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arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Task Offloading for End-Edge-Cloud Orchestrated Computing in Mobile Networks

Task Offloading for End-Edge-Cloud Orchestrated Computing in...

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IEEE Conference on Wireless Communications and Networking

作者： Chuan Sun L. Hui Xiuhua Li Junhao We Qingyu Xiongl Xiaofei Wang Victor C.M. Leun School of Big Data Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

Recently, mobile edge computing has received widespread attention, which provides computing infrastructure via pushing cloud computing, network control, and storage to the network edges. To improve the resource utilization and Quality of Service, we investigate the issue of task offloading for End-EdgeCloud orchestrated computing in mobile networks. Particularly, we jointly optimize the server selection and resource allocation to minimize the weighted sum of the average cost. A cost minimization problem is formulated underjoint the constraints of cache resource and communication/computation resource of edge servers. The resultant problem is a Mixed-Integer Non-linear Programming, which is NP-hard. To tackle this problem, we decompose it into simpler subproblems for server selection and resource allocation, respectively. We propose a low-complexity hierarchical heuristic approach to achieve server selection, and a Cauchy-Schwards Inequality based closed-form approach to efficiently determine resource allocation. Finally, simulation results demonstrate the superior performance of the proposed scheme on reducing the weighted sum of the average cost in the network.

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MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series

MERITS: Medication Recommendation for Chronic Disease with I...

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IEEE International Conference on data Mining (ICDM)

作者： Shuai Zhang Jianxin Li Haoyi Zhou Qishan Zhu Shanghang Zhang Danding Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Shenyuan Honors College Beihang University China School of Software Beihang University China National University of Singapore >Singapore

ISBN: (纸本)9781665423991

Medication recommendation for chronic diseases based on the complex historical electronic medical records (EMR) is an important and challenging research problem in medical informatics because the medical records are often irregularly sampled and contain many missing data. However, most existing approaches fail to explore the irregular time-series dependencies and ignore the consecutive correlation in dynamic prescription history. To fill this gap, we propose the MEdication Recommendation network on Irregular Time-Series (MERITS), which captures the irregular time-series dependencies with the neural ordinary differential equations (Neural ODE). Meanwhile, it leverages a drug-drug interaction knowledge graph and two learned medication relation graphs to explore the co-occurrence and sequential correlations of the medications. We further propose an attention-based encoder-decoder framework to combine the historical information of patients and medications from EMR. Besides, we collect and annotate a diabetes inpatient medication dataset and demonstrate the effectiveness of MERITS by comparing it with several state-of-the-art methods of medication recommendations.

关键词： Correlation data visualization Ordinary differential equations data models Diabetes History Electronic medical records

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Value Function Dynamic Estimation in Reinforcement Learning based on data Adequacy 20

Value Function Dynamic Estimation in Reinforcement Learning ...

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4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on big data and Artificial Intelligence, BDAI 2020

作者： Gao, Huifan Pan, Yinghui Tang, Jing Zeng, Yifeng Chai, Peihua Cao, Langcai Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University China Newcastle Business School Northumbria University United Kingdom Department of Computer and Information Sciences Northumbria University United Kingdom

ISBN: (纸本)9781450375603

In recent years, reinforcement learning has played an important role in the study of decision problem in computer games. To solve the problem of how to better estimate the value function with limited computational resources, this paper proposes a dynamic estimation method of value function based on data adequacy. In consideration of the varying complexity of each state in the MDP model, we propose a dynamic value function estimation method which is different from the fixed value function estimation method in traditional methods. Based on the PigChase challenge of the Malmo project launched by Microsoft in 2017, we compare the new method with the existing techniques. Experimental results show that the performance of the proposed algorithm is better than traditional algorithms. © 2020 ACM.

关键词： Dynamic programming

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A mutual authentication lightweight rfid protocol for iot devices 2nd

A mutual authentication lightweight rfid protocol for iot de...

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2nd International Cognitive Cities Conference, IC3 2019

作者： Lu, Yong Wang, Anxi Liu, Shuai School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou550025 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou510632 China

ISBN: (纸本)9789811561122

The IoT data security is a hot topic at a data time. Personal privacy data may be easily leaked to data collectors in an open network environment. If the collectors get enough data, they can analyze the habit and interest of every IoT user, which may give helpful advice or cause endless harass. RFID authentication protocol in IoT is a powerful way to avoid the essential data leaked to data collectors. The previous protocols protect the security of RFID system. But they cannot guard the privacy of RFID system. The protocol we proposed achieves both the privacy and security. © Springer Nature Singapore Pte Ltd 2020.

