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检索条件"任意字段=23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021"

共 280 条记录，以下是91-100 订阅

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Predicting the Global Impact of Authors from the Learning Analytics Community - A Case Study grounded in CNA 23

Predicting the Global Impact of Authors from the Learning An...

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23rd international conference on control systems and computer science technologies, cscs 2021

作者： Ionita, Remus Florentin Corlatescu, Dragos-Georgian Dascalu, Mihai McNamara, Danielle S. University Politehnica of Bucharest Computer Science Department Bucharest Romania Arizona State University Psychology Department TempeAZ United States

ISBN: (纸本)9781665439398

Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the international conference of Learning Analytics Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact. © 2021 IEEE.

关键词： Semantics

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Knowledge-Assisted DRL for Energy Harvesting Based Multi-Access Wireless Communications 23

Knowledge-Assisted DRL for Energy Harvesting Based Multi-Acc...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Luo, Sheng Zhang, Haodi Li, Qifan Wu, Kaishun College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665494571

This paper investigates the use of deep reinforcement learning (DRL) to maximize the long-term throughput of multi-access wireless communication systems. Specifically, we consider the scenario in which an access point (AP) without dedicated power supply harvests energy from the ambient environment and uses the harvested energy to deliver data packets to multiple users. We formulate the access control problem as a Markov Decision Process (MDP) with unknown system dynamics and apply the deep reinforcement learning (DRL) approach to solve this problem, i.e., the optimal access strategy is approximated by using a double deep Q-network (DDQN). As the considered system usually has large state and action spaces, some domain knowledge is embedded into the training process to improve the performance of DDQN. It is shown that with the help of domain knowledge, access control strategies which can achieve a higher system throughput can be found by the knowledge-embedded DDQN. Moreover, the performance of the access control policy obtained by using the knowledge-embedded DDQN is compared with that of several conventional transmission schemes. Experiment results show that the transmission policy obtained by using our proposed model can achieve better performance. © 2021 IEEE.

关键词： Access control Wireless communication Training System dynamics Smart cities Power supplies Process control

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Flocking of Battery-Powered Mobile Agents with Energy-Aware Potential Function 23

Flocking of Battery-Powered Mobile Agents with Energy-Aware ...

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作者： Jiang, Fu Xia, Yaoxin Li, Heng Zhang, Zhongke Zhang, Xiaoyong Peng, Jun School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665494571

Flocking of mobile agents has been well studied in the literature. However, since most practical mobile agents, e.g., robots, UAVs, are typically powered by onboard batteries, it is of practical significance to consider the battery consumption in the control design, and explore how to optimize the distribution of energy consumption. In this paper, we consider the flocking of battery-powered mobile agents using the potential field approach. We first analyze the limitations of the classical potential field method. Next, we propose a new potential function by considering the battery consumption, and design the flocking control law related to the gradient of the proposed potential functions correspondingly. We verified the effectiveness of the proposed method with both simulations and a group of UAVs. The results of experiment verify that the proposed method can optimize the energy consumption of the flocking system when compared with the existing methods, which is benefit to the endurance of the system. © 2021 IEEE.

关键词： Energy consumption Smart cities control design Mobile agents Batteries Robots Optimization

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Quality in Color: Using Knowledge Graphs for Enhanced Quality control in an Automotive Paintshop 23rd

Quality in Color: Using Knowledge Graphs for Enhanced Qual...

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23rd international Semantic Web conference, ISWC 2024

作者： Steenwinckel, Bram Soete, Colin Moens, Pieter Mussche, Joris Hoecke, Sofie Van Ongenae, Femke IDLab Ghent University – imec Gent9000 Belgium Volvo Cars Group Gent9000 Belgium

ISBN: (纸本)9783031778469

Sensors and their derived data have reshaped manufacturing by enabling real-time monitoring, improved quality control and enhanced safety, particularly in automotive processes. Despite these benefits, challenges arise within the realm of data discovery when they have to align all this data, originating from different systems and built by different manufacturers using different technologies. This paper shows our solution to resolve these challenges within the Volvo Cars paintshop. By organizing sensor metadata, link them with the originating devices and the place where the data will be stored in a knowledge graph, our approach facilitates seamless data integration for multiple paintshop applications. We show how the resulting knowledge graph helps with dashboard techniques, machine learning, and semantic reasoning to provide insights for the paintshop operators. The obtained findings clearly show the potential of knowledge graphs in production lines, paving the way for future advancements in automotive manufacturing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Metadata

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of Things and Smart Spaces, ruSMART 2023

23rd International Conference on Next Generation Wired/Wirel...

