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SNAP

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)

Measuring Similarity Similarly

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 article... (more)

CSM

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)

SPrank

In most real-world scenarios, the ultimate goal of recommender system applications is to suggest a short ranked list of items, namely top-N recommendations, that will appeal to the end user. Often, the problem of computing top-N recommendations is mainly tackled with a two-step approach. The system focuses first on predicting the unknown ratings,... (more)

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their... (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)

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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
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.

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.

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.

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.

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.

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

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.

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.

Bibliometrics

Publication Years 2010-2016
Publication Count 415
Citation Count 4422
Available for Download 415
Downloads (6 weeks) 4548
Downloads (12 Months) 45028
Downloads (cumulative) 180373
Average downloads per article 435
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)
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 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)
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
Yue Shi 2
Alan Hanjalic 2
Charles Ling 2
Daqing Zhang 2
Mohan Kankanhalli 2
Zhengjun Zha 2
Yue Gao 2
Yuval Elovici 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
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
Venkatramanan Subrahmanian 2
Maria Sapino 2
Guirong Xue 2
Iván Cantador 2
Ido Guy 2
Bohao Chen 2
Yixin Chen 2
Fuzheng Zhang 2
Nathan Eagle 2
Manish Marwah 2
Hanqing Lu 2
Tao Mei 2
Pablo Castells 2
Meir Kalech 2
Daxin Jiang 2
Xuning Tang 2
Francesco Bonchi 2
Katia Sycara 2
Rino Falcone 2
Jinshi Cui 2
Jia Zeng 2
Dana Nau 2
Shoude Lin 2
Ling Guan 2
Laiwan Chan 2
Michael Fire 2
Neil Yorke-Smith 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
Zhi Geng 2
Kun Zhang 2
Bernhard Schölkopf 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
Daqing Zhang 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
Claudio Biancalana 2
Giuseppe Sansonetti 2
Anlei Dong 2
Robin Cohen 2
Luca Cagliero 2
Hao Wang 1
Norbert Schorghofer 1
Hao Wang 1
Markus Endler 1
Alberto Rosi 1
Weihong Qian 1
John O’Donovan 1
