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检索条件"任意字段=Proceedings of the 22nd International Conference on Distributed Computing and Networking"

共 1646 条记录，以下是31-40 订阅

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Retrieval-Augmented Generation Powered by a Multi-agent System to Assisted the Operation of Industries 22nd

Retrieval-Augmented Generation Powered by a Multi-agent Syst...

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22nd international conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Oliveira, Francisco Gomes, Luis Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advancement Innovation and Development Polytechnic of Porto Porto Portugal LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9783031704147

The introduction of large language models in recent years transformed the use of artificial intelligence in our daily life, making it available to common users and allowing them to interact with natural language processing machines. However, the common and general knowledge of large language models is not reliable when it comes to the quality of the response and its veracity. The proposed work integrates the use of retrieval-augmented generation with large language models with a multi-agent system framework to enable the creation of trees of knowledge where multiple agents have domain specific knowledge. The proposed agent-based solution was tested in a realistic case study where multiple knowledge domains are distributed amongst six different agents. The case study evaluates the capacity of agents to delegate questions according to its topics and the capacity of correctly replying to questions. The results are promising, demonstrating the ability of large language models to assess and process information that wasn’t used in its training process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Towards Implementation of Privacy-Preserving Federated Learning Aggregation Using Multi-key Homomorphic Encryption 25th

Towards Implementation of Privacy-Preserving Federated Lear...

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25th international conference on Internet computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

作者： Boudko, Svetlana Norwegian Computing Center Oslo Norway

ISBN: (纸本)9783031859229

Federated Learning is a machine learning approach where a model is trained across multiple decentralized edge devices. Since the data are not uploaded to a server, this approach is particularly useful for data protection and efficient computation. Further, it can be combined with privacy-preserving technologies, e.g., homomorphic encryption for enhanced data protection. Considering all these elements, a practical solution will require an efficient multikey homomorphic encryption as well as an effective integration of federated model aggregation and multikey generation processes. The paper studies the related work for homomorphic encryption in the context of federated learning and outlines the rationale behind practical design of secure federated learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： distributed computer systems

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proceedings 22nd international conference on distributed computing Systems

Proceedings 22nd International Conference on Distributed Com...

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international conference on distributed computing Systems

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proceedings 22nd international conference on distributed computing Systems Workshops

Proceedings 22nd International Conference on Distributed Com...

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international conference on distributed computing Systems Workshop

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Identifying and Architecting Microservices for Edge computing

Identifying and Architecting Microservices for Edge Computin...

引用

IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Urjaswala Vora College of Information Sciences and Technology Pennsylvania State University State College Unites States

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Microservices architecture (MSA) paradigm extends Service Oriented Architecture (SOA) to focus on specific aspects such as design of small lightweight services as independent cohesive components, development and service management using agile and DevOps methodologies and service delivery based on distributed data management and decentralized service governance and more. MSA thus has become a widely used architectural style for designing modern applications due to advantages like granular scalability and maintainability, especially in today's world of ‘system of systems’. Edge computing facilitates segregation of computing responsibilities between edge devices and cloud infrastructure to process and analyze data acquired by edge devices. The number of IoT devices is increasing at such a high rate that the need for Edge AI is expected to reach the value of investment to USD 72.6 billion by 2030. The edge analytics bring correlated research challenges from the areas of cyber and physical security, data privacy, maintenance support, access, latency, reliability, power consumption and remoteness into the scope and require innovation at architecture level as does the improvement in all systems' quality related attributes. In this tutorial we discuss how microservices paradigm can be used effectively for edge computing-based systems resulting in a sustainable architecture with lower dependency on the data-center / cloud architecture.

关键词： Cloud computing Technological innovation Power demand Software architecture Scalability Microservice architectures Computer architecture Tutorials Quality of service Security

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Pricing Strategies in Cybersecurity Markets with Network Effects

Pricing Strategies in Cybersecurity Markets with Network Eff...

