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IEEE International Conference on Services Computing (SCC)

作者： Yang Li JinPeng Huai Hailong Sun Ting Deng Huipeng Guo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

With the rapid development of SOC (Service oriented computing), the automated service composition has become an important research direction. Through automated service composition, business processes need not to be constructed in advance, which helps to improve the flexibility of service composition. The current research on automated service composition is mainly based on AI techniques, and a common domain-oriented knowledge base is usually required to perform the heuristic planning. In practice, it is impossible for the knowledge base to characterize the personalized requirements of different users, so the AI-based methods can not apply to the user-centric application scenarios. In this paper, we propose PASS, a novel approach to personalized automated service composition. With PASS, both the hard-constraints represented by user's initial state, and the soft-constraints represented by user preferences can be satisfied in the process of automated service composition. Furthermore, three algorithms are designed to implement preference-aware automated service composition. In these algorithms, the Pareto dominance principle and relaxation degree are used to select the most satisfied composite service for users. Finally, comprehensive simulations are conducted to evaluate the performance and effectiveness of the proposed algorithms.

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Constructing a Degree-Constrained Low Delay Application Layer Multicast Tree

Constructing a Degree-Constrained Low Delay Application Laye...

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The 4th International Conference on Wireless Communications, Networking and Mobile Computing(第四届IEEE无线通信、网络技术及移动计算国际会议)

作者： Tan Chen Baosong Shan Xin Xiong State Key Laboratory of Software Development Environment Beijing University of Aeronautics and Astro State Key Laboratory of Software Development Environment Beijing University of Aeronautics and Astro School of Computer Science and Engineering Zhengzhou University of Light Industry Zhengzhou China

Application layer multicast is probably the most efficient scheme to sustain the group communication for delay sensitive applications in Internet due to its rapid deployment. However, the problem of building a degree-constrained minimum delay application layer multicast tree is proven to be NP-hard. In this paper, we investigate this optimization problem and propose a distributed protocol to explore the method of constructing distribution tree based on members' relative position in underlying network, which utilizes physical network topology information to assist the optimizing of delivery tree through employing proximity aware technique for overlay network. Evaluation results show our scheme achieves efficiency as well as may support real time applications effectively.

关键词： Delay Network topology Peer to peer computing Application software Buildings Programming Optimization methods Large-scale systems computer science Aerospace industry

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Searching nearest neighbor in overlay network

Searching nearest neighbor in overlay network

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IEEE Symposium on computers and Communications (ISCC)

作者： Tan Chen Xin Xiong State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9781424427024

For overlay network, the mismatch between logical network and underlying physical network could cause a high number of substrate hops resulting in increased delay and network traffic, as well as degrade performance of application significantly. Thus selecting the nearest neighbor for each node in terms of network latency, will bridge this gap efficiently. In this paper, we present a novel approach to search nearest neighbor in overlay network, which combines distance prediction using network coordinate and round-trip time measurements to generate proximity information, achieving both efficiency and accuracy. Our proposal can be used to improve the performance of many large scale distributed applications like network-aware overlay construction and selection of nearby resource in the network.

关键词： Accuracy Distance measurement Nearest neighbor searches Peer to peer computing Economic indicators Network topology Coordinate measuring machines

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Polar: An efficient finding nearest neighbor algorithm for overlay network 2

Polar: An efficient finding nearest neighbor algorithm for o...

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2nd International Conference on Scalable Information Systems, InfoScale 2007

作者： Chen, Tan Sheng, Xiangzhi Shan, Baosong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing University of Aeronautics and Astronautics Beijing China

ISBN: (纸本)9781595937575

Obviously, network proximity is an essential characteristic for overlay network construction. By selecting the nearest neighbor for the target node, large-scale distributed applications can decrease the communication cost and improve the performance significantly. Nevertheless, existing mechanisms for exploiting network proximity information are either inaccurate or low efficiency. In this paper, we present a novel and practical algorithm to find the nearest neighbor in overlay network, called Polar. Performing direct measurement based on the result of distance prediction using network coordinate, Polar achieves accuracy as while as it has low overhead. Meanwhile, our proposal does not rely on the static landmark so it has strong scalability and high robustness. Polar is a generic architecture that can be used for a variety of overlay network with different requirement. Copyright 200X ACM

关键词： Nearest neighbor Network coordinate Overlay network

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Pseudo trust: Zero-knowledge based authentication in anonymous peer-to-peer protocols

Pseudo trust: Zero-knowledge based authentication in anonymo...

