Vien Tran 1
You Xu 1
Weixiong Zhang 1
Chingyung Lin 1
Josh Ying 1
Wenchih Peng 1
Claudio Schifanella 1
Nardine Osman 1
Daniel Sui 1
Zhihui Jin 1
Yang Gao 1
Giulia Bruno 1
Silvia Chiusano 1
VS Subrahmanian 1
Yao Zhao 1
Lieve Macken 1
Haodong Yang 1
Arpad Berta 1
Shihwen Huang 1
Chen Luo 1
Mingxuan Yuan 1
Michael Strintzis 1
Kuanta Chen 1
Irwin King 1
Nirwan Sharma 1
Christina Katsimerou 1
Shengdong Zhao 1
Ya Zhang 1
Furu Wei 1
Marjan Momtazpour 1
Aonghus Lawlor 1
Jason Hong 1
Licia Capra 1
Ouri Wolfson 1
Eoghan Furey 1
Dan Lin 1
Juan Cao 1
Byron Gao 1
Tao Li 1
Ankit Shah 1
Tao Gu 1
Jiangbo Jia 1
Xingshe Zhou 1
Anna Monreale 1
Shazia Sadiq 1
Sergio Oramas 1
Jie Cheng 1
Massimo Mecella 1
Yuesong Wang 1
Zhenmin Tang 1
Franco Nardini 1
Stevende Jong 1
Mingli Song 1
Liping Xie 1
Jiajun Bu 1
Ah Tsoi 1
Matthew Kyan 1
Guoyu Sun 1
Paisarn Muneesawang 1
Yufei Wang 1
Tianzhu Zhang 1
Kuiyu Chang 1
Paul Schermerhorn 1
Matthias Scheutz 1
Alex Smola 1
Nadia Figueroa 1
Daniel Bryce 1
Michael Verdicchio 1
Abder Benaskeur 1
Na Shan 1
Chao Xu 1
Marina Demeshko 1
Hadrien Hours 1
Ernst Biersack 1
Patrick Loiseau 1
Alfredo Milani 1
Ralph Ewerth 1
Lingfang Li 1
Hong Chang 1
Ashok Ramadass 1
Timothy Rogers 1
Stefano Spaccapietra 1
Bin Xu 1
Diane Cook 1
Marco Mamei 1
Achla Marathe 1
Masahiro Kimura 1
Olivia Buzek 1
Shimei Pan 1
Luigi Di Caro 1
Chengbin Zeng 1
Huamin Qu 1
Ming Hao 1
Eibe Frank 1
Azhar Ibrahim 1
Ibrahim Venkat 1
Zechao Li 1
Jing Liu 1
Changxing Ding 1
Yantao Jia 1
Qiang Li 1
Xiaolong Jin 1
Michael Hardegger 1
Wanrong Jih 1
Chiachun Lian 1
Xiaoqinshelley Zhang 1
James Michaelis 1
James Hendler 1
Geoffrey Levine 1
Zhexuan Song 1
Lukas Mandrake 1
Kristina Lerman 1
Bin Li 1
Yong Ge 1
Maosong Sun 1
Aristides Gionis 1
Léon Bottou 1
Patrick Roos 1
Yudong Guang 1
Mohamed Bouguessa 1
Bo Zhang 1
Gang Pan 1
Hua Lu 1
Saisai Ma 1
Siddhartha Ghosh 1
Yuchin Juan 1
Carla Gomes 1
Michela Milano 1
Ming Ji 1
Yintao Yu 1
Matthew Boyce 1
Michael Steinbach 1
Yang Mu 1
Hengshu Zhu 1
Tieyan Liu 1
Marco Ribeiro 1
Anísio Lacerda 1
Adriano Veloso 1
Ümit Çatalyürek 1
Amos Azaria 1
Weiming Hu 1
Bin Chen 1
Jinbo Bi 1
Yu Wu 1
Stephen Armeli 1
Thomas Hoens 1
Wenyuan Zhu 1
Waynexin Zhao 1
Bin Wu 1
Bo Long 1
Lihong Li 1
Elisa Marengo 1
Timothy Norman 1
Olivier Colot 1
Qun Jin 1
Huijing Zhao 1
Xiangfeng Luo 1
Enrico Pontelli 1
Lora Aroyo 1
Wangchien Lee 1
Alice Leung 1
Chenghua Lin 1
Paola Mello 1
Ching Law 1
Paolo Garza 1
Marta Arias 1
Ramon Xuriguera 1
Janyl Jumadinova 1
Xing Xie 1
Miyoung Kim 1
Feng Wu 1
George Karypis 1
David Wilkie 1
Jinha Kang 1
Deborah Estrin 1
Bin Guo 1
Nagarajan Natarajan 1
Kevin Mcnally 1
Xufei Wang 1
Huan Liu 1
Barry Smyth 1
Hua Wu 1
Thuc Vu 1
Belén Díaz-Agudo 1
Ernesto De Luca 1
Wolfgang Nejdl 1
Fernando Diaz 1
Mohammad Bozchalui 1
Xingyu Gao 1
Yang Zhou 1
NhatHai Phan 1
Jialei Wang 1
Chen Cheng 1
Mirco Nanni 1
Jungeun Kim 1
Julio Carabias-Orti 1
François Pachet 1
Karl Tuyls 1
Yoshihide Sekimoto 1
Yiqiang Chen 1
Chao Sun 1
J Benton 1
Seth Flaxman 1
Furui Liu 1
Judea Pearl 1
Yuriy Pepyolyshev 1
Zhikun Wang 1
Lin Liu 1
Bingyu Sun 1
Réjean Plamondon 1
Aidan Delaney 1
Dhaval Patel 1
Mingbo Zhao 1
Jianke Zhu 1
Deng Cai 1
Xiaofeng Tong 1
Tao Wang 1
Jeremy Frank 1
Rushi Bhatt 1

Affiliation Paper Counts
Ryukoku University 1
University of Connecticut Health Center 1
University of Lausanne 1
Federal University of Amazonas 1
University of Macedonia 1
Demokritos National Centre for Scientific Research 1
University of Michigan 1
Anhui University 1
National Taitung University Taiwan 1
University of Sheffield 1
Ehime University 1
University of Haifa 1
University of Perugia 1
Joint Institute for Nuclear Research, Dubna 1
Instituto Superior Tecnico 1
University of Auckland 1
Bogazici University 1
University of Houston 1
University of Pennsylvania 1
University of Koblenz-Landau 1
Guangdong University of Technology 1
Northwestern University 1
Smithsonian National Museum of Natural History 1
Hebrew University of Jerusalem 1
Osaka Prefecture University 1
Duke University 1
Vrije Universiteit Amsterdam 1
Birkbeck University of London 1
Educational Testing Service 1
IBM Almaden Research Center 1
Wayne State University 1
Northeast Normal University China 1
Central European University 1
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 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
Vienna University of Technology 1
Washington State University Pullman 1
Office of Naval Research 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
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 Chicago 1
University of Southern California 1
European Space Agency - ESA 1
University of California, Riverside 1
Institute of Applied Physics and Computational Mathematics 1
Columbia University 1
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
North Dakota State University 1
University of Electro-Communications 1
Pontifical Catholic University of Rio de Janeiro 1
University of Jyvaskyla 1
University of Chittagong 1
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
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 China, Nanjing 2
University of Lugano 2
University of Missouri-Kansas City 2
University of Wolverhampton 2
University of Texas at El Paso 2
University of Brighton 2
Utrecht University 2
National University of Ireland, Galway 2
Hong Kong Polytechnic University 2
University of Massachusetts Dartmouth 2
Universite des Sciences et Technologies de Lille 2
University of Kent 2
RMIT University 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
Academia Sinica Taiwan 2
National Tsing Hua University 2
Xiamen University 2
Technical University of 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 University Linz 2
Queen Mary, University of London 2
University of Central Florida 2
Waseda University 2
Lancaster University 2
University of Massachusetts Boston 2
University of Ferrara 2
Sam Houston State University 2
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
Southern Illinois University at Carbondale 2
Universite de Rennes 1 2
University of California System 2
NEC Corporation 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
University of Oregon 3
Philipps-Universitat Marburg 3
Beihang University 3
Universidad de Jaen 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
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
University of 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
University of 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
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
Massachusetts Institute of Technology 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
Korea Advanced Institute of Science & Technology 4
Eindhoven University of Technology 4
Nanjing University 4
Cornell Tech 4
Institute for Infocomm Research, A-Star, Singapore 5
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
Max Planck Institute for Intelligent Systems 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
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
University of Bari 7
Universidad Autonoma de Madrid 7
HP Labs 8
National Chiao Tung University Taiwan 8
Roma Tre University 8
Drexel University 8
University of Aberdeen 8
University of California, Berkeley 8
Virginia Tech 8
Ghent University 8
Northwestern Polytechnical University China 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 University Weimar 9
University of Turin 10
Beijing University of Posts and Telecommunications 10
Swiss Federal Institute of Technology, Lausanne 10
University of Minnesota Twin Cities 10
Federal University of Minas Gerais 10
Nanjing University of Science and Technology 11
Georgia Institute of Technology 11
Nokia 11
Zhejiang University 12
University of Technology Sydney 12
Shanghai Jiaotong University 12
Delft University of Technology 13
Polytechnic Institute of Turin 13
Chinese University of Hong Kong 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
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 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 2, November 2016  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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