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Consumer Communications and networking conference, CCNC IEEE

作者： Jing Hou Xuyu Wang Amy Z. Zeng Department of Computer Science and Information Systems California State University San Marcos San Marcos CA USA School of Computing and Information Sciences Florida International University Miami FL USA Sawyer Business School Suffolk University Boston MA USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The cybersecurity market is rapidly growing in the face of ubiquitous vulnerabilities, with vendors delivering solutions ranging from basic software-based approaches to high-end services for diverse business needs. The sustainability of a cybersecurity business relies on its ability to update its solutions to address ever-changing cyber threats continuously. It requires a substantial customer base for data collection and service enhancement, which, in turn, can attract more customers. Therefore, a novel business model that acknowledges this positive network effect and provides incentives for potential customers is crucial to ensure the success of the cybersecurity business. In this paper, we develop an analytical framework with optimization and a Stackelberg game approach to model the cybersecurity market scenario for a cybersecurity vendor offering both a basic software-based service and a premium service. We delve into the market evolution and characterize the conditions under which the dynamic market converges to a unique equilibrium. The optimal pricing strategy for the vendor is analyzed to leverage the network effects for profit maximization.

关键词： Analytical models Pricing Games Data collection Distance measurement Computer security Sustainable development Optimization Faces Business

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Detection Techniques for Cell-Free Massive MIMO

Detection Techniques for Cell-Free Massive MIMO

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Mahmoud A. Albreem Hassan Idries Sam Ansari Saeed Abdallah Shahriar Shahabuddin Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Research Institute of Science and Engineering University of Sharjah Sharjah United Arab Emirates School of Electrical and Computer Engineering Oklahoma State University Stillwater Oklahoma United States of America

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Cell-free massive multiple-input multiple-output (CF-MMIMO) systems represent a transformative evolution in wireless communication, characterized by a distributed architecture of numerous low-cost, low-power access points (APs) connected to a centralized network controller. Unlike traditional cellular systems, CF-MMIMO eliminates the concept of cells, with every AP collaboratively serving all users simultaneously. This design eradicates inter-cell interference, significantly enhancing network capacity, reliability, and spectral efficiency. As a corner-stone for future 6G networks, CF-MMIMO aligns seamlessly with the vision of ultra-reliable, low-latency communication, massive connectivity, and ubiquitous coverage. By leveraging distributed APs, CF-MMIMO systems pave the way for energy-efficient, high-capacity networks that cater to the demands of next-generation wireless systems. This paper conducts an in-depth exploration of various detection techniques, including minimum mean square error (MMSE), zero-forcing (ZF), linear precoding MMSE (LP-MMSE), and regularized conjugate beamforming (RCB). Through rigorous simulations and performance analysis, the results demonstrate that while ZF achieves the best bit error rate (BER) performance, RCB emerges as a robust contender, especially under practical conditions with imperfect channel state information. MMSE and LP-MMSE exhibit moderate performance, with LP-MMSE being comparatively less efficient in mitigating interference and noise.

关键词： Array signal processing Spectral efficiency Precoding Bit error rate Interference Mean square error methods Ultra reliable low latency communication Energy efficiency Intercell interference Next generation networking

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Depression Detection Using a Hybrid CNN-BiGRU-BiLSTM Model With BERT Embedding and Data Augmentation

Depression Detection Using a Hybrid CNN-BiGRU-BiLSTM Model W...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Saadat M. Alhashmi Saleema Hashim Ahmed Mohamed Khedr College of Computing and Informatics University of Sharjah UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. Also called major depressive disorder or clinical depression, it affects how you feel, think, and behave and can lead to a variety of emotional and physical problems. Automatic detection, as in a clinical interview can be derived from various modalities which includes video, audio, and text. Among them, detection from text data has emerged as a crucial task in mental health monitoring. In this study, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM) layers to enhance depression detection accuracy. Using BERT embedding for robust feature extraction, the proposed model processes social media or clinical text data to identify signs of depression. Additionally, we incorporate a novel data augmentation technique using synonym replacement to address data imbalance and improve generalization. Evaluations are conducted using key performance metrics, including accuracy, precision, recall, and F1 score. With an accuracy of 86.2 %, the results demonstrate that the combined CNN-BiGRU-BiLSTM architecture, alongside BERT embedding and augmented data, significantly improves classification performance compared to traditional models. This approach shows promise in contributing to more effective automatic depression detection systems.

关键词： Deep learning Accuracy Social networking (online) Bidirectional long short term memory Bidirectional control Depression Data augmentation Encoding Data models Convolutional neural networks

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22nd international conference on Scientific computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

22nd International Conference on Scientific Computing and Bi...