Xueying Li 1
Shriram Revankar 1
VinhTuan Thai 1
Lixin Shi 1
Ke Zhou 1
K Subramanian 1
Ahamad Khader 1
Dingquan Wang 1
Xueming Wang 1
Xueqi Cheng 1
Lirong Xia 1
Alberto Calatroni 1
Jennifer Moody 1
Jamie Ward 1
Hans Gellersen 1
Danny Wyatt 1
James Kitts 1
Bing Liu 1
Quanquan Gu 1
Fabian Loose 1
Paolo Rosso 1
Tad Hogg 1
Darren Appling 1
Elizabeth Whitaker 1
Deborah McGuinness 1
Antons Rebguns 1
Gerald DeJong 1
Reid MacTavish 1
Jinhong Guo 1
Sergej Sizov 1
Nello Cristianini 1
Bin Cheng 1
Anusua Trivedi 1
Piyush Rai 1
Véronique Hoste 1
István Hegedűs 1
Levente Kocsis 1
András Benczúr 1
Bo Xin 1
Hao Yan 1
Disneyyan Lam 1
Dario Antonelli 1
Krishnaprasad Thirunarayan 1
Md Ullah 1
Jun Du 1
Adnan Ansar 1
Xiaojin Zhu 1
Melissa Bunte 1
Lele Chang 1
Shanchiehjay Yang 1
John Salerno 1
Andrea Apolloni 1
Asuman Ozdaglar 1
Gal Kaminka 1
Herbert Gintis 1
Shiqi Zhao 1
Mirella Lapata 1
Alexander Quinn 1
Weijia Cai 1
Yangqiu Song 1
Babak Araabi 1
Peipei Li 1
Wenbin Zhang 1
Christian Rohrdantz 1
Umeshwar Dayal 1
Daniel Keim 1
Xiaoyan Li 1
Zhaoyan Ming 1
Sajal Das 1
Stephan Doerfel 1
Xinlong Bao 1
Jane Hsu 1
Paul Lukowicz 1
Jeff Bilmes 1
Amac Herdagdelen 1
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Matthai Philipose 1
Katayoun Farrahi 1
Zhijun Yin 1
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Chengkai Li 1
Cong Yu 1
Christian KöRner 1
José Troyano 1
Li Ding 1
Prasad Tadepalli 1
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Alvaro Rosero 1
Tianyi Ma 1
Kristen Venable 1
Linlin You 1
Yueying He 1
Petros Daras 1
Benjamin Lok 1
Omar Alonso 1
Praveen Paritosh 1
Andrzej Romanowski 1
Morten Fjeld 1
Hao Ma 1
Carlos Castillo 1
Chunnan Hsu 1
Justin Ma 1
Geoffrey Voelker 1
Moshe Tennenholtz 1
Eran Toch 1
Rebecca Goolsby 1
Jilin Chen 1
Yun Lu 1
Chang Liu 1
Naphtali Rishe 1
Asmaa Elbadrawy 1
S Nolen 1
Guozhong Dai 1
Elizabeth Salmon 1
Xiatian Zhang 1
Rongyao Fu 1
Lara Quijano-Sánchez 1
Yoav Shoham 1
Chunping Li 1
Shlomo Berkovsky 1
Paul Cook 1
Timothy Baldwin 1
Hongyuan Zha 1
Xiao Gu 1
Patrick Butler 1
Liqiang Nie 1
Helmut Prendinger 1
Mitsuru Ishizuka 1
Jiaching Ying 1
Clemens Drews 1
Yicheng Song 1
Come Etienne 1
Xiang Wu 1
Derek Green 1
Diana Spears 1
Santiago Ontañón 1
Jainarayan Radhakrishnan 1
Ashwin Ram 1
Damhnait Gleeson 1
Umaa Rebbapragada 1
Erel Uziel 1
Shikui Wei 1
Mike Thelwall 1
Christopher Lambin 1
Senzhang Wang 1
Zhoujun Li 1
Abraham Bernstein 1
Radu Jurca 1
David Huynh 1
Jacek Kucharski 1
ShihHsien Tai 1
Siming Li 1
Stefan Savage 1
Ron Hirschprung 1
Lotfi Romdhane 1
Yicheng Chen 1
J Carr 1
Huan Liu 1
Mingjin Zhang 1
Jiunlong Huang 1
Shijian Li 1
Z Khalapyan 1
Cameron Ketcham 1
Haoyi Xiong 1
Maurice Coyle 1
Feng Tian 1
Xiaolong Zhang 1
Zhanyi Liu 1
Wentao Zheng 1
Andrei Broder 1
Chengbo Zhang 1
Luheng He 1
Min Zhao 1
Isamu Okada 1
Enrique Chavarriaga 1