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of Things and Smart Spaces, ruSMART 2023

ISBN: (纸本)9783031609961

The proceedings contain 67 papers. The special focus in this conference is on Next Generation Wired/Wireless Networking. The topics include: Detecting PII Leakage Using DPI and Machine Learning in an Enterprise Environment;deep Learning for Preventing Botnet Attacks on IoT;Design of Multi-polarisation MIMO Antenna for Heterogeneous Smartphone Applications;potential Possibilities of Voice Pattern Recognition by a Distributed Fiber Optic Sensor;Enhancing Wireless Connectivity Through Bayesian-Optimized UAV-BS Positioning and Charging;comparison of Centralized and Federated Machine Learning Techniques for Beamtracking in 5G/6G systems;exploring the Efficiencies and Vulnerabilities of Smart Door control systems: A Systematic Review;System-Level Model for SINR and HPBW Evaluation in 5G mmWave UDN with Location-Aware Beamforming;reinforcement Learning Based Power Allocation for 6G Heterogenous Networks;The Impact of Capacity Averaging in Packet-Level Modeling of 5G NR with Blockage and Micromobility;Interference Mitigation for Reconfigurable Intelligent Surface (RIS)-Aided Non-terrestrial Base Station (NTBS) in NOMA Downlink HetNets;modeling a Digital Avatar of a Car Drivers Based on the Quantification of the Information Environment;A New Blockage Detection Approach for 6G THz systems;real-Time Anomaly Detection in Network Traffic Using Graph Neural Networks and Random Forest;addressing Security and Privacy Issues in a Smart Environment by Using Blockchain as a Preemptive Technique;the Role of Cryptocurrencies in the Development of Countries and the Fight Against Financial Problems;Designing the UzBCS Lending Platform Network Based on Blockchain Technology and Ensure Transaction Security;decentralized Blockchain Networks and Economic Security: Balancing Scalability and Security Tradeoffs;study of Machine Learning Models for IoT Based Efficient Classroom Usage;transforming Higher Education: A Comprehensive Analysis of Blockchain technologies and Digitalization.

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of Things and Smart Spaces, ruSMART 2023

23rd International Conference on Next Generation Wired/Wirel...

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of Things and Smart Spaces, ruSMART 2023

ISBN: (纸本)9783031609930

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IoT-based Ambient Parameters Monitoring Device for in-Home Elderly Care 23

IoT-based Ambient Parameters Monitoring Device for in-Home E...

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23rd international conference on control systems and computer science technologies, cscs 2021

作者： Elena-Anca, Paraschiv Ovidiu, Bica Cristian, Petrache Ianculescu, Marilena National Institute for Research and Development in Informatics Systems and Applications for Society Department Bucharest Romania Doctoral School of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665439398

During the last decades, more and more demographic, social and economic changes have influenced the percentage of the ageing population, leading thus to an increasing number of elderly people, correlated with a direct and dramatic influence on the healthcare and social services. Furthermore, the grown longevity of the elderly, the shortage of an appropriate number of caretakers and the raising empowerment of the seniors regarding the self-management of their health status, have led to the stringent necessity of implementing smart environments inside their homes in order to benefit of a prolonged independent, active, healthy and secure life. The Internet of Things (IoT) technology has gained a lot of attention due to its strong potential for supporting these smart environments through devices that can remotely monitor elderly's health and daily activities, without the need to be constantly assisted by a medical specialist. For a comprehensive elderly-targeted remote monitoring able to sustain proactive and preventive measures, the ambient factors should be closely measured and correlated with the physiological and physical vital parameters. RO-SmartAgeing is an undergoing project which aims to create such a smart environment in the home of an elderly person for providing accurate and less-intrusive health and lifestyle monitoring and support services. This paper highlights a component of RO-SmartAgeing project, namely a system that guarantees the collection of ambient-related parameters in order to keep under control the home environment of the patient. Apart from the suggested literature review, as to demonstrate the feasibility of the proposed system, both a proof of concept is used in order to describe the idea and the functionality of the product, and also the conclusions drawn after some preliminary tests have been performed. © 2021 IEEE.

关键词： Internet of things

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Multi-agent deep reinforcement learning for traffic signal control with Nash Equilibrium 23

Multi-agent deep reinforcement learning for traffic signal c...

