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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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Resource-Aware Classification via Model Management Enabled Data Stream Optimization 20

Resource-Aware Classification via Model Management Enabled D...

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20th ieee international conference on Pervasive computing and Communications (ieee PerCom)

作者： Suenkel, Michael Elmamooz, Golnaz Grawunder, Marco Phan Thai Hoang Rauch, Elke Schmeling, Lara Thurner, Stefan Nicklas, Daniela Univ Bamberg Chair Mobile Syst Bamberg Germany Carl von Ossietzky Univ Oldenburg Dept Comp Sci Oldenburg Germany Bavarian State Res Ctr Inst Agr Engn & Anim Husb Freising Weihenstephan Germany Ludwig Maximilians Univ Munchen Chair Anim Welf Ethol Anim Hyg & Anim Husb Dept Vet Sci Fac Vet Med D-80539 Munich Germany

ISBN: (纸本)9781665416436

The integration of machine learning (ML) approaches in sensor-based applications in the field of pervasive computing is becoming increasingly prominent due to the increasing number of sensor-based applications in general and the continuous adaption of ML approaches to new domains. Several ML models are used within a processing pipeline that operates on the same sensor data. Still, the cloud computing approach is a straightforward solution where all sensor data is sent to the cloud before processing, which is inefficient according to resource utilization. Appropriate management of the different processing tasks for ML models enhances resource utilization. This paper proposes an architecture for resource-aware classification empowered by an ML model management (MLMM) framework and a distributed data stream management system (DDSMS). First, the classification pipeline is decomposed and implemented as data stream operators. Second, ML models are retrieved from an MLMM framework considering preprocessing, segmentation, and feature alignment to enable an effective redundancy elimination. Finally, the classification pipeline is deployed using resource-aware operator placement optimization. The evaluation results on a real-world scenario of a sensor-based activity classification pipeline for dairy cows show that our approach can reduce network utilization up to 98.9%.

关键词： distributed data stream management machine learning model management redundancy elimination operator placement pervasive computing activity classification

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An Architecture for Predictive Resource Allocation in Cloud Capacity Management Using Neural Networks

An Architecture for Predictive Resource Allocation in Cloud ...

引用

2023 ieee international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Amaral, Janete Lima, Alberto Sampaio De Souza, Jose Neuman Moura, Jose Antao Beltrao Federal University of Ceará Computer Science Department Cear Fortaleza Brazil Federal University of Ceará Teleinformatics Engineering Department CE Fortaleza Brazil Federal University of Ceará Computer Science Department CE Fortaleza Brazil Federal University of Campina Grande Department of Systems and Computing Paraiba Campina Grande Brazil

ISBN: (纸本)9798350304602

Cloud service providers (CSP) offer shared environments to meet customers' fluctuating requirements. The challenge is to use mechanisms capable of optimizing the use of resources and ensuring that the performance of these services continues to comply with quality metrics and the respective Service Level Agreements (SLA). CSPs must offer autonomous mechanisms to readily promote resource scalability, while customers must be confident in the performance and costs involved. In Cloud Capacity Management, the target of research in Business-driven IT Management (BDIM), predictive approaches have already been proposed to overcome the limitations of reactive methods. Such techniques have yet to demonstrate satisfactory results. This research suggests an Autonomous Architecture for Predictive Resource Allocation in Cloud Capacity Management using Neural Networks, combining reactive and predictive approaches for resource scheduling. A deep learning-based method supported predictive resource provisioning through the SLSTM (Stacked long short-term memory) neural network. This proposal adopted an autonomic approach using the MAPE-K (Monitor-Analyze-Plan-Execute over a Knowledge base) loop. A comparison between statistical methods, classical neural networks LSTM (Long short-term memory), and SLSTM demonstrated the proposal's feasibility. © 2023 ieee.

关键词： Long short-term memory

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Fortifying the Academic Perimeter: A Study on Network Sniffers in University computing systems 15

Fortifying the Academic Perimeter: A Study on Network Sniffe...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pradhan, Rajeshwari Kaur, Inderdeep Singh, Yudhvir Jakhar, Nitil Heena Chandigarh University Computer Science and Engineering Mohali India

ISBN: (纸本)9798350370249

This study presents a specialised Java-based network sniffer designed for department-level monitoring of commercial computer systems on university networks. The sniffer is a powerful, platform-independent tool for real-time traffic analysis and intrusion detection. The research looks into the operational complexities of educational environments and compares them to alternative technologies. The paper emphasises its application by detailing the sniffer's smooth integration with university computer systems, overcoming scaling problems. For detecting the presence of packet sniffers, advanced detection approaches like as Address Resolution Protocol(ARP) detection, Round Trip Time (RTT) analysis, and Simple Network Management Protocol(SNMP) monitoring are examined. The outcomes provide a full performance review, recognising issues and suggesting technological remedies. Potential upgrades and collaborative opportunities are among the future work considerations. The abstract emphasises the research's unique application in departmental-level monitoring. The abstract underscores the research's specific application in departmental-level monitoring, emphasizing its contributions to efficient and secure network practices within university environments. © 2024 ieee.