Bo Han 1
Shaodian Zhang 1
Kathleen Carley 1
Hai Yang 1
Eric Lu 1
Valentina Sintsova 1
Paolo Trunfio 1
Wenkui Ding 1
Shaojie Zhuo 1
Daniel Hennes 1
Ling Zhong 1
You Yang 1
Richard Souvenir 1
Abdulmotaleb Saddik 1
Gao Cong 1
Marcello Cirillo 1
Lars Karlsson 1
Jianhua Guo 1
Kevin Curran 1
Froduald Kabanza 1
Amy Fire 1
Xiaogang Dong 1
Jiji Zhang 1
Peng Ding 1
Thucduy Le 1
Nicholas Jennings 1
Weisheng Chin 1
Yong Zhuang 1
Zhao Zhang 1
Chenglin Liu 1
Rong Jin 1
Andreas Krause 1
Jameson Toole 1
Perukrishnen Vytelingum 1
Nicholas Jennings 1
Pauline Berry 1
Mitchell Ai-Chang 1
Juan Castilla-Rubio 1
Wei Ding 1
Wei Chen 1
RubéN Lara 1
Dell Zhang 1
Erik Saule 1
Edleno Moura 1
Howard Tennen 1
Aaron Steele 1
Sukjin Lee 1
Saranya Krishnamoorthy 1
Yubin Park 1
Pasquale Lops 1
Christopher Yang 1
Simon Pool 1
Gavin McArdle 1
Qi Liu 1
Hsunping Hsieh 1
Chengte Li 1
Dejing Dou 1
Zhu Wang 1
Zhenyu Chen 1
Bin Luo 1
Loris Belcastro 1
Juanzi Li 1
Emil Stefanov 1
Elaine Shi 1
Dan Ventura 1
Amit Chopra 1
Munindar Singh 1
Matteo Venanzi 1
Mohamed Daoudi 1
Liangtien Chia 1
Timothy Shih 1
Yo Ehara 1
Khoi Nguyen 1
Feiyue Wang 1
Pakkin Wong 1
Ruoyun Huang 1
Zhihua Zhou 1
Emilio Ferrara 1
Geert Houben 1
Neil Rubens 1
Thomas Porta 1
Myungcheol Doo 1
Ling Liu 1
Wei Gao 1
François Poulet 1
Federico Chesani 1
Luigi Grimaudo 1
Prithviraj Dasgupta 1
Jianhui Ye 1
Hiroyuki Yoshida 1
Anshul Sawant 1
MohammadTaghi Hajiaghayi 1
Valeria Soto-Mendoza 1
Jesús Favela 1
Maythe Rojas 1
Pengfei Xu 1
Xiaoshuai Sun 1
Shengping Zhang 1
Julian Panetta 1
Karl Aberer 1
Daqing Zhang 1
Lingyin Wei 1
Thomas Springer 1
Hiroshi Motoda 1
Haifeng Wang 1
Bonnie Dorr 1
Taesun Moon 1
Houda Bouamor 1
Arthur Asuncion 1
Kasim Candan 1
Xuegang Hu 1
Christos Anagnostopoulos 1
Hong Zhou 1
Steven Skiena 1
Bernhard Pfahringer 1
Jie Tang 1
Philippe De Wilde 1
Rodrygo Santos 1
Jiaul Paik 1
Zhifeng Li 1
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Qi Liu 1
Daniel Gatica-Perez 1
Markus Strohmaier 1
Dominik Benz 1
Elad Yom-Tov 1
Haofen Wang 1
Ethan Trewhitt 1
Chongjie Zhang 1
Phillip DiBona 1
Martin Hofmann 1
Yunchao Wei 1
Guangchan Liu 1
Mohammad Hossain 1
Ghulam Muhammad 1
Sarah Schulz 1
Huaming Rao 1
Robert Morris 1
Advaith Siddharthan 1
Richard Comont 1
Xiaoming Zhang 1
Elaine O'mahony 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
Marina Spivak 1
Lawrence Saul 1
Evangelos Milios 1
Inderjit Dhillon 1
Hang Li 1
Vanja Josifovski 1
Lance Riedel 1
Weishi Zhang 1
Yi Chang 1
Li Song 1
Yiliang Zhao 1
Wei Jiang 1
Ali Hurson 1
David Kil 1
Tianben Wang 1
Roberto Trasarti 1
Pearl Pu 1
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Kupsze Choi 1
Hongzhi Yin 1
William Groves 1
Fabrizio Silvestri 1
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Frederic Font 1
Fanchieh Cheng 1
Tao Guan 1
Liya Duan 1
Meng Wang 1
