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Identifying the effectiveness of arabica drip coffee on individual human brainwave 4

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Identifying the effectiveness of arabica drip coffee on indi...

4th Joint International Conference on Digital Arts, Media and technology and 2nd ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT-NCON 2019

作者： Chenghu, Cui Jinna, Hu Visavakitcharoen, Arnon Temdee, Punnarumol Chaisricharoen, Roungsan Brain Science and Engineering Innovation Research Group School of Information Technology Mae Fah Luang University Chiang Rai Thailand State Key Laboratory of Integrated Services Network Xidian Univerisity Shaanxi China Department of Computer Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9781538680728

Caffeine is one of the core substances in coffee. It can directly influence human brain nerve status. Arabica drip coffee is a widely known beverage;drinker often drinks it to keep the brain awake, sensitive, and focus on mental activity. Previous studies have shown that the level of attention in the human brain is controlled by Beta frequency and caffeine can affect the level of Beta frequency. Therefore, this study focuses on the Influence of attention level of local Arabica coffee to the individual human brainwave, thus proving the value of the local Arabica coffee. The research applied twenty participants: ten males and ten females which age between 19 to 22. This research performed human brainwave detection via Electroencephalography (EEG) and interpreted the data through the k-means algorithm. The t-Test study verified that the individual Beta frequency increases significantly after drinking the coffee. Additionally, the classification of k-mean showed that the individual effectiveness could be classified into three groups including low, medium, and high-efficiency level for both before and after drinking the coffee. © 2019 IEEE.

关键词： Electroencephalography

A Distributed Privacy-Preserving Learning Dynamics in General Social Networks

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arXiv 2020年

作者： Tao, Youming Chen, Shuzhen Li, Feng Yu, Dongxiao Yu, Jiguo Sheng, Hao School of Computer Science and Technology Shandong University Qingdao266237 China Big Data Institute Qilu University of Technology Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

Reinforcement Learning with Dual-Observation for General Video Game Playing

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arXiv 2020年

作者： Hu, Chengpeng Wang, Ziqi Shu, Tianye Tong, Hao Togelius, Julian Yao, Xin Liu, Jialin Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The University of Birmingham BirminghamB15 2TT United Kingdom The New York University New York11201 United States

Reinforcement learning algorithms have performed well in playing challenging board and video games. More and more studies focus on improving the generalisation ability of reinforcement learning algorithms. The General Video Game AI Learning Competition aims to develop agents capable of learning to play different game levels that were unseen during training. This paper summarises the five years' General Video Game AI Learning Competition editions. At each edition, three new games were designed. The training and test levels were designed separately in the first three editions. Since 2020, three test levels of each game were generated by perturbing or combining two training levels. Then, we present a novel reinforcement learning technique with dual-observation for general video game playing, assuming that it is more likely to observe similar local information in different levels rather than global information. Instead of directly inputting a single, raw pixel-based screenshot of the current game screen, our proposed general technique takes the encoded, transformed global and local observations of the game screen as two simultaneous inputs, aiming at learning local information for playing new levels. Our proposed technique is implemented with three state-of-the-art reinforcement learning algorithms and tested on the game set of the 2020 General Video Game AI Learning Competition. Ablation studies show the outstanding performance of using encoded, transformed global and local observations as input. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

Applications of Game Theory in Vehicular Networks: A Survey

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

Parallel Exploration via Negatively Correlated Search

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arXiv 2019年

作者： Yang, Peng Yang, Qi Tang, Ke Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Effective exploration is a key to successful search. The recently proposed Negatively Correlated Search (NCS) tries to achieve this by parallel exploration, where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited simultaneously. Various applications have verified the advantages of such novel search behaviors. Nevertheless, the mathematical understandings are still lacking as the previous NCS was mostly devised by intuition. In this paper, a more principled NCS is presented, explaining that the parallel exploration is equivalent to the explicit maximization of both the population diversity and the population solution qualities, and can be optimally obtained by partially gradient descending both models with respect to each search process. For empirical assessments, the reinforcement learning tasks that largely demand exploration ability is considered. The new NCS is applied to the popular reinforcement learning problems, i.e., playing Atari games, to directly train a deep convolution network with 1.7 million connection weights in the environments with uncertain and delayed rewards. Empirical results show that the significant advantages of NCS over the compared state-of-the-art methods can be highly owed to the effective parallel exploration ability. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

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computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

Adversarial directed graph embedding

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arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

Corrections to "Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based brain Network"

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IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2022年 30卷 2970页

作者： Fali Li Yuqin Li Hui Zheng Lin Jiang Dongrui Gao Cunbo Li Yueheng Peng Zehong Cao Yangsong Zhang Dezhong Yao Tao Xu Ti-Fei Yuan Peng Xu MOE Key Laboratory for Neuroinformation The Clinical Hospital of Chengdu Brain Science Institute and the Center for Information in Medicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center School of Medicine Shanghai Jiao Tong University Shanghai China School of Computer Science Chengdu University of Information Technology Chengdu China STEM University of South Australia Mawson Lakes Campus Adelaide SA Australia School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Department of Anesthesiology Affiliated Shanghai Sixth People’s Hospital Shanghai Jiao Tong University Shanghai China Department of Anesthesiology Affiliated Tongzhou People’s Hospital Nantong University Nantong China Co-Innovation Center of Neuroregeneration Nantong University Nantong China Translational Research Institute of Brain and Brain-Like Intelligence Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine Shanghai China

In the above article [1], to track the loss of consciousness (LOC) induced by general anesthesia (GA), we first developed the multi-channel cross fuzzy entropy method to construct the time- varying networks, whose temporal fluctuations were then explored and quantitatively evaluated. Since time-varying network topologies were found to fluctuate from long-range frontal-occipital to short-range prefrontal-frontal connectivity during the LOC period, a new parameter, i.e., the long-range connectivity (LRC) that measured the number of frontal-occipital connectivity, was accordingly calculated and then investigated between the coherence (COH) and cross fuzzy entropy (C-FuzzyEn) approaches, as displayed in Fig. 1. The distinct time-varying fluctuations of both approaches were indeed found within this period, where only C-FuzzyEn effectively captured the consciousness fluctuation induced by the GA.

关键词： Fluctuations Network topology Coherence Anesthesia Entropy Time-varying systems

Transferring knowledge distillation for multilingual social event detection

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings