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arXiv 2021年

作者： Ding, Zhijun Yang, Ru Cui, Puwen Zhou, MengChu Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Department of Computer Science and Technology Tongji University Shanghai201804 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Mobile computing systems, service-based systems and some other systems with mobile interacting components have recently received much attention. However, because of their characteristics such as mobility and disconnection, it is difficult to model and analyze them by using a structure-fixed model. This work proposes a new Petri net model called Variable Petri Net (VPN) for modeling and analyzing these systems. The definition, firing rule, and related analysis technology of VPN are introduced in detail. In a VPN, the possible interaction interfaces are abstracted as a new kind of places called virtual places, and the occurrences of (dis)connections are described by new functions, which makes it appropriate to describe the component collaboration in systems and realize the scalability and pluggability of systems. Moreover, to overcome the shortcoming that markings cannot reflect link capability of a system, VPNs add a constraint function along with a marking to represent a complete system configuration. Several examples are used to demonstrate the newly proposed model and method. Copyright © 2021, The Authors. All rights reserved.

关键词： embedded systems

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Admission Control for Non-preprovisioned service Flow in Wireless Metropolitan Area Networks

Admission Control for Non-preprovisioned Service Flow in Wir...

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European Conference on Universal Multiservice Networks (ECUMN)

作者： Liping Wang Fuqiang Liu Yusheng Ji Nararat Ruangchaijatupon The Key Laboratory of |Embedded System and Service Computing| Ministry of Education China Ministry of Education Tongji University Shanghai China Graduate University of Advanced Studies (SOKENDAI) Tokyo Japan National Institute of Information Japan School of Electronics and Information Engineering University of Tongji Shanghai China The Graduate University for Advanced Studies Tokyo Japan National Institute of Informatics Japan

Improving the admission control process in wireless metropolitan area networks (WMAN) based on the IEEE 802.16e standard can improve the QoS. Because IEEE 802.16e supports five classes of service as well as mobility, an admission control policy for non-preprovisioned service flow is proposed. This policy is compatible with the QoS parameters for each service class defined in the standard. As improvements, the guard channel scheme is used to give handover connections higher priority and a novel proportional bandwidth-borrowing scheme is added to prioritize the different service classes. The simulation results show the proposed admission control policy maximizes the bandwidth utilization and the use of the two schemes provides a reasonable priority order of new originated connections and handover connections of different service classes. The connection-blocking and connection-dropping probabilities were reduced to the extent that they can be ignored

关键词： Admission control Wireless LAN Metropolitan area networks Bandwidth Media Access Protocol laboratories embedded system embedded computing Informatics Identity management systems

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Joint Transmit Power and Trajectory Optimization for Two-Way Multi-Hop UAV Relaying Networks

Joint Transmit Power and Trajectory Optimization for Two-Way...

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IEEE International Conference on Communications Workshops, ICC

作者： Bing Li Shengjie Zhao Rongqing Zhang Hao Zhang Hanli Wang Liuqing Yang School of Software Engineering Tongji University Shanghai China National Mobile Communications Research Laboratory Southeast University China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China School of Electronic and Information Engineer Tongji University Shanghai China Department of Electrical & Computer Engineering Colorado State University CO USA

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Unmanned aerial vehicle (UAV) has been regarded as a promising means to supplement ground communications. As flying relays, UAVs can be rapidly and flexibly deployed to assist data transmissions in many practical scenarios. In this paper, we investigate a two-way multi-hop UAV relaying network, where there are two ground users as sources and multiple UAVs as relays to help the two ground sources exchange information. We first provide an efficient two-way multi-hop UAV relaying pattern, which can achieve a data rate of 2/4 data packets per time slot with decode-and-forward (DF) protocol. Then, we further formulate a joint transmit power and trajectory optimization problem for the UAVs in this two-way multi-hop relaying scenario. The formulated problem is non-convex which makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution. Numerical results demonstrate that our proposed network achieves significant throughput gains.

关键词： Relays Protocols Throughput Trajectory optimization Data communication

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GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

arXiv

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arXiv 2024年

作者： Qu, Sanqing Zou, Tianpei Röhrbein, Florian Lu, Cewu Chen, Guang Tao, Dacheng Jiang, Changjun The School of Automotive Engineering Department of Computer Science Tongji University China The Faculty of Computer Science Chemnitz University of Technology Germany The Department of Computer Science Shanghai Jiao Tong University China The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Computer Science Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Asymptotic throughput for large-scale wireless networks with general node density

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Wireless Networks 2012年第5期19卷 559-575页

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Yunhao Liu Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China The Tsinghua National Laboratory for Information Science and Technology (TNLIST) Department of Computer Science and Engineering Tongji University Shanghai China The Department of Computer Science Illinois Institute of Technology Chicago USA The TNLIST and School of Software Tsinghua University Beijing China CSE The Hong Kong University of Science and Technology Kowloon Hong Kong