引用

22nd international conference on Scientific computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

ISBN: (纸本)9783031859014

The proceedings contain 31 papers. The special focus in this conference is on Scientific computing and Bioinformatics,. The topics include: Austere Runway Simulated Identification;using Rhai to Create and Benchmark Bevy Entities;pyO3: Building Python Extension Modules in Native Rust with Performance and Safety in Mind;Leveraging Python Interpreters for Concurrency in SeQUeNCe;benchmark Performance of a Rust-Based Python Extension;quantum Error Correction in Repetition Codes;investigating Selective Reliability with the Laminar networking Package;an Entanglement Swapping Throughput Analysis for Quantum Networks Using Linear Quantum Optics;notes on Symmetric Generalized Tent Map: Route to Chaos;scalable Service Model and Scheduler for Delay-Sensitive Services;regret-Minimization Heuristics for Identifying Monotone Boolean Functions;multi-class Classification of Satellite Orbits for Database Quality Control;real-Time Motion Planning for Autonomous Vehicles in Dynamic Environments;A Novel Deep Learning Method for Solving PDE’s Applied to a Shallow Water Problem;exploring the Impact of Social Media on Mental Health and Well-Being: A Multi-dimensional Analysis;the Strategic Optimization of a Tabletop Role-Playing Game;Problem-Solving Using Logic and Reasoning, Mathematics, Algorithms, Python, and Generative AI: Part Two;acquisition and Analysis of Mobile Data Using Digital Forensics Tools and Techniques;structural Health Monitoring for Risk Assessment and Reliability of a Structure After Extreme Loads;scientific Constructing Adequate Statistical Decision Rules Under Parametric Uncertainty of Applied Mathematical Models via the Smart Use of Pivotal Quantities and Ancillary Statistics;a Smart Air Quality Analysis and Pollutant Diffusion Detection and Prediction System Based on Tree Canopy Shape Research Using Machine Learning and Artificial Intelligence;a High Order Scheme for Modelling Viscous Incompressible Fluid Flow in a Channel with a Step;Feasibility Study of Neutron M

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Llama-Recipe - Fine-Tuned Meta's Llama LLM, PBOM and NFT Enabled 5G Network-Slice Orchestration and End-to-End Supply-Chain Verification Platform 22

Llama-Recipe - Fine-Tuned Meta's Llama LLM, PBOM and NFT Ena...

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22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Bandara, Eranga Bouk, Safdar H. Shetty, Sachin Roy, Sandip Mukkamala, Ravi Rahman, Abdul Foytik, Peter Liang, Xueping Keong, Ng Wee De Zoysa, Kasun Old Dominion University NorfolkVA United States Deloitte & Touche Llp United States Florida International University United States Nanyang Technological University Singapore University of Colombo School of Computing Sri Lanka

ISBN: (纸本)9798331508050

Modern 5G networks offer a network-sliced infrastructure where each network slice contains a dedicated 5G core software service layer. The 5G core software services in each slice shares common core network resources to meet specific customer needs. A primary challenge in 5G network slicing involves resource sharing and efficient network slice orchestration. Container-based methodologies, including tools like Docker and Kubernetes, have become popular for orchestrating 5G network slice services and managing configurations in microservices-based cloud-native service deployment. However, despite their utility, these tools present significant challenges. Their complexity often necessitates dedicated DevOps teams for effective management, while configuration management can prove arduous, and end-to-end supply chain oversight is lacking. To address these challenges, this paper introduces 'Llama-Recipe,' a cloud-native 5G-core service deployment and orchestration platform integrating Generative AI, SBOM, PBOM and NFT. 5G-core service configurations across different network slices are represented as 'HOCON (Human-Optimized Config Object Notation)' config objects adhering to the GitOps paradigm. Leveraging custom-trained Meta's Llama2 LLM, Llama-Recipe generates the Kubernetes manifests for network-sliced 5G-core services based on the defined HOCON configurations. The generated Kubernetes manifests of the 5G-core services are deployed in designated Kubernetes clusters utilizing GitOps tools (e.g., ArgoCD), ensuring seamless and automated deployment processes. Additionally, Llama-Recipe introduced a novel mechanism to handle end-to-end supply chain verification of 5G-core software services using Software-Bill of Materials (SBOM) and Pipeline-Bill of Materials (PBOM). SBOMs track all the dependencies and PBOMs facilitate the comprehensive tracking of end-to-end supply chain data for 5G-core software services, enhancing transparency and security. These PBOMs are also generated

关键词： DevOps

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