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arXiv 2021年

作者： Yang, Ru Ding, Zhijun Jiang, Changjun Zhou, MengChu The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University The Department of Computer Science and Technology Tongji University Shanghai201804 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Due to the mobility and frequent disconnections, the correctness of mobile interaction systems, such as mobile robot systems and mobile payment systems, are often difficult to analyze. This paper introduces three critical properties of systems, called system connectivity, interaction soundness and data validity, and presents a related modeling and analysis method, based on a kind of Petri nets called VPN. For a given system, a model including component nets and interaction structure nets is constructed by using VPNs. The component net describes the internal process of each component, while the interaction structure net reflects the dynamic interaction between components. Based on this model, three properties are defined and analyzed. The case study of a practical mobile payment system shows the effectiveness of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Petri nets

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Two-stream Hierarchical Similarity Reasoning for Image-text Matching

arXiv

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arXiv 2022年

作者： Chen, Ran Wang, Hanli Wang, Lei Kwong, Sam The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China DeepBlue Academy of Sciences Shanghai200336 China The Department of Computer Science City University of Hong Kong Hong Kong

Reasoning-based approaches have demonstrated their powerful ability for the task of image-text matching. In this work, two issues are addressed for image-text matching. First, for reasoning processing, conventional approaches have no ability to find and use multi-level hierarchical similarity information. To solve this problem, a hierarchical similarity reasoning module is proposed to automatically extract context information, which is then co-exploited with local interaction information for efficient reasoning. Second, previous approaches only consider learning single-stream similarity alignment (i.e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching. To address this issue, a two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation. These two issues are investigated by a unifying framework that is trained in an end-to-end manner, namely two-stream hierarchical similarity reasoning network. The extensive experiments performed on the two benchmark datasets of MSCOCO and Flickr30K show the superiority of the proposed approach as compared to existing state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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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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Model checking cooperative multi-agent systems in BDI logic

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Journal of Information and Computational Science 2012年第5期9卷 1185-1194页

作者： Chen, Qingliang Su, Kaile Wu, Lijun Xu, Zhaocheng School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Department of Computer Science Jinan University Guangzhou 510632 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Traditional temporal logics such as LTL (Linear Temporal Logic) and CTL (Computation Tree Logic) have shown tremendous success in specifying and verifying hardware and software systems. However, this kind of logic can only model the dynamic behaviors for a single system, and fall short in capturing the joint and concurrent aspects in the composition of systems, such as the interactions and coordinations of agents in Multi-agent systems. Although ATL (Alternating-time Temporal Logic) can solve the problem to a certain extent, by preceding the temporal operators with a selective path quantifier, to specify that the interactions between entities inside the system can make sure that there is a strategy to reach a certain state, it can not formalize the rational mental attitudes of the agents such as the Belief, Desire and Intention (BDI) which plays an essential role in decision making during cooperation with other agents. This paper will investigate this problem by proposing a logic of ATLBDI (Alternating-time Temporal Logic of Belief Desire and Intention) and discuss the algorithmic issues such as model checking algorithms and the computational complexity, along with examples to show its effectiveness. We conclude that model checking for ATLBDI is completely tractable and is in PTIME-Complete, which is quite an optimistic and promising result for further applications. © 2012 by Binary Information Press.

关键词： Multi agent systems

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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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arXiv

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arXiv 2017年

作者： Lang, Guangming School of Mathematics and Statistics Changsha University of Science and Technology Changsha Hunan410114 China Department of Computer Science and Technology Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Mathematics and Econometrics Hunan University Changsha Hunan410004 China

In practice, there are many dynamic covering decision information systems, and knowledge reduction of dynamic covering decision information systems is a significant challenge of covering-based rough sets. In this paper, we first study mechanisms of constructing attribute reducts for consistent covering decision information systems when adding objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when adding objects. Then we investigate mechanisms of constructing attribute reducts for consistent covering decision information systems when deleting objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when deleting objects. Finally, the experimental results illustrates that the related family-based methods are effective to perform attribute reduction of dynamic covering decision information systems when object sets are varying with time. Copyright © 2017, The Authors. All rights reserved.

关键词： Rough set theory

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arXiv

引用

arXiv 2017年

作者： Lang, Guangming School of Mathematics and Statistics Changsha University of Science and Technology Changsha Hunan410114 China Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Mathematics and Econometrics Hunan University Changsha Hunan410004 China

In practical situations, there are many dynamic covering information systems with variations of attributes, but there are few studies on related family-based attribute reduction of dynamic covering information systems. In this paper, we first investigate updated mechanisms of constructing attribute reducts for consistent and inconsistent covering information systems when varying attribute sets by using related families. Then we employ examples to illustrate how to compute attribute reducts of dynamic covering information systems with variations of attribute sets. Finally, the experimental results illustrates that the related family-based methods are effective to perform attribute reduction of dynamic covering information systems when attribute sets are varying with time. Copyright © 2017, The Authors. All rights reserved.

关键词： Information use

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Incorporation of Ordinal Optimization into Learning Automata for High Learning Efficiency

Incorporation of Ordinal Optimization into Learning Automata...

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IEEE International Conference on Automation Science and Engineering

作者： JunQi Zhang Cheng Wang Di Zang MengChu Zhou Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Department of Electrical and Computer Engineering New Jersey Institute of Technology

ISBN: (纸本)9781467381840

Learning automata (LA) represent important leaning mechanisms with applications in automated system design, biological system modeling, computer vision, and transportation. They play the critical roles in modeling a process as well as generating the appropriate signal to control it. They update their action probabilities in accordance with the inputs received from the environment and can improve their own performance during operations. The action probability vector in LA takes charge of two functions: 1) The cost of convergence, i.e., the size of sampling budget;2) The allocation of sampling budget among actions to identify the optimal one. These two intertwined functions lead to a problem: The sampling budget mostly goes to the currently estimated optimal action due to its high action probability regardless whether it can help identify the real optimal action or not. This work proposes a new class of LA that separates the allocation of sampling budget from the action probability vector. It uses the action probability vector to determine the size of sampling budget and then uses Optimal computing Budget Allocation (OCBA) to accomplish the allocation of sampling budget in a way that maximizes the probability of identifying the true optimal action. Simulation results verify its significant speedup ranging from 10.93% to 65.94% over the best existing LA algorithms.

关键词： Convergence Optimization Resource management Learning automata Computational modeling Pursuit algorithms Accuracy

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Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

Random Occlusion Recovery with Noise Channel for Person Re-i...

引用

16th International Conference on Intelligent computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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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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