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ORCIDACM Transactions on Intelligent Systems and Technology, Volume 13
Volume 13, Number 1, February 2022
- Kai Zheng, Yong Li, Cyrus Shahabi, Hongzhi Yin
:
Introduction to the Special Issue on Intelligent Trajectory Analytics: Part I. 1:1-1:2 - Yuandong Wang, Hongzhi Yin, Tong Chen, Chunyang Liu, Ben Wang, Tianyu Wo, Jie Xu:
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs. 2:1-2:25 - Wen-Cheng Chen, Wan-Lun Tsai, Huan-Hua Chang, Min-Chun Hu, Wei-Ta Chu:
Instant Basketball Defensive Trajectory Generation. 3:1-3:20 - Fan Zhou, Pengyu Wang, Xovee Xu, Wenxin Tai, Goce Trajcevski:
Contrastive Trajectory Learning for Tour Recommendation. 4:1-4:25 - Meng Chen, Qingjie Liu, Weiming Huang, Teng Zhang, Yixuan Zuo, Xiaohui Yu:
Origin-Aware Location Prediction Based on Historical Vehicle Trajectories. 5:1-5:18 - Christoffer Löffler, Luca Reeb, Daniel Dzibela, Robert Marzilger, Nicolas Witt, Björn M. Eskofier, Christopher Mutschler:
Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories. 6:1-6:23 - Heli Sun, Xianglan Guo, Zhou Yang, Xuguang Chu, Xinwang Liu, Liang He:
Predicting Future Locations with Semantic Trajectories. 7:1-7:20 - Hui Luo, Zhifeng Bao, Gao Cong, J. Shane Culpepper, Nguyen Lu Dang Khoa:
Let Trajectories Speak Out the Traffic Bottlenecks. 8:1-8:21 - Hongting Niu, Hengshu Zhu, Ying Sun, Xinjiang Lu, Jing Sun, Zhiyuan Zhao, Hui Xiong, Bo Lang:
Exploring the Risky Travel Area and Behavior of Car-hailing Service. 9:1-9:22 - Yanliang Zhu, Dongchun Ren, Yi Xu, Deheng Qian, Mingyu Fan, Xin Li, Huaxia Xia:
Simultaneous Past and Current Social Interaction-aware Trajectory Prediction for Multiple Intelligent Agents in Dynamic Scenes. 10:1-10:16 - Xin Bi, Chao Zhang, Fangtong Wang, Zhixun Liu, Xiangguo Zhao, Ye Yuan, Guoren Wang:
An Uncertainty-based Neural Network for Explainable Trajectory Segmentation. 11:1-11:18
- Marcin Waniek, Tomasz P. Michalak, Michael J. Wooldridge, Talal Rahwan:
How Members of Covert Networks Conceal the Identities of Their Leaders. 12:1-12:29 - Shih-Chia Huang, Quoc-Viet Hoang, Da-Wei Jaw:
Self-Adaptive Feature Transformation Networks for Object Detection in low luminance Images. 13:1-13:11 - Yu Ting Wen, Hui-Kuo Yang, Wen-Chih Peng:
Mining Willing-to-Pay Behavior Patterns from Payment Datasets. 14:1-14:19 - Yu Zhou, Haixia Zheng, Xin Huang, Shufeng Hao, Dengao Li, Jumin Zhao:
Graph Neural Networks: Taxonomy, Advances, and Trends. 15:1-15:54 - Cheng-Te Li, Cheng Hsu, Yang Zhang:
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings. 16:1-16:21
Volume 13, Number 2, April 2022
- Senzhang Wang, Junbo Zhang, Yanjie Fu, Yong Li:
Introduction to the Special Issue on Deep Learning for Spatio-Temporal Data: Part 2. 17:1-17:4 - Divya Saxena, Jiannong Cao:
Multimodal Spatio-Temporal Prediction with Stochastic Adversarial Networks. 18:1-18:23 - He Li, Xuejiao Li, Liangcai Su, Duo Jin, Jianbin Huang, Deshuang Huang:
Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction. 19:1-19:21 - Zhilong Lu, Weifeng Lv, Zhipu Xie, Bowen Du, Guixi Xiong, Leilei Sun, Haiquan Wang:
Graph Sequence Neural Network with an Attention Mechanism for Traffic Speed Prediction. 20:1-20:24 - Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Quanjun Chen, Xuan Song, Ryosuke Shibasaki:
Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System. 21:1-21:24 - Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, Flora D. Salim:
Generative Adversarial Networks for Spatio-temporal Data: A Survey. 22:1-22:25 - Yingxue Zhang, Yanhua Li, Xun Zhou, Jun Luo, Zhi-Li Zhang:
Urban Traffic Dynamics Prediction - A Continuous Spatial-temporal Meta-learning Approach. 23:1-23:19 - Haomin Wen, Youfang Lin, Huaiyu Wan, Shengnan Guo, Fan Wu, Lixia Wu, Chao Song, Yinghui Xu:
DeepRoute+: Modeling Couriers' Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction. 24:1-24:23 - Zhe Jiang, Wenchong He, Marcus Stephen Kirby, Arpan Man Sainju, Shaowen Wang, Lawrence V. Stanislawski, Ethan Shavers, E. Lynn Usery:
Weakly Supervised Spatial Deep Learning for Earth Image Segmentation Based on Imperfect Polyline Labels. 25:1-25:20 - Wenchong He, Arpan Man Sainju, Zhe Jiang, Da Yan, Yang Zhou:
Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training. 26:1-26:22 - Han Bao, Xun Zhou, Yiqun Xie, Yingxue Zhang, Yanhua Li:
COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features. 27:1-27:23 - Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, Xinbing Wang, Haisong Zhang:
Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network. 28:1-28:25
- Liang Wang, Zhiwen Yu, Bin Guo, Dingqi Yang, Lianbo Ma, Zhidan Liu, Fei Xiong:
Data-driven Targeted Advertising Recommendation System for Outdoor Billboard. 29:1-29:23 - Yulin He, Xuan Ye, Joshua Zhexue Huang, Philippe Fournier-Viger:
Bayesian Attribute Bagging-Based Extreme Learning Machine for High-Dimensional Classification and Regression. 30:1-30:26 - Qian Li, Hao Peng, Jianxin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He:
A Survey on Text Classification: From Traditional to Deep Learning. 31:1-31:41 - Guangliang Gao, Zhifeng Bao, Jie Cao, A. Kai Qin, Timos Sellis:
Location-Centered House Price Prediction: A Multi-Task Learning Approach. 32:1-32:25 - Weichao Liang, Zhiang Wu, Zhe Li, Yong Ge:
CrimeTensor: Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal Consistency. 33:1-33:24
Volume 13, Number 3, June 2022
- Kai Zheng, Yong Li, Cyrus Shahabi, Hongzhi Yin:
Introduction to the Special Issue on Intelligent Trajectory Analytics: Part II. 34:1-34:2 - Liwei Deng, Hao Sun, Rui Sun, Yan Zhao, Han Su:
Efficient and Effective Similar Subtrajectory Search: A Spatial-aware Comprehension Approach. 35:1-35:22 - Arun Sharma, Shashi Shekhar:
Analyzing Trajectory Gaps to Find Possible Rendezvous Region. 36:1-36:23 - Bolong Zheng, Lingfeng Ming, Qi Hu, Zhipeng Lü, Guanfeng Liu, Xiaofang Zhou:
Supply-Demand-aware Deep Reinforcement Learning for Dynamic Fleet Management. 37:1-37:19 - Senzhang Wang, Meiyue Zhang, Hao Miao, Zhaohui Peng, Philip S. Yu:
Multivariate Correlation-aware Spatio-temporal Graph Convolutional Networks for Multi-scale Traffic Prediction. 38:1-38:22 - Yifan Zhang, Jinghuai Zhang, Jindi Zhang, Jianping Wang, Kejie Lu, L. Jeff Hong:
Integrating Algorithmic Sampling-Based Motion Planning with Learning in Autonomous Driving. 39:1-39:27 - Chenglong Fang, Feng Wang, Bin Yao, Jianqiu Xu:
GPSClean: A Framework for Cleaning and Repairing GPS Data. 40:1-40:22 - Jianbin Huang, Longji Huang, Meijuan Liu, He Li, Qinglin Tan, Xiaoke Ma, Jiangtao Cui, De-Shuang Huang:
Deep Reinforcement Learning-based Trajectory Pricing on Ride-hailing Platforms. 41:1-41:19 - Lin Yao, Zhenyu Chen, Haibo Hu, Guowei Wu, Bin Wu:
Privacy Preservation for Trajectory Publication Based on Differential Privacy. 42:1-42:21 - Nan Han, Shaojie Qiao, Kun Yue, Jianbin Huang, Qiang He, Tingting Tang, Faliang Huang, Chunlin He, Chang-an Yuan:
Algorithms for Trajectory Points Clustering in Location-based Social Networks. 43:1-43:29 - Zhirun Zheng, Zhetao Li, Jie Li, Hongbo Jiang, Tong Li, Bin Guo:
Utility-aware and Privacy-preserving Trajectory Synthesis Model that Resists Social Relationship Privacy Attacks. 44:1-44:28
- Cheolhyeong Kim, Haeseong Moon, Hyung Ju Hwang:
NEAR: Neighborhood Edge AggregatoR for Graph Classification. 45:1-45:17 - Lili Wei, Congyan Lang, Liqian Liang, Songhe Feng, Tao Wang, Shidi Chen:
Weakly Supervised Video Object Segmentation via Dual-attention Cross-branch Fusion. 46:1-46:20 - Runze Yan, Xinwen Liu, Janine M. Dutcher, Michael J. Tumminia, Daniella K. Villalba, Sheldon Cohen, J. David Creswell, Kasey G. Creswell, Jennifer Mankoff, Anind K. Dey, Afsaneh Doryab:
A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams. 47:1-47:27 - Yang Hu, Adriane Chapman, Guihua Wen, Wendy Hall:
What Can Knowledge Bring to Machine Learning? - A Survey of Low-shot Learning for Structured Data. 48:1-48:45 - Fandel Lin, Hsun-Ping Hsieh:
Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data. 49:1-49:25 - Vinayak Gupta, Srikanta Bedathur:
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer. 50:1-50:24 - Nurendra Choudhary, Charu C. Aggarwal, Karthik Subbian, Chandan K. Reddy:
Self-supervised Short-text Modeling through Auxiliary Context Generation. 51:1-51:21
Volume 13, Number 4, August 2022
- Qiang Yang, Yongxin Tong, Yang Liu, Yangqiu Song, Hao Peng, Boi Faltings:
Introduction to the Special Issue on the Federated Learning: Algorithms, Systems, and Applications: Part 1. 52:1-52:3 - Jun Zhou, Longfei Zheng, Chaochao Chen, Yan Wang, Xiaolin Zheng, Bingzhe Wu, Cen Chen, Li Wang, Jianwei Yin:
Toward Scalable and Privacy-preserving Deep Neural Network via Algorithmic-Cryptographic Co-design. 53:1-53:21 - Rodolfo Stoffel Antunes, Cristiano André da Costa, Arne Küderle, Imrana Abdullahi Yari, Björn M. Eskofier:
Federated Learning for Healthcare: Systematic Review and Architecture Proposal. 54:1-54:23 - Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, Philip S. Yu:
Federated Social Recommendation with Graph Neural Network. 55:1-55:24 - Meng Jiang, Taeho Jung, Ryan Karl, Tong Zhao:
Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance. 56:1-56:23 - Ziheng Hu, Hongtao Xie, Lingyun Yu, Xingyu Gao, Zhihua Shang, Yongdong Zhang:
Dynamic-Aware Federated Learning for Face Forgery Video Detection. 57:1-57:25 - Zhenghang Ren, Liu Yang, Kai Chen:
Improving Availability of Vertical Federated Learning: Relaxing Inference on Non-overlapping Data. 58:1-58:20 - Di Chai, Leye Wang, Kai Chen, Qiang Yang:
Efficient Federated Matrix Factorization Against Inference Attacks. 59:1-59:20 - Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu, Lizhen Cui:
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning. 60:1-60:21 - Sicong Che, Zhaoming Kong, Hao Peng, Lichao Sun, Alex D. Leow, Yong Chen, Lifang He:
Federated Multi-view Learning for Private Medical Data Integration and Analysis. 61:1-61:23 - Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, Xing Xie:
FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data. 62:1-62:19 - Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He:
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems. 63:1-63:32 - Yan Kang, Yang Liu, Xinle Liang:
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training. 64:1-64:16 - Hanchi Ren, Jingjing Deng, Xianghua Xie:
GRNN: Generative Regression Neural Network - A Data Leakage Attack for Federated Learning. 65:1-65:24 - Yuanyishu Tian, Yao Wan, Lingjuan Lyu, Dezhong Yao, Hai Jin, Lichao Sun:
FedBERT: When Federated Learning Meets Pre-training. 66:1-66:26 - Yuzhu Mao, Zihao Zhao, Guangfeng Yan, Yang Liu, Tian Lan, Linqi Song, Wenbo Ding:
Communication-Efficient Federated Learning with Adaptive Quantization. 67:1-67:26 - Xu Guo, Han Yu, Boyang Li, Hao Wang, Pengwei Xing, Siwei Feng, Zaiqing Nie, Chunyan Miao:
Federated Learning for Personalized Humor Recognition. 68:1-68:18
Volume 13, Number 5, October 2022
- Qiang Yang, Yongxin Tong, Yang Liu, Yangqiu Song, Hao Peng, Boi Faltings:
Preface to Federated Learning: Algorithms, Systems, and Applications: Part 2. 69:1-69:2 - Xiaolong Xu, Wentao Liu, Yulan Zhang, Xuyun Zhang, Wanchun Dou, Lianyong Qi, Md. Zakirul Alam Bhuiyan:
PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing. 70:1-70:22 - Zhengyi Zhong, Weidong Bao, Ji Wang, Xiaomin Zhu, Xiongtao Zhang:
FLEE: A Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge, and End Device. 71:1-71:24 - Trung Kien Dang, Xiang Lan, Jianshu Weng, Mengling Feng:
Federated Learning for Electronic Health Records. 72:1-72:17 - Shenghui Li, Edith C. H. Ngai, Fanghua Ye, Thiemo Voigt:
Auto-weighted Robust Federated Learning with Corrupted Data Sources. 73:1-73:20 - Xue Jiang, Xuebing Zhou, Jens Grossklags:
SignDS-FL: Local Differentially Private Federated Learning with Sign-based Dimension Selection. 74:1-74:22 - Bixiao Zeng, Xiaodong Yang, Yiqiang Chen, Hanchao Yu, Yingwei Zhang:
CLC: A Consensus-based Label Correction Approach in Federated Learning. 75:1-75:23 - Chien-Lun Chen, Sara Babakniya, Marco Paolieri, Leana Golubchik:
Defending against Poisoning Backdoor Attacks on Federated Meta-learning. 76:1-76:25 - Lunchen Xie, Jiaqi Liu, Songtao Lu, Tsung-Hui Chang, Qingjiang Shi:
An Efficient Learning Framework for Federated XGBoost Using Secret Sharing and Distributed Optimization. 77:1-77:28 - Dimitris Stripelis, Paul M. Thompson, José Luis Ambite:
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings. 78:1-78:29 - Georgios Damaskinos, Rachid Guerraoui, Anne-Marie Kermarrec, Vlad Nitu, Rhicheek Patra, François Taïani:
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction. 79:1-79:30 - Yijing Liu, Dongming Han, Jianwei Zhang, Haiyang Zhu, Mingliang Xu, Wei Chen:
Federated Multi-task Graph Learning. 80:1-80:27
- Fuxian Li, Jie Feng, Huan Yan, Depeng Jin, Yong Li:
Crowd Flow Prediction for Irregular Regions with Semantic Graph Attention Network. 81:1-81:14 - Zongwei Wang, Min Gao, Jundong Li, Junwei Zhang, Jiang Zhong:
Gray-Box Shilling Attack: An Adversarial Learning Approach. 82:1-82:21 - Kai Di, Yifeng Zhou, Fuhan Yan, Jiuchuan Jiang, Shaofu Yang, Yichuan Jiang:
A Foraging Strategy with Risk Response for Individual Robots in Adversarial Environments. 83:1-83:29 - Sawan Rai, Ramesh Chandra Belwal, Atul Gupta:
