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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 ...

引用

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

Micro-expression recognition convolutional network based on ...

引用

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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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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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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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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Impulsive Containment control for Linear Multi-Agent Systems with Self-Feedback and Aperiodic Sampling 11

Impulsive Containment Control for Linear Multi-Agent Systems...

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11th International Conference on Information Science and Technology, ICIST 2021

作者： Chen, Jiejie Chen, Boshan Zeng, Zhigang Jiang, Ping Zhu, Yi Hubei Normal University College of Computer and Information Huangshi435002 China Hubei Normal University College of Mathematics and Statistics Huangshi435002 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan435000 China Hubei PolyTechnic University Computer School Huangshi435002 China

ISBN: (纸本)9781665412667

In this paper we discuss the impulsive containment control problem of continuous-time linear multi-agent systems. Firstly, an impulsive control protocol with self-feedback and aperiodic sampling is proposed, in which the coupling matrix do not have to be Laplacian. Utilizing the proposed protocol, the impulsive containment control problem for continuous-time linear multi-agent systems is equivalently transformed into the stabilization problem of some independent linear discrete-time switched systems. Then a mode-dependent switching-type Lyapunov function is constructed, some sufficient conditions are showed by using some linear matrix inequalities to achieve the containment control purpose. In addition, based on a linear matrix inequality, the controller gain matrix can be designed to solve the containment control problem. In obtained results, the self-feedback gain, the size and the switching frequency of the sampling intervals can play all important roles. Finally, two simulation examples are employed to validate the effectiveness of the theoretical results. © 2021 ieee.

关键词： Multi agent systems

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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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Evaluation of air quality variability in Timioara, Romania 14

Evaluation of air quality variability in Timioara, Romania

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14th ieee International symposium on Applied computational intelligence and Informatics, SACI 2020

作者： Gruicin, Iasmina Ionascu, Marian-Emanuel Popa, Mircea University Politehnica of Timioara Faculty of Automation and Computers Timioara300223 Romania

ISBN: (纸本)9781728173764

This study presents the evaluation and analysis of air pollution levels in Timioara, Romania, regarding the main air pollutants. We examined diurnal, monthly and seasonal variations, using datasets of measurements from the four existing fixed air quality monitoring stations in Timioara. The results show that the station TM-1, located in the south of the city, has overall much higher values that the other stations. All pollutants except for ozone show peaks in the diurnal variation in the morning and late in the afternoon, which can be correlated to the traffic rush hours, and fall and winter dominance in the seasonal variation. Ozone variation follows the chemistry of its formation, showing proportional increases with the increase of sunlight and ultraviolet radiation. © 2020 ieee.

关键词： Air quality

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Remote Sensing Inversion for River Turbidity Estimation Based on Noise Injection and Ensemble Learning

Remote Sensing Inversion for River Turbidity Estimation Base...

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2021 China automation Congress, CAC 2021

作者： Zhou, Yazhen Shi, Shuang Liu, Hongyan Zhang, Yonghui Gu, Ke Qiao, Junfei Faculty of Information Technology Beijing University of Technology Beijing 100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute

ISBN: (纸本)9781665426473

Turbidity is one main indicator of quality typically used for monitoring water pollution and water eutrophication. The monitoring technology of river water quality can be divided into contact monitoring technology based on physical sensors and non-contact monitoring technology based on remote sensing satellites. The traditional contact monitoring technology has limited monitoring spatial resolution. As a new technology, the remote sensing monitoring technology of the river can conveniently solve the above-mentioned shortcomings and realize the coverage of the whole river basin. However, due to the scarcity of remote sensing data and the measurement errors of remote sensing satellites themselves, learning-based turbidity measurement methods have weak generalization ability and thus are easily prone to produce large deviations. To effectively address such difficulty, a noise injection method is introduced into a river turbidity measurement model based on ensemble learning. To be specific, we randomly generate many noise sets and add them to the remote sensing data to make it closer to the real application scene, and then adopt the ensemble learning model to estimate river turbidity. Experiments validate that the random noise injection strategy can effectively improve the generalization performance of river turbidity measurement. © 2021 ieee

关键词： Rivers

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