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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Liu, Junjun Sun, Haichun Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

ISBN: (纸本)9783662462478

With the explosive growth of Internet information, it is more and more important to fetch real-time and related information. And it puts forward higher requirement on the speed of webpage classification which is one of common methods to retrieve and manage information. To get a more efficient classifier, this paper proposes a webpage classification method based on locality sensitive hash function. In which, three innovative modules including building feature dictionary, mapping feature vectors to fingerprints using Localitysensitive hashing, and extending webpage features are contained. The compare results show that the proposed algorithm has better performance in lower time than the naïve bayes one. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Hash functions

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TAGnn: Time Adjoint Graph Neural Network for Traffic Forecasting 28th

TAGnn: Time Adjoint Graph Neural Network for Traffic Foreca...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

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

ISBN: (纸本)9783031306365

Spatial-temporal modeling considering the particularity of traffic data is a crucial part of traffic forecasting. Many methods take efforts into relatively independent time series modeling and spatial mining and then stack designed space-time blocks. However, the intricate deep structures of these models lead to an inevitable increment in the training cost and the interpretability difficulty. Moreover, most previous methods generally ignore the meaningful time prior and the spatial correlation across time. To address these, we propose a novel Time Adjoint Graph Neural Network (TAGnn) for traffic forecasting to model entangled spatial-temporal dependencies in a concise structure. Specifically, we inject time identification (i.e., the time slice of the day, the day of the week) which locates the evolution stage of traffic flow into node representation. Secondly, based on traffic propagation, we connect data across time slices to generate the time-adjoint hidden feature and spatial correlation matrix, allowing the spatial-temporal semantics to be captured by a simple graph convolution layer. And we introduce a time residual connection in generating predictions to capture the future traffic evolution. Experiments on four traffic flow datasets demonstrate that our method outperforms the state-of-the-art baselines efficiently. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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Adaptively selecting working frequency to reduce disturbances in high frequency radar

Adaptively selecting working frequency to reduce disturbance...

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作者： Gui, R.Z. Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai 200092 China

In high frequency radar, we should avoid noise disturbances in the radar's working-frequency segment. Moreover, the sidelobes of strong targets interfere with the detection of weak targets. A new method based on an adaptive selecting working-frequency is proposed. Frequency spectrum monitor is designed for selecting quiet frequency segment for the radar. Frequency spectrum monitor and the receiver of the radar are arranged to work according to special time periods respectively. So the radar can work in the frequency segments with lower noise disturbances. Moreover, there is no correlation between the noise and the useful echo signal, though the correlation between noises over very short time periods is strong, the noise data produced by frequency spectrum monitor can be exploited effectively Adjusting system parameters in real-time by adaptive methods can be utilized to reduce noise disturbances. Algorithm based on the properties of crosscorrelation between noise and target is exploited for suppressing sidelobe disturbances of strong targets. Lastly, the feasibility of the methods is verified by processing actual radar data.

关键词： Data handling

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Survey of locomotion control of legged robots inspired by biological concept

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Science in China(Series F) 2009年第10期52卷 1715-1729页

作者： WUQiDi LIU ChengJu ZHANG JiaQi CHEN QiJun School of Electronics and Information Engineering Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

Compared with wheeled mobile robots, legged robots can easily step over obstacles and walk through rugged ground. They have more flexible bodies and therefore, can deal with complex environment. Nevertheless, some other issues make the locomotion control of legged robots a much complicated task, such as the redundant degree of freedoms and balance keeping. From literatures, locomotion control has been solved mainly based on programming mechanism. To use this method, walking trajectories for each leg and the gaits have to be designed, and the adaptability to an unknown environment cannot be guaranteed. From another aspect, studying and simulating animals＇ walking mechanism for engineering application is an efficient way to break the bottleneck of locomotion control for legged robots. This has attracted more and more attentions. Inspired by central pattern generator （CPG）, a control method has been proved to be a successful attempt within this scope. In this paper, we will review the biological mechanism, the existence evidences, and the network properties of CPG. From the en- gineering perspective, we will introduce the engineering simulation of CPG, the property analysis, and the research progress of CPG inspired control method in locomotion control of legged robots. Then, in our research, we will further discuss on existing problems, hot issues, and future research directions in this field.

关键词： biological inspired control central pattern generator (CPG) locomotion control

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Dynamic frequency selection and spatial interaction fusion for robust person search

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Information Fusion 2025年 124卷

作者： Qixian Zhang Duoqian Miao Qi Zhang Cairong Zhao Hongyun Zhang Ye Sun Ruizhi Wang School of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China School of Computer Science Fudan University Shanghai 200438 China

Person search aims to locate target individuals in large image databases captured by multiple non-overlapping cameras. Existing models primarily rely on spatial feature extraction to capture fine-grained local details, which is vulnerable to background clutter and occlusions and leads to unstable feature representations. To address the issues, we propose a Dynamic Frequency Selection and Spatial Interaction Fusion Network (PS-DFSI), marking the first attempt to introduce frequency decoupling and selection into person search. By integrating frequency and spatial features, PS-DFSI enhances feature expressiveness and robustness. Specifically, it comprises two core modules: the Dynamic Frequency Selection Module (DFSM) and the Spatial Frequency Interaction Module (SFIM). DFSM decouples feature maps into low-frequency and high-frequency components using learnable low-pass and high-pass filters, and a frequency selection modulator emphasizes key frequency components via channel attention. SFIM refines local details by fusing frequency-enhanced features with high-level semantic representations, leveraging multi-scale receptive fields and cross-feature attention for efficient spatial-frequency integration. Extensive experiments on CUHK-SYSU and PRW demonstrate that PS-DFSI significantly improves person search performance, validating its effectiveness and robustness.

