ACM Transactions on

Intelligent Systems and Technology (TIST)

Latest Articles

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

Virtual Metering: An Efficient Water Disaggregation Algorithm via Nonintrusive Load Monitoring

Concept and Attention-Based CNN for Question Retrieval in Multi-View Learning

A Novel Image-Centric Approach Toward Direct Volume Rendering

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

A Bayesian Approach to Intervention-Based Clustering

Sparse Passive-Aggressive Learning for Bounded Online Kernel Methods

Modeling Queries with Contextual Snippets for Information Retrieval


Recent TIST News: 

ACM Transactions on Intelligent Systems and Technology (TIST) is ranked No.1 in all ACM journals in terms of citations received per paper. Each paper published at TIST in the time span (from Jan. 2010 to Dec. 2014) has received 18 citations on average in ACM Digital Library in the past fiscal year (from July 1 2015 to June 30 2016).  

ACM Transactions on Intelligent Systems and Technology (TIST) has been a success story.  Submissions to the journal have increase 76 percent from 2013 to 2015, from 278 original papers and revisions to 488.  Despite this increase, the journal acceptance rate has remained at a steady rate of approximately 24 percent. Furthermore, the TIST Impact Factor increased from 1.251 in 2014 to 3.19 in 2016.  

Journal Metric (2016)

  • - Impact Factor: 3.19
  • - 5-year Impact Factor: 10.47
  • - Avg. Citations in ACM DL: 18 

About TIST

ACM Transactions on Intelligent Systems and Technology (ACM TIST) is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. READ MORE

Forthcoming Articles
Learning Urban Community Structures: A Collective Embedding Perspective with Periodic Spatial-temporal Mobility Graphs

Learning urban community structures refers to the efforts of quantifying, summarizing, and representing an urban community's (i) static structures, e.g., Point-Of-Interests (POIs) buildings and corresponding geographic allocations, and (ii) dynamic structures, e.g., human mobility patterns among POIs. By learning the community structures, we can better quantitatively represent urban communities and understand their evolutions in the development of cities. This can help us boost commercial activities, enhance public security, foster social interactions, and, ultimately, yield livable, sustainable and viable environments. However, due to the complex nature of urban systems, it is traditionally challenging to learn the structures of urban communities. To address this problem, in this paper, we propose a collective embedding framework to learn the community structure from multiple periodic spatial-temporal graphs of human mobility. Specifically, we first exploit a probabilistic propagation based approach to create a set of mobility graphs from periodic human mobility records. In these mobility graphs, the static POIs are regarded as vertexes, the dynamic mobility connectivity between POI pairs are regarded as edges, and the edge weights periodically evolve over time. A collective deep auto-encoder method is then developed to collaboratively learn the embeddings of POIs from multiple spatial-temporal mobility graphs. In addition, we develop a UGWA method (Unsupervised Graph based Weighted Aggregation), in order to align and aggregate the POI embeddings into the representation of the community structure. As an application, we apply the proposed embedding framework to rank high-rated residential communities to evaluate the performance of our proposed method. Extensive experimental results on real-world urban communities and human mobility data demonstrate the effectiveness of the proposed collective embedding framework.

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

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

On Incremental High Utility Sequential Pattern Mining

High utility sequential pattern (HUSP) mining is an emerging topic in pattern mining, and only a few algorithms have been proposed to address it. In practice, most sequence databases usually grow over time, and it is inefficient for existing algorithms to mine HUSPs from scratch when databases grow with a small portion of updates. In view of this, we propose the IncUSP-Miner + algorithm to mine HUSPs incrementally. Specifically, to avoid redundant re-computations, we propose a tighter upper bound of the utility of a sequence, called TSU (standing for Tight Sequence Utility), and then design a novel data structure, called the candidate pattern tree, to buffer the sequences whose TSU values are greater than or equal to the minimum utility threshold in the original database. Accordingly, to avoid keeping a huge amount of utility information for each sequence, a set of concise utility information is designed to be stored in each tree node. To improve the mining efficiency, several strategies are proposed to reduce the amount of computation for utility update and the scopes of database scans. Moreover, several strategies are also proposed to properly adjust the candidate pattern tree for the support of multiple database updates. Experimental results on some real and synthetic datasets show that IncUSP-Miner + is able to efficiently mine HUSPs incrementally.

The Effect of Pets on Happiness: A Large-scale Multi-Factor Analysis using Social Multimedia

From reducing stress and loneliness, to boosting productivity and overall well-being, pets are believed to play a significant role in people's daily lives. Many traditional studies have identified that frequent interactions with pets could make individuals become healthier and more optimistic, and ultimately enjoy a happier life. However, most of those studies are not only restricted in scale, but also may carry biases by using subjective self-reports, interviews, and questionnaires as the major approaches. In this paper, we leverage large-scale data collected from social media and the state-of-the-art deep learning technologies to study this phenomenon in depth and breadth. Our study includes four major steps: 1) collecting timeline posts from around 20,000 Instagram users; 2) using face detection and recognition on 2-million photos to infer users' demographics, relationship status, and whether having children, 3) analyzing a user's degree of happiness based on images and captions via smiling classification and textual sentiment analysis; 3) applying transfer learning techniques to retrain the final layer of the Inception v3 model for pet classification; and 4) analyzing the effects of pets on happiness in terms of multiple factors of user demographics. Our main results have demonstrated the efficacy of our proposed method with many new insights. We believe this method is also applicable to other domains as a scalable, efficient, and effective methodology for modeling and analyzing social behaviors and psychological well-being.

Combination Forecasting Reversion Strategy for Online Portfolio Selection

Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. A popular and state-of-the-art family of strategies is to explore the reversion phenomenon through online learning algorithms and statistical prediction models. Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. However, such model selection is often unstable and may cause unnecessarily high variability in the final estimation, leading to poor prediction performance in real datasets and thus non-optimal portfolios. To overcome the drawbacks, in this paper, we propose to exploit the reversion phenomenon by using combination forecasting estimators, and design a novel online portfolio selection strategy, named Combination Forecasting Reversion (CFR), which outputs optimal portfolios based on the improved reversion estimator. We further present an efficient CFR implementation based on online Newton step (ONS) and online gradient descent (OGD) algorithms, and theoretically analyze regret bounds of the proposed algorithms, which guarantee that the online CFR model performs as good as the best CFR model in hindsight. We evaluate the proposed algorithms on various real markets with extensive experiments. Empirical results show that CFR can effectively overcome the drawbacks of existing reversion strategies and achieve the state-of-the-art performance.

