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RHUPS: Mining Recent High Utility Patterns with Sliding Window–based Arrival Time Control over Data Streams ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2021-01-13 Yoonji Baek; Unil Yun; Heonho Kim; Hyoju Nam; Hyunsoo Kim; Jerry Chun-Wei Lin; Bay Vo; Witold Pedrycz
Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently generated data has a greater influence than the previously generated data. These are called the time-sensitive non-binary stream databases, and they
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Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2021-01-13 Felix Winter; Nysret Musliu
Factories in the automotive supply industry paint a large number of items requested by car manufacturing companies on a daily basis. As these factories face numerous constraints and optimization objectives, finding a good schedule becomes a challenging task in practice, and full-time employees are expected to manually create feasible production plans. In this study, we propose novel constraint programming
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Self-weighted Robust LDA for Multiclass Classification with Edge Classes ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-12-17 Caixia Yan; Xiaojun Chang; Minnan Luo; Qinghua Zheng; Xiaoqin Zhang; Zhihui Li; Feiping Nie
Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the others, i.e., edge class, which occurs frequently in multi-class classification. First, the existence of edge classes often makes the total mean biased
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Deep Learning Thermal Image Translation for Night Vision Perception ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-12-22 Shuo Liu; Mingliang Gao; Vijay John; Zheng Liu; Erik Blasch
Context enhancement is critical for the environmental perception in night vision applications, especially for the dark night situation without sufficient illumination. In this article, we propose a thermal image translation method, which can translate thermal/infrared (IR) images into color visible (VI) images, called IR2VI. The IR2VI consists of two cascaded steps: translation from nighttime thermal
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On Representation Learning for Road Networks ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-12-15 Meng-Xiang Wang; Wang-Chien Lee; Tao-Yang Fu; Ge Yu
Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks to learn embeddings of intersections and road segments in road networks. To implement the RLRN framework
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Uncovering Media Bias via Social Network Learning ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-12-17 Yiyi Zhou; Rongrong Ji; Jinsong Su; Jiaquan Yao
It is known that media outlets, such as CNN and FOX, have intrinsic political bias that is reflected in their news reports. The computational prediction of such bias has broad application prospects. However, the prediction is difficult via directly analyzing the news content without high-level context. In contrast, social signals (e.g., the network structure of media followers) provide inspiring cues
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A Theoretical Revisit to Linear Convergence for Saddle Point Problems ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-12-07 Wendi Wu; Yawei Zhao; En Zhu; Xinwang Liu; Xingxing Zhang; Lailong Luo; Shixiong Wang; Jianping Yin
Recently, convex-concave bilinear Saddle Point Problems (SPP) is widely used in lasso problems, Support Vector Machines, game theory, and so on. Previous researches have proposed many methods to solve SPP, and present their convergence rate theoretically. To achieve linear convergence, analysis in those previouse studies requires strong convexity of φ(z). But, we find the linear convergence can also
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CSL+: Scalable Collective Subjective Logic under Multidimensional Uncertainty ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-11-21 Adil Alim; Jin-Hee Cho; Feng Chen
Using unreliable information sources generating conflicting evidence may lead to a large uncertainty, which significantly hurts the decision making process. Recently, many approaches have been taken to integrate conflicting data from multiple sources and/or fusing conflicting opinions from different entities. To explicitly deal with uncertainty, a belief model called Subjective Logic (SL), as a variant
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Pricing-aware Real-time Charging Scheduling and Charging Station Expansion for Large-scale Electric Buses ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-11-21 Guang Wang; Zhihan Fang; Xiaoyang Xie; Shuai Wang; Huijun Sun; Fan Zhang; Yunhuai Liu; Desheng Zhang
We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment
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Deep Energy Factorization Model for Demographic Prediction ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-11-16 Chih-Te Lai; Cheng-Te Li; Shou-De Lin
Demographic information is important for various commercial and academic proposes, but in reality, few of these data are accessible for analysis and research. To solve this problem, several studies predict demographic attributes from users’ behavioral data. However, previous works suffer from different kinds of disadvantages. Handling data sparseness and defining useful features remain especially challenge
