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Variable petri nets for mobility

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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

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

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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

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

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

Generalized Visual Quality Assessment of GAN-Generated Face Images

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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

Scaling Laws of Multicast Capacity for Power-Constrained Wireless Networks under Gaussian Channel Model

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IEEE Transactions on Computers 2012年第5期61卷 713-725页

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Shaojie Tang Yuan He Xufei Mao Yunhao Liu Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Engineering Building of Electronics and Information Engineering University of Tongji Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL USA Tsinghua National Laboratory for Information Science and Technology (TNLIST) Tsinghua University Beijing China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Hong Kong University of Science and Technology Kowloon Hong Kong China Tsinghua National Laboratory for Information Science and Technology (TNLIST) School of Software Tsinghua University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

We study the asymptotic networking-theoretic multicast capacity bounds for random extended networks (REN) under Gaussian channel model, in which all wireless nodes are individually power-constrained. During the transmission, the power decays along path with attenuation exponent α >; 2. In REN, n nodes are randomly distributed in the square region of side length √n. There are n s randomly and independently chosen multicast sessions. Each multicast session has n d + 1 randomly chosen terminals, including one source and n d destinations. By effectively combining two types of routing and scheduling strategies, we analyze the asymptotic achievable throughput for all n s = ω(1) and nd. As a special case of our results, we show that for n s = Θ(n), the per-session multicast capacity for REN is of order Θ(1/√n d n) when nd = O(n/(log n) a+1 ) and is of order Θ(1/n d · (log n) -n/2 ) when n d = Ω(n/log n).

关键词： Lattices Throughput Upper bound Road transportation Receivers Interference Routing

Aggregation capacity of wireless sensor networks: Extended network case

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Aggregation capacity of wireless sensor networks: Extended n...

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Cheng Wang Changjun Jiang Yunhao Liu Xiang-Yang Li Shaojie Tang Huadong Ma Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China TNLIST School of Software Tsinghua University China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781424499199

A critical function of wireless sensor networks (WSNs) is data gathering. While, one is often only interested in collecting a relevant function of the sensor measurements at a sink node, rather than downloading all the data from all the sensors. This paper studies the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. It focuses on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power *** derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive perfectly compressible functions and type-threshold perfectly compressible functions, the aggregation capacities for random extended WSNs with n nodes are of order Θ ((log n) -β/2-1 ) and Θ (((log n) -β/2 )/(log log n)), respectively, where β >; 2 denotes the power attenuation exponent in the generalized physical model.

关键词： Wireless sensor networks Signal to noise ratio Interference Throughput Lattices Routing PROM

Spatial sparse subspace clustering for compressive spectral imaging

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arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

CSformer: Bridging Convolution and Transformer for Compressive Sensing

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arXiv 2021年

作者： Ye, Dongjie Ni, Zhangkai Wang, Hanli Zhang, Jian Wang, Shiqi Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong 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 Shanghai200092 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518052 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Convolution neural networks (CNNs) have succeeded in compressive image sensing. However, due to the inductive bias of locality and weight sharing, the convolution operations demonstrate the intrinsic limitations in modeling the long-range dependency. Transformer, designed initially as a sequence-to-sequence model, excels at capturing global contexts due to the self-attention-based architectures even though it may be equipped with limited localization abilities. This paper proposes CSformer, a hybrid framework that integrates the advantages of leveraging both detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. In the sampling module, images are measured block-by-block by the learned sampling matrix. In the reconstruction stage, the measurement is projected into dual stems. One is the CNN stem for modeling the neighborhood relationships by convolution, and the other is the transformer stem for adopting global self-attention mechanism. The dual branches structure is concurrent, and the local features and global representations are fused under different resolutions to maximize the complementary of features. Furthermore, we explore a progressive strategy and window-based transformer block to reduce the parameter and computational complexity. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing, which achieves superior performance compared to state-of-the-art methods on different datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing