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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61-70 订阅

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POSEIDON: Efficient Function Placement at the Edge Using Deep Reinforcement Learning 22nd

POSEIDON: Efficient Function Placement at the Edge Using Dee...

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22nd international conference on Service Oriented computing

作者： Jain, Prakhar Singhal, Prakhar Pandey, Divyansh Quatrocchi, Giovanni Vaidhyanathan, Karthik Int Inst Informat Technol Software Engn Res Ctr Hyderabad India Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9789819608041;9789819608058

Edge computing allows for reduced latency and operational costs compared to centralized cloud systems. In this context, serverless functions are emerging as a lightweight and effective paradigm for managing computational tasks on edge infrastructures. However, the placement of such functions in constrained edge nodes remains an open challenge. On one hand, it is key to minimize network delays and optimize resource consumption;on the other hand, decisions must be made in a timely manner due to the highly dynamic nature of edge environments. In this paper, we propose POSEIDON, a solution based on Deep Reinforcement Learning for the efficient placement of functions at the edge. POSEIDON leverages Proximal Policy Optimization (PPO) to place functions across a distributed network of nodes under highly dynamic workloads. A comprehensive empirical evaluation demonstrates that POSEIDON significantly reduces execution time, network delay, and resource consumption compared to state-of-the-art methods.

关键词： Edge computing Serverless Function Placement Deep Reinforcement Learning

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RESEARCH ON distributed POWER QUALITY MONITORING DEVICE WITH 25.6K SAMPLING RATE 2

RESEARCH ON DISTRIBUTED POWER QUALITY MONITORING DEVICE WITH...

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2nd international conference on Power, Communication, computing and networking Technologies, PCCNT 2024

作者： Wu, Wenting Gao, Sen Wei, Ziwei Deng, Jindong NR Electric Co. Ltd Nanjing211102 China

ISBN: (纸本)9781837242672

Aiming at the problem that CVT increases C3 needs on-site sampling and the insufficient sampling rate of digital substations leads to insufficient accuracy of power quality monitoring. A distributed power quality monitoring device with a sampling rate of 25.6k is proposed. The device mainly realizes local sampling through the merging unit, and the merging unit sends the sample data to the power quality monitoring terminal through the multi-ASDU IEC61850-9-2 data frame format to complete the calculation and analysis of various indicators of power quality. The performance indicators of the distributed power quality monitoring device prototype have been verified by a third-party type test. As a stable and reliable power quality monitoring solution for substations, it has pretty promotion prospects. © The Institution of Engineering & Technology 2024.

关键词： 25.6K SAMPLING RATE distributed POWER QUALITY MONITORING

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Kua: A distributed Object Store over Named Data networking 9

Kua: A Distributed Object Store over Named Data Networking

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22nd international conference on Information Centric networking

作者： Patil, Varun Desai, Hemil Zhang, Lixia Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781450392570

Applications such as machine learning training systems or log collection generate and consume large amounts of data. Object storage systems provide a simple abstraction to store and access such large datasets. These datasets are typically larger than the capacities of individual storage servers, and require fault tolerance through replication. In this paper, we present Kua, a distributed object storage system built over Named Data networking (ndN). The data-centric nature of ndN helps Kua maintain a simple design while catering to requirements of storing large objects, providing fault tolerance, low latency and strong consistency guarantees, along with data-centric security. Our prototype Kua implementation provides easy-to-use primitives to let applications store and access data securely, and our initial evaluation suggests that Kua can leverage ndN's capabilities of multicast data delivery and in-network caching to achieve higher efficiency than existing object storage systems.

关键词： Named Data networking Object Store distributed Storage

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Consensus in the Presence of Overlapping Faults and Total Omission 22nd

Consensus in the Presence of Overlapping Faults and Tota...

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22nd Theory of Cryptography conference, TCC 2024

作者： Loss, Julian Shi, Kecheng Stern, Gilad CISPA Helmholtz Center for Information Security Saarbrücken Germany Tel Aviv University Tel Aviv Israel Saarland University Saarbrücken Germany

ISBN: (纸本)9783031780103

Understanding the fault tolerance of Byzantine Agreement protocols is an important question in distributed computing. While the setting of Byzantine faults has been thoroughly explored in the literature, the (arguably more realistic) omission fault setting is far less studied. In this paper, we revisit the recent work of Loss and Stern who gave the first protocol in the mixed fault model tolerating t Byzantine faults, s send faults, and r receive faults, when 2t+r+s2, or a broadcast protocol for s+r=n and s>1 even without overlapping faults. © international Association for Cryptologic Research 2025.

关键词： distributed computer systems

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Customized adversarial training enhances the performance of knowledge tracing tasks 22

Customized adversarial training enhances the performance of ...

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22nd IEEE international Symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Zhang, Huitao Zhang, Xiankun Shang, Yuhu Luo, Xin Zhang, Yiying Tianjin University of Science and Technology College of Artificial Intelligence Tianjin China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798331509712

Knowledge Tracing (KT) involves using deep neural networks (DNNs) to track students' learning progress, but over-fitting can be an issue with small datasets. Adversarial examples have been introduced to improve generalization, but they may overlook individual student differences. To address this issue, we propose a new model called Customized Adversarial Training Knowledge Tracing (CATKT). The model generates unique adversarial perturbations for each sample based on the characteristics of knowledge tracking tasks, thereby better adapting to students' learning traits and enhancing the effectiveness and accuracy of knowledge tracking tasks. Specifically, CATKT can dynamically adjust the level of perturbations according to the difficulty of the knowledge, adding non-uniform and effective perturbations to each interaction embedding, and replacing the original labels with adaptively smoothed labels to improve task accuracy. Experimental results show that CATKT outperforms previous knowledge tracking methods in terms of performance and provides new ideas and methods for teaching assessment and personalized learning in the education field. © 2024 IEEE.

关键词： Adversarial machine learning

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DMTP: Deadline-aware Multipath Transport Protocol 22

DMTP: Deadline-aware Multipath Transport Protocol

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22nd international-Federation-for-Information-Processing (IFIP) networking conference (IFIP networking)

作者： John, Tony Perrig, Adrian Hausheer, David OVGU Magdeburg Networks & Distributed Syst Lab Magdeburg Germany Swiss Fed Inst Technol Network Secur Grp Zurich Switzerland

ISBN: (纸本)9783903176577

Increasing demand for real-time applications such as teleoperation, remote control of a process, and live video streaming has led to the need for more efficient and reliable transport protocols that can satisfy strict latency requirements. Emerging multipath communication can help to achieve such goals. However, existing multipath protocols, such as Multipath TCP and Multipath QUIC fall short on real-time applications, as they rely on retransmissions and congestion control, leading to higher latencies on lossy links. This paper proposes DMTP, a new deadline-aware multipath transport protocol based on the SCION Internet architecture to support real-time applications with strict latency requirements. Our evaluations show that DMTP outperforms Multipath TCP and Multipath QUIC over lossy links by more than 40% in terms of the number of packets transferred within their deadline when the loss rate is above 2%.

关键词： Multipath communication deadline-aware transport protocol SCION Internet Architecture

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Food Nutrient Extraction Based on Image Recognition and Entity Extraction 19

Food Nutrient Extraction Based on Image Recognition and Enti...

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19th international conference on Wireless and Mobile computing, networking and Communications (WiMob)

作者： Gao, Hanzhong Liu, Yanjun Li, Jingjuan Gao, Jianwei Columbian Coll Arts Sci Phillips Hall801 22nd St NW Washington DC 20052 USA Shandong Acad Agr Sci Shandong Key Lab Greenhouse Vegetable Biol Shandong BranchNatl Vegetable Improvement Ctr Inst VegetablesHuanghuai Reg Vegetable Sci StnM Jinan 250100 Peoples R China

ISBN: (纸本)9798350336672

Nutrition is an important aspect of public health, and in recent years, there has been increasing interest in the nutritional information of food. However, processing this information can be a challenging task due to the large amounts of data involved. Machine learning (ML) has emerged as a useful tool to address this challenge. In this paper, we present a data resource that uses the FoodData Central (FDC) nutrient database to explore the combination of food images, nutritional information, and text with ML. We begin by providing an overview of machine learning and its applications in nutrition research, including the use of ML algorithms to identify food intake patterns, predict nutrient intakes, and evaluate dietary guidelines. We then describe the features and applications of Inception-v3, Inception-v4, and MobileNetV2 in ML, highlighting how these models can be used to extract nutritional information from food images. To further explore the potential of ML in nutrition research, we developed a quick search app that integrates images, text, and nutritional information. This app uses image recognition algorithms to identify food items in pictures, and text processing techniques to extract food information from text data. Users can simply take a picture of a food item and the app will provide the details of its nutritional content. This app can be used to facilitate the study of food and nutrition information and help promote healthier eating habits. In conclusion, the development of data resources and apps that use ML algorithms can be particularly helpful in processing large amounts of nutrition data and making it more accessible to the public. By harnessing the power of ML, we can advance our understanding of the relationship between diet and health, and ultimately work towards improving public health outcomes.