关键词： Internet of things

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Self-Adaptive Visual Background Extraction with Ghost Regions Elimination

Self-Adaptive Visual Background Extraction with Ghost Region...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Ling Liu Shuai Liu Zhong Qu Dongyang Zhou School of Big Data and Software Engineering College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665446006

The ViBe (Visual Background Extraction) algorithm uses the first frame to initialize the pixel background model for each pixel, which may introduce the ghost regions in the following video frames. Although ViBe can use its background model update mechanism to eliminate the ghost regions, it is time-consuming. Firstly, a ghost elimination algorithm is proposed in this paper in order to reduce the elimination time for the ghost regions based on its properties. Secondly, a fixed global segmentation decision threshold is used to segment pixels in ViBe algorithm. However, because it cannot adapt to the dynamic background, a self-adaptive segmentation decision threshold based on single pixel is used to adjust to the different backgrounds. Finally, a self-adaptive update factor is presented, which makes the background model more reliable and adjusts to the background changes through the self-adaptive processing. The experimental result shows that this improved method can quickly eliminate the ghost regions, and adapt to the different kinds of backgrounds, ensuring more accurate results.

关键词： Visualization Adaptation models Heuristic algorithms Image processing Conferences Lighting Reliability

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Animating turbulent fluid with a robust and efficient high-order advection method

Animating turbulent fluid with a robust and efficient high-o...

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作者： Zhu, Jian Li, Silong Cai, Ruichu Hao, Zhifeng Huang, Guoheng Sheng, Bin Wu, Enhua School of Computers Guangdong University of Technology Guangzhou China School of Mathematics and Big Data Foshan University Foshan China Department of Computer Science & Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

The accuracy of advection has a great influence on the visual effect of fluid simulation. Constrained interpolation profile (CIP) method has been an important advection scheme because of its third-order accuracy and the fact that it only needs to be performed over a compact stencil, but extending it to high-dimensional advection equations is not easy, because it involves complex calculations and large memory overheads, and is usually unstable. In this article, we propose a stable and efficient three-dimensional (3D) CIP scheme which can maintain high accuracy but requires low computation and memory cost. We first construct an efficient two-dimensional (2D) CIP scheme based on dimensional splitting and local Taylor expansions, and then propose an effective way to extend it for 3D applications without decreasing the computational accuracy or affecting the stability. The experimental results show the advantages of our method over the state-of-the-art advection schemes. © 2020 John Wiley & Sons, Ltd.

关键词： Advection

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Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

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第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

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Interpretation Area-Guided Detection of Adversarial Samples

Interpretation Area-Guided Detection of Adversarial Samples

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： JiaLi Wei Ming Fan Xi Xu Ang Jia Zhou Xu Lei Xue School of Cyber Science and Engineering Xi'an Jiaotong University China School of Big Data and Software Engineering Chongqing University China The Hong Kong Polytechnic University China

ISBN: (数字)9781728189154

ISBN: (纸本)9781728189161

Deep learning systems are known to be vulnerable to adversarial samples, which are implemented to change the prediction results by adding small perturbations to benign samples. It is significant to defend against an adversarial attack in critical fields such as automatic drive. In this paper, we propose an interpretation area-guided detection method of adversarial samples, which can improve the performance of the typical feature squeezing method by combining the generated interpretation results. Specifically, we divide the input image into two main parts, the interpretation part, and the non-interpretation part. Then we only squeeze the non-interpretation part, which can reduce the side-effect for benign samples. We evaluate our approach on two widely used datasets, and the results demonstrate that our approach outperforms the original feature squeezing method.

关键词： Deep learning Perturbation methods Conferences software quality Feature extraction software reliability Security

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Tourism service recommendation based on user influence in social networks and time series 21

Tourism service recommendation based on user influence in so...

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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： Ye, Jianxin Xiong, Qingyu Li, Qiude Gao, Min Xu, Rui Key Laboratory of Dependable Service Computing Cyber Physical Society Chongqing University Ministry of Education School of Big Data and Software Engineering Chongqing China

ISBN: (纸本)9781728120584

In the mobile network, user can share their trip stories at any time and place, the massive user data can provides an opportunity to mining users' travel preferences. Tourism recommendation has been studied by lots of researchers, although they took into account the social relation of users and the need of travelers, but if multi-dimensional social information and the seasonality of tourism service are considered, it may lead to better personality efficiency for tourism recommendation system. In this paper, we propose a novel personalized tourism service recommendation model termed GA-LSTM-CSInf, which fused multi-modality travel information include the multi-dimensional social information, time series and tourism service category. The main work is listed as follows:(1)we combined genetic algorithm (GA) and LSTM network for predicting the popularity and score of tourism service.(2) Considering the multi-dimensional social information of users in social networks, we proposed the Explicit Social Influence (ExSoInf) and the Implicit Social Influence (ImSoInf) of users, and then integrated them into SVD algorithm.(3) Calculating user preferences for tourism service categories using matrix factorization technology. And then, the calculation results of the three methods are weighted and summed to obtain a fusion tourism service recommendation algorithm (GA-LSTM-CSInf). We conduct a comprehensive performance evaluation for our fusion recommendation algorithm using real-world dataset collected from Yelp. Experimental results show that our method achieves significantly superior recommendation quality compared to other state of art recommendation methods. © 2019 IEEE.

关键词： Genetic algorithms

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