Chongyu Chen 1
Jiadong Zhang 1
Haiyan Li 1
Nan Dong 1
Guodong Guo 1
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Kartik Talamadupula 1
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Daniel Neill 1
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Hoda Sepehri Rad 1
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Andrew Sung 1
Mengyu Qiao 1
Jiebo Luo 1
Karen Haigh 1
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Jinfeng Zhuang 1
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Anton Hengel 1
Brandon Gozick 1
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Deendayal Dinakarpandian 1
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Klaus Herrmann 1
Rong Yan 1
Jiang Bian 1
Nicoletta Fornara 1
Michael Wooldridge 1
Cristiano Castelfranchi 1
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Yiqun Hu 1
Liu Wenyin 1
Ofrit Lesser 1
Luigi Caro 1
Jiawei Han 1
John Debenham 1
Jiqing Han 1
Tieran Zheng 1
Liubin Wang 1
Dingkun Ma 1
Alen Docef 1
Aijun Bai 1
Randy Goebel 1
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Mark Beattie 1
Ana Martínez-García 1
Md Seddiqui 1
Alice Chan 1
Benjamin Bornstein 1
Michele Judd 1
Homer Chen 1
Xilin Chen 1
Xin Sun 1
Pietro Pala 1
Yohan Jin 1
Junming Xu 1
Christine Parent 1
Pavel Serdyukov 1
Kouzou Ohara 1
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Chang Hu 1
Yuval Marton 1
Katrin Erk 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
Hweepink Tan 1
Jintao Ye 1
Domonkos Tikk 1
Marco Baroni 1
Benno Stein 1
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Roman Kern 1
Charles Parker 1
Ugur Kuter 1
Daniel Corkill 1
Daniel Tran 1
Robert Pappalardo 1
Guy De Pauw 1
Orphée De Clercq 1
Walter Daelemans 1
Lingjing Hu 1
Yashar Moshfeghi 1
Goran Radanovic 1
Peng Dai 1
Vasant Dhar 1
Chen Chen 1
Yuzhou Zhang 1
Edward Chang 1
Gilles Gasso 1
Bo Liu 1
Huibo Wang 1
Erik Edrosa 1
Guande Qi 1
Ping Tan 1
Ingemar Cox 1
Chiyin Chow 1
Houqiang Li 1
Meiyu Huang 1
Yu Zhu 1
Jonathan Doherty 1
Zhou Jin 1
Takashi Washio 1
Peter Rodgers 1
Sahar Changuel 1
Nicolas Labroche 1
Massimiliano Cattafi 1
Yuan Zhou 1
Lei Wu 1
Jia Liu 1
Paolo Cagnoli 1
Bart Peintner 1
Tomasz Stepinski 1
Onur Küçüktunç 1
Zhiguo Gong 1
Chunhua Shen 1
Ram Dantu 1
Vincenzo D'Elia 1
Gregory Cooper 1
Kurt Rothermel 1
Yicheng Chen 1
Jinpeng Wang 1
Di Fu 1
Matthijs Leeuwen 1
Neilzhenqiang Gong 1
Dawn Song 1
Nithya Ramanathan 1
D George 1
Berkant Savas 1
Marek Lipczak 1
Vishvas Vasuki 1
Lei Tang 1
Juan Rogers 1
Yingying Jiang 1
Michele Gelfand 1
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Sheng Li 1
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Dietmar Jannach 1
Hitoshi Yamamoto 1
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Ming Zhou 1
Ruiqiang Zhang 1
Keyi Shen 1
Yiping Han 1
Xiangyu Wang 1