RelationLines: Visual Reasoning of Egocentric Relations from Heterogeneous Urban Data

The increased accessibility of urban sensor data and the popularity of social network applications is enabling the discovery of crowd mobility and personal communication patterns. However, studying the egocentric relationships of an individual (i.e., the egocentric relations) can be very challenging because available data may refer to direct contacts, such as phone calls between individuals, or indirect contacts, such as paired location presence. In this paper, we develop methods to integrate three facets extracted from heterogeneous urban data (timelines, calls and locations) through a progressive visual reasoning and inspection scheme. Our approach uses a detect-and-filter scheme, such that, prior to visual refinement and analysis, a coarse detection is performed to extract the target individual and construct the timeline of the target. It then detects spatio-temporal co-occurrences or call-based contacts to develop the egocentric network of the individual. The filtering stage is enhanced with a line-based visual reasoning interface that facilitates flexible and comprehensive investigation of egocentric relationships and connections in terms of time, space and social networks. The integrated system, RelationLines, is demonstrated using a dataset that contains taxi GPS data, cell-base mobility data, mobile calling data, microblog data and POI data of a city with millions of citizens. We conduct three case studies to examine the effectiveness and efficiency of our system.

DeepTracker: Visualizing the Training Process of Convolutional Neural Networks

Deep convolutional neural networks (CNNs) have achieved remarkable success in various fields. However, training an excellent CNN is practically a trial-and-error process that consumes a tremendous amount of time and computer resources. To accelerate the training process and reduce the number of trials, experts need to understand what has occurred in the training process and why the resulting CNN behaves as such. However, current popular training platforms, such as TensorFlow, only provide very little and general information, such as training/validation errors, which is far from enough to serve this purpose. To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log. Specifically, we combine a hierarchical index mechanism and a set of hierarchical small multiples to help experts explore the entire training log from different levels of detail. We also introduce a novel cube-style visualization to reveal the complex correlations among multiple types of heterogeneous training data including neuron weights, validation images, and training iterations. Three case studies are conducted to demonstrate how DeepTracker provides its users with valuable knowledge in an industry-level CNN training process, namely in our case, training ResNet-50 on the ImageNet dataset. We show that our method can be easily applied to other state-of-the-art "very deep" CNN models.

Visual Analytics of Heterogeneous Data using Hypergraph Learning

For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. These models represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., more than two patient groups admitted by the same hospital in 2014). In this paper, we propose a visual analytics approach for the classification of heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a uni ed hypergraph structure. The hypergraph learning algorithm is used for predicting the missing labels in the datasets. To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. We also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. We demonstrate the capability of our approach via two real-world cases.

Integrate and Conquer: Double-Sided Two-Dimensional K-Means Via Integrating of Projection and Manifold Construction

For 2-dimensional (2D) data, current clustering algorithms usually need to convert them to vectors in a pre-processing step, which, unfortunately, severely damages 2D spatial information and omits the inherent structures and correlations in the original data. In this paper, we develop a novel clustering method, which addresses these issues to enhance the clustering capability. The proposed method mutually enhances three goals, including seeking projections, learning manifolds, and constructing data representations, in a seamlessly integrated model. In particular, we seek two projection matrices with optimal number of directions to project the data into optimally low-rank, noise reduced, most expressive subspaces, in which manifolds are constructed and data representations are sought. The manifolds are adaptively updated according to the projections, and the new data representations are sought with respect to the projected data. Consequently, the learned manifolds are clean and more expressive, and the new data representations are representative and robust. Extensive experimental results have verified the effectiveness of the proposed method in clustering.

Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis

Recognizing human activities using supervised learning methods has been widely studied in the literature. However, for some applications like elderly care, what activities to be identied for analysis are very often unknown. In this paper, we focus on automatic extraction of behavioral patterns as the representations of activities from the trajectory data of an individual. The underlying challenges lie on the need to model the long-range dependency and spatio-temporal variations within the trajectory data. We propose to rst represent the trajectory data using a behavior-aware ow graph which is a probabilistic nite state automaton with its nodes and edges attributed with local behavioral features. We then identify the underlying subows as the behavioral patterns using the kernel k-means algorithm. With the activities automatically identied, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily activity routines. The performance of the proposed methodology has been compared with a number of existing methods using both synthetic and publicly available real smart home data sets with promising results obtained. We also discuss how the proposed unsupervised methodology can be used to support exploratory behavior analysis for elderly care.

Visual Interfaces for Recommendation Systems: Finding Similar and Dissimilar Peers

Recommendation applications can guide users in making important life choices by referring to the activities of similar peers. For example, students making academic plans may learn from the data of similar students, while patients and their physicians may explore data from similar patients to select the best treatment. Selecting an appropriate peer group has a strong impact on the value of the guidance that can result from analyzing the peer group data. In this paper, we describe a visual interface that helps users review the similarity and differences between a seed record and a group of similar records, and refine the selection. We introduce the LikeMeDonuts, Ranking Glyph, and History Heatmap visualizations. The interface was refined through three rounds of formative usability evaluation with 12 target users and its usefulness was evaluated by a case study with a student review manager using real student data. We describe three analytic workflows observed during use and summarize how users' input shaped the final design.

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition but still remains an important challenge. Data-driven supervised approaches, especially the ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.

Multi-view Discrete Hashing for Scalable Multimedia Search

Hashing techniques have recently gained increasing research interests in multimedia studies. Most existing hashing methods only employ single feature for hash code learning. Multi-view data with each view corresponding to a type of feature generally provides more comprehensive information. How to efficiently integrate multiple views for learning compact hash codes still remains challenging. In this paper, we propose a novel unsupervised hashing method, dubbed multi-view discrete hashing (MvDH), by effectively exploring multi-view data. Specifically, MvDH performs matrix factorization to generate the hash codes as the latent representations shared by multiple views, during which spectral clustering is performed simultaneously. The joint learning of hash codes and cluster labels enables that MvDH can generate more discriminative hash codes, which are optimal for classification. An efficient alternating algorithm is developed to solve the proposed optimization problem with guaranteed convergence and low computational complexity. The binary codes are optimized via discrete cyclic coordinate descent (DCC) method to reduce the quantization errors. Extensive experimental results on three large-scale benchmark datasets demonstrate the superiorities of the proposed method over several state-of-the-art methods in terms of both accuracy and scalability.

D-Map+: Interactive Visual Analysis and Exploration of Ego-centric and Event-centric Information Diffusion Patterns in Social Media

Popular social media platforms could rapidly propagate vital information over social networks among a significant number of people. In this work we present D-Map+ (Diffusion Map), a novel visualization method to support exploration and analysis of social behaviors during such information diffusion and propagation on typical social media through a map metaphor. In D-Map+, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected and mapped to a hexagonal grid based on their behavior similarities and in chronological order of the repostings. With additional interaction and linking, D-Map+ is capable of providing visual profilings of the influential users, describing their social behaviors and analyzing the siginificant events evolution in social media. A comprehensive visual analysis system is developed to support interactive exploration with D-Map+. We evaluate our work with real world social media data and find interesting patterns among users. Key players, important information diffusion paths, and interactions among social communities can be identified.