关键词： Network intrusion

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PolyNet: Cost- and Performance-Aware Multi-Criteria Link Selection in Software-Defined Edge-to-Cloud Overlay Networks 10

PolyNet: Cost- and Performance-Aware Multi-Criteria Link Sel...

引用

10th international conference on Network Softwarization (NetSoft)

作者： Daneshmand, Vahid Subratie, Kensworth C. Figueiredo, Renato J. Univ Florida Dept Elect & Comp Engn Gainesville FL 32610 USA

ISBN: (纸本)9798350369588;9798350369595

In the ever-evolving networking landscape, the demand for efficient and adaptable Virtual Private Network (VPN) solutions is growing. Software-Defined Networks (SDNs), particularly Peer-to-Peer (P2P) overlay VPNs, offer a practical approach for networks spanning various edge and cloud providers. However, existing decentralized VPNs, while resilient and scalable, typically utilize a single tunnel type and overlook data plan costs and link performance in their selection processes. This oversight can lead to cost and performance inefficiencies, especially in edge-to-cloud networks where diverse nodes have unique needs that generic solutions fail to meet effectively. Although SDN facilitates the integration of multiple link types in overlay VPNs, existing systems lack efficient policies for selecting favorable tunnels. To bridge this gap, we introduce PolyNet, a Multi-Criteria approach designed to make cost- and performance-aware policy decisions in hybrid-link overlay networks. PolyNet employs a dynamic link selection policy during runtime that evaluates latency using Vivaldi network coordinates and considers cost, and integrates with SDN-based P2P overlays to enhance link management capabilities and support multiple link types. This paper presents the design of PolyNet and evaluates its performance through simulations and prototype testing. Results demonstrate that PolyNet achieves up to a 19.1% cost reduction and a 14.1% latency improvement over traditional methods in Symphony P2P topologies. Additionally, tests with a software prototype confirm the advantages of hybrid links, showing that kernel-layer GENEVE tunnels can increase throughput by up to 8.9 times compared to user-layer Nebula and WebRTC tunnels in edge clusters.

关键词： overlay network P2P VPN SDN Hybrid-link VPN edge computing cloud computing

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Animal Intrusion Detection Using Yolo V8 10

Animal Intrusion Detection Using Yolo V8

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Kathir, M. Balaji, V. Ashwini, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

This project presents an inventive Animal Intrusion Detection System using Yolo v8 object detection algorithm. Addressing the problem of wildlife intrusions in agriculture as its main goal, the presented work focuses on precise animal identification and the activation of appropriate warning systems. The technology effectively identifies animals in agricultural fields, protecting crops as well as guaranteeing the welfare of wildlife. An intelligent monitoring system that can detect the image of an animal entering, sending alert messages to the farmers or landlords is proposed. It may lessen conflicts between people and wildlife, safeguard crops, and maintain biodiversity, all of which would be advantageous to both wildlife conservation and agriculture. The presented work uses YOLO v8, the most recent instantaneous object detector in the YOLO family, because it performs exceptionally well in terms of accuracy and speed. YOLOv8 offers new features and enhancements, building on the strengths of earlier YOLO versions. This makes it perfect for object detection in a range of applications. © 2024 ieee.

关键词： Livestock

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Deep Multimodal Emotion Recognition: Fusion of Facial Features and Neurophysiological Signals 20

Deep Multimodal Emotion Recognition: Fusion of Facial Featur...

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20th international conference on Body Sensor Networks

作者： Safavi, Farshad Venkannagari, Vikas Reddy Vinjamuri, Ramana Kumar Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21228 USA

ISBN: (纸本)9798331530143

Multimodal emotion recognition through the fusion of facial and neurophysiological features plays an important role in various applications, such as advertising, the automotive industry, wearable devices, and human-computer interactions. Fusing human facial expressions and neurophysiological signals traditionally requires domain-specific knowledge and complex preprocessing steps. However, with the advent of deep learning, we can fully leverage the end-to-end capabilities of these techniques for the intermediate integration of facial and neurophysiological signals in emotion recognition systems. As a result, we introduce a novel end-to-end deep network that leverages transformers to learn rich feature representations of neurophysiological signals, integrated with a transformer-inspired technique for facial expression recognition and emotion classification. By integrating transformers and deep neural networks, our approach successfully captures complex temporal and spatial patterns in the data. This combination allows for more robust analysis, enhancing the system's overall performance in recognizing and classifying emotions accurately. We validated our approach through experiments on the well-known DEAP dataset, achieving performance comparable to the state-of-the-art, with accuracy rates of 97.64% for valence and 97.78% for arousal.

关键词： Multimodal Emotion Recognition Affective computing Deep learning Emotion Detection Transformer

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A distributed Critical Node Detection Algorithm for UAV Swarm Networks 3

A Distributed Critical Node Detection Algorithm for UAV Swar...

