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IEEE International Conference on Computer Science and Automation Engineering (CSAE)

作者： Zhao Mingqing Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji Shanghai China

In order to strengthen the traditional information system's capacity of describing practical problems, preference relation is introduced into information system and the concepts of preference information system, preference decision table, and information utility function are offered in this paper. In addition, the existence theorem of information utility function and several applications of information utility function in knowledge acquisition are provided.

关键词： Information systems Knowledge acquisition Economics Clustering methods Transforms Information science Approximation methods

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Quality assessment for color images with tucker decomposition

Quality assessment for color images with tucker decompositio...

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IEEE International Conference on Image Processing

作者： Cheng, Cheng Wang, Hanli Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

As an extension of the singular value decomposition based approaches, a novel metric based on Tucker decomposition for color image quality assessment is proposed in this paper. It extracts both the spacial and chromatic information of a color image with Tucker decomposition. As compared to most of the other existing quality metrics for color images, the key advantage of the proposed metric is that it treats the color image as a whole entity instead of an assembly of three independent visual channels, and thus the interrelation among the different visual channels is well involved. Experimental results on the LIVE Database Release 2 demonstrate that the proposed metric can generally achieve similar or better performance than other metrics.

关键词： Tensile stress Measurement Image color analysis Color Matrix decomposition Image quality Vectors

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An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4026-4041页

作者： Zhu, Kun Zhang, Nana Ding, Weiping Jiang, Changjun the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China the School of Computer Science and Technology Donghua University Shanghai201620 China the School of Information Science and Technology Nantong University Nantong226019 China

Heterogeneous fraud detection is an important means of credit card security assurance, which can utilize historical transaction records in a source and target domain to build an effective fraud detection model. Nevertheless, large feature distribution differences between source and target transaction instances and the complex intrinsic structure hidden behind transaction data make it difficult for existing credit card fraud detection (CCFD) models to capture and transfer the most informative feature representations and seriously hinder detection performance. In this work, we propose a novel adaptive heterogeneous CCFD model named adaptive heterogeneous credit card fraud detection model based on deep reinforcement training subset selection (RTAHC) based on deep reinforcement training subset selection, which mainly contains two components: selection distribution generator (SDG) and transaction fraud detector (TFD, including feature extractor with an attention mechanism and classifier). The SDG can generate the selection probability distribution vector via the reinforcement reward mechanism, and then transaction instances in the source domain relevant to the target domain are selected. The feature extractor with an attention mechanism can learn the abstract deep semantic feature representations of selected source transaction instances and the target domain. The joint training of SDG and TFD can provide more real-time and accurate transaction feature representations to reduce the distribution discrepancy between the two domains. We verify the detection performance of RTAHC across a large real-world credit card transaction dataset and four public datasets. Experimental results demonstrate that the RTAHC model can exhibit competitive CCFD performance. Impact Statement—With the rise of artificial intelligence (AI)generated models, credit card fraud has become increasingly rampant, which also causes tens of billions of U.S. dollars in credit card losses worldwide every year

关键词： Hidden Markov models

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Reader activation scheduling in multi-reader RFID systems: A study of general case

Reader activation scheduling in multi-reader RFID systems: A...

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25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011

作者： Tang, Shaojie Wang, Cheng Li, Xiang-Yang Jiang, Changjun Department of Computer Science Illinois Institute of Technology Chicago IL 60616 United States Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9780769543857

Radio frequency identification (RFID) is a technology where a reader device can "sense" the presence of a close by object by reading a tag device attached to the object. To guarantee the coverage quality, multiple RFID readers can be deployed in the given region. In this paper, we consider the problem of activation schedule for readers in a multi-reader environment. In particular, we try to design a schedule for readers to maximize the number of served tags per time-slot while avoiding various interferences. We first develop a centralized algorithm under the assumption that different readers may have different interference and interrogation radius. Next, we propose a novel algorithm which does not need any location information of the readers. Finally, we extend the previous algorithm in distributed manner in order to suit the case where no central entity exists. We conduct extensive simulations to study the performances of our proposed algorithm. And our evaluation results corroborate our theoretical analysis. © 2011 IEEE.

关键词： Graph theory

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Single camera 3D lane detection and tracking based on EKF for urban intelligent vehicle

Single camera 3D lane detection and tracking based on EKF fo...