X-CLEAVER: Learning Ranking Ensembles by Growing and Pruning Trees

Learning-to-Rank (LtR) solutions are commonly used in large-scale information retrieval systems such as Web search engines where high-quality documents need to be returned in response to a user query within a fraction of a second. The most effective LtR algorithms, e.g., »-MART, adopt a gradient boosting approach to build an additive ensemble of weighted regression trees. Since the required ranking effectiveness is achieved with very large ensembles, the impact on response time and query throughput of these solutions is not negligible. In this paper we propose X-CLEaVER, an iterative meta-algorithm able to build more efficient and effective ranking ensembles. X-CLEaVER interleaves the iterations of a given ensemble learning algorithm with pruning and re-weighting phases. First, redundant trees are removed from the ensemble generated, then the weights of the remaining trees are fine-tuned by optimizing the desired ranking loss function. We propose and analyse several pruning strategies and assess their bene ts showing that interleaving pruning and re-weighting phases during learning is more effective than applying a single post-learning optimization step. Experiments conducted using two publicly available LtR datasets show that X-CLEaVER is very effective in optimizing »-MART models both in terms of effectiveness and efficiency.

Interactive Visual Graph Mining and Learning

This paper presents a platform for interactive graph mining and relational learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators/models (including new block model approaches), and a variety of multi-level network analysis techniques.

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

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

Exploiting Multi-Label Information for Noise Resilient Feature Selection

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

Learning Facial Expressions with 3D Mesh Convolutional Neural Network

Making machines understand human expressions enables various useful applications in human-machine interaction. In this paper, we present a novel facial expression recognition approach with 3D Mesh Convolutional Neural Network (3DMCNN) and a visual analytics guided 3DMCNN design and optimization scheme. From a RGBD camera, we first reconstruct a 3D face model of a subject with facial expressions and then compute the geometric properties of the surface. Instead of using regular Convolutional Neural Network (CNN) to learn intensities of the facial images, we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We design a geodesic distance-based convolution method to overcome the difficulties raised from the irregular sampling of the face surface mesh. We further present an interactive visual analytics for the purpose of designing and modifying the networks to analyze the learned features and cluster similar nodes in 3DMCNN. By removing low activity nodes in the network, the performance of the network is greatly improved. We compare our method with the regular CNN-based method by interactively visualizing each layer of the networks and analyze the effectiveness of our method by studying representative cases. Testing on public datasets, our method achieves a higher recognition accuracy than traditional image-based CNN and other 3D CNNs. The proposed framework, including 3DMCNN and interactive visual analytics of the CNN, can be extended to other applications.

Traffic Simulation and Visual Verification in Smog

Smog causes low visibility on the road and it can impact the safety of traffic. Modeling traffic in smog will have a significant impact on realistic traffic simulation. Most of the existing traffic models assume that drivers have optimal vision in the simulations. These simulations are not suitable for modeling smog weather conditions. In this paper, we introduce the smog full velocity difference model (SMOG-FVDM) for a realistic simulation of traffic in smog weather conditions. In this model, we present a stadia model for drivers in smog weather. We then introduce it into the car-following traffic model through ``Psychological Force'' and ``Body Force'', and then introduce the SMOG-FVDM. Considering that there are lots of parameters in the SMOG-FVDM, we design a visual verification system based on the SMOG-FVDM to get an adequate solution, which can show visual simulation results in different road scenarios and different smog degrees by reconciling the parameters. Experiments results show that our model can give a realistic and efficient traffic simulation in smog weather conditions.

CapVis: Towards Better Understanding of Visual-Verbal Saliency Consistency

When looking at an image, humans shift their attention towards interesting regions, making sequences of eye fixations. When describing an image, they also come up with simple sentences that highlight the key elements in the scene. What is the correlation between where people look and what they describe in an image? To investigate this problem intuitively, we develop a visual analytics system CapVis to look into eye fixations and image captions, two types of subjective annotations that are relatively task-free and natural. From the annotations, we propose a word-weighting scheme to extract visual and verbal saliency ranks to compare against each other. In our approach, a number of low-level and semantic-level features relevant to the visual-verbal saliency consistency are proposed and visualized in multiple facts for better understanding of image content. Our method also shows the different ways human and computational model look and describe, which provides reliable information for the diagnosis of captioning model. Experiment also shows that the visualized feature can be integrated into a computational model, to effectively predict the consistency between the two modalities on image dataset with both types of annotations.

Optimum Velocity Profile of Multiple Bernstein-Bézier Curves Subject to Constraints for Mobile Robots

This paper deals with trajectory planning that is suitable for nonholonomic differentially driven wheeled mobile robots. The path is approximated with a spline which consist of multiple Bernstein-Bézier curves that are merged together in a way that continuous curvature of the spline is achieved. The paper presents the approach for optimization of velocity profile of Bernstein-Bézier spline subject to velocity and acceleration constraints. For the purpose of optimization velocity and turning points are introduced. Based on these singular points local segments are defined where local velocity profiles are optimized independently of each other. From the locally optimum velocity profiles the global optimum velocity profile is determined. The proposed optimization approach is experimentally evaluated and validated in simulation environment and on real mobile robots.


