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Elastic and Energy-Efficient Virtual Machine Allocation on Edge Servers for Internet of Vehicles

Elastic and Energy-Efficient Virtual Machine Allocation on E...

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International symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xianger Li Aiwen Huang Yiqi Fan Zhihai Tang Le Chang Tian Wang School of Automation Guangdong University of Technology Guangzhou China Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Edge computing and virtualization are two important technologies to support future Internet of Vehicles (IoV). Edge servers deployed at Road-Side Units (RSUs) employ Virtual Machines (VMs) to provision realtime, close-distance and low-latency task offloading services to the vehicles on road. Nevertheless, the spatiotemporal traffic dynamics introduce a great challenge in managing the VMs. The local traffic surge can overwhelm the VM capacity of a server, and the VMs have to be frequently powered on and off to catch up with the traffic change, which consumes a great amount of energy. To address these issues, we propose an elastic and energy-efficient VM allocation strategy on edge servers for IoV systems. First, we conduct a trace study on a real traffic trajectory dataset, which reveals the pervasive complementary traffic patterns across neighboring regions. Such insight inspires us to propose an elastic VM allocation strategy by expanding the service range of an edge server to its neighbors. We build a mathematical model of the VM allocation problem, and design an online heuristic algorithm to solve it based on the temporal difference between the current number of VMs and the traffic load in the following round in each region. Extensive trace-driven simulations verify that our algorithm can achieve an up to 35.1%-reduction of the energy consumption compared with other popular approaches, while ensuring that all the vehicles are served by the VMs.

关键词： Telecommunication traffic Traffic control Virtual machines Mathematical models Energy efficiency Trajectory Servers Resource management Vehicle dynamics Internet of Vehicles

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Research on synergy based on correlation analysis of weighted samples of grasping action

Research on synergy based on correlation analysis of weighte...

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2021 China automation Congress, CAC 2021

作者： Yu, Jianjun Zhang, Zihao Zuo, Guoyu Liu, Gengyuan Li, Xuchen Beijing Key Laboratory of Computational Intelligence and Intelligent System College of Electronic and Control Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

The main research goal of underactuated manipulator is to reduce the number of controllers and improve the bionic ability. In this kind of research, underactuated hand design synergy is the mainstream research direction in recent years. Human hand synergy space is a parameterized representation of human hand motion based on a large number of human hand motion data In the past synergy meta-analysis, samples were selected only based on grasping classification without considering people's daily habits, which led to the lack of bionic ability. In this paper, based on the most authoritative 33 kinds of grabbing classification, the samples are weighted and reconstructed according to the daily use habits;Principal component analysis is used to analyze the reconstructed samples, and the correlation between joints is calculated to obtain the synergy of human hand The comparative experiments show that the underactuated transmission scheme based on the synergy design of reconstructed samples has higher similarity and stronger bionic ability than the ordinary sample scheme in most grasping scenes. On this basis, this paper studies the following ability between fingers through the calculation of correlation coefficient and clustering method, so as to further reduce the number of controllers. © 2021 ieee

关键词： Principal component analysis

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Soft-Sensing Method of Effluent Ammonia Nitrogen Based on Emotional Neural Network

Soft-Sensing Method of Effluent Ammonia Nitrogen Based on Em...

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2021 China automation Congress, CAC 2021

作者： Zhang, Hongye Yang, Cuili Qiao, Junfei Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

In the process of wastewater treatment process (WWTP), the concentration of effluent ammonia nitrogen is one of the important indexes to test whether the water quality reaches the standard. Soft sensing technology is a method which can use some easily measurable auxiliary variables to predict key variables online. In this paper, we designed a soft sensing model of effluent NH4-N based on radial basis emotional neural network (RBENN), and then a feature selection (FS) method of sticky binary particle swarm optimization (SBPSO) algorithm based on mutual information (MI) and symmetric uncertainty (SU) is proposed to obtain the appropriate feature variables while balancing correlation and redundancy, and then the network parameters are optimized by comprehensive learning particle swarm optimization (CLPSO) algorithm. Simulation results show that the proposed method effectively improves the prediction performance of the model. © 2021 ieee

关键词： Effluents

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A Document Processing Scheme for Journal Submissions Based on Locality Sensitive Hashing and Scale-invariant Feature Transform 13

A Document Processing Scheme for Journal Submissions Based o...