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Changshing Perng 1
Sushil Jajodia 1
Jing Lv 1
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Ranieri Baraglia 1
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Jianfei Cai 1
Yang Yang 1
Bruce Elder 1
Chunyan Miao 1
Wenbin Chen 1
Fan Liu 1
Zhen Hai 1
Paul McKevitt 1
Marc Cavazza 1
Fred Charles 1
Éric Beaudry 1
Miaojing Shi 1
Hua Chen 1
Xiaohua Zhou 1
Jixue Liu 1
Elias Bareinboim 1
Yi Chang 1
Matteo Baldoni 1
Zhengxiang Wang 1
Jeremiah Folsom-Kovarik 1
Shunxuan Wang 1
Qi Guo 1
Fabian Abel 1
Wil Van Der Aalst 1
Argimiro Arratia 1
Yi Wang 1
Zhenlong Sun 1
Tao Li 1
Haiyin Shen 1
John Champaign 1
Xiaoping Chen 1
Pramod Anantharam 1
Osmar Zaïane 1
Eunju Kim 1
Chris Nugent 1
Patricia Serrano-Alvarado 1
Steve Chien 1
Jiming Liu 1
Tara Estlin 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
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
Gerd Stumme 1
David Glass 1
Toon De Pessemier 1
Bernadette Bouchon-Meunier 1
Kyle Feuz 1
Chidansh Bhatt 1
Gem Stapleton 1
Beryl Plimmer 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
Ashish Garg 1
Ari Jónsson 1
Lourenço Bandeira 1
Ricardo Ricardo 1
Tianyu Cao 1
Raju Balakrishnan 1
Rushi Bhatt 1
Azin Ashkan 1
Leong U 1
Anthony Dick 1
Jiangwen Sun 1
Parisa Rashidi 1
Joydeep Ghosh 1
William Cushing 1
Philip Hendrix 1
John Yen 1
Ameet Talwalkar 1
Ling Huang 1
David Norton 1
Jie Zhang 1
Jaewon Yang 1
Frank Dignum 1
Elisabetta Erriquez 1
Chris Burnett 1
Scott Gerard 1
Hedi Tabia 1
Sae Schatz 1
Takashi Ninomiya 1
Vien Tran 1
You Xu 1
Weixiong Zhang 1
Chingyung Lin 1
Michelle Zhou 1
Liangliang Cao 1
José Cortizo 1
Yong Yu 1
Janardhan Doppa 1
Bhavesh Shrestha 1
Victor Lesser 1
Daniel McFarlane 1
Yosi Mass 1
Hal Daumé 1
Jiashi Feng 1
Teng Li 1
Waitat Fu 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
Mohammad Obaid 1
Paweł Woźniak 1
Richong Zhang 1
Wenjun Zhou 1
Chihchung Chang 1
Oded Maimon 1
Dana Nau 1
Bernardo Huberman 1
Kyumin Lee 1
Hongtai Li 1
Wangsheng Zhang 1
Brent Longstaff 1
Joshua Selsky 1
Atesmachew Hailegiorgis 1
Aris Anagnostopoulos 1
Guillermo Jiménez-Díaz 1
Yuchun Shen 1
Fatih Gedikli 1
Hongbin Zha 1
Furu Wei 1
Ya Zhang 1
Marjan Momtazpour 1
Jason Hong 1
Licia Capra 1
Ouri Wolfson 1
Claudio Schifanella 1
Wenchih Peng 1
Nardine Osman 1
Qing Li 1
Josh Ying 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
Stefano Spaccapietra 1
Bin Xu 1
Diane Cook 1
Achla Marathe 1
Marco Mamei 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
Changxing Ding 1
Zechao Li 1
Jing Liu 1
Qiang Li 1
Yantao Jia 1
Xiaolong Jin 1
Michael Hardegger 1
Chiachun Lian 1
Wanrong Jih 1
Kristina Lerman 1
Xiaoqinshelley Zhang 1
James Michaelis 1
James Hendler 1
Geoffrey Levine 1
Juan Cao 1
Eoghan Furey 1
Aonghus Lawlor 1
Dan Lin 1
Yexi Jiang 1
Byron Gao 1
Javid Ebrahimi 1
Graham Pinhey 1
Hasan Cam 1
Hongbo Ni 1
Rok Sosič 1
Tieke He 1
Fabrizio Marozzo 1
Domenico Talia 1
Zhenmin Tang 1
Franco Nardini 1
Mingli Song 1
Jiajun Bu 1
Ah Tsoi 1
Stevende Jong 1
Yuesong Wang 1
Matthew Kyan 1
Guoyu Sun 1
Paisarn Muneesawang 1
Yufei Wang 1
Tianzhu Zhang 1
Nadia Figueroa 1
Kuiyu Chang 1
Paul Schermerhorn 1
Matthias Scheutz 1
Daniel Bryce 1
Michael Verdicchio 1
Abder Benaskeur 1
Alex Smola 1
Na Shan 1
Chao Xu 1
Hadrien Hours 1
Ernst Biersack 1
Patrick Loiseau 1
Saisai Ma 1
Marina Demeshko 1
Yoshinobu Kawahara 1
Siddhartha Ghosh 1
Yuchin Juan 1
Michela Milano 1
Carla Gomes 1
Ming Ji 1
Yintao Yu 1
Zhexuan Song 1
Lukas Mandrake 1
Yao Zhao 1
Lieve Macken 1
Arpad Berta 1
Chen Luo 1
Mingxuan Yuan 1
Shihwen Huang 1
Nirwan Sharma 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
Mohamed Bouguessa 1
Patrick Roos 1
Yudong Guang 1
George Karypis 1
Bo Zhang 1
Hua Lu 1
Deborah Estrin 1
Gang Pan 1
Jinha Kang 1
Bin Guo 1
David Wilkie 1
Kevin Mcnally 1
Barry Smyth 1
Nagarajan Natarajan 1
Xufei Wang 1
Huan Liu 1
Hua Wu 1
Thuc Vu 1
Belén Díaz-Agudo 1
Ernesto De Luca 1
Fernando Diaz 1
Wolfgang Nejdl 1
Mohammad Bozchalui 1
Xingyu Gao 1
Anca Sailer 1
Ignacio Silva-Lepe 1
Brigitte Piniewski 1
Matthew Boyce 1
Michael Steinbach 1
Yang Mu 1
Hengshu Zhu 1
Tieyan Liu 1
Ümit Çatalyürek 1
Marco Ribeiro 1
Anísio Lacerda 1
Adriano Veloso 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
Lihong Li 1
Bo Long 1
Timothy Norman 1
Elisa Marengo 1
Olivier Colot 1
Qun Jin 1
Huijing Zhao 1
Enrico Pontelli 1
Lora Aroyo 1
Alice Leung 1
Chenghua Lin 1
Paola Mello 1
Xiangfeng Luo 1
Wangchien Lee 1
Marta Arias 1
Ramon Xuriguera 1
Janyl Jumadinova 1
Ching Law 1
Xing Xie 1
Paolo Garza 1
Feng Wu 1
Payam Barnaghi 1
Amit Sheth 1
Miyoung Kim 1
José García-Macías 1
David Hayden 1
Markus Mühling 1
Yujin Zhang 1
Rajesh Ganesan 1
Valerio Grossi 1
Dino Pedreschi 1
Zhaohong Deng 1
Hisao Ishibuchi 1
Shitong Wang 1
Karl Tuyls 1
Yiqiang Chen 1
J Benton 1
Furui Liu 1
Chao Sun 1
Seth Flaxman 1
Zhikun Wang 1
Lin Liu 1
Bingyu Sun 1
Yuriy Pepyolyshev 1
Aidan Delaney 1
Dhaval Patel 1
Judea Pearl 1
Réjean Plamondon 1
Jianke Zhu 1
Deng Cai 1
Mingbo Zhao 1
Xiaofeng Tong 1
Tao Wang 1
Jeremy Frank 1
Olivier Chapelle 1
Eren Manavoglu 1
Zhongxue Chen 1
Chao Chen 1
Meiling Shyu 1
Hang Li 1
Chandan Reddy 1
Davide Susta 1
Jian Su 1
Hamed Valizadegan 1
Federica Cena 1
Suhyin Lee 1
Xiaoming Li 1
Nan Li 1
Xiangnan Kong 1
Qi He 1
Haizheng Zhang 1
Lester Mackey 1
Marco Colombetti 1
Pınar Yolum 1
Wiebe Hoek 1
Michele Piunti 1
Xianming Liu 1
Shiguang Shan 1
Shaohui Liu 1
Myunghoon Suk 1
Mary Pendleton Hoffer 1
Daniel Schuster 1
Benjamin Hung 1
Stephan Kolitz 1
Yakov Kronrod 1
Yi Zhang 1
Aurélien Max 1
Anne Vilnat 1
Tobias Höllerer 1
Hadi Moradi 1
Hossein Hajimirsadeghi 1
Siegfried Handschuh 1
Jing Bai 1
Carolina Batista 1