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Distributed collaborative vehicle tracking in embedded smart camera networks 12

Distributed collaborative vehicle tracking in embedded smart...

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12th IEEE International Conference on Dependable, Autonomic and Secure computing, DASC 2014

作者： Zhang, Yaying Lu, Xiuqing Ye, Chen Key Laboratory on Embedded System and Service Computing College of Electronics and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9781479950799

A vehicle tracking mode based on dynamic roles is proposed in the smart camera networks. The tracking for a specific vehicle is organized collaboratively and automatically in smart camera network. The tracking is completed autonomously by smart cameras in a distributed manner. The decentralized control mode can effectively decrease the working pressure of server system, reduce the requirements of network bandwidth for real-time video transmission, and make the system flexible and fault-tolerant. An information transmission mechanism is also presented in the smart camera network to ensure the collaborative tracking. Finally, a demo system for vehicle tracking in intelligent traffic surveillance system is implemented to verify the proposed methods. © 2014 IEEE.

关键词： Image communication systems

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Autonomic Performance and Power Control on Virtualized Servers:Survey, Practices, and Trends

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Journal of Computer Science & Technology 2014年第4期29卷 631-645页

作者：周笑波蒋昌俊 IEEE ACM Department of Computer Science University of Colorado Colorado Springs U.S.A. The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University CCF Department of Computer Science and Technology Tongji University

Modern datacenter servers hosting popular Internet services face significant and multi-facet challenges in performance and power control. The user-perceived performance is the result of a complex interaction of complex workloads in a very complex underlying system. Highly dynamic and bursty workloads of Internet services fluctuate over multiple time scales, which has a significant impact on processing and power demands of datacenter servers. High-density servers apply virtualization technology for capacity planning and system manageability. Such virtuMized computer systems are increasingly large and complex. This paper surveys representative approaches to autonomic performance and power control on virtualized servers, which control the quality of service provided by virtualized resources, improve the energy efficiency of the underlying system, and reduce the burden of complex system management from human operators. It then presents three designed self-adaptive resource management techniques based on machine learning and control for percentile-based response time assurance, non-intrusive energy-efficient performance isolation, and joint performance and power guarantee on virtualized servers. The techniques were implemented and evaluated in a testbed of virtualized servers hosting benchmark applications. Finally, two research trends are identified and discussed for sustainable cloud computing in green datacenters.

关键词： autonomic computing joint performance and power control virtualized server Internet application sustainable computing

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Large-scale video compression： recent advances and challenges

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Frontiers of Computer Science 2018年第5期12卷 825-839页

作者： Tao TIAN Hanli WANG Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Engineering Research Center of Industrial Vision Perception & Intelligent Computing Shanghai 200092 China

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.

关键词： large-scale video compression fast video coding compression efficiency

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A perspective on Petri Net learning

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Frontiers of Computer Science 2023年第6期17卷 1-3页

作者： Hongda QI Changjun JIANG Key Laboratory of Embedded System and Service Computing(Ministry of Education) Tongji UniversityShanghai 201804China National(Province-Ministry Joint)Collaborative Innovation Center for Financial Network Security Tongji UniversityShanghai 201804China

Petri Nets(PNs)are used for modeling and analyzing discreteevent systems,such as communication protocols,trafficsystems,human-computer interaction,and fault ***’state space explosion problem means that the state spaceof PNs grows exponentially with PNs’*** thefundamental reachability problem is still an NP-Hard problemin *** has been proved that the equivalence problem forthe reachability sets of arbitrary PNs is undecidable except forsome subclass of PNs[1].That is,the reachability problem ofarbitrary PNs cannot be solved ***,there is noefficient and accurate algorithm to solve the problem.10172In recent years,with the emergence of big data and thedevelopment of computing hardware,a series ofbreakthroughs have been achieved in machine learning,suchas AlphaGo,AlphaFold,and ChatGPT[2−4].As a data-drivenapproach,machine learning can learn potential mappingrelationships between inputs and outputs from large-scaledata.

关键词： breakthrough hardware learning

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CDMC2011 data mining competition: LEVIS group results overview

CDMC2011 data mining competition: LEVIS group results overvi...

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2012 International Conference on system Simulation, ICUSS 2012

作者： Zhao, Q.C. Liu, J.M. Wang, X. You, M.Y. Li, G.Z. MOE Key Laboratory of Embedded Systems and Service Computing Department of Control Science and Engineering Tongji University Shanghai 201804 China

ISBN: (纸本)9781849195386

The task for the data mining contest organized in conjunction with the ICONIP2011 conference was to learn three predictive models (i.e. a classifier) capable of distinguishing between different classes for three separate tasks. Different combinations of data preprocess, feature selection and classifier learning methods were tried to obtain the best results.

关键词： Intrusion detection

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