A Review on Source Code Documentation. 84:1-84:44 - Xiaoyu Chen, Yingyan Zeng, Sungku Kang, Ran Jin:
INN: An Interpretable Neural Network for AI Incubation in Manufacturing. 85:1-85:23 - Ke Li, Bin Guo, Jiaqi Liu, Jiangtao Wang, Haoyang Ren, Fei Yi, Zhiwen Yu:
Dynamic Probabilistic Graphical Model for Progressive Fake News Detection on Social Media Platform. 86:1-86:24
Volume 13, Number 6, December 2022
- Rohit Verma, Sugandh Pargal, Debasree Das, Tanusree Parbat, Sai Shankar Kambalapalli, Bivas Mitra, Sandip Chakraborty:
Impact of Driving Behavior on Commuter's Comfort During Cab Rides: Towards a New Perspective of Driver Rating. 87:1-87:25 - Lin Zhang, Lixin Fan, Yong Luo, Ling-Yu Duan:
Intrinsic Performance Influence-based Participant Contribution Estimation for Horizontal Federated Learning. 88:1-88:24 - Siyuan Ren, Bin Guo, Longbing Cao, Ke Li, Jiaqi Liu, Zhiwen Yu:
DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction. 89:1-89:22 - Qian Zheng, Yueming Wang, Zhenfang Hu, Xiaobo Zhang, Zhaohui Wu, Gang Pan:
Jointly Optimizing Expressional and Residual Models for 3D Facial Expression Removal. 90:1-90:17 - Wided Hammedi, Sidi Mohammed Senouci, Philippe Brunet, Metzli Ramirez-Martinez:
Two-Level Optimization to Reduce Waiting Time at Locks in Inland Waterway Transportation. 91:1-91:30 - Mohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne:
CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems. 92:1-92:23 - Yasan Ding, Bin Guo, Yan Liu, Yunji Liang, Haocheng Shen, Zhiwen Yu:
MetaDetector: Meta Event Knowledge Transfer for Fake News Detection. 93:1-93:25 - Yao Zhang, Wenping Fan, Qichen Hao, Xinya Wu, Min-Ling Zhang:
CAFE and SOUP: Toward Adaptive VDI Workload Prediction. 94:1-94:28 - Junyi Dong, Qingze Huo, Silvia Ferrari:
A Holistic Approach for Role Inference and Action Anticipation in Human Teams. 95:1-95:24 - Marius Hogräfer, Marco Angelini, Giuseppe Santucci, Hans-Jörg Schulz:
Steering-by-example for Progressive Visual Analytics. 96:1-96:26 - Xian Wu, Chao Huang, Pablo Robles-Granda, Nitesh V. Chawla:
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series. 97:1-97:21 - Edgar Eduardo Ceh-Varela, Huiping Cao, Hady W. Lauw:
Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral Groups. 98:1-98:26 - Anirban Das, Timothy Castiglia, Shiqiang Wang, Stacy Patterson:
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning. 99:1-99:27 - Ángel Navia-Vázquez, Roberto Díaz-Morales, Marcos Fernández Díaz:
Budget Distributed Support Vector Machine for Non-ID Federated Learning Scenarios. 100:1-100:25 - Yue Hu, Ao Qu, Dan Work:
Detecting Extreme Traffic Events Via a Context Augmented Graph Autoencoder. 101:1-101:23 - Tamir Tassa, Alon Ben Horin:
Privacy-preserving Collaborative Filtering by Distributed Mediation. 102:1-102:26 - Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya, Abir De:
Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes. 103:1-103:26 - Jinxiang Ou, Yunheng Shen, Feng Wang, Qiao Liu, Xuegong Zhang, Hairong Lv:
AggEnhance: Aggregation Enhancement by Class Interior Points in Federated Learning with Non-IID Data. 104:1-104:25 - Miguel Costa, Diogo Costa, Tiago Gomes, Sandro Pinto:
Shifting Capsule Networks from the Cloud to the Deep Edge. 105:1-105:25
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