Jiankang Deng 1
Yuanzhuo Wang 1
Tie Luo 1
Dingqi Yang 1
Guangming Guo 1
Luc Martens 1
Paolo Cremonesi 1
Yue Zhou 1
Tanzeem Choudhury 1
Guan Wang 1
Jiawei Han 1
Francisco Carrero 1
Linyun Fu 1
Wengkeen Wong 1
Huzaifa Zafar 1
Kenneth Whitebread 1
Zhenxing Wang 1
Scott DuVall 1
Zhenfeng Zhu 1
Yanhui Xiao 1
Márk Jelasity 1
Quan Fang 1
Yizhou Wang 1
Joemon Jose 1
Gianmario Motta 1
Anne Robinson 1
Matthew Johnson 1
Chris Mellish 1
Rene Van Der Wal 1
Cristina Conati 1
Qiang Lu 1
Jyhren Shieh 1
Pasquale De Meo 1
Kamfai Wong 1
Juan Cruz 1
Cécile Bothorel 1
Carles Sierra 1
Fabrizio Maggi 1
Lifeng Wang 1
Yanfang Ye 1
Edgar Chávez 1
Clement Leung 1
Yuanxi Li 1
David Thompson 1
Qingming Huang 1
Dityan Yeung 1
Balakrishnan Prabhakaran 1
Lijun Zhu 1
Franco Zambonelli 1
Natalie Fridman 1
Kazumi Saito 1
Nitin Madnani 1
Xiaoxiao Lian 1
Svetlin Bostandjiev 1
Majid Ahmadabadi 1
Lars Haug 1
Jussara Almeida 1
Marcos Gonçalves 1
Tongliang Liu 1
Jinhui Tang 1
David Ben Shimon 1
Guy Shani 1
Bracha Shapira 1
Daniel Roggen 1
Robert Jäschke 1
Le Wu 1
Simon Dooms 1
Thomas Huang 1
Wei Jin 1
Hala Mostafa 1
Steve Chien 1
Vasileios Lampos 1
Georgios Paltoglou 1
Bart Desmet 1
Yoshinobu Kawahara 1
Joris Albeda 1
Tomasz Jaworski 1
Aristidis Pappaioannou 1
Michal Feldman 1
Wangchien Lee 1
Zheng Song 1
Jian Ma 1
Zhaohui Wu 1
Chengkang Hsieh 1
John Jenkins 1
Leye Wang 1
J Gibson 1
Michael O’Mahony 1
Zhengdong Lu 1
Zhen Liao 1
Claudio Cioffi-Revilla 1
Hongan Wang 1
Hilal Khashan 1
Peter Prettenhofer 1
Shiwan Zhao 1
Fernando Díez 1
Yoshiyuki Inagaki 1
Alena Neviarouskaya 1
Wenning Kuo 1
Alexei Pozdnoukhov 1
Jintao Li 1
Tao Li 1
Jiankai Sun 1
Ankit Shah 1
Tao Gu 1
Jiangbo Jia 1
Xingshe Zhou 1
Anna Monreale 1
Shazia Sadiq 1
Fan Liu 1
Cristina Muntean 1
Tatjen Cham 1
Ke Lu 1
Qionghai Dai 1
Scott Spurlock 1
Ioannis Refanidis 1
Xinghai Sun 1
Stephen Roberts 1
Denilson Barbosa 1
Wei Liu 1
Richang Hong 1
Marco Gavanelli 1
Kaixu Liu 1
James Lindsey 1
Michalis Lazaridis 1
Isabel Micheel 1
Kevyn Collins-Thompson 1
Shuguang Han 1
Judith Redi 1
Ramendra Sahoo 1
Alejandro Jaimes 1
Fang Wu 1
William Bainbridge 1
Chiharold Liu 1
Wendong Wang 1
Hongsuda Tangmunarunkit 1
J Ooms 1
Faisal Alquaddoomi 1
Jie Zhu 1
Runhe Huang 1
Jitao Sang 1
Peter Briggs 1
Haifeng Wang 1
Quan Yuan 1
Juan Recio-García 1
Yuchih Chen 1
Nathannan Liu 1
Pranam Kolari 1
Yan Liu 1
Jianmin Wu 1
Xiaokang Yang 1
Kenneth Joseph 1
Rui Zhang 1
Yang Zhou 1
NhatHai Phan 1
Mirco Nanni 1
Xiubo Geng 1
Xudong Zhang 1
Yan Song 1
Maria Gini 1
Yongsheng Dong 1
Xavier Serra 1
Yong Rui 1
Jun Wang 1
Jianmin Zheng 1
Guangming Shi 1
Ao Tang 1
Jie Huang 1
Yi Zhen 1
Wen Ji 1
Carlos Guestrin 1
Steven Klooster 1
Elif Kürklü 1
Youxi Wu 1
Kamer Kaya 1
Lin Lin 1
Panagiotis Adamopoulos 1
Alexander Tuzhilin 1
Zinovi Rabinovich 1
Claudia Goldman 1
Kalyan Subbu 1
