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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhuang, Yunkai Yang, Shangdong Li, Wenbin Gao, Yang Nanjing University of Posts and Telecommunications Nanjing China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9781728163277

The graphical game provides an effective method for modeling different kinds of sparse interactions in multi-agent reinforcement learning. Most previous work on game abstraction lacks theoretical guarantees of convergence. In this paper, we adopt the N-step return signal to detect interactions between agents and build the Markov graphical game based on it. We analyze that the solution of the Markov graphical game is an ϵ-Nash equilibrium which guarantees the convergence of the proposed NSR-G2NashQ algorithm theoretically. Also, we have done experiments in different multi-agent reinforcement learning tasks with both tabular and function approximation solutions. The results show the NSR-G2NashQ algorithm accelerates the convergence of agents to the optimal policy. © 2023 IEEE.

关键词： game abstraction Graphical game multi-agent reinforcement learning sparse interaction

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Test Case Classification via Few-Shot Learning 9

Test Case Classification via Few-Shot Learning

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Zhao, Yuan Tang, Haojie Zhang, Quanjun Liu, Sining Wang, Jin Liu, Jia Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Crowdsourcing testing can reduce testing costs and improve testing efficiency. However, crowdsourcing testing generates many test cases, from which testers need to select task-related test cases for execution. Furthermore, it is difficult to get the labeled test cases, and it is also costly to mark the test cases manually. Even in the set of labeled test cases, there is still an uneven distribution of labels. To address the difficulties mentioned above, this paper proposes a test case classification framework based on Few-shot Learning. The framework augments test cases and extracts relevant information for vectorization, collects the category lexicon of test cases using the BiLSTM model, and finally classifies the test cases. To verify the effectiveness of the classification framework, we selected thousands of test cases from three crowd testing projects to conduct in-usability evaluation experiments. The experimental results show that our classification method has a higher accuracy rate than existing classification methods. © 2022 IEEE.

关键词： Crowdsourcing

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Latency-First Smart Contract: Overclock the Blockchain for a while 42

Latency-First Smart Contract: Overclock the Blockchain for a...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Qi, Huayi Xu, Minghui Cheng, Xiuzhen Lyu, Weifeng Shandong University School of Computer Science and Technology China Beihang University State Key Laboratory of Software Development Environment China

ISBN: (纸本)9798350334142

Blockchain systems can become overwhelmed by a large number of transactions, leading to increased latency. As a consequence, latency-sensitive users must bid against each other and pay higher fees to ensure that their transactions are processed in priority. However, most of the time of a blockchain system (78% in Ethereum), there is still a lot of unused computational power, with few users sending transactions. To address this issue and reduce latency for users, we propose the latency-first smart contract model in this paper, which optimistically accepts committed transactions. This allows users to submit a commitment during times of high demand, and then complete the rest of the work at a lower priority. From the perspective of the blockchain, this temporarily "overclocks"the system. We have developed a programming tool for our model, and our experiments show that the proposed latency-first smart contract model can greatly reduce latency during the periods of high demand. © 2023 IEEE.

关键词： Smart contract

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A Blockchain-enabled Secure Access Management Method in Edge Computing 29

A Blockchain-enabled Secure Access Management Method in Edge...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jiang, Xutong Zhang, Guoming Tian, Hao Xiang, Haolong Zhang, Xuyun Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Macquarie University School of Computing Sydney Australia

ISBN: (纸本)9798350330717

Edge computing has emerged as a transformative paradigm with applications ranging from IoT to smart homes and transportation systems. However, its decentralized nature presents significant challenges in securing sensitive data, particularly in scenarios involving data sharing. To address these issues, this paper introduces a secure data access control system for edge computing by leveraging blockchain to foster mutual trust among edge servers and employing attribute-based encryption for secure data sharing. Our proposed solution seeks to enhance data security in distributed environments. We validate our method through practical experiments conducted on multiple edge servers, confirming its effectiveness in safeguarding data integrity and privacy. © 2023 IEEE.

关键词： blockchain edge computing encryption security

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Buffered asynchronous SGD for byzantine learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 9761-9822页

作者： Yi-Rui Yang Wu-Jun Li National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Distributed learning has become a hot research topic due to its wide application in cluster-based large-scale learning, federated learning, edge computing, and so on. Most traditional distributed learning methods typically assume no failure or attack. However, many unexpected cases, such as communication failure and even malicious attack, may happen in real applications. Hence, Byzantine learning (BL), which refers to distributed learning with failure or attack, has recently attracted much attention. Most existing BL methods are synchronous, which are impractical in some applications due to heterogeneous or offline workers. In these cases, asynchronous BL (ABL) is usually preferred. In this paper, we propose a novel method, called buffered asynchronous stochastic gradient descent (BASGD), for ABL. To the best of our knowledge, BASGD is the first ABL method that can resist non-omniscient attacks without storing any instances on the server. Furthermore, we also propose an improved variant of BASGD, called BASGD with momentum (BASGDm), by introducing local momentum into BASGD. Compared with those methods which need to store instances on server, BASGD and BASGDm have a wider scope of application. Both BASGD and BASGDm are compatible with various aggregation rules. Moreover, both BASGD and BASGDm are proved to be convergent and able to resist failure or attack. Empirical results show that our methods significantly outperform existing ABL baselines when there exists failure or attack on workers.

