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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Kong, Xujiao Li, Peng Zhang, Tao Tu, Meimei Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Mobile crowdsensing is an effective way to collect sensing data about regions of interest. Effective incentive mechanisms are invaluable in mobile crowd sensing, for recruiting a number of mobile users to fulfill coverage requirement in the interested regions. In this paper, we exploit the potential of mobile users by designing a spatial-temporal crowd sensing incentive mechanism for smartphone users. We first use the logistic regression technique to mine user preferences from the user's historical data. Then, we propose a sub-module approximation algorithm to solve the participant recruitment problem, which is proved to be an NP-hard problem. Moreover, we design the winner auction algorithm (WAA) based on the reverse auction mechanism to solve the critical payment allocation problem. Finally, we demonstrate that our mechanism achieves truthfulness, individual rationality, and high computation efficiency through both theoretical analysis and experimental simulation. © 2019 IEEE.

关键词： Approximation algorithms

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Minimum cost task assignment for vehicle-based crowdsensing with deterministic and nondeterministic trajectory 21

Minimum cost task assignment for vehicle-based crowdsensing ...

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作者： Tu, Meimei Li, Peng Zhang, Tao Zhou, Qiang Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Vehicle-based crowdsensing (VCS) is a special case in crowdsourcing, and task assignment is a basic and important problem. In this paper, we investigate the minimum cost task assignment (MCTA) problem for vehicle-based crowdsensing. The VCS platform hopes to recruit some vehicles to complete given spatial-temporal tasks with the minimum cost. As vehicle trajectories are dynamic, we divide the MCTA problem into two sub-problems, the deterministic trajectory (D-MCTA) problem, and nondeterministic trajectory (N-MCTA) problem. For D-MCTA problem, we prove that the D-MCTA problem is NP-hard, and we propose a greedy algorithm to solve this problem. For N-MCTA problem, firstly, we determine the probability of each vehicle's trajectory through the logistic regression method. Then, we exploit the semi-Markov method to calculate the probability of each vehicle executing the task. Moreover, a greedy algorithm is proposed and the theoretical analysis is given. Finally, extensive simulations have been conducted to show the performance of our proposed algorithms is superior to the other algorithms. © 2019 IEEE.

关键词： Vehicles

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Budget constraint roadside units placement for traffic flows monitoring system with reliability in vehicular networks 21

Budget constraint roadside units placement for traffic flows...

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作者： Jiang, Peng Li, Peng Zhang, Tao Huang, Weiyi He, Heng Nie, Lei Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

In Traffic Flows Monitoring System (TFMS), the key problem is how to install Roadside Units (RSUs) reliably to monitor traffic flows with the constrained budget. The reliability of Traffic Flows Monitoring System(TFMS) is determined by the number of RSUs each traffic flow pass through, and the RSUs placement number is determined by the budget and the placement strategy. In this paper, we investigate the budget constraint RSUs placement problem for traffic flows monitoring system with reliability in vehicular networks. Given a budget constraint, the problem is how to install RSUs to ensure the reliability of TFMS, that is each traffic flow pass through RSUs as many as possible with the limited budget. We prove that this problem is NP-hard. Then, we propose two greedy algorithms to solve the problem according to the pigeonhole principle. Moreover, approximation ratios of proposed algorithms are given. Finally, we verify the feasibility of proposed algorithms through extensive experiments, and the results show that the proposed algorithms are superior to the other algorithms. © 2019 IEEE.

关键词： Reliability

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Multi-view ontology alignment visualization (MOAV): A human-cognition based ontology alignment visualization technique

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第4期5卷 198-210页

作者： Chen, Jie Zhang, Jie Xue, Xingsi Huang, Yikun Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guangxi Guilin541004 China Department of Information Technology Concord University College Fujian Normal University No.68 Xueyuan Road University Town Minhou FuzhouFujian350117 China

To further improve the quality of ontology alignment, it is necessary for an ontology matcher to introduce a user’s knowledge into its automatic matching process, which yields the development of interactive ontology matching techniques. Since validating problematic entity correspondences is a difficult cognition task, user interaction based on Ontology Alignment Visualization (OAV) has become the critical component of an interactive ontology matcher, which directly affects the quality of validating result and the efficiency of validating process. The existing OAV tools are mainly developed based on their designers’ subjective feelings and experiences, which do not take into consideration the law of human cognition. To improve the efficiency of the ontology alignment validating process, in this paper, a Multi-view OAV (MOAV) is proposed, which synthetically utilizes the human cognitive theory-information visualization and human-computer inter-action. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test the performance of our proposal, and the experimental results show that MOAV can effectively improve a user’s validating efficiency. © 2020, Taiwan Ubiquitous information. All rights reserved.

关键词： Visualization

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Improved Quality Aware Network Using Quality and Max Pooling Based Late Feature Fusion for Video-Based Person Re-Identification

Improved Quality Aware Network Using Quality and Max Pooling...

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作者： Yude Shi Xiao Ke Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University

Video-based person re-identification is an important research area in computer vision. Quality Aware Network(QAN) is a model that is robust to noise in the videos. It calculates the weighted average of all the frames' image features, with each frame's quality score as the weight, and generates a video feature. However, the QAN has poor ability to distinguish pedestrians with highly similar clothing. To address this problem, we analyze the advantages and shortcomings of Quality Pooling and Max Pooling in theory, and propose the Frame Quality and Max Pooling based Late Feature Fusion Method(QMFF). It aims at enhancing the QAN's ability to distinguish pedestrians with similar clothing. First, use Quality Pooling to get the initial ranking list. Then, for each Query, judge whether the top-N candidates in the ranking list are highly similar in clothing to one another. If the Query meets the similarity criterion, Max Pooling with Frame Quality Filter is used. Local salient features selected by Max Pooling help distinguish pedestrians more accurately. Experimental results on the iLIDS-VID dataset show that the proposed method is superior to the QAN and many recently proposed methods.