Publication Years 2010-2018
Publication Count 532
Citation Count 6641
Available for Download 532
Downloads (6 weeks) 6028
Downloads (12 Months) 48546
Downloads (cumulative) 251373
Average downloads per article 473
Average citations per article 12
First Name Last Name Award
Rakesh Agrawal ACM Fellows (2003)
Benjamin B Bederson ACM Distinguished Member (2011)
Andrei Broder ACM Paris Kanellakis Theory and Practice Award (2012)
ACM Fellows (2007)
Carlos A. Castillo ACM Senior Member (2014)
Charles L A Clarke ACM Distinguished Member (2015)
Ingemar J. Cox ACM Fellows (2013)
ACM Distinguished Member (2011)
Umeshwar Dayal ACM Fellows (2008)
Alberto Del Bimbo ACM Distinguished Member (2016)
Inderjit Dhillon ACM Fellows (2014)
Deborah Estrin ACM Athena Lecturer Award (2006)
ACM Fellows (2000)
Christos Faloutsos ACM Fellows (2010)
Wen Gao ACM Fellows (2013)
Maria L Gini ACM Distinguished Member (2006)
Carla Gomes ACM Fellows (2017)
Jiawei Han ACM Fellows (2003)
James Hendler ACM Fellows (2016)
Xian-Sheng Hua ACM Distinguished Member (2015)
ACM Senior Member (2009)
Ramesh C Jain ACM Fellows (2003)
Sarit Kraus ACM Fellows (2014)
Vipin Kumar ACM Fellows (2005)
Chih-Jen Lin ACM Fellows (2015)
ACM Distinguished Member (2011)
ACM Senior Member (2010)
C.L. Liu ACM Karl V. Karlstrom Outstanding Educator Award (1989)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Dana Nau ACM Fellows (2013)
Jeffrey Nichols ACM Senior Member (2013)
Judea Pearl ACM Fellows (2015)
ACM A. M. Turing Award (2011)
ACM AAAI Allen Newell Award (2003)
Jian Pei ACM Fellows (2015)
ACM Senior Member (2007)
Keith Ross ACM Fellows (2012)
Yong Rui ACM Fellows (2017)
ACM Distinguished Member (2009)
ACM Senior Member (2006)
Michael Rung-Tsong Lyu ACM Fellows (2015)
Stefan Savage ACM Prize in Computing (2015)
ACM Fellows (2010)
Cyrus Shahabi ACM Distinguished Member (2009)
Stuart Shieber ACM Fellows (2014)
Yoav Shoham ACM AAAI Allen Newell Award (2012)
ACM Fellows (2012)
Padhraic Smyth ACM Fellows (2013)
Gita Reese Sukthankar ACM Senior Member (2013)
Jie Tang ACM Senior Member (2017)
Jaime Teevan ACM Senior Member (2012)
Moshe Tennenholtz ACM AAAI Allen Newell Award (2012)
Paolo Trunfio ACM Senior Member (2017)
Sebastian Ventura ACM Senior Member (2013)
Geoffrey M Voelker ACM Fellows (2017)
Feiyue Wang ACM Distinguished Member (2007)
Ouri Wolfson ACM Fellows (2001)
Michael Wooldridge ACM Fellows (2015)
Xing Xie ACM Senior Member (2010)
Hui Xiong ACM Distinguished Member (2014)
ACM Senior Member (2010)
Shuicheng Yan ACM Distinguished Member (2016)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)
Philip S Yu ACM Fellows (1997)
Franco Zambonelli ACM Distinguished Member (2012)
ACM Senior Member (2009)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)