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21st International Parallel and Distributed Processing Symposium, IPDPS 2007

作者： Lu, Li Han, Jinsong Hu, Lei Huai, Jinpeng Liu, Yunhao Ni, Lionel M. State Key Lab. of Information Security Graduate School Chinese Academy of Sciences Beijing China Dept. of Computer Science and Engineering Hong Kong University of Science and Technology Kowloon Hong Kong School of Computer Science Beihang University Beijing China State Key Lab. of Software Developing Environment Beihang University Beijing China

ISBN: (纸本)1424409101

Most of the current trust models in peer-to-peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inherent tradeoff between trust and anonymity. To the best of our knowledge, there is currently no P2P protocol that provides complete mutual anonymity as well as authentication and trust management. We propose a zero-knowledge authentication scheme called Pseudo Trust (PT), where each peer, instead of using its real identity, generates an unforgeable and verifiable pseudonym using a one-way hash function. A novel authentication scheme based on Zero-Knowledge Proof is designed so peers can be authenticated without leaking any sensitive information. With the help of PT, most existing identity-based trust management schemes become applicable in mutual anonymous P2P systems. We analyze the levels of security and anonymity in PT, and evaluate its performance using trace-driven simulations and a prototype implementation. The strengths of Pseudo Trust include the lack of need for a centralized trusted party or CA, high scalability and security, low traffic and cryptography processing overheads, and man-in-middle attack resistance. We aim for the Pseudo Trust design to be included in the P2P trust and anonymity context. © 2007 IEEE.

关键词： Distributed computer systems

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Crowdsourcing 1

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丛书名： Progress in IS

1000年

作者： Wei Li Michael N. Huhns Wei-Tek Tsai Wenjun Wu

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Location-based IP Addressing in IP-enable Wireless Sensor Networks

Location-based IP Addressing in IP-enable Wireless Sensor Ne...

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2011 2nd Asia-Pacific Conference on Wearable Computing Systems (APWCS 2011);2011 International Conference on Intelligent Control and Information Technology(ICICIT 2011)

作者： Rong DING Haiying DU State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University

Network addressing is a traditional problem in communications network. IP addressing which can fully reflect the characteristics of Wireless Sensor Network will provide good support for the design of

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Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on Blockchain

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ACM Transactions on Sensor Networks 1000年

作者： Du Bowen Wang Haiquan Li Yuxuan Jiejie Zhao Yanbo Ma Huang Runhe School of Transportation Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China School of Software Beihang University Beijing China Zhongguancun Laboratory Beijing China Pontosense Inc Toronto Canada Computer and Information Sciences Hosei University Chiyoda-ku Japan

As an emerging learning paradigm, Federated Learning (FL) enables data owners to collaborate training a model while keeps data locally. However, classic FL methods are susceptible to model poisoning attacks and Byzantine failures. Despite several defense methods proposed to mitigate such concerns, it is challenging to balance adverse effects while allowing that each credible node contributes to the learning process. To this end, a Fair and Robust FL method is proposed for defense against model poisoning attack from malicious nodes, namely FRFL. FRFL can learn a high-quality model even if some nodes are malicious. In particular, we first classify each participant into three categories: training node, validation node, and blockchain node. Among these, blockchain nodes replace the central server in classic FL methods while enabling secure aggregation. Then, a fairness-aware role rotation method is proposed to periodically alter the sets of training and validation nodes in order to utilize the valuable information included in local datasets of credible nodes. Finally, a decentralized and adaptive aggregation mechanism cooperating with blockchain nodes is designed to detect and discard malicious nodes and produce a high-quality model. The results show the effectiveness of FRFL in enhancing model performance while defending against malicious nodes.

关键词： Federated Learning Blockchain Model Poisoning Attack Malicious Participant Detection

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Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

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Large Language Model-Aware In-Context Learning for Code Generation

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ACM Transactions on software engineering and Methodology 1000年

作者： Jia Li Chongyang Tao Ge Li Zhi Jin Huangzhao Zhang Zheng Fang Fang Liu Key Lab of High Confidence Software Technology (Peking University) MoE China Beihang University China The State Key Laboratory of Software Development Environment (SKLSDE) SEI School of Computer Science & Engineering Beihang University China

Large Language Models (LLMs) have shown impressive In-Context Learning (ICL) ability in code generation. LLMs take a prompt context consisting of a few demonstration examples and a new requirement as input, and output new programs without any parameter update. Existing studies have found that the performance of ICL-based code generation heavily depends on the quality of demonstration examples and thus arises research on selecting demonstration examples: given a new requirement, a few demonstration examples are selected from a candidate pool, where LLMs are expected to learn the pattern hidden in these selected demonstration examples. Existing approaches are mostly based on heuristics or randomly selecting examples. However, the distribution of randomly selected examples usually varies greatly, making the performance of LLMs less robust. The heuristics retrieve examples by only considering textual similarities of requirements, leading to sub-optimal *** fill this gap, we propose a Large language model-Aware selection approach for In-context-Learning-based code generation named LAIL. LAIL uses LLMs themselves to select examples. It requires LLMs themselves to label a candidate example as a positive example or a negative example for a requirement. Positive examples are helpful for LLMs to generate correct programs, while negative examples are trivial and should be ignored. Based on the labeled positive and negative data, LAIL trains a model-aware retriever to learn the preference of LLMs and select demonstration examples that LLMs need. During the inference, given a new requirement, LAIL uses the trained retriever to select a few examples and feed them into LLMs to generate desired programs. We apply LAIL to four widely used LLMs and evaluate it on five code generation datasets. Extensive experiments demonstrate that LAIL outperforms the state-of-the-art (SOTA) baselines by 11.58%, 3.33%, and 5.07% on CodeGen-Multi-16B, 1.32%, 2.29%, and 1.20% on CodeLlama-3

关键词： Code generation in-context-learning large language model

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