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Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019 ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-11-13 Jens Adamczak; Yashar Deldjoo; Farshad Bakhshandegan Moghaddam; Peter Knees; Gerard-Paul Leyson; Philipp Monreal
In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe is vitally important for recommendation systems research in the area of hotel search, from both academic and industry perspectives. In this article
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A Novel Multi-task Tensor Correlation Neural Network for Facial Attribute Prediction ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-11-13 Mingxing Duan; Kenli Li; Keqin Li; Qi Tian
Multi-task learning plays an important role in face multi-attribute prediction. At present, most researches excavate the shared information between attributes by sharing all convolutional layers. However, it is not appropriate to treat the low-level and high-level features of the face multi-attribute equally, because the high-level features are more biased toward the specific content of the category
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Bayesian Nonparametric Unsupervised Concept Drift Detection for Data Stream Mining ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-11-13 Junyu Xuan; Jie Lu; Guangquan Zhang
Online data stream mining is of great significance in practice because of its ubiquity in many real-world scenarios, especially in the big data era. Traditional data mining algorithms cannot be directly applied to data streams due to (1) the possible change of underlying data distribution over time (i.e., concept drift) and (2) delayed, short, or even no labels for streaming data in practice. A new
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BiNeTClus: Bipartite Network Community Detection Based on Transactional Clustering ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-11-13 Mohamed Bouguessa; Khaled Nouri
We investigate the problem of community detection in bipartite networks that are characterized by the presence of two types of nodes such that connections exist only between nodes of different types. While some approaches have been proposed to identify community structures in bipartite networks, there are a number of problems still to solve. In fact, the majority of the proposed approaches suffer from
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DeepApp: Predicting Personalized Smartphone App Usage via Context-Aware Multi-Task Learning ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-10-29 Tong Xia; Yong Li; Jie Feng; Depeng Jin; Qing Zhang; Hengliang Luo; Qingmin Liao
Smartphone mobile application (App) usage prediction, i.e., which Apps will be used next, is beneficial for user experience improvement. Through an in-depth analysis on a real-world dataset, we find that App usage is highly spatio-temporally correlated and personalized. Given the ability to model complex spatio-temporal contexts, we aim to apply deep learning to achieve high prediction accuracy. However
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Human-computer Coalition Formation in Weighted Voting Games ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-10-17 Moshe Mash; Roy Fairstein; Yoram Bachrach; Kobi Gal; Yair Zick
This article proposes a negotiation game, based on the weighted voting paradigm in cooperative game theory, where agents need to form coalitions and agree on how to share the gains. Despite the prevalence of weighted voting in the real world, there has been little work studying people’s behavior in such settings. This work addresses this gap by combining game-theoretic solution concepts with machine
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Fast DistributedkNN Graph Construction Using Auto-tuned Locality-sensitive Hashing ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-10-12 Carlos Eiras-Franco; David Martínez-Rego; Leslie Kanthan; César Piñeiro; Antonio Bahamonde; Bertha Guijarro-Berdiñas; Amparo Alonso-Betanzos
The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. Approximate solutions have been described in the literature using diverse techniques, among which Locality-sensitive Hashing (LSH) is a promising alternative that still has unsolved problems. We
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Multiple Elimination of Base Classifiers in Ensemble Learning Using Accuracy and Diversity Comparisons ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-10-04 Zohaib Md. Jan; Brijesh Verma
When generating ensemble classifiers, selecting the best set of classifiers from the base classifier pool is considered a combinatorial problem and an efficient classifier selection methodology must be utilized. Different researchers have used different strategies such as evolutionary algorithms, genetic algorithms, rule-based algorithms, simulated annealing, and so forth to select the best set of
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SafeRoute: Learning to Navigate Streets Safely in an Urban Environment ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-09-27 Sharon Levy; Wenhan Xiong; Elizabeth Belding; William Yang Wang
Recent studies show that 85% of women have changed their traveled routes to avoid harassment and assault. Despite this, current mapping tools do not empower users with information to take charge of their personal safety. We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime. Unlike other street navigation applications, SafeRoute introduces
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BOXREC: Recommending a Box of Preferred Outfits in Online Shopping ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-09-25 Debopriyo Banerjee; Krothapalli Sreenivasa Rao; Shamik Sural; Niloy Ganguly