First Name Last Name Paper Counts
Dacheng Tao 9
Enhong Chen 8
Xing Xie 8
Tatseng Chua 7
Steven Hoi 6
Shuicheng Yan 6
Nicholasjing Yuan 5
Yu Zheng 5
Xiansheng HUA 5
Jinhui Tang 5
Hui Xiong 5
Xuan Song 4
Ryosuke Shibasaki 4
Yuval Elovici 4
Alex Rogers 4
Richang Hong 4
Changsheng Xu 4
Wenchih Peng 4
Qiang Yang 4
Michelle Zhou 4
Ya'akov Gal 3
Quanshi Zhang 3
Boi Faltings 3
Martha Larson 3
Philip YU 3
Christopherchuen Yang 3
Wen Gao 3
Xue Li 3
Liqiang Nie 3
Rongrong Ji 3
Xiaowei Shao 3
Francesco Bonchi 3
Huanhuan Cao 3
Ling Guan 3
Ratnesh Sharma 3
Meng Wang 3
Rebecca Castaño 3
Irwin King 3
VS Subrahmanian 3
Qi Tian 3
Naren Ramakrishnan 3
Daqing Zhang 3
Tao Li 3
Haoyi Xiong 2
Shulamit Reches 2
Ron Hirschprung 2
Jamal Bentahar 2
Kyumin Lee 2
James Caverlee 2
Wangchien Lee 2
Thomas Dietterich 2
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Juan Rogers 1
Yingying Jiang 1
Michele Gelfand 1
Lei Tang 1
Sheng Li 1
Jingdong Wang 1
Evgeniy Gabrilovich 1
Yushi Lin 1
Guiguang Ding 1
Belén Díaz-Agudo 1
Dietmar Jannach 1
Hitoshi Yamamoto 1
Ruiqiang Zhang 1
Xiaohua Liu 1
Ching Law 1
Marta Arias 1
Ramon Xuriguera 1
Janyl Jumadinova 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
Xianming Liu 1
Shiguang Shan 1
Myunghoon Suk 1
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Mary Pendleton Hoffer 1
Daniel Schuster 1
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Stephan Kolitz 1
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Anne Vilnat 1
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Hossein Hajimirsadeghi 1
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Luc Martens 1
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Tie Luo 1
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Tanzeem Choudhury 1
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Francisco Carrero 1
Wengkeen Wong 1
Huzaifa Zafar 1
Kenneth Whitebread 1
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Oukhellou Latifa 1
Chang Tan 1
Sashi Gurung 1
Peng Dai 1
Goran Radanovic 1
Chen Chen 1
Walter Daelemans 1
Guy De Pauw 1
Orphée De Clercq 1
Lingjing Hu 1
Yashar Moshfeghi 1
Jinghe Zhang 1
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Zhao Kang 1
Xibin Zhao 1
Eepeng Lim 1
Rong Yan 1
Jiang Bian 1
W Towne 1
Jing Lv 1
Changshing Perng 1
Dingwen Zhang 1
Jimmyxiangji Huang 1
Bingsheng Wang 1
Enhong Chen 1
Yu Su 1
Joan Serrà 1
Ranieri Baraglia 1
Bowei Chen 1
Jianfei Cai 1
Yang Yang 1
Bruce Elder 1
Chunyan Miao 1
Wenbin Chen 1
Fan Liu 1
Zhen Hai 1
Paul McKevitt 1
Miaojing Shi 1
Marc Cavazza 1
Fred Charles 1
Éric Beaudry 1
Jixue Liu 1
Pedro Vera-Candeas 1
Hua Chen 1
Elias Bareinboim 1
Linyun Fu 1
Zhenxing Wang 1
Scott DuVall 1
Aristidis Pappaioannou 1
Michal Feldman 1
Mauricio Chiazzaro 1
Yang Li 1
Maria Glenski 1
Amin Khezerlou 1
Wangchien Lee 1
Zheng Song 1
Jian Ma 1
Zhaohui Wu 1
Chengkang Hsieh 1
John Jenkins 1
J Gibson 1
Michael O’Mahony 1
Zhengdong Lu 1
Claudio Cioffi-Revilla 1
Zhen Liao 1
Hongan Wang 1
Peter Prettenhofer 1
Hilal Khashan 1
Shiwan Zhao 1
Fernando Díez 1
Yoshiyuki Inagaki 1
Wenning Kuo 1
Alexei Pozdnoukhov 1
Alena Neviarouskaya 1
Annie Robinson 1
Gianmario Motta 1
Chris Mellish 1
René Van Der Wal 1
Jiankai Sun 1
Joris Albeda 1
Tomasz Jaworski 1
Yanhui Xiao 1
Zhenfeng Zhu 1
Matthew Johnson 1
Yizhou Wang 1
Márk Jelasity 1
Joemon Jose 1
Dawei Song 1
Xinbo Gao 1
Wenyuan Zhu 1
Waynexin Zhao 1
Bin Wu 1
Sebastian SardiñA 1
Yang Liu 1
Ning Zhang 1
Xiaohua Zhou 1
Nicholas Sidiropoulos 1
Gem Stapleton 1
Bernadette Bouchon-Meunier 1
Kyle Feuz 1
Wei Gong 1
Guannan Liu 1
Hien To 1
Fan Zhang 1
Kotagiri Ramamohanarao 1
Egemen Tanin 1
Md Bashar 1
Raymondyiu Lau 1
Lichao Yan 1
Fang Liu 1
Wei Zhang 1
Dongming Lei 1
Chen Wang 1
Nathan Self 1
Lina Feng 1
Jie Yu 1
Guojun Qi 1
Chidansh Bhatt 1
Yimin Zhang 1
Zhenhui Li 1
Fusun Yaman 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
Xiao Han 1
Guodao Sun 1
Sanda Harabagiu 1
Raju Balakrishnan 1
Azin Ashkan 1
Leong U 1
Anthony Dick 1
Jiangwen Sun 1
Lihong Li 1
Ankit Shah 1
Tao Gu 1
Jiangbo Jia 1
Xingshe Zhou 1
Tao Li 1
Changtien Lu 1
Jiajia Li 1
Fan Liu 1
Cristina Muntean 1
Karl Tuyls 1
Tatjen Cham 1
Ke Lu 1
Qionghai Dai 1
Scott Spurlock 1
Xinghai Sun 1
Ioannis Refanidis 1
Andrea Marrella 1
Pradeep Varakantham 1
Hongliang Guo 1
Mi Tian 1
Stephen Roberts 1
Denilson Barbosa 1
Pengfei Wang 1
Jianhui Li 1
Desheng Zhang 1
Perfecto Herrera-Boyer 1
Wei Liu 1
Kai Zhu 1
Jincheng Zhang 1
Ranveer Chandra 1
Chisheng Zhang 1
Hsinhan Huang 1
Frederik Auffenberg 1
Sebastian Stein 1
Nikhil Muralidhar 1
Hongyuan Zha 1
Carlos Guestrin 1
Marco Gavanelli 1
Elif Kürklü 1
Steven Klooster 1
Youxi Wu 1
Cornelia Caragea 1
Yexun Zhang 1
Qing He 1
Kamer Kaya 1
Bo Long 1
Lin Lin 1
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Anne Ngu 1
Luoqi Liu 1
Qiang Yang 1
Zhenyu Chen 1
Sarvapali Ramchurn 1
Jie Zhang 1
Nancy Yacovzada 1
Frank Dignum 1
Scott Gerard 1
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Chris Burnett 1