Riccardo Molinari 1
Cristopher Yang 1
Iyad Batal 1
Amip Shah 1
Naren Ramakrishnan 1
Chuan Shi 1
Eui Shin 1
Derrall Heath 1
Frank Dignum 1
Munindar Singh 1
Amit Chopra 1
Cristina Baroglio 1
Neil Yen 1
Nobuyuki Shimizu 1
Hiroshi Nakagawa 1
Tianshi Chen 1
Lena Tenenboim-Chekina 1
Rami Puzis 1
Mario Cataldi 1
Mehdi Elahi 1
Juan Pane 1
Ziqiang Shi 1
Alessandro Fiori 1
Hao Fu 1
Aston Zhang 1
Xuemin Zhao 1
Naeem Mahoto 1
Sumi Helal 1
Daniel Gaines 1
Robert Anderson 1
Michael Burl 1
Yantao Zheng 1
Deming Zhai 1
Stefano Berretti 1
Ronald Greeley 1
Shanshan Huang 1
Peizhe Cheng 1
Subbarao Kambhampati 1
Peng Luo 1
Seungchan Kim 1
Julie Porteous 1
Songchun Zhu 1
Pingfeng Xu 1
George Baciu 1
Einat Minkov 1
Areej Malibari 1
Luis Leiva 1
Daniel Martín-Albo 1
Xin Jin 1
Nenghai Yu 1
Yuanlong Shao 1
Bolin Ding 1
Varun Mithal 1
Vipin Kumar 1
Kuifei Yu 1
Tao Qin 1
Jun Wang 1
Yiyang Yang 1
Charles Clarke 1
Nívio Ziviani 1
Marina Blanton 1
Shumei Sun 1
Silvia Chiusano 1
Miloš Hauskrecht 1
Atif Khan 1
Julita Vassileva 1
Antonina Dattolo 1
Yulan He 1
Layne Watson 1
Taesup Moon 1
Andrew Jones 1
Jean Vandeborre 1
Hongbin Zha 1
Trancao Son 1
Guoliang Chen 1
Jaling Wu 1
Yunji Chen 1
Ling Li 1
Zhiwei Xu 1
Francesco Ricci 1
Yulan He 1
Fiona McNeill 1
Marco Montali 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
RMIT 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
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
NICTA 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
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
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
University of Oregon 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
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
Intel Corp., China 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
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
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
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
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 Minnesota Twin Cities 9
Chinese University of Hong Kong 10
University of Turin 10
Beijing University of Posts and Telecommunications 10
Swiss Federal Institute of Technology, Lausanne 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

2016

Volume 8 Issue 1, September 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

2015

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

2014

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

2013

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

2012

Volume 3 Issue 4, September 2012
Volume 3 Issue 3, May 2012
Volume 3 Issue 2, February 2012

2011

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

2010

Volume 1 Issue 2, November 2010
Volume 1 Issue 1, October 2010
 
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