Fashionable outfits are generally created by expert fashionistas, who use their creativity and in-depth understanding of fashion to make attractive outfits. Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features)
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A Joint Neural Model for User Behavior Prediction on Social Networking Platforms ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-09-25 Junwei Li; Le Wu; Richang Hong; Kun Zhang; Yong Ge; Yan Li
Social networking services provide platforms for users to perform two kinds of behaviors: consumption behavior (e.g., recommending items of interest) and social link behavior (e.g., recommending potential social links). Accurately modeling and predicting users’ two kinds of behaviors are two core tasks in these platforms with various applications. Recently, with the advance of neural networks, many
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Contextual Anomaly Detection in Solder Paste Inspection with Multi-Task Learning ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-09-18 Zimu Zheng; Jie Pu; Linghui Liu; Dan Wang; Xiangming Mei; Sen Zhang; Quanyu Dai
In this article, we study solder paste inspection (SPI), an important stage that is used in the semiconductor manufacturing industry, where abnormal boards should be detected. A highly accurate SPI can substantially reduce human expert involvement, as well as reduce the waste in disposing of the boards in good condition. A key difference today is that because of increasing demand in board customization
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Latent Unexpected Recommendations ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-09-15 Pan Li; Alexander Tuzhilin
Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Previous unexpected recommendation methods only focus on the straightforward relations between current recommendations and user expectations by modeling unexpectedness in the feature
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An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-09-11 Bayu Adhi Tama; Marco Comuzzi; Jonghyeon Ko
There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the performance of different encoding windows and of using
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From Appearance to Essence: Comparing Truth Discovery Methods without Using Ground Truth ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-09-11 Xiu Susie Fang; Quan Z. Sheng; Xianzhi Wang; Wei Emma Zhang; Anne H. H. Ngu; Jian Yang
Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes
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Adaptive HTF-MPR: An Adaptive Heterogeneous TensorFlow Mapper Utilizing Bayesian Optimization and Genetic Algorithms ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-08-21 Ahmad Albaqsami; Maryam S. Hosseini; Masoomeh Jasemi; Nader Bagherzadeh
Deep neural networks are widely used in many artificial intelligence applications. They have demonstrated state-of-the-art accuracy on many artificial intelligence tasks. For this high accuracy to occur, deep neural networks require the right parameter values. This is achieved by a process known as training. The training of large amounts of data via many iterations comes at a high cost in regard to
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A Survey of Unsupervised Deep Domain Adaptation ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-05 Garrett Wilson; Diane J. Cook
Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data
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Practical Privacy Preserving POI Recommendation ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-05 Chaochao Chen; Jun Zhou; Bingzhe Wu; Wenjing Fang; Li Wang; Yuan Qi; Xiaolin Zheng
Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the basis of collecting users’ data. Both private data and models are held by the recommender, which causes serious privacy concerns. In this article, we propose a novel Privacy preserving POI Recommendation (PriRec) framework
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Cut-n-Reveal: Time Series Segmentations with Explanations ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-28 Nikhil Muralidhar; Anika Tabassum; Liangzhe Chen; Supriya Chinthavali; Naren Ramakrishnan; B. Aditya Prakash
Recent hurricane events have caused unprecedented amounts of damage on critical infrastructure systems and have severely threatened our public safety and economic health. The most observable (and severe) impact of these hurricanes is the loss of electric power in many regions, which causes breakdowns in essential public services. Understanding power outages and how they evolve during a hurricane provides
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Multi-Task Learning for Entity Recommendation and Document Ranking in Web Search ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-25 Jizhou Huang; Haifeng Wang; Wei Zhang; Ting Liu
Entity recommendation, providing users with an improved search experience by proactively recommending related entities to a given query, has become an indispensable feature of today’s Web search engine. Existing studies typically only consider the query issued at the current timestep while ignoring the in-session user search behavior (short-term search history) or historical user search behavior across
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STARS: Defending against Sockpuppet-Based Targeted Attacks on Reviewing Systems ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-24 Rui Liu; Runze Liu; Andrea Pugliese; V. S. Subrahmanian