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3rd international conference on computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： He, Zihang Gou, Haosong Wu, Xiong Zhang, Gaoyi Du, Pengfei Zhai, Daosen School of Electronics and Information Northwestern Polytechnical University Xi’an China Sichuan Co. Ltd. China Mobile Group Chengdu China School of Physics and Electronics Hunan University Changsha China Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Xihua University Chengdu China

ISBN: (纸本)9798331528386

Unmanned aerial vehicle (UAV) swarm can be widely used in cooperative detection and strike across wide geographic areas. To support the collaboration of multiple UAVs, a connected network topology is essential. Thus, critical nodes, whose removal will disconnect the network into two or more separate components, will play an important role in UAV swarm networks. This paper investigate the distributed critical node detection algorithm for UAV swarm networks. Firstly, we analyze the logical relationship between critical node, robustness, and k-hop subgraph. Guided by the analysis, we propose a distributed critical node detection algorithm by using the geometrical relationship of neighbors and the k-hop local topology. Abundant simulation results indicate that the proposed algorithms can strike a good balance between accuracy and overhead and greatly outperform the global detection algorithms especially in large-scale networks. Besides, the simulation study provides some useful design guidance for robust topology control of UAV swarm networks. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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A distributed transaction performance optimization method 5

A distributed transaction performance optimization method

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5th international conference on Electronics and Communication, Network and Computer Technology, ECNCT 2023

作者： Wang, Yunhui Yang, Lei Southwest University of Science and Technology School of Computer Science and Technology Mianyang China

ISBN: (纸本)9798350328660

With the wide application of distributed systems, complex transaction processing involving multiple nodes has become an important challenge. The difficulty lies in how to ensure the data consistency of each node and how to reduce the system delay to increase the transaction throughput. In order to solve the distributed transaction problem, the 2PC(two-phase commit) solution has been proposed by predecessors, but this solution brings some problems such as resource blocking, which has a great impact on the performance of distributed system. This paper proposes an asynchronous transaction processing scheme on the basis of 2PC protocol and BASE theory. It has been verified and evaluated that although 2PC scheme still performs well in solving distributed transaction problems, compared with 2PC scheme, the proposed scheme in this paper has slightly larger transaction throughput and less resource consumption, and the effectiveness of the solution has been verified in experiments. © 2023 ieee.

关键词： asynchronous BASE theory distributed systems transaction throughput two-phase commit

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Empirical Analysis of Prompt-Based LLMs for Structural Classification of Indian Legal Judgements 5

Empirical Analysis of Prompt-Based LLMs for Structural Class...

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5th ieee international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Chandana, Sidda Siri Bysani, Hemanth Garg, Shubham Prasoona, Ghanana Gupta, Deepa Sinha, Sushant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India India Kanoon Karnataka Bangalore Urban India

ISBN: (纸本)9798350356236

Legal judgment is a decision formally issued by a court as a conclusion to legal proceedings, analyzing the court's findings, reasoning, and rulings on the matters brought before it. This study explores the analysis of these legal judgments and classifies these judgments with the help of large language models and incorporating prompting techniques. The dataset considered in this study facilitates comprehensive model training and evaluation by assessing performance metrics such as Accuracy, Precision, Recall, and F1-score both Macro and Weighted. The Mistral model, of version OpenHermes-2.5-Mistral-7B-16k-GGUF, achieved an accuracy score of 0.61, performing best using the 0- shot prompting technique. This research aims to provide insights into the efficacy of these Large Language Models in automating legal research tasks and streamlining document processing workflows. The findings shed light on how Large Language Models interact with and reason through documents in the complex legal domain, offering valuable implications for practitioners and researchers. © 2025 ieee.

关键词： Natural language processing systems

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Enhancing Access Control Mechanisms for Data Stored in Cloud computing 5

Enhancing Access Control Mechanisms for Data Stored in Cloud...

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5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Madavarapu, Jhansi Bharathi Yalamanchili, Radha Krishna Madavarapu, Rahul Charan Bose University of the Cumberland's Department of Information Technology WilliamsburgKY40769 United States Governor's State University Department of Computer Science University ParkIL60484 United States University of Central Florida Department of ECE OrlandoFL32816 United States

ISBN: (纸本)9798350395235

The security and access control of data kept in the cloud are becoming increasingly important to the popularity of cloud computing services. In cloud computing, the access control can limit the data stored for further access. Several access controls explore the need for enhancing access control mechanisms to ensure data confidentiality, integrity, and availability in cloud environments. Traditional access control models may need to address the unique challenges posed by the dynamic and distributed nature of cloud computing fully. This study reviews existing access control models and proposes a set of enhancements to increase the levels of access in the cloud. Cloud environments are dynamic, with resources and users constantly changing. Dynamic access control mechanisms can adapt to these changes in real time, ensuring that only authorized entities can access data, even as the environment evolves. This study describes the Ensemble Access Control Framework (EACF) that provides access to the data stored in the cloud. The EACF contains several components that offer high protection to the cloud data. The proposed approach focused on safety, access control, and effective key management. Finally, the performance of several security algorithms shows the strength of the algorithms. © 2024 ieee.

关键词： Dynamics

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