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13th International symposium on computational intelligence and Design (ISCID)

作者： Wu, Dezhi Shi, Peng Xu, Liang Wang, Hao Tianjin Univ Technol Tianjin 300384 Peoples R China Luoyang Inst Electroopt Equipment AVIC Beijing 100076 Peoples R China Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Elect & Elect Engn Tianjin 300384 Peoples R China

ISBN: (纸本)9781728184463

This paper proposes a blockchain-based document processing scheme that can be used in the journal submission system. The Hash algorithm and Merkle tree are used to realize the immutability of information;the interplanetary file system (IPFS) and asymmetric encryption are used to realize document storage and sharing. Through the Local Sensitive Hash Algorithm (LSH) and SIFT algorithm to achieve multiple submission recognition. The solution has the characteristics of decentralization, security and credibility, and cannot be tampered with at will. It can realize multi-platform data sharing and decentralized processing without revealing the original text, and can realize the identification of multiple submissions of the same paper in different journals.

关键词： Blockchain Journal submission system Document sharing

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Micro-expression recognition convolutional network based on dual-stream temporal-domain information interaction 13

Micro-expression recognition convolutional network based on ...

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13th International symposium on computational intelligence and Design (ISCID)

作者： Zhu, Weijie Chen, Ying Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781728184463

There is a shortage of experimental data in the use of deep learning method for micro expression recognition. The neural network can obtain limited knowledge in the learning process and it is difficult to improve the accuracy. To tackle the problems, a dual-stream temporal-domain information interaction neural convolution network (Dual Scale Temporal Interactive Convolution Neural Network, DSTICNN) network is constructed to process micro-expression sequences and realize the automatic recognition of micro-expression. By improving the deep mutual learning strategy to guide the network to learn different temporal domain information of the same image sequence, the final recognition rate can be improved. The algorithm is tested on CASMEII and SMIC databases, and the results show that the algorithm in this paper can effectively improve the recognition rate of micro expressions, and the overall algorithm is better than existing algorithms.

关键词： micro-expression recognition Convolution Neural Network DSTICNN mutual learning

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Self-Adaptation of Meta-Parameters for Lamarckian-Inherited Neuromodulated Neurocontrollers in the Pursuit-Evasion Game

Self-Adaptation of Meta-Parameters for Lamarckian-Inherited ...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Showalter, Ian Schwartz, Howard Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781728125473

Determining meta-parameter settings is a long-standing challenge in evolutionary computing, and often involves running repeated simulations with successive estimated values until acceptable values are found. The term meta-evolution is often used to describe the optimization of one or more of the meta-parameters that control, for example, the rates of selection and variation in evolutionary optimization algorithms. Here we use self-adaptation to simultaneously evolve all of the practical meta-parameters together with the other evolved parameters as part of the main evolutionary algorithm. The meta-parameters are added to the gene, and evolved along with the other parameters. The evolved neurocontrollers with self-adaptation are compared to those with manually selected meta-parameters. Secondly, self-adapted meta-parameters from the best neurocontroller are used to evolve a further set of non-self-adapted neurocontrollers, and compared with the self-adapted and manually selected results. The effects of self-adaptation are determined through a series of experiments using a previously demonstrated multi-objective Lamarckian-inherited neuromodulated evolutionary neurocontroller. The fitness of the evolved neurocontrollers is determined through their operation of a simulated vehicle pursuing a basic evader vehicle in the pursuit-evasion game. Both vehicles are subject to the effects of mass and drag. It is shown that self-adaptation can he used to automatically tune and control meta-parameters during evolution. Only a trivial amount of computational cost is added, and no degradation in evolutionary performance was observed. Under some circumstances self-adaptation may lead to improved performance of the evolutionary algorithm.

关键词： Evolutionary Meta-parameters Hebbian Learning Lamarckian Inheritance Multi-objective Neuroevolulion Neuromodulation Parameter control Parameter Tuning Pursuit-Evasion Unsupervised Learning

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Malware Family Classification with Explainable BERT (xBERT) Using API Calls

Malware Family Classification with Explainable BERT (xBERT) ...