Customers of virtually all online marketplaces rely upon reviews in order to select the product or service they wish to buy. These marketplaces in turn deploy review fraud detection systems so that the integrity of reviews is preserved. A well-known problem with review fraud detection systems is their underlying assumption that the majority of reviews are honest-this assumption leads to a vulnerability
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A Discriminative Convolutional Neural Network with Context-aware Attention ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-25 Yuxiang Zhou; Lejian Liao; Yang Gao; Heyan Huang; Xiaochi Wei
Feature representation and feature extraction are two crucial procedures in text mining. Convolutional Neural Networks (CNN) have shown overwhelming success for text-mining tasks, since they are capable of efficiently extracting n-gram features from source data. However, vanilla CNN has its own weaknesses on feature representation and feature extraction. A certain amount of filters in CNN are inevitably
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Shapelet-transformed Multi-channel EEG Channel Selection ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-08-10 Chenglong Dai; Dechang Pi; Stefanie I. Becker
This article proposes an approach to select EEG channels based on EEG shapelet transformation, aiming to reduce the setup time and inconvenience for subjects and to improve the applicable performance of Brain-Computer Interfaces (BCIs). In detail, the method selects top-k EEG channels by solving a logistic loss-embedded minimization problem with respect to EEG shapelet learning, hyperplane learning
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Querying Recurrent Convoys over Trajectory Data ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-08-03 Munkh-Erdene Yadamjav; Zhifeng Bao; Baihua Zheng; Farhana M. Choudhury; Hanan Samet
Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. We observe that existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the
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Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-25 Xiaoguang Tu; Zheng Ma; Jian Zhao; Guodong Du; Mei Xie; Jiashi Feng
Face anti-spoofing aims to detect presentation attack to face recognition--based authentication systems. It has drawn growing attention due to the high security demand. The widely adopted CNN-based methods usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of novel patterns
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Moment-Guided Discriminative Manifold Correlation Learning on Ordinal Data ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-05 Qing Tian; Wenqiang Zhang; Meng Cao; Liping Wang; Songcan Chen; Hujun Yin
Canonical correlation analysis (CCA) is a typical and useful learning paradigm in big data analysis for capturing correlation across multiple views of the same objects. When dealing with data with additional ordinal information, traditional CCA suffers from poor performance due to ignoring the ordinal relationships within the data. Such data is becoming increasingly common, as either temporal or sequential
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Dancing with Trump in the Stock Market: A Deep Information Echoing Model ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-07-05 Kun Yuan; Guannan Liu; Junjie Wu; Hui Xiong
It is always deemed crucial to identify the key factors that could have significant impact on the stock market trend. Recently, an interesting phenomenon has emerged that some of President Trump’s posts in Twitter can surge into a dominant role on the stock market for a certain time period, although studies along this line are still in their infancy. Therefore, in this article, we study whether and
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Mapping Points of Interest Through Street View Imagery and Paid Crowdsourcing ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-08-10 Eddy Maddalena; Luis-Daniel Ibáñez; Elena Simperl
We present the Virtual City Explorer (VCE), an online crowdsourcing platform for the collection of rich geotagged information in urban environments. Compared to other volunteered geographic information approaches, which are constrained by the number and availability of mapping enthusiasts on the ground, the VCE uses digital street imagery to allow people to virtually explore a city from anywhere in
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BISTRO: Berkeley Integrated System for Transportation Optimization ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-06-24 Sidney A. Feygin; Jessica R. Lazarus; Edward H. Forscher; Valentine Golfier-Vetterli; Jonathan W. Lee; Abhishek Gupta; Rashid A. Waraich; Colin J. R. Sheppard; Alexandre M. Bayen
The current trend toward urbanization and adoption of flexible and innovative mobility technologies will have complex and difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the increasingly uncertain future state of the urban mobility landscape do not yet exist. Furthermore, few approaches have enabled the massive ingestion of urban
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Geosocial Co-Clustering ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-06-13 Jungeun Kim; Jae-Gil Lee; Byung Suk Lee; Jiajun Liu
As location-based services using mobile devices have become globally popular these days, social network analysis (especially, community detection) increasingly benefits from combining social relationships with geographic preferences. In this regard, this article addresses the emerging problem of geosocial community detection. We first formalize the problem of geosocial co-clustering, which co-clusters