Sae Schatz 1
Hedi Tabia 1
Qing Li 1
Vien Tran 1
Takashi Ninomiya 1
You Xu 1
Weixiong Zhang 1
Chingyung Lin 1
Claudio Schifanella 1
Wenchih Peng 1
Nardine Osman 1
Daniel Sui 1
Zhihui Jin 1
Yang Gao 1
Giulia Bruno 1
Silvia Chiusano 1
Haodong Yang 1
Alfredo Milani 1
Lingfang Li 1
Ralph Ewerth 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
Luigi Di Caro 1
Shimei Pan 1
Chengbin Zeng 1
Huamin Qu 1
Ming Hao 1
Kalyan Subbu 1
Panagiotis Adamopoulos 1
Alexander Tuzhilin 1
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Claudia Goldman 1
Cristopher Yang 1
Iyad Batal 1
Riccardo Molinari 1
Lina Yao 1
Xinyu Ou 1
Jianguo Jiang 1
Christos Dimitrakakis 1
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Matan Monitz 1
Frank Dignum 1
Munindar Singh 1
Cristina Baroglio 1
Neil Yen 1
Nobuyuki Shimizu 1
Hiroshi Nakagawa 1
Tianshi Chen 1
Lena Tenenboim-Chekina 1
Rami Puzis 1
Mehdi Elahi 1
Ziqiang Shi 1
Mario Cataldi 1
Juan Pane 1
Xuemin Zhao 1
Alessandro Fiori 1
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Naeem Mahoto 1
Sumi Helal 1
Michael Burl 1
Yantao Zheng 1
Daniel Gaines 1
Robert Anderson 1
Deming Zhai 1
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Julian Panetta 1
Ronald Greeley 1
Norbert Schorghofer 1
Hao Wang 1
Hao Wang 1
Alberto Rosi 1
Markus Endler 1
John O’Donovan 1
Eibe Frank 1
Changxing Ding 1
Zechao Li 1
Yantao Jia 1
Xiaolong Jin 1
Azhar Ibrahim 1
Ibrahim Venkat 1
Qiang Li 1
Chiachun Lian 1
Wanrong Jih 1
Xiaoqinshelley Zhang 1
James Michaelis 1
James Hendler 1
Geoffrey Levine 1
Lukas Mandrake 1
Zhexuan Song 1
Kristina Lerman 1
Yong Ge 1
Maosong Sun 1
Aristides Gionis 1
Bin Li 1
Léon Bottou 1
Patrick Roos 1
Belkacem Chikhaoui 1
Yong Rui 1
Tim Weninger 1
Yong Ge 1
Yudong Guang 1
Mohamed Bouguessa 1
George Karypis 1
Sen Wu 1
Bo Zhang 1
Gang Pan 1
Hua Lu 1
Deborah Estrin 1
Jinha Kang 1
David Wilkie 1
Bin Guo 1
Kevin Mcnally 1
Barry Smyth 1
Nagarajan Natarajan 1
Xufei Wang 1
Hua Wu 1
Jing Liu 1
Michael Hardegger 1
Thuc Vu 1
Ernesto De Luca 1
Wolfgang Nejdl 1
Weihong Qian 1
Xueying Li 1
Shriram Revankar 1
Vinhtuan Thai 1
Lixin Shi 1
Ke Zhou 1
Dingquan Wang 1
Xueming Wang 1
Alberto Calatroni 1
Lirong Xia 1
Jennifer Moody 1
Jamie Ward 1
Hans Gellersen 1
K Subramanian 1
Ahamad Khader 1
James Kitts 1
Danny Wyatt 1
Quanquan Gu 1
Bing Liu 1
Fabian Loose 1
Paolo Rosso 1
Darren Appling 1
Elizabeth Whitaker 1
Deborah McGuinness 1
Antons Rebguns 1
Gerald Dejong 1
Reid MacTavish 1
Jinhong Guo 1
Anusua Trivedi 1
Piyush Rai 1
Tad Hogg 1
Sergej Sizov 1
Nello Cristianini 1
Carlos Castillo 1
Chunnan Hsu 1
Geoffrey Voelker 1
Justin Ma 1
Hao Ma 1
Moshe Tennenholtz 1
Shengrui Wang 1
Rebecca Goolsby 1
Dong Wang 1
Jun Tao 1
Fan Zhang 1
Guifeng Wang 1
Jilin Chen 1
Yun Lu 1
Fernando Diaz 1
Mohammad Bozchalui 1
Nirwan Sharma 1
Shihwen Huang 1
Michael Strintzis 1
Kuanta Chen 1
Irwin King 1
Christina Katsimerou 1
Shengdong Zhao 1
Yao Zhao 1
Lieve Macken 1
Arpad Berta 1
Chen Luo 1
Mingxuan Yuan 1
Yu Huang 1
Kevin Leach 1
Peng Zhang 1
Ling Chen 1
Xuelong Li 1
Ming Zong 1
William Cushing 1
Philip Hendrix 1
John Yen 1
Ameet Talwalkar 1
Ling Huang 1
Han Hu 1
Brigitte Piniewski 1
David Norton 1
Jaewon Yang 1
Ignacio Silva-Lepe 1
Anca Sailer 1
Zhaohong Deng 1
Hisao Ishibuchi 1
Shitong Wang 1
Valerio Grossi 1
Dino Pedreschi 1
Nisansa De Silva 1
Riadh Ksantini 1
Doyen Sahoo 1
J Benton 1
Furui Liu 1
Seth Flaxman 1
Zhikun Wang 1
Lin Liu 1
Bingyu Sun 1
Yiqiang Chen 1
Chao Sun 1
Réjean Plamondon 1
Asmaa Elbadrawy 1
Naphtali Rishe 1
S Nolen 1
Guozhong Dai 1
Elizabeth Salmon 1
Xiatian Zhang 1
Rongyao Fu 1
Yoav Shoham 1
Chunping Li 1
Lara Quijano-Sánchez 1
Shlomo Berkovsky 1
Paul Cook 1
Timothy Baldwin 1
Hongyuan Zha 1
Xiao Gu 1
Patrick Butler 1
Clemens Drews 1
Yicheng Song 1
Helmut Prendinger 1
Mitsuru Ishizuka 1
Come Etienne 1
Xiang Wu 1
Linlin You 1
Tianyi Ma 1
Praveen Paritosh 1
Petros Daras 1
Benjamin Lok 1
Ingrid Heynderickx 1
Andrzej Romanowski 1
Morten Fjeld 1
Bin Cheng 1
Kristen Venable 1
Véronique Hoste 1
Bo Xin 1
András Benczúr 1
István Hegedűs 1
Levente Kocsis 1
Wenyuan Dai 1
Yueying He 1
Alvaro Rosero 1
Laura Barnes 1
Bin Hu 1
Qiang Cheng 1
Jun Xu 1
Chang Liu 1
Debo Cheng 1
Changsheng Xu 1
Julio Carabias-Orti 1
François Pachet 1
Songtao Wu 1
Dhaval Patel 1
Yuichi Kawamoto 1
William Yeoh 1
Cen Chen 1
Yuriy Pepyolyshev 1
Judea Pearl 1
Aidan Delaney 1
Mingbo Zhao 1
Xiaowen Dong 1
Jungeun Kim 1
Hairuo Xie 1
Yoshihide Sekimoto 1
Xu Zhang 1
Francesca Pratesi 1
Deng Cai 1
Xiaofeng Tong 1
Tao Wang 1
Jeremy Frank 1
Animesh Pathak 1
Chao Chen 1
Yingcai Wu 1
Olivier Chapelle 1
Eren Manavoglu 1
Rushi Bhatt 1
Fuzhen Zhuang 1
Suhas Ranganath 1
Zhongxue Chen 1
Chao Chen 1
Meiling Shyu 1
Jian Su 1
Hang Li 1
Hamed Valizadegan 1
Davide Susta 1
Pasquale Lops 1
Federica Cena 1
Zhenhen Hu 1
Meng Wang 1
Brammert Ottens 1
Zhong Ming 1
Tamir Mendel 1
Iradben Gal 1
Marco Colombetti 1
Pınar Yolum 1
Yongdong Zhang 1
Hadas Schwartz-Chassidim 1

Affiliation Paper Counts
Ryukoku University 1
University of Connecticut Health Center 1
University of Lausanne 1
Max Planck Institute for Informatics 1
Federal University of Amazonas 1
University of Macedonia 1
Demokritos National Centre for Scientific Research 1
University of Michigan 1
National Taitung University Taiwan 1
University of Sheffield 1
Ehime University 1
University of Haifa 1
University of Perugia 1
Iowa State University 1
Northumbria University 1
Joint Institute for Nuclear Research, Dubna 1
Instituto Superior Tecnico 1
University of Auckland 1
Bogazici University 1
University of Houston 1
University of Pennsylvania 1
University of Koblenz-Landau 1
Northwestern University 1
Smithsonian National Museum of Natural History 1
Hebrew University of Jerusalem 1
Osaka Prefecture University 1
Tohoku University 1
Duke University 1
Vrije Universiteit Amsterdam 1
Sabanci University 1
Birkbeck University of London 1
Educational Testing Service 1
IBM Almaden Research Center 1
Wayne State University 1
Northeast Normal University China 1
Central European University 1
Rissho University 1
Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende 1
The University of British Columbia 1
Dartmouth College 1
Hohai University 1
The University of Western Ontario 1
Donghua University 1
Citigroup 1
Lingnan University, Hong Kong 1
University of Dublin, Trinity College 1
King's College London 1
Center for Mathematics and Computer Science - Amsterdam 1
University of Messina 1
State University of New York at Albany 1
University of Shizuoka 1
Aoyama Gakuin University 1
United States National Science Foundation 1
Ionian University 1
University of Passau 1
Eastman Kodak Company 1
Imperial College London 1
University of Saskatchewan 1
University of Tennessee, Knoxville 1
General Electric Company 1
New York State Museum 1
National Taipei University 1
China Electric Power Research Institute 1
Charles Stark Draper Lab Inc 1
University of Sussex 1
Defence Research and Development Canada 1
Vienna University of Technology 1
Washington State University Pullman 1
University of Science and Technology Beijing 1
Office of Naval Research 1
Polytechnic School of Montreal 1
Macquarie University 1
Boston University 1
Netherlands Organisation for Applied Scientific Research - TNO 1
Texas A and M University System 1
Capital Normal University China 1
Binghamton University State University of New York 1
American University 1
Massachusetts General Hospital and Harvard Medical School 1
University of Surrey 1
Nanjing University of Aeronautics and Astronautics 1
Aalborg University 1
Naresuan University 1
Weber State University 1
Politecnico di Milano 1
Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences 1
Chongqing University 1
Shanghai University 1
Soka University 1
Ecole Centrale Paris 1
National University of Defense Technology China 1
National Central University Taiwan 1
Dublin City University 1
Catholic University of Leuven, Leuven 1
The University of North Carolina at Chapel Hill 1
University of Cincinnati 1
Institute of Software Chinese Academy of Sciences 1
INSA Rouen 1
University of Udine 1
Institute of Intelligent Machines Chinese Academy of Sciences 1
University of Exeter 1
Capital Medical University China 1
National Chengchi University 1
United States Military Academy 1
University of Quebec in Montreal 1
Yunnan University 1
Lanzhou University 1
Berlin University of Applied Sciences 1
Northeastern University 1
Fairleigh Dickinson University 1
Research Organization of Information and Systems National Institute of Informatics 1
University of Hawaii System 1
University of Chicago 1
European Space Agency - ESA 1
Institute of Applied Physics and Computational Mathematics 1
Columbia University 1
Ecole des Mines de Paris 1
Hosei University 1
Boeing Corporation 1
Santa Fe Institute 1
University of Western Australia 1
Indian Institute of Technology Roorkee 1
North Dakota State University 1
University of Electro-Communications 1
Pontifical Catholic University of Rio de Janeiro 1
University of Jyvaskyla 1
Shanghai University of Finance and Economics 1
University of Chittagong 1
University of Seville 1
York University Canada 1
Mehran University of Engineering & Technology 1
University of Sousse 1
Nanjing University of Information Science and Technology 1
Know-Center, Graz 1
Ecole Superieure des Communications de Tunis 1
Institute for Cancer Research and Treatment, Candiolo 1
Reykjavik University 1
Macau University of Science and Technology 1
SONY Computer Science Laboratory, Paris 1
Google Switzerland GmbH 1
Intel Research Laboratories 1, Inc. 1
Nanyang Technological University School of Computer Engineering 1
Fondazione Bruno Kessler 1
Florida Institute for Human & Machine Cognition 1
Fujitsu America, Inc. 1
Shandong Academy of Sciences 1
Austrian Institute of Technology 1
Laboratoire d'Informatique de Nantes-Atlantique 1
Communaute d'Universites et d'Etablissements Lille Nord de France 1
Yuncheng University 1
Liverpool Hope University 1
Qatar Foundation 1
CSIRO Data61 1
Facebook, Inc. 1
University of Verona 2
TU Dortmund University 2
Intel Corporation 2
American University of Beirut 2
Universite Paris-Est 2
Shandong University of Finance 2
Shenzhen Institute of Advanced Technology 2
Telecom & Management SudParis 2
New York University Shanghai 2
IBM Ireland Limited 2
Anhui University 2
Universidad de Cordoba 2
University of Texas at Arlington 2
University of Manchester 2
King Abdulaziz University 2
Southeast University China, Nanjing 2
University of Electronic Science and Technology of China 2
University of Lugano 2
University of Missouri-Kansas City 2
Guangdong University of Technology 2
University of Wolverhampton 2
University of Texas at El Paso 2
University of Brighton 2
Utrecht University 2
National University of Ireland, Galway 2
University of Massachusetts Dartmouth 2
Universite des Sciences et Technologies de Lille 2
Harvard University 2
University of Arizona 2
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RMIT University 2
Technical University of Berlin 2
University of Sherbrooke 2
Open University 2
University of Zurich 2
University of Antwerp 2
King Saud University 2
Telecom Bretagne 2
Aston University 2
University of Hawaii at Hilo 2
Aristotle University of Thessaloniki 2
University of Southern California, Information Sciences Institute 2
Dalhousie University 2
Beijing Institute of Technology 2
Academia Sinica Taiwan 2
National Tsing Hua University 2
Xiamen University 2
Technical University of Dresden 2
Tamkang University 2
University of Nebraska at Omaha 2
University of Bristol 2
Communication University of China 2
Ecole d' Ingenieurs Telecom Lille 1 2
Johannes Kepler University Linz 2
Queen Mary, University of London 2
Waseda University 2
Lancaster University 2
University of Massachusetts Boston 2
University of Fribourg 2
University of Ferrara 2
Sam Houston State University 2
University of Sydney 2
Zhejiang University of Technology 2
University of Rochester 2
University of Edinburgh 2
Jerusalem College of Technology 2
Hungarian Academy of Sciences 2
New Mexico Institute of Mining and Technology 2
University of Athens 2
University of California, Riverside 2
Texas State University-San Marcos 2
Universite de Rennes 1 2
University of California System 2
NEC Corporation 2
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
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University College Dublin 3
Brigham Young University 3
Jiangnan University 3
Universitat Politecnica de Catalunya 3
Orebro University 3
University of Wyoming 3
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Free University of Bozen-Bolzano 3
Queensland University of Technology 3
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Changchun University of Technology 3
Auburn University 3
BBN Technologies 3
Philipps-Universitat Marburg 3
Universidad de Jaen 3
Georgia Tech Research Institute 3
University of Stuttgart 3
Kassel University 3
Wright State University 3
Xerox Corporation 3
Graz University of Technology 3
University of Central Florida 3
University of Macau 3
University of Connecticut 3
Universite Paris-Sud XI 3
University of Utah 3
University of Konstanz 3
Xidian University 3
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Center For Research And Technology - Hellas 3
Stony Brook University 3
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EURECOM Ecole d'Ingenieurs & Centre de Recherche en Systemes de Communication 3
Utah State University 3
University of Wisconsin Madison 3
University of Roma La Sapienza 3
U.S. Army Research Laboratory 3
Intel Corporation, China 3
Toyohashi University of Technology 4
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The University of North Carolina at Charlotte 4
University of Vermont 4
University of Adelaide 4
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University of Pavia 4
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Technical University of Lodz 4
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University of Liverpool 4
Indiana University 4
West Virginia University 4
New York University 4
University of Florence 4
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University of Tehran 4
University of Iowa 4
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South China University of Technology 4
Swiss Federal Institute of Technology, Zurich 4
National University of Ireland, Maynooth 4
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Complutense University of Madrid 4
Sharif University of Technology 4
University of Ottawa, Canada 4
University of California, Santa Barbara 4
University of Miami 4
University of Southern California 4
University of California, San Diego 4
Michigan State University 4
Korea Advanced Institute of Science & Technology 4
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Cornell Tech 4
Shenzhen University 5
New Mexico State University Las Cruces 5
North Carolina State University 5
University of California, Irvine 5
University of Texas at Dallas 5
Soochow University 5
University of Oregon 5
Istituto Di Scienze E Tecnologie Della Cognizione, Rome 5
University of Massachusetts Amherst 5
National Cheng Kung University 5
University of Washington, Seattle 5
University of Pittsburgh 5
Nankai University 5
CSIC - Instituto de Investigacion en Inteligencia Artificial 5
Tianjin University 5
East China Normal University 5
University College London 5
Beijing Jiaotong University 5
Osaka University 5
Rensselaer Polytechnic Institute 5
Southern Illinois University at Carbondale 5
Max Planck Institute for Intelligent Systems 5
Rutgers, The State University of New Jersey 5
TECH Lab 5
Nanjing University 5
Yahoo Research Barcelona 5
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 5
Rutgers University-Newark Campus 6
Pennsylvania State University 6
Virginia Commonwealth University 6
Institute for Infocomm Research, A-Star, Singapore 6
Bar-Ilan University 6
Google Inc. 6
Washington University in St. Louis 6
National Taipei University of Technology 6
City University of Hong Kong 6
Hong Kong Baptist University 6
Simon Fraser University 6
Texas A and M University 6
TELECOM ParisTech 6
University of Alberta 6
University of South Australia 6
Centro de Investigacion Cientifica y de Educacion Superior de Ensenada 6
Universitat Pompeu Fabra 6
Stanford University 7
Oregon State University 7
IBM Thomas J. Watson Research Center 7
George Mason University 7
Institute of Automation Chinese Academy of Sciences 7
University of Ulster 7
Massachusetts Institute of Technology 7
University of Bari 7
Microsoft Corporation 7
Universidad Autonoma de Madrid 7
University of Pisa 7
HP Labs 8
Roma Tre University 8
Drexel University 8
University of Aberdeen 8
NEC Laboratories America, Inc. 8
Huazhong University of Science and Technology 8
Ryerson University 8
University of Queensland 8
Shandong University 8
Ghent University 8
NASA Ames Research Center 9
University of Texas at Austin 9
University of Waterloo 9
University of Notre Dame 9
Missouri University of Science and Technology 9
University of California, Berkeley 9
University of Illinois at Chicago 9
Bauhaus University Weimar 9
University of Turin 10
Beijing University of Posts and Telecommunications 10
Federal University of Minas Gerais 10
Hefei University of Technology 11
Northwestern Polytechnical University China 11
Hong Kong Polytechnic University 11
Nanjing University of Science and Technology 11
National Chiao Tung University Taiwan 11
University of Melbourne 11
Nokia Corporation 11
University of Minnesota Twin Cities 11
Hong Kong University of Science and Technology 12
Georgia Institute of Technology 12
Delft University of Technology 13
Polytechnic Institute of Turin 13
University of Southampton 13
University of Technology Sydney 14
University of California, Los Angeles 14
Swiss Federal Institute of Technology, Lausanne 14
Zhejiang University 15
Florida International University 15
Shanghai Jiaotong University 15
Tel Aviv University 16
Yahoo Research Labs 16
Chinese University of Hong Kong 18
University of Tokyo 18
Arizona State University 19
Carnegie Mellon University 19
Singapore Management University 20
Harbin Institute of Technology 20
National Taiwan University 21
Virginia Tech 21
Microsoft Research 23
Microsoft Research Asia 23
Jet Propulsion Laboratory 24
IBM Research 24
Peking University 24
Nanyang Technological University 25
Tsinghua University 25
Ben-Gurion University of the Negev 25
University of Illinois at Urbana-Champaign 26
Institute of Computing Technology Chinese Academy of Sciences 28
University of Maryland 29
National University of Singapore 38
University of Science and Technology of China 43
Chinese Academy of Sciences 48