关键词： computer vision quality aware network late feature fusion video-based person re-identification max pooling attention mechanism

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A Topic-Triggered Translation Model for Statistical Machine Translation

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Chinese Journal of Electronics 2017年第1期26卷 65-72页

作者： SU Jinsong WANG Zhihao WU Qingqiang YAO Junfeng LONG Fei ZHANG Haiying Software School Xiamen University Provincial Key Laboratory for Computer Information Processing Technology Soochow University Automation Department Xiamen University

Translation model containing translation rules with probabilities plays a crucial role in statistical machine translation. Conventional method estimates translation probabilities with only the consideration of cooccurrence frequencies of bilingual translation units, while ignoring document-level context information. In this paper, we extend the conventional translation model to a topic-triggered one. Specifically, we estimate topic-specific translation probabilities of translation rules by leveraging topical context information, and online score selected translation rules according to topic posterior distributions of translated sentences. As compared with the conventional model, our model allows for more fine-grained distinction among different translations. Experiment results on large data set demonstrate the effectiveness of our model.

关键词： Statistical machine translation Topictriggered translation model Topical context information

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A Novel Economical Design of Multistage Decimators for Low Latency Audio Applications

A Novel Economical Design of Multistage Decimators for Low L...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Wei Hu Chongwei Zheng Yonghao Wang Jing Liu Cen Chen Zeng Zeng College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Digital Media Technology lab Birmingham City University Birmingham UK Institute for Infocomm Research Singapore

ISBN: (纸本)9781728176499;9781728176505

The oversampling sigma-delta analog-to-digital converter (ADC) is widely used in digital audio systems for its low hardware cost and high precision. The digital filter part of the ADC is the primary source of the silicon and power cost. In this work, we proposed an economical multistage decimation filter architecture to meet the requirements of commercial ADC specifications with a further novel multi-objective optimization strategy. We took group delay and computational cost as objective functions and used annealing algorithm to readjust the stopband attenuation, stopband edge and passband ripple of each filter. Our multistage architecture combines cascaded integrator-comb (CIC) filters, compensating filters and equiripple finite impulse response (FIR) filters at the different stages to balance the overall performance. The experimental results showed that our design approach worked with multi-objective optimization strategy could achieve low group delay with lower cost compared to traditional designs.

关键词： Sigma-delta modulation Finite impulse response filters computer architecture Filtering algorithms Attenuation Linear programming Silicon

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Efficient Design of Hybrid Half-band Multi-stage Filter Based on Simulated Annealing Algorithm

Efficient Design of Hybrid Half-band Multi-stage Filter Base...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Wei Hu Chongwei Zheng Yonghao Wang Jin Zhang Cen Chen Zeng Zeng College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Digital Media Technology lab Birmingham City University Birmingham UK Institute for Infocomm Research Singapore

ISBN: (纸本)9781728176499;9781728176505

The oversampling sigma-delta based analog-to-digital converter and digital-to-analog converter (ADC/DAC) architecture can achieve high resolution with effective low hardware cost. It normally introduces latency due to the internal high-performance finite impulse response (FIR) digital filters. In this work, we proposed a novel approach to reduce latency and accurately predict latency caused by the group delay. We propose a filter structure applied to ADC, using the infinite impulse response (IIR) filter and half-band filter (HBF) to construct a hybrid half-band filter (HHBF). A novel modified Simulated Annealing (SA) algorithm is proposed to optimize the best distribution of passband ripple and transition bandwidth that is suitable for audio processing with low latency and computation cost. The experiments show that hybrid HBF proposed in this paper improves the efficiency of the filter in terms of filter cost and group delay comparing to traditional design.

关键词： Sigma-delta modulation Finite impulse response filters IIR filters Simulated annealing Bandwidth Filtering algorithms Prediction algorithms

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A Campus Carpooling System Based on GPS Trajectories 1

引用

12th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2019

作者： Wang, Xuesong Liu, Yizhi Jiang, Zhengtao Peng, You Yin, Tianhao Liao, Zhuhua Zhao, Jingqiang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Xiangtan411201 China Network Information Center Hunan University of Science and Technology Xiangtan411201 China

ISBN: (数字)9783030249007

ISBN: (纸本)9783030248994

College students are special because they relatively have tighter in economy but have greater consistency in leisure time. They prefer to go out together with schoolfellows due to higher trusts and closeness. Moreover, the electronic map is difficult to be updated. Campus-roads recently are updated rapidly. And many alleys in campuses are not shown in the electronic map. Therefore, we devise and implement a campus carpooling system based on GPS trajectories. It includes three parts. Firstly, the campus road network is extracted based on GPS trajectories. Next, the shortest sharing path in the campus is computed in terms of the campus road network. Then, passengers are matched automatically by the carpooling matching algorithm (CMA) in our system. Experiments show that our system is able to provide a safer and more comfortable carpooling experience for college students. © 2019, Springer Nature Switzerland AG.

关键词： Trajectories

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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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