We study the asymptotic throughput for a large-scale wireless ad hoc network consisting of n nodes under the generalized physical model. We directly compute the throughput of multicast sessions to unify the unicast and broadcast throughputs. We design two multicast schemes based on the so-called ordinary arterial road system and parallel arterial road system, respectively. Correspondingly, we derive the achievable multicast throughput by taking account of all possible cases of n s = ω(1) and 1 ≤ n d ≤ n − 1, rather than only the cases of $$n_s=\Uptheta(n)$$ as in most related works, where n s and n d denote the number of sessions and the number of destinations of each session, respectively. Furthermore, we consider the network with a general node density $$\lambda \in [1,n]$$ , while the models in most related works are either random dense network (RDN) or random extended network (REN) where the density is λ = n and λ = 1, respectively, which further enhances the generality of this work. Particularly, for the special case of our results by letting λ = 1 and $$n_s=\Uptheta(n)$$ , we show that for some regimes of n d , the multicast throughputs achieved by our schemes are better than those derived by the well-known percolation-based schemes.

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Forecasting of Forex Time Series Data Based on Deep Learning

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Procedia Computer Science 2019年 147卷 647-652页

作者： Lina Ni Yujie Li Xiao Wang Jinquan Zhang Jiguo Yu Chengming Qi College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China Beijing Union University Beijing 100101 China

This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy.

关键词： Deep learning Recurrent neural network Convolutional neural network Foreign Exchange Rate Time series analysis

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Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks

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Procedia Computer Science 2019年 147卷 643-646页

作者： Jinquan Zhang Bowen Zhao Guochao Song Lina Ni Jiguo Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China

Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm.

关键词： Privacy protection Data publishing Social networks (α, L)-diversity K-anonymity Clustering feature (CF) tree

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An improved PSO algorithm with simulated annealing for constrained function optimization

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 175-179页

作者： Si, Cheng-Yong Yang, Dong-Sheng Tian, Hong-Jun Wang, Lei Wu, Qi-Di College of Electronics and Information Engineering Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Economic and Information Committee Shanghai 200040 China

To solve the problem of infeasible solutions in the constrained function optimization, a constraint-handling technique based on simulated annealing is proposed. The improved particle swarm optimization is also used to verify the effectiveness of this technique. The experimental results show that the proposed algorithm is suitable for solving such problems.

关键词： Simulated annealing

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Integrated verification model atsFPM based on functional, time and spatial constraints

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Jisuanji Xuebao/Chinese Journal of Computers 2009年第4期32卷 740-750页

作者： Niu, Jun Zeng, Guo-Sun Chen, Bo Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing of Ministry of Education Shanghai 201804 China College of Information Engineering Zhejiang Business Technology Institute Ningbo 315012 China

Nowadays complex information system's integrated formal models of function verification and performance evaluation lack properties constraint about space aspect. This paper presents an integrated verification model atsFPM by defining a space requirement function over the states of the considered information system. The patterns of paths which are based on regular expressions is proposed in order to specify the functional specifications. The syntax and semantic of the model atsFPM is defined. A conversion product model is obtained by the combination of the system model and the automaton of the pattern of paths which expresses the functional specifications. The verification of the model atsFPM is tackled by the performance verification technique of Markov Reward Model. Experimental results show that the atsFPM model and its verification approach can satisfy the modeling of information system and verification of functional and performance specifications.

关键词： Information systems

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Generalized Visual Quality Assessment of GAN-Generated Face Images

arXiv

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arXiv 2022年

作者： Tian, Yu Ni, Zhangkai Chen, Baoliang Wang, Shiqi Wang, Hanli Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Tongji University Shanghai 200092 China The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been developed to produce vivid face images towards different application scenarios. However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model. Herein, we make the first attempt to study the subjective and objective quality towards generalized quality assessment of GFIs. More specifically, we establish a large-scale database consisting of GFIs from four GAN algorithms, the pseudo labels from image quality assessment (IQA) measures, as well as the human opinion scores via subjective testing. Subsequently, we develop a quality assessment model that is able to deliver accurate quality predictions for GFIs from both available and unseen GAN algorithms based on meta-learning. In particular, to learn shared knowledge from GFIs pairs that are born of limited GAN algorithms, we develop the convolutional block attention (CBA) and facial attributes-based analysis (ABA) modules, ensuring that the learned knowledge tends to be consistent with human visual perception. Extensive experiments exhibit that the proposed model achieves better performance compared with the state-of-the-art IQA models, and is capable of retaining the effectiveness when evaluating GFIs from the unseen GAN algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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