关键词： distributed machine learning momentum asynchronous Byzantine learning buffer stochastic gradient descent

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InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning

arXiv

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arXiv 2024年

作者： Liang, Yan-Shuo Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity). Recently, parameter-efficient fine-tuning (PEFT), which involves freezing a pre-trained model and injecting a small number of learnable parameters to adapt to downstream tasks, has gained increasing popularity in continual learning. Although existing continual learning methods based on PEFT have demonstrated superior performance compared to those not based on PEFT, most of them do not consider how to eliminate the interference of the new task on the old tasks, which inhibits the model from making a good trade-off between stability and plasticity. In this work, we propose a new PEFT method, called interference-free low-rank adaptation (InfLoRA), for continual learning. InfLoRA injects a small number of parameters to reparameterize the pre-trained weights and shows that fine-tuning these injected parameters is equivalent to fine-tuning the pre-trained weights within a subspace. Furthermore, InfLoRA designs this subspace to eliminate the interference of the new task on the old tasks, making a good trade-off between stability and plasticity. Experimental results show that InfLoRA outperforms existing state-of-the-art continual learning methods on multiple datasets. Code is available at https://***/liangyanshuo/InfLoRA. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Distributed GAN: Toward a Faster Reinforcement-Learning-Based Architecture Search

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第3期3卷 391-401页

作者： Shi, Jiachen Fan, Yi Zhou, Guoqiang Shen, Jun Nanjing University of Posts and Telecommunications College of Computer Science Nanjing210046 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210046 China University of Wollongong School of Computing and Information Technology WollongongNSW2522 Australia

In the existing reinforcement learning (RL)-based neural architecture search (NAS) methods for a generative adversarial network (GAN), both the generator and the discriminator architecture are usually treated as the search objects. In this article, we take a different perspective to propose an approach by treating the generator as the search objective and the discriminator as the judge to evaluate the performance of the generator architecture. Consequently, we can convert this NAS problem to a GAN-style problem, similar to using a controller to generate sequential data via reinforcement learning in a sequence GAN, except that the controller in our methods generates serialized data information of architecture. Furthermore, we adopt an RL-based distributed search method to update the controller parameters θ. Generally, the reward value is calculated after the whole architecture searched, but as another novelty in this article, we employ the reward shaping method to judge the intermediate reward and assign it to every cell in the architecture to encourage the diversity and the integrity of all cells. The main contribution of this article is to provide a novel performance estimation mechanism, which could speed up the efficiency of architecture search, and improve the searching results with specific supplementary strategies. Crucially, this estimation mechanism can be applied to most RL-based NAS methods for the GAN. The experiments demonstrate that our methods achieve satisfactory results against our design objectives. © 2020 IEEE.

关键词： Reinforcement learning

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Vision-Language Models are Strong Noisy Label Detectors 38

Vision-Language Models are Strong Noisy Label Detectors

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Tong Li, Hao-Tian Li, Chun-Shu Shi, Jiang-Xin Li, Yu-Feng Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education China National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled data in real-world applications poses a significant obstacle during the fine-tuning process. To address this challenge, this paper presents a Denoising Fine-Tuning framework, called DEFT, for adapting vision-language models. DEFT utilizes the robust alignment of textual and visual features pretrained on millions of auxiliary image-text pairs to sieve out noisy labels. The proposed framework establishes a noisy label detector by learning positive and negative textual prompts for each class. The positive prompt seeks to reveal distinctive features of the class, while the negative prompt serves as a learnable threshold for separating clean and noisy samples. We employ parameter-efficient fine-tuning for the adaptation of a pre-trained visual encoder to promote its alignment with the learned textual prompts. As a general framework, DEFT can seamlessly fine-tune many pre-trained models to downstream tasks by utilizing carefully selected clean samples. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DEFT in both noisy label detection and image classification tasks. Our source code is available at https://***/HotanLee/DeFT. © 2024 Neural information processing systems foundation. All rights reserved.

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A Heuristic-based Dynamic Scheduling and Routing Method for Industrial TSN Networks 10

A Heuristic-based Dynamic Scheduling and Routing Method for ...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Chen, Honglong Liu, Mindong Huang, Jing Zheng, Zhiling Huang, Weihong Xiao, Yufeng Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Hunan Industry Polytechnic Information Center Changsha410125 China

ISBN: (纸本)9798350312461

In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization. © 2023 IEEE.

关键词： Heuristic algorithms

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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Frontiers of computer science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

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