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2007 International Conference on Parallel Processing Workshops, ICPPW 2007

作者： Eom, Minyoung Oh, Jaegeun Seon, Wook Kim Compiler and Embedded System Laboratory Department of Electronics and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)0769529348

As 3G became the general tendency of mobile industry, new mobile handsets are challenged to show more and more functions. In order to process high-resolution images, the high-speed data transfer technology that was originally used in communication system or server system started to be adopted in mobile handsets. It is the purpose of this paper to describe and compare some technologies for high-speed data transfer between camera and ISP(Image Signal Processor) focusing on specifications in the PHY layer, after going over the structural features of the mobile handsets. Most of these technologies are based on serial interface and they show excellence in terms of power, bandwidth and EMI in comparison to the conventional parallel interface based on the LVCMOS technology. In order to verify this fact, this paper presents a comparison of the SMIA interfaced camera module which represents the serial interface (as an example of the subLVDS technology) and the parallel interfaced camera module which represents the parallel interface (as an example of a general LVCMOS technology). © 2007 IEEE.

关键词： Cameras

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Will the fibers of the future be considered fibers?

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National Science Review 2024年第10期11卷 15-17页

作者： Dewen Xu Ya Liu Hailiang Wang Wei Yan Changjun Jiang Meifang Zhu State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and Engineering Donghua University Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Department of Computer Science and Technology Tongji University

Fiber materials are key materials that have changed human history and promoted the progress of human civilization. In ancient times, humans used feathers and animal skins for clothing, and later they widely employed natural fibers such as cotton, hemp, silk and wool to make fabrics(Fig. 1a). Chinese ancestors had mastered the art of natural fiber weaving as early as the Neolithic *** thousand years ago, people were already familiar with and adept at techniques for spinning natural fibers [1].

关键词： future will considered fibers

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Constructing prioritized interaction test suite with interaction relationship

Constructing prioritized interaction test suite with interac...

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2nd International Workshop on Education Technology and computer Science, ETCS 2010

作者： Yuan, Jianjun Jiang, Zuowen Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai China Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9780769539874

Interaction testing has addressed some issues on how to select a small subset of test cases. In many systems where interaction testing is needed, the entire test suite is not executed because of time or budget constraints. It is important to prioritize the test cases in these situations. On the other hand, there are not always interactions among any factors in real systems. Moreover, some factors may need N-way (N>2) testing since there is a closer relationship among them. We present a model for prioritized interaction testing with interaction relationship and propose a greedy algorithm for generating variable strength covering arrays with bias. © 2010 IEEE.

关键词： Testing

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Application of a self-organizing fuzzy neural network controller with group-based genetic algorithm to greenhouse

Application of a self-organizing fuzzy neural network contro...

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2011 7th International Conference on Natural Computation, ICNC 2011

作者： Yao, Yuan Zhang, Kailong Zhou, Xingshe Shaanxi Provincial Key Embedded System Technology Laboratory School of Computer Northwestern Polytechnical University Xian China

ISBN: (纸本)9781424499533

As a complex nonlinear system, greenhouse can not be controlled perfectly by traditional control strategies. This paper proposes a self-organizing fuzzy neural network controller (SOFNNC) with group-based genetic algorithm (GGA) to drive the internal climate of the greenhouse. SOFFNNC is a hybrid control strategy which combines fuzzy control and neural network organically. It generates or prunes neurons automatically by the structure learning algorithm, which can adaptively strike a balance between the rule number and the desired performance. In other to avoid the shortage of the original learning algorithm to SOFNNC, we come up with an improved structure learning method and a new parameter learning method with GGA. Based on a greenhouse model established by an Elman neural network (ENN), we test the performance of SOFNNC. Simulation and comparison results prove that SOFNNC can achieve outstanding control effect with high efficiency. © 2011 IEEE.

关键词： Genetic algorithms

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Intrusion Detection system for IoMT through Blockchain-based Federated Learning 15

Intrusion Detection System for IoMT through Blockchain-based...

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15th IEEE International Conference on Security of Information and Networks, SIN 2022

作者： Zaabar, Bessem Cheikhrouhou, Omar Abid, Mohamed University of Sfax Computer Embedded System Laboratory CES-ENIS Tunisia Higher Institute of Computer Science of Mahdia University of Monastir Tunisia

ISBN: (数字)9781665454650

ISBN: (纸本)9781665454650

Federated Learning (FL) is a feasible technology to collaboratively train a model without sharing private data. This approach differs from traditional machine learning techniques, which aggregate local datasets in a single server. Thus, FL is adopted in the Healthcare sector to preserve the privacy of collected sensitive medical data from heterogenous and resource-constrained Internet of Medical Things (IoMT). However, FL requires aggregating all trained local models in a central server that presents a single point of failure. To address this issue, we propose a novel Blockchain-based Federated Learning architecture, which is applied to detect malicious network traffic in IoMT environments. In this paper, the proposed architecture takes advantage of a Hyperledger Fabric channel coupled with FL to manage efficiently and securely the learning process of an Intrusion Detection system (IDS). The Blockchain channel replaces the commonly used central server with the traditional FL approach. Moreover, the proposed approach benefits from the inherent features of Blockchain and FL. Besides, it secures patient data collection from the IoMT by examining the network traffic for unauthorised behaviour or policy breaches. © 2022 IEEE.

关键词： Blockchain

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Observable liveness of Petri nets with controllable and observable transitions

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Science China(Information Sciences) 2017年第11期60卷 256-264页

作者： Guanjun LIU Changjun JIANG Department of Computer Science Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University E-commerce Transactions and Information Services Collaborative Innovation Center of Shanghai University Knowledge Service Platform Tongji University

Observability and/or controllability are paid much attention to by researchers not only from the control science field[1–3]but also from the computer science field[4–9].Recently,Desel and Kilinc[4]proposed the notion of observable liveness for Petri nets with(un-)observable and(un-)controllable *** purpose of defining observable liveness is to represent that"a user can always enforce the occurrence of any observable transition only by stimulating the net by choosing appropriate enabled controllable transition"[4].An important assumption of

关键词： net cot PN Observable liveness of Petri nets with controllable and observable transitions

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A credible individual behavior profiling method for online payment fraud detection 21

A credible individual behavior profiling method for online p...

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4th International Conference on Data Storage and Data Engineering, DSDE 2021

作者： Cui, Jipeng Yan, Chungang Wang, Cheng Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science and Engineering Tongji University China

ISBN: (纸本)9781450389303

Online payment fraud detection relies on a credible characterization of individual behaviors. Existing individual behavior profiling methods have three problems in credibility. Firstly, they solely regard users as individuals to model and fail to credibly characterize the whole behavior patterns of transactions. Secondly, they cannot make full use of the general information among similar individuals due to the heterogeneity of transaction attributes. Thirdly, they are not capable of utilizing the label information of transactions credibly. Faced with these challenges, we propose a credible method for individual behavior profiling that consists of two steps. The first step is the construction of the credible individual behavior profiling framework. To begin with, the concept of individuals is generalized and the credible transaction description is given. Afterwards, based on the co-occurrence information of transaction attributes, the credible behavior condition is defined to set a limit to individual behavior patterns. The second step is the implementation of the framework. For this purpose, the original fraud detection problem is transformed into a pseudo-recommender system problem with a co-occurrence mapping process. In order to guarantee the credible behavior constraint, a credible recommendation algorithm is designed to collaboratively restore both the ranking and rating information of the pseudo-rating matrices. Besides, an embedding based approach is adopted to parameterize the proposed model. Experiments on an online payment transaction dataset demonstrate the effectiveness of our proposed method. © 2021 ACM.

关键词： Crime

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer systems Science & Engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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Robust Learning to Rank Based on Portfolio Theory and AMOSA Algorithm

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IEEE Transactions on systems, Man, and Cybernetics: systems 2017年第6期47卷 1007-1018页

作者： Li, Jinzhong Liu, Guanjun Yan, Chungang Jiang, Changjun Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Watershed Ecology and Geographical Environment Monitoring NASG Department of Computer Science and Technology Jinggangshan University Ji'an343009 China

Effectiveness is the most important factor considered in the ranking models yielded by algorithms of learning to rank (LTR). Most of the related ranking models only focus on improving the average effectiveness but ignore robustness. When a ranking model ignores robustness, the effectiveness for many queries is possibly very poor although the average effectiveness for all queries is relatively high. Therefore, Wang et al. first consider robustness in their ranking models. However, the robustness formula defined by Wang et al. cannot characterize those queries whose effectiveness are hurt seriously in comparison with the baseline model. In order to overcome this shortcoming, we propose a novel formula of characterizing robustness based on portfolio theory, and construct a multiobjective optimization model of the robust LTR in which the formula is used. Based on this model, we propose an approach of risk-sensitive and robust LTR, named as R2Rank, which is based on the framework of archived multiobjective simulated annealing algorithm and the idea of preference ranking organization method for enrichment evaluation. The experimental results show that the ranking models produced by our proposed R2Rank approach are better in both effectiveness and robustness than those produced by three state-of-the-art LTR approaches. © 2016 IEEE.

关键词： Simulated annealing

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On complementary effect of blended behavioral analysis for identity theft detection in mobile social networks 1

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13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017

作者： Wang, Cheng Luo, Jing Yang, Bo Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (数字)9789811088902

ISBN: (纸本)9789811088896

User behavioral analysis is expected to act as a promising technique for identity theft detection in the Internet. The performance of this paradigm extremely depends on a good individual-level user behavioral model. Such a good model for a specific behavior is often hard to obtain due to the insufficiency of data for this behavior. The insufficiency of specific data is mainly led by the prevalent sparsity of users’ collectable behavioral footprints. This work aims to address whether it is feasible to effectively detect identify thefts by jointly using multiple unreliable behavioral models from sparse individual-level records. We focus on this issue in mobile social networks (MSNs) with multiple dimensions of collectable but sparse data of user behavior, i.e., making check-ins, posing tips and forming friendships. Based on these sparse data, we build user spatial distribution model, user post interest model and user social preference model, respectively. Here, as the arguments, we validate that there is indeed a complementary effect in multi-dimensional blended behavioral analysis for identity theft detection in MSNs. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Social networking (online)

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