关键词： Nutrient Information Machine Learning Image Recognition Text Recognition

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GuRuChain: Guarantee and Reputation-based Blockchain Service Trading Platform 22

GuRuChain: Guarantee and Reputation-based Blockchain Service...

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22nd international-Federation-for-Information-Processing (IFIP) networking conference (IFIP networking)

作者： Bouchiha, Mouhamed Amine Ghamri-Doudane, Yacine Rabah, Mourad Champagnat, Ronan La Rochelle Univ L3i Lab La Rochelle France

ISBN: (纸本)9783903176577

Blockchain (BC) is a promising distributed Ledger Technology (DLT) that has attracted attention in recent years due to its characteristics of maintaining privacy and security. This technology, which removes the need for a trusted third party, can be applied in various trust-related domains, such as energy trading, crowdsourcing, and the Internet of Things. This paper presents GuRuChain, a platform framework for online service trading. It combines on-chain and off-chain trust management to provide a blockchain system suitable for trust-related applications. The platform does not only ensure the recording of online transactions;but also the deliverance of offline services. In GuRuChain, we propose a mathematical model to assess the trustworthiness of each participant. We also introduce an incentive mechanism based on reputation and guarantee to monitor the participants' behavior. Finally, we maintain the consistency of the BC through the proposed consensus scheme called Proof Guarantee and Reputation (PoGR). PoGR selects nodes based on a scoring formula that uses three parameters, reputation score, guarantee balance, and risk taken to ensure fairness. The results of the performance evaluation demonstrate the feasibility, efficiency, and scalability of GuRuChain.

关键词： Reputation distributed Ledger Lightweight Consensus Real-world Correlation Fairness Reliability

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Efficient 2-Segment Routing Based on Local Search With Failure Recovery 22

Efficient 2-Segment Routing Based on Local Search With Failu...

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22nd IEEE international Symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Chen, Jingjing Ling, Xueyun Wang, Ying Qiu, Xuesong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798331509712

Segment Routing (SR) is a flexible and efficient source-routing technology. It can forward traffic along arbitrary paths in the network and has good scalability without the maintenance of routing information at intermediate nodes. These advantages make SR widely used in data centers, WANs, MANs, and other networks, particularly in the context of parallel and distributed processing. However, current SR traffic engineering has some shortcomings in terms of network load balancing, failure resiliency, and complete use of SR characteristics, which prevents better optimization of comprehensive network performance. In this paper, we propose a heuristic traffic engineering algorithm (2-SRLS) based on a two-segment routing model (2-SR), which incorporates a traffic-splitting strategy, adjacency segments and failure resiliency. The algorithm supports the technical characteristic of adjacency segments in SR with a flexible source node traffic splitting strategy and can efficiently recover from single-link failures. Experimental results show that our algorithm is close to the theoretical optimum in terms of the performance of reducing the maximum link utilization and has a 10%-16% improvement in active link coverage compared to existing algorithms. The running time is reduced by 70% compared to existing heuristics. In addition, the proposed algorithm can effectively recover from single-link failures on the basis of guaranteed the stability of the maximum link utilization (MLU). © 2024 IEEE.

关键词： Local search (optimization)

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HTKT: Knowledge Tracing Based on Hypergraph Transformer 22

HTKT: Knowledge Tracing Based on Hypergraph Transformer

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22nd IEEE international Symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Guo, Xiaojin Chang, Xilong Zhang, Xiankun Liang, Kun Shang, Yuhu Zhang, Yiying Tianjin University of Science and Technology College of Artificial Intelligence Tianjin300457 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

ISBN: (纸本)9798331509712

Knowledge tracing(KT) is one of the supporting technologies for adaptive learning. It obtains learners' knowledge level by analyzing their online historical answer records, thereby predicting their future answer performance. Since there are a large number of continuous or repeated exercises in KT datasets, and traditional graph structures can usually only represent one-to-one or one-to-many relationships. Some existing models tend to ignore the potential connections between knowledge concept, and thus fail to accurately obtain high-level representations of students' knowledge state. Hypergraphs can directly represent many-to-many relationships and have advantages in extracting high-level information and processing complex relationships. In this paper, we propose a KT method (HTKT) based on Hypergraph Transformer. This method is based on the Encoder-only architecture and introduces a hypergraph attention mechanism to obtain a high-order feature representation of students' knowledge state. Experiments on multiple public and general datasets show that our proposed HTKT method outperforms existing baseline methods. © 2024 IEEE.

关键词： Students

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