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An Attention-based Rumor Detection Model with Tree-structured Recursive Neural Networks ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-06-08 Jing Ma; Wei Gao; Shafiq Joty; Kam-Fai Wong
Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to
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Superpixel Region Merging Based on Deep Network for Medical Image Segmentation ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-31 Hui Liu; Haiou Wang; Yan Wu; Lei Xing
Automatic and accurate semantic segmentation of pathological structures in medical images is challenging because of noisy disturbance, deformable shapes of pathology, and low contrast between soft tissues. Classical superpixel-based classification algorithms suffer from edge leakage due to complexity and heterogeneity inherent in medical images. Therefore, we propose a deep U-Net with superpixel region
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CNN-based Multiple Manipulation Detector Using Frequency Domain Features of Image Residuals ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-31 Divya Singhal; Abhinav Gupta; Anurag Tripathi; Ravi Kothari
Increasingly sophisticated image editing tools make it easy to modify images. Often these modifications are elaborate, convincing, and undetectable by even careful human inspection. These considerations have prompted the development of forensic algorithms and approaches to detect modifications done to an image. However, these detectors are model-driven (i.e., manipulation-specific) and the choice of
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Domain-attention Conditional Wasserstein Distance for Multi-source Domain Adaptation ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-31 Hanrui Wu; Yuguang Yan; Michael K. Ng; Qingyao Wu
Multi-source domain adaptation has received considerable attention due to its effectiveness of leveraging the knowledge from multiple related sources with different distributions to enhance the learning performance. One of the fundamental challenges in multi-source domain adaptation is how to determine the amount of knowledge transferred from each source domain to the target domain. To address this
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DeepKey ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-31 Xiang Zhang; Lina Yao; Chaoran Huang; Tao Gu; Zheng Yang; Yunhao Liu
Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or
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Knowledge-aware Attentive Wasserstein Adversarial Dialogue Response Generation ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-28 Yingying Zhang; Quan Fang; Shengsheng Qian; Changsheng Xu
Natural language generation has become a fundamental task in dialogue systems. RNN-based natural response generation methods encode the dialogue context and decode it into a response. However, they tend to generate dull and simple responses. In this article, we propose a novel framework, called KAWA-DRG (Knowledge-aware Attentive Wasserstein Adversarial Dialogue Response Generation) to model conversation-specific
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Understanding the Long-Term Evolution of Electric Taxi Networks ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-28 Guang Wang; Fan Zhang; Huijun Sun; Yang Wang; Desheng Zhang
Due to the ever-growing concerns over air pollution and energy security, more and more cities have started to replace their conventional taxi fleets with electric ones. Even though environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures, and inadequate
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Video Object Segmentation and Tracking ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-23 Rui Yao; Guosheng Lin; Shixiong Xia; Jiaqi Zhao; Yong Zhou
Object segmentation and object tracking are fundamental research areas in the computer vision community. These two topics are difficult to handle some common challenges, such as occlusion, deformation, motion blur, scale variation, and more. The former contains heterogeneous object, interacting object, edge ambiguity, and shape complexity; the latter suffers from difficulties in handling fast motion
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CoFi-points ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-23 Lin Li; Weike Pan; Zhong Ming
With the explosive growth of web resources, an increasingly important task in recommender systems is to provide high-quality personalized services by learning users’ preferences from historically observed information. As an effective preference learning technology, collaborative filtering has been widely extended to model the one-class or implicit feedback data, which is known as one-class collaborative
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Mining High-utility Temporal Patterns on Time Interval–based Data ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-23 Jun-Zhe Wang; Yi-Cheng Chen; Wen-Yueh Shih; Lin Yang; Yu-Shao Liu; Jiun-Long Huang
In this article, we propose a novel temporal pattern mining problem, named high-utility temporal pattern mining, to fulfill the needs of various applications. Different from classical temporal pattern mining aimed at discovering frequent temporal patterns, high-utility temporal pattern mining is to find each temporal pattern whose utility is greater than or equal to the minimum-utility threshold. To