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IEEE International Conference on Vehicular Electronics and Safety, ICVES 2006

作者： Min, Tian Fuqiang, Liu Zhencheng, Hu IEEE Key Laboratory of Embedded System and Service Computing Ministry of Education China Electronic and Information Engineering Department Tongji University Shanghai 201804 China Department of Computer Science Kumamoto University Kumamoto 860-8555 Japan

ISBN: (纸本)1424407591

Road boundary detection and tracking is an important and integral function in advanced driver-assistance system. This paper proposes an algorithm, which can follow multi-kinds of lane, straight and curved, quickly and robustly. The algorithm uses several masks to extract blobs of road markings, combining with KNN function to remove the disturbance. Further more, road is modeled as a 3D surface, and some important parameters of current lane are provided on real-time by tracking based on Extended Kalman Filter (EKF). The results of experiments, which have been done in urban road, show that the algorithm is adapted to many road conditions. Even in a complex driving environment, it also has a good performance. © 2006 IEEE.

关键词： Extended Kalman filters

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Moving object detection through efficient detection and clustering of reliable singular points

Advances in Intelligent and Soft Computing

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Advances in Intelligent and Soft computing 2011年 104卷 381-385页

作者： Cui, Jianzhu Wang, Ping Li, Zhipeng Li, Jing Li, Yan Key Laboratory of Embedded System and Service Computing Supported by Ministry of Education Tongji University Cao'an Highway Shanghai 4800 China

ISBN: (纸本)9783642237768

This paper presents a method to detect vehicles from a moving camera. The detection component involves a cascade of modules. First, motion estimation of singular points in video sequences is used to detect moving vehicle. Finding the image points is the selection of singular points. We impose restrictions about the gradient magnitude and the cornerness. A sparse motion vector field outlines the image motion. We obtain excellent results in real sequences in China. At last, vehicles are detected by clustering of singular points. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Motion estimation

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Top-n recommendation algorithms for cloud data

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2015年第1期43卷 54-61页

作者： Huang, Zhen-Hua Department of Computer and Technology Tongji University Shanghai200092 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

Top-n recommendation technology has recently received a lot of attention in information service community. In this paper, we study the problem of top-n recommendation under the cloud data. Firstly, we propose a multilayer distributed storage architecture(MDSA)which can fit the need of top-n recommendation. Secondly, based on the consideration of reducing the cost of network transmission, we present the data encoding pattern which is based on the MDSA architecture. Lastly, we fast realize top-n recommendation using the map/reduce distributed programming model. For satisfying the realistic requirement, we further present three application variants of top-n recommendation. Massive experiments demonstrate that our approach is efficient and has high overall performance. ©, 2015, Chinese Institute of Electronics. All right reserved.

关键词： MapReduce

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A Prediction Based on Clustering and Personality Questionnaire Data for IGD Risk

A Prediction Based on Clustering and Personality Questionnai...

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International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery

作者： Xiaoliang Gong Kun Fang Yichu Hou Bozhong Long Zongling Di Lei Cao National Key Laboratory of Embedded System and Service Computing (Ministry of Education) Tongji University Shanghai China

ISBN: (纸本)9781509040940

With the development of the Internet, a lot of people trapped in the network, especially the adolescent depending on the network game and disturbing their normal life. 579 freshmen participated in this work who were collected the personality questionnaires in the first week they came in university and their grades points average (GPA) after half year. The questionnaires were including Self-Control (SCS), Barratt impulse Inventory (BIS) and Chinese Big Five Personality (CBF). This work used multi-clustering algorithms to construct the models of predicting for Internet game disorder (IGD) risk, including FCM, K-means, and Hierarchical for training model. This is the first try to predict the risk of IGD by personality traits. The results shown the questionnaire data were well separated by different clustering algorithms into three groups who were shared the analogous personality traits which has a relationship with the behavior of IGD. But compared to the GPA of each group, the efficiency of the prediction model seems not so satisfactory. There need more efforts to optimized the model in the future.

关键词： Personality questionnaire Data IGD Risk FCM K-means Hierarchical

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A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

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IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 934-945页

作者： Liang Qi Mengchu Zhou Wenjing Luan IEEE Department of Computer Scicnce and Technology Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102USA Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghai Electronic Transactions and Information Service Collaborative Innovation CenterDepartment of Com-puter ScienceTongji UniversityShanghai 201804China

Advanced information and communication technolo-gies can be used to facilitate traffic incident *** an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this ***-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well *** results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is *** results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

关键词： Cell transmission model(CTM) intelligent transportation systems(ITS) traffic incident management(TIM) urban traffic congestion

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ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting

arXiv

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arXiv 2024年

作者： Zheng, Qi Yao, Zihao Zhang, Yaying The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods;2) overlooking spatial correlations across variables over time;3) limitations of efficiency and scalability in existing self-supervised learning methods. To tackle these, we propose a lightweight representation-learning model ST-ReP, integrating current value reconstruction and future value prediction into the pre-training framework for spatial-temporal forecasting. And we design a new spatial-temporal encoder to model fine-grained relationships. Moreover, multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability. Experimental results across diverse domains demonstrate that the proposed model surpasses pre-training-based baselines, showcasing its ability to learn compact and semantically enriched representations while exhibiting superior scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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