ACM Transactions on Intelligent Systems and Technology (TIST) - Research Survey and Regular Papers

Volume 9 Issue 4, February 2018 Research Survey and Regular Papers
Volume 9 Issue 3, February 2018 Regular Papers and Special Issue: Urban Intelligence
Volume 9 Issue 2, January 2018 Regular Papers

Volume 9 Issue 1, October 2017 Regular Papers and Special Issue: Data-driven Intelligence for Wireless Networking
Volume 8 Issue 5, September 2017
Volume 8 Issue 6, September 2017 Survey Paper, Regular Papers and Special Issue: Social Media Processing
Volume 8 Issue 4, July 2017 Special Issue: Cyber Security and Regular Papers
Volume 8 Issue 3, April 2017 Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers
Volume 8 Issue 2, January 2017 Survey Paper, Special Issue: Intelligent Music Systems and Applications and Regular Papers

Volume 8 Issue 1, October 2016
Volume 7 Issue 4, July 2016 Special Issue on Crowd in Intelligent Systems, Research Note/Short Paper and Regular Papers
Volume 7 Issue 3, April 2016 Regular Papers, Survey Papers and Special Issue on Recommender System Benchmarks
Volume 7 Issue 2, January 2016 Special Issue on Causal Discovery and Inference

Volume 7 Issue 1, October 2015
Volume 6 Issue 4, August 2015 Regular Papers and Special Section on Intelligent Healthcare Informatics
Volume 6 Issue 3, May 2015 Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence
Volume 6 Issue 2, May 2015 Special Section on Visual Understanding with RGB-D Sensors
Volume 6 Issue 1, April 2015
Volume 5 Issue 4, January 2015 Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers

Volume 5 Issue 3, September 2014 Special Section on Urban Computing
Volume 5 Issue 2, April 2014 Special Issue on Linking Social Granularity and Functions

Volume 5 Issue 1, December 2013 Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Volume 4 Issue 4, September 2013 Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Volume 4 Issue 3, June 2013 Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Volume 4 Issue 2, March 2013 Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Volume 4 Issue 1, January 2013 Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context

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

Volume 3 Issue 1, October 2011
Volume 2 Issue 4, July 2011
Volume 2 Issue 3, April 2011
Volume 2 Issue 2, February 2011
Volume 2 Issue 1, January 2011

Volume 1 Issue 2, November 2010
Volume 1 Issue 1, October 2010
All ACM Journals | See Full Journal Index

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