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End-to-End Text-to-Image Synthesis with Spatial Constrains ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-23 Min Wang; Congyan Lang; Liqian Liang; Songhe Feng; Tao Wang; Yutong Gao
Although the performance of automatically generating high-resolution realistic images from text descriptions has been significantly boosted, many challenging issues in image synthesis have not been fully investigated, due to shapes variations, viewpoint changes, pose changes, and the relations of multiple objects. In this article, we propose a novel end-to-end approach for text-to-image synthesis with
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A Traffic Density Estimation Model Based on Crowdsourcing Privacy Protection ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-19 Yapei Huang; Yun Tian; Zhijie Liu; Xiaowei Jin; Yanan Liu; Shifeng Zhao; Daxin Tian
Acquiring traffic condition information is of great significance in transportation guidance, urban planning, and route recommendation. To date, traffic density data are generally acquired by road sound analysis, video data analysis, or in-vehicle network communication, which are usually financially or temporally expensive. Another way to get traffic conditions is to collect track data by crowdsourcing
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Copula-Based Anomaly Scoring and Localization for Large-Scale, High-Dimensional Continuous Data ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-13 Gábor Horváth; Edith Kovács; Roland Molontay; Szabolcs Nováczki
The anomaly detection method presented by this article has a special feature: it not only indicates whether or not an observation is anomalous but also tells what exactly makes an anomalous observation unusual. Hence, it provides support to localize the reason of the anomaly. The proposed approach is model based; it relies on the multivariate probability distribution associated with the observations
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Understand Dynamic Regret with Switching Cost for Online Decision Making ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-13 Yawei Zhao; Qian Zhao; Xingxing Zhang; En Zhu; Xinwang Liu; Jianping Yin
As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in much previous research, we still have little knowledge about the relation between the dynamic regret and the switching cost. In the article, we investigate the relation for two classic online settings:
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WiSign ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-13 Lei Zhang; Yixiang Zhang; Xiaolong Zheng
In this article, we propose WiSign that recognizes the continuous sentences of American Sign Language (ASL) with existing WiFi infrastructure. Instead of identifying the individual ASL words from the manually segmented ASL sentence in existing works, WiSign can automatically segment the original channel state information (CSI) based on the power spectral density (PSD) segmentation method. WiSign constructs
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Learning Three-dimensional Skeleton Data from Sign Language Video ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-13 Heike Brock; Felix Law; Kazuhiro Nakadai; Yuji Nagashima
Data for sign language research is often difficult and costly to acquire. We therefore present a novel pipeline able to generate motion three-dimensional (3D) skeleton data from single-camera sign language videos only. First, three recurrent neural networks are learned to infer the three-dimensional position data of body, face, and finger joints for a high resolution of the signer’s skeleton. Subsequently
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A Causal Dirichlet Mixture Model for Causal Inference from Observational Data ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-13 Adi Lin; Jie Lu; Junyu Xuan; Fujin Zhu; Guangquan Zhang
Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today’s data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains
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Modeling with Node Popularities for Autonomous Overlapping Community Detection ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-13 Di Jin; Bingyi Li; Pengfei Jiao; Dongxiao He; Hongyu Shan; Weixiong Zhang
Overlapping community detection has triggered recent research in network analysis. One of the promising techniques for finding overlapping communities is the popular stochastic models, which, unfortunately, have some common drawbacks. They do not support an important observation that highly connected nodes are more likely to reside in the overlapping regions of communities in the network. These methods
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Deep Neighborhood Component Analysis for Visual Similarity Modeling ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-13 Xueliang Liu; Xun Yang; Meng Wang; Richang Hong
Learning effective visual similarity is an essential problem in multimedia research. Despite the promising progress made in recent years, most existing approaches learn visual features and similarities in two separate stages, which inevitably limits their performance. Once useful information has been lost in the feature extraction stage, it can hardly be recovered later. This article proposes a novel
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HERA ACM Trans. Intell. Syst. Technol. (IF 2.672) Pub Date : 2020-05-13 Gengyu Lyu; Songhe Feng; Yidong Li; Yi Jin; Guojun Dai; Congyan Lang
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this paper, we propose a novel PLL approach called HERA, which simultaneously incorporates