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ieee/ACM International symposium on Big Data Computing (BDC)

作者： Ruba Kharsae Fatih Kurugollu Ashiq Anjum Abbes Amira Ahmed Bouridane Department of CS University of Sharjah Sharjah UAE School of Computer and Mathematic Sciences University of Leicester Leicester UK

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Malicious Software (Malware) is a primary element of many cyber crimes and attacks, causing massive damage and financial losses to organizations. Accordingly, malware detection and classification has become a crucial security field resulting in various attempts from researchers to develop solutions including signature-based approaches to Artificial intelligence (AI) models showing their efficacy in detecting malware. Yet, users still have reservations about AI models due to the ambiguity and mysteriousness of their decisions resulting from their black-box nature. To address this problem, this paper proposes to develop explainable AI models to classify the malware families robustly. The proposed method uses text classification of API call sequences generated by these families by considering two datasets. A weighted training methodology is used to solve the dataset imbalance problem. Subsequently, the method presents an eXplainable AI (XAI) approach to establish an understandable and interpretable relationship between the API call sequences and the Bidirectional Encoder Representations from Transformers (BERT) model decisions, which enhance the model accountability and usability by employing the Local Interpretable Model-Agnostic Explanation (LIME) and the Shapley Additive Explanations (SHAP) platforms. The results reveal that the BERT model outperforms its counterparts considering F1 score, Balanced Accuracy (BA), and Matthews correlation coefficient (MCC).

关键词： Training Explainable AI computational modeling Transfer learning Text categorization Bidirectional control Transformers Malware Encoding Usability

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Evolutionary automation of Coordinated Autonomous Vehicles

Evolutionary Automation of Coordinated Autonomous Vehicles

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ieee Congress on Evolutionary Computation (CEC) as part of the ieee World Congress on computational intelligence (ieee WCCI)

作者： Huang, Chien-Lun Nitschke, Geoff Univ Cape Town Dept Comp Sci Cape Town South Africa

ISBN: (纸本)9781728169293

Recently, there has been increased research on adaptive control systems for vehicles that operate on autonomous vehicle only roads. Specifically, roads without current infrastructure constraints of traffic lights, stop signals at intersections or vehicle lanes. This study investigates controller automation for vehicles that must navigate and coordinate with each other on such autonomous vehicle only roads. We comparatively evaluate fitness-function (objective) versus behavior-based (novelty search) versus hybridized objective-novelty evolutionary search for synthesizing autonomous vehicle coordinated driving behavior. The goal of such evolved coordinated driving behavior is to maximize effective (safe) and efficient (expedient) autonomous vehicle traffic throughput for given roads. Results indicate that while novelty and hybrid search evolved effective and efficient driving behaviors, these behaviors did not generalize to new roads as well as driving behaviors evolved with objective-based search.

关键词： Evolutionary algorithms

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Meat Factory Cell: Assisting meat processors address sustainability in meat production

Meat Factory Cell: Assisting meat processors address sustain...

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International symposium on computational intelligence and Informatics

作者： A. Mason O. Korostynska L. E. Cordova-Lopez I. Esper D. Romanov S. Ross K. Takács T. Haidegger Norwegian University of Life Sciences (NMBU) Ås Norway Animalia AS Oslo Norway OsloMet University Oslo Norway Óbuda University EKIK Budapest Hungary

ISBN: (纸本)9781665426855

This paper provides a brief overview of the novel Meat Factory Cell and discusses its concept in the context of increasing sustainability in the meat sector. Job quality, environment, health risks, industrial development and education are discussed as sustainability goals that can be mapped against some of the United Nations Sustainable Development Goals (SDG). Technology can arguably help to improve related processes on a societal level, and to achieve the SDGs.

关键词： Program processors automation Education Production facilities Sustainable development Informatics computational intelligence

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Optimal Consensus control for Second-Order Discrete-Time Multi-Agent Systems: Using Online Policy Iteration Algorithm

Optimal Consensus Control for Second-Order Discrete-Time Mul...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Li, Jun Ji, Lianghao Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China

ISBN: (纸本)9781728125473

In this paper, the optimal consensus control problem for multi-agent systems is discussed via a model-free reinforcement learning algorithm. Considering the second-order discrete-time multi-agent systems(MASs) with directed topology which is composed of the leader and the follower agents, a novel solution to the Hamilton-Jacobi-Bellman(HJB) equation combining the optimal consensus problem with the system characteristics including the second-order discrete-time systems with cooperative interactions under directed topology is presented. By graph theory, matrix analysis, deep learning and optimal control method, the necessary conditions and strategies for solving the optimal consensus problem of the dynamic systems are obtained. The results show that the online policy iteration algorithm not only stabilizes the distributed second-order dynamic systems, but also achieves the optimal consensus. Finally, the correctness of our theoretical results are illustrated by the designing actor-critic networks through several numerical simulations.

关键词： Reinforcement learning optimal consensus control online policy iteration neural networks multi-agent systems

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