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作者： Wei, Wei Wu, Qiang Wu, Jianqing Du, Bo Shen, Jun Li, Tinghong School of Computer Science and Engineering Xi'an University of Technology Xi'an China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China University of Wollongong Smart Infrastructure Facility Wollongong Australia China Mobile Communications Corporation Lanzhou China

ISBN: (纸本)9781665494571

Traffic signal control is an essential and chal-lenging real-world problem, which aims to alleviate traffic congestion by coordinating vehicles' movements at road in-tersections. Deep reinforcement learning (DRL) combines deep neural networks (DNNs) with a framework of reinforcement learning, which is a promising method for adaptive traffic signal control in complex urban traffic networks. Now, multi-agent deep reinforcement learning (MARL) has the potential to deal with traffic signal control at a large scale. However, current traffic signal control systems still rely heavily on simplified rule- based methods in practice. In this paper, we propose: (1) a MARL algorithm based on Nash Equilibrium and DRL, namely Nash Asynchronous Advantage Actor-Critic (Nash-A3C);(2) an urban simulation environment (SENV) to be essentially close to the real-world scenarios. We apply our method in SENV, obtaining better performance than benchmark traffic signal control methods by 22.1%, which proves that Nash-A3C to be more suitable for large industrial level deployment. © 2021 IEEE.

关键词： Performance evaluation Deep learning Computational modeling Roads Neural networks Reinforcement learning Nash equilibrium

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DDoS Defense Scheme Based on Machine Learning in Software-Defined Networking 23

DDoS Defense Scheme Based on Machine Learning in Software-De...

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作者： Zhong, Hong Yu, Chao Cui, Jie Sun, Xiuwen Gu, Chengjie Liu, Lu School of Computer Science and Technology Anhui University Hefei China Security Research Institute New H3C Group Hefei China

ISBN: (纸本)9781665494571

Since software-defined networking (SDN) has the ad-vantages of central control programmability and global view, it is suitable for industrial network. However, large-scale security risks such as distributed denial of service (DDoS) attacks endanger the security and reliability of services in the SDN-based system. Therefore, a comprehensive and efficient solution to this issue is crucial. This paper proposes a scheme for mitigating DDoS attack traffic deployed on an SDN application plane. The framework periodically obtains the traffic statistics of the switch ports and flow tables. After obtaining the traffic characteristics, a machine learning model is used for training. The trained model can identify benign and malicious traffic. After the attack is found, the rules are sent to the switch to control the flow and resist DDoS attacks. We use the CICDDoS2019 dataset to perform experiments. The experimental results show that compared to other schemes, the proposed scheme achieves more accurate classification. © 2021 IEEE.

关键词： Training Support vector machines Switches Machine learning Denial-of-service attack Feature extraction control systems

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StatekeepSpec: Securing Speculative Execution via forbidding Change-Of-State 23

StatekeepSpec: Securing Speculative Execution via forbidding...

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作者： Lv, Zhiyuan Zhao, Youjian Huawei Technologies Beijing China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9781665494571

Speculation-based attacks, e.g., Spectre and Meltdown, affect hundreds of millions of computers. These attacks utilize speculative execution to access privileged data and leak it through a side-channel, e.g., typical a cache channel. Existing hardware-based defense solutions require major changes to the microprocessor architecture and incur large overheads, while software-based solutions address only specific exploit techniques. In this paper, we propose StatekeepSpec, a novel defense against Spectre attacks by forbidding speculative instructions' side effects to the cache hierarchy. Specifically, given a speculative load, if it triggers a cache hit then StatekeepSpec enforces it reading data from the cache without updating its replacement information;otherwise a cache miss occurs then StatekeepSpec enforces it reading data directly from the next level cache or memory, without updating the current level cache. As a result, StatekeepSpec blocks micro-architectural covert and side channels through the cache hierarchy caused by speculative loads. We have implemented a prototype of StatekeepSpec by modifying the design of an x86-64 processor using the Gem5 full system simulator. The evaluation results show that StatekeepSpec provides a strong security guarantee against cache based Spectre attacks. Furthermore, our simulations with 23 SPEC CPU workloads show that the performance overhead introduced by StatekeepSpec is less than 29%. © 2021 IEEE.

关键词： computers Smart cities Microprocessors Prototypes computer architecture Hardware Central Processing Unit

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