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Federated Machine Learning: Concept and Applications

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by... (more)

A Survey of Zero-Shot Learning: Settings, Methods, and Applications

Most machine-learning methods focus on classifying instances whose classes have already been seen in training. In practice, many applications require classifying instances whose classes have not been seen previously. Zero-shot learning is a powerful and promising learning paradigm, in which the classes covered by training instances and the classes... (more)

Goal and Plan Recognition Design for Plan Libraries

This article provides new techniques for optimizing domain design for goal and plan recognition using plan libraries. We define two new problems: Goal... (more)

Enumerating Connected Subgraphs and Computing the Myerson and Shapley Values in Graph-Restricted Games

At the heart of multi-agent systems is the ability to cooperate to improve the performance of... (more)

Understanding Event Organization at Scale in Event-Based Social Networks

Understanding real-world event participation behavior has been a subject of active research and can offer valuable insights for event-related... (more)

Inferring Online Social Ties from Offline Geographical Activities

As mobile devices are becoming ubiquitous nowadays, the geographical activities and interactions of human beings can be easily recorded and accessed.... (more)

Reconstruction of Hidden Representation for Robust Feature Extraction

This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically... (more)

A Simple Baseline for Travel Time Estimation using Large-scale Trip Data

The increased availability of large-scale trajectory data provides rich information for the study of urban dynamics. For example, New York City Taxi... (more)

Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area,... (more)

NEWS

Recent TIST News: 

ACM Transactions on Intelligent Systems and Technology (TIST) is ranked as one of the best  journals in all ACM journals in terms of citations received per paper. Each paper published at TIST in the time span (from 2010 to 2018) has received 12.8 citations  on average in ACM Digital Library.  

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 (2018)

  • - Impact Factor: 3.19
  • - 5-year Impact Factor: 10.47
  • - Avg. Citations in ACM DL: 12.8 

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
Local Learning Approaches for Finding Effects of a Specified Cause and Their Causal Paths

Causal networks are used to describe and to discover causal relationships among variables and data generating mechanisms. There have been many approaches for learning a global causal network of all observed variables. In many applications, we may be interested in finding what are the effects of a specified cause variable and what are the causal paths from the cause variable to its effects. Instead of learning a global causal network, we propose several local learning approaches for finding all effects (or descendants) of the specified cause variable and the causal paths from the cause variable to some effect variable of interest. We discuss the identifiability of the effects and the causal paths from observed data and prior knowledge. For the case that the causal paths are not identifiable, our approaches try to find a path set which contains the causal paths of interest.

A Local Mean Representation-Based K-Nearest Neighbor Classifier

K-nearest neighbor classification has been well-known and widely used in pattern recognition. As one of the top 10 algorithms in data mining, k-nearest neighbor classifier (KNN) is a very simple and yet effective nonparametric technique. However, due to the selective sensitiveness of the neighborhood size k, the simple majority vote and the conventional metric measure, the KNN-based classification performance can be easily degraded, especially in the small training sample size cases. In this article, to further improve the classification performance and overcome the main issues in the KNN-based classification, we propose a local mean representation-based k-nearest neighbor classifier (LMRKNN). The rationale of the proposed LMRKNN is as follows. Firstly, k categorical nearest neighbors of a query sample are found and then used to calculate the corresponding k categorical local mean vectors which can represent different local class-specific sample distributions. Secondly, the query sample is represented by the linear combination of k categorical local mean vectors and the representation coefficient of each local mean vector is obtained as the contribution to representing and classifying the query sample. Finally, the class-specific representation-based distance between the query sample and k categorical local mean vectors per class is adopted to determine the class label of the query sample. Extensive experiments on many UCI and KEEL data sets and three popular face databases are carried out by comparing LMRKNN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed LMRKNN method outperforms the related competitive KNN-based methods with the more robustness and effectiveness.

Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users? traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this paper, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize the revenue of network operators. Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

Using Social Dependence to Enable Neighbourly Behaviour in Open Multi-agent Systems

Agents frequently coordinate their behaviour and collaborate with their neighbours, which is especially needed when resources constrained, to achieve a shared goal or to accomplish a complex task that they cannot do alone. In agent neighbourhoods with a single shared resource, agents' cooperation and neighbourly behaviour is the key to any successful collaborative process. However, such behaviour is particularly challenging in open multi-agent multi-neighbourhood systems, where agents are self-interested and continuously and unpredictably leave and join neighbourhoods. In current approaches, social reasoning is used to capture agents' capabilities in disjoint neighbourhoods to support selection of a qualified set of participants to accomplish a complex task. However, these approaches are not useful in systems where agents do not depend on each other to accomplish complex tasks, but they may depend on each other when using shared resources and share the overall costs and benefits. In this paper, using social dependencies, agents are enabled to be cooperative and demonstrate good neighbourly behaviour in open multi-neighbourhood systems. Agents use both self-adaptation and social reasoning techniques to adjust their level of involvement in cooperative processes and to balance their level of self-interest and cooperation. Each agent builds and maintains a social dependency model, which enhances agents' understanding of their own goal dependencies on their neighbours. The dependency model enables agents to effectively adjust their behaviour or move between different neighbourhoods to contribute to lowering shared costs or increasing shared benefits. The proposed model is evaluated in a multi-neighbourhood setting with 100 agents sharing the constrained resources available in each neighbourhood under varying levels of agents' mobility and neighbourhoods' density. The results from the proposed model is compared to collaborative and competitive scenarios to evaluate agents' success at achieving multiple dependant goals while sharing constraint resources. The results obtained from the most dense and mobile scenario show a 97.6\% success rate at achieving the shared goal, with up to 50\% lower communication and computation cost, while 100\% individual goals are met.

Location-Specific Influence Quantification in Location-based Social Networks

Location-based social networks (LBSNs) such as Foursquare o er a platform for users to share and be aware of each others physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a model called LoCaTe, that combines (a) a user mobility model based on kernel density estimates; (b) a model of the semantics of the location using topic models; and (c) a model of inter-check-in time using exponential distribution. We show the applicability of LoCaTe for location promotion and location recommendation tasks using LBSNs. Our model is validated using a long-term crawl of Foursquare data collected between Jan 2015 aAS' Feb 2016, as well as other publicly available LBSN datasets. Our experiments demonstrate the efficacy of LoCaTe in capturing location-specific influence between users. We also show that LoCaTe improves over state-of-the-art models for the coarse-grained task of location promotion.

CNNs based Viewpoint Estimation for Volume Visualization

Viewpoint estimation from 2D rendered images is helpful in understanding how users select viewpoints for volume visualization and guiding users to select better viewpoints based on previous visualizations. In this paper, we propose a viewpoint estimation method based on Convolutional Neural Networks (CNNs) for volume visualization. We first design an overfit-resistant image rendering pipeline to generate the training images with accurate viewpoint annotations, and then train a category-specific viewpoint classification network to estimate the viewpoint for the given rendered image. Our method can achieve good performance on images rendered with different transfer functions and rendering parameters in several categories. We apply our model to recover the viewpoints of the rendered images in publications, and show how experts look at volumes. We also introduce a CNN feature-based image similarity measure for similarity voting based viewpoint selection, which can suggest semantically meaningful optimal viewpoints for different volumes and transfer functions.

A Semi-Boosted Nested Model with Sensitivity-based Weighted Binarization for Multi-Domain Network Intrusion Detection

Effective network intrusion detection techniques are required to thwart evolving cybersecurity threats. Historically, traditional enterprise networks have been researched extensively in this regard. However, the cyber threat landscape has grown to include wireless networks. In this paper, the authors present a novel model that can be trained on completely different feature sets and applied to two distinct intrusion detection applications: traditional enterprise networks and 802.11 wireless networks. This is the first method that demonstrates superior performance in both aforementioned applications. The model is based on a one-versus-all (OVA) binary framework comprising multiple nested sub-ensembles. To provide good generalization ability, each sub-ensemble contains a collection of sub-learners, and only a portion of the sub-learners implement boosting. A class weight based on the sensitivity metric (true positive rate), learned from the training data only, is assigned to the sub-ensembles of each class. The use of pruning to remove sub-learners that do not contribute to or have an adverse effect on overall system performance is investigated as well. The results demonstrate that the proposed system can achieve exceptional performance in applications to both traditional enterprise intrusion detection and 802.11 wireless intrusion detection.

Co-saliency Detection with Graph Matching

Recently, co-saliency detection which aims to automatically discover common and salient objects appeared in several relevant images has attracted increasing interest in computer vision community. In this paper, we present a novel graph-matching based model for co-saliency detection in image pairs. A solution of graph matching is proposed to integrate the visual appearance, saliency coherence and spatial structural continuity for detecting co-saliency collaboratively. Since the saliency and the visual similarity have been seamlessly integrated, such a joint inference schema is able to produce more accurate and reliable results. More concretely, the proposed model first computes the intra saliency for each image by aggregating multiple saliency cues. The common and salient regions across multiple images are thus discovered via a graph matching procedure. Then, a graph reconstruction scheme is proposed to refine the intra saliency iteratively. Compared to existing co-saliency detection methods that only utilize visual appearance cues, our proposed model can effectively exploit both visual appearance and structure information to better guide co-saliency detection. Extensive experiments on several challenging image pair databases demonstrate that our model outperforms state-of-the-art baselines significantly.

Combating Fake News: A Survey on Identification and Mitigation Techniques

The proliferation of fake news on social media has opened up new directions of research for timely identifi- cation and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news using content based solutions, that deter- mine the truthfulness of a piece of news based on its text contents only, or using feedback based solutions that exploit users? activities towards the news on social media, such as propagation patterns or comments, there has been a rising interest in active intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the problem of fake news and the technical challenges associated with identification and mitigation of fake news. We present an overview of existing methods and techniques applicable to fake news identification and mitigation, along with insights and details of the sig- nificant advances in various methods, and the practical advantages and limitations of each. Further, we enumerate a list of challenges and open problems that outline new directions of research, and provide a comprehensive list of available datasets with a summarization of their characteristic features, in order to facilitate future research and enable the development of solutions that are interdisciplinary and effective in practice.

Online Heterogeneous Transfer Learning by Knowledge Transition

In this paper, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called Online Heterogeneous Knowledge Transition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.

Motion-aware Compression and Transmission of Mesh Animation Sequences

With the increasing demand in using 3D mesh data over networks, supporting effective compression and efficient transmission of meshes have caught lots of attention in recent years. This paper introduces a novel compression method for 3D mesh animation sequences, supporting user-defined and progressive transmissions over networks. Our motion-aware approach starts with clustering animation frames based on their motion similarities, dividing a mesh animation sequence into fragments of varying lengths. This is done by a novel temporal clustering algorithm, which measures motion similarity based on the curvature and torsion of a space curve formed by corresponding vertices along a series of animation frames. We further segment each cluster based on mesh vertex coherence, representing topological proximity within an object under certain motion. To produce a compact representation, we perform intra-cluster compression based on Graph Fourier Transform (GFT) and Set Partitioning In Hierarchical Trees (SPIHT) coding. Optimized compression results can be achieved by applying GFT due to the proximity in vertex position and motion. We adapt SPIHT to support progressive transmission and design a mechanism to transmit mesh animation sequences with user-defined quality. Experimental results show that our method can obtain a high compression ratio while maintaining a low reconstruction error.

Predicting Academic Performance for College Students: A Campus Behavior Perspective

Detecting abnormal behaviors of students in time and providing personalized intervention and guidance at the early stage is important in educational management. Academic performance prediction is an important building block to enabling this pre-intervention and guidance. Most of the previous studies are based on questionnaire surveys and self-reports, which suffer from a small sample size and social desirability bias. In this paper, we collect longitudinal behavioral data from 6,597 students' smart cards and propose three major types of discriminative behavioral factors, diligence, orderliness, and sleep patterns. Empirical analysis demonstrates these behavioral factors are strongly correlated with academic performance. Furthermore, motivated by social influence theory, we analyze the correlation of each student's academic performance with his/her behaviorally similar students'. Statistical tests indicate this correlation is significant. Based on these factors, we further build a multi-task predictive framework based on a learning-to-rank algorithm for academic performance prediction. This framework captures inter-semester correlation, inter-major correlation and integrates student similarity to predict students' academic performance. The experiments on a large-scale real-world dataset show the effectiveness of our methods for predicting academic performance and the effectiveness of proposed behavioral factors.

Recognizing Multi-Agent Plans When Action Models and Team Plans Are Both Incomplete

Multi-Agent Plan Recognition (MAPR) aims to recognize team structures (which are composed of team plans) from the observed team traces (action sequences) of a set of intelligent agents. In this paper, we introduce the problem formulation of Multi-Agent Plan Recognition based on partially observed team traces, and present a weighted MAX-SAT based framework to recognize multi-agent plans from partially observed team traces with the help of two types of auxiliary knowledge to help recognize multi-agent plans, i.e., a library of \emph{incomplete} team plans and a set of \emph{incomplete} action models. Our framework functions with two phases. We first build a set of \emph{hard} constraints that encode the correctness property of the team plans, and a set of \emph{soft} constraints that encode the optimal utility property of team plans based on the input team trace, incomplete team plans and incomplete action models. After that, we solve all the constraints using a weighted MAX-SAT solver and convert the solution to a set of team plans that best \emph{explain} the structure of the observed team trace. We empirically exhibit both effectiveness and efficiency of our framework in benchmark domains from International Planning Competition (IPC).

Accounting for hidden common causes when inferring cause and effect from observational data

Hidden common causes make it difficult to infer causal relationships from observational data. Here, we consider a new method to account for a hidden common cause that infers its presence from the data. As with other approaches that can account for common causes, this approach is successful only in some cases. We describe such a case taken from the field of genomics, wherein one tries to identify which genomic markers causally influence a trait of interest.

Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection

Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this paper, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on center saliency and dark channel priors. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. Extensive evaluations over four benchmark datasets demonstrate that our proposed method performs favorably against most of the state-of-the-art approaches. Besides, we further discuss the application of the proposed algorithm in small target detection and demonstrate the universal value of center-dark channel priors in the field of object detection.

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