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

作者： Zhang, Jie Song, Jian Li, Xiang Tian, Xuesen Zhao, Zhigang Wu, Lu Wang, Chunxiao Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model's performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers. © 2023 IEEE.

关键词： Contrastive Learning HPC Power Consumption Prediction

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Optimization Strategy for Remote Differential Upgrade of IoT Firmware 11

Optimization Strategy for Remote Differential Upgrade of IoT...

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11th Joint International information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Ding, Wenkang Sun, Ruirui Meng, Qinglong Wan, Zhong Cheng, Guanghe Hao, Huijuan Bai, Jinqiang Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350333664

With the continuous increase of IoT devices, the management and optimization of devices become particularly important. The update cycle of device functions is short, and in order to facilitate management and optimize device performance, a fast and convenient upgrade method is needed to solve this problem. Remote upgrade technology is mainly divided into full package upgrade and differential upgrade. Compared to full package upgrade, differential upgrade has obvious advantages in remote upgrade of large files. The main problem with current differential upgrade is the problem of slow generation speed while ensuring a certain size range of differential packages. This article proposes an improved Bsdiff algorithm to solve the problem of slow generation speed of differential packages. Experimental verification shows that the efficiency of remote upgrade has been improved, Improved the speed of differential packet generation and upgrade efficiency while ensuring the size of differential packets. © 2023 IEEE.

关键词： Block Optimization Strategy Bsdiff Difference Algorithm IoT Firmware Libdivsufsort Remote Upgrade

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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

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Journal of computer Science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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Distributed user pairing and effective computation offloading in aerial edge networks

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Chinese Journal of Aeronautics 2024年第4期37卷 378-390页

作者： Wei LIANG Shuhui WEN Lixin LI Jingjing CUI School of Electronics and Information Northwestern Polytechnical UniversityXi’an CN710072China The State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an CN710126China School of Electronics and Computer Science University of SouthamptonSouthampton SO167NSUK

Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas,such as mountainous ***,the aerial networks are the key framework for the future communications,in which the aerial vehicle could act as the access ***,Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial *** this paper,we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing,power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks,namely CR-MEC aerial ***,this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation *** the conceived CR-MEC aerial networks,we propose a User Pairing and Computation Offloading Management Algorithm (UPCOMA) based on three-sided matching theory,aiming to minimize the system’s energy *** the first step of UPCOMA,multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user ***,the stable user pairs would match to a high altitude platform who act as the base station,which is for appropriately allocating Transmission Time Slots (TSs) at the second step of ***,a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual ***,the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low ***,the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.

关键词： Aerial edge computing Binary offloading Cognitive radio Hybrid offloading Resource management

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Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts 41

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning ...

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41st International Conference on Machine Learning, ICML 2024

作者： Shi, Jiang-Xin Wei, Tong Zhou, Zhi Shao, Jie-Jing Han, Xin-Yan Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Multi-dimensional Classification via Selective Feature Augmentation

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Machine Intelligence Research 2022年第1期19卷 38-51页

作者： Bin-Bin Jia Min-Ling Zhang School of Computer Science and Engineering Southeast UniversityNanjing 210096China College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina

In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented *** this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented ***, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

关键词： Machine learning multi-dimensional classification feature augmentation feature selection class dependencies

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Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition 41

Learning Label Shift Correction for Test-Agnostic Long-Taile...

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41st International Conference on Machine Learning, ICML 2024

作者： Wei, Tong Mao, Zhen Zhou, Zi-Hao Wan, Yuanyu Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Software Technology Zhejiang University Ningbo China

Long-tail learning primarily focuses on mitigating the label distribution shift between long-tailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing long-tail learning approaches, consistently improving their performance. The source code is available at https://***/Stomach-ache/label-shift-correction. Copyright 2024 by the author(s)

关键词： Black-box testing

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LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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An Accountable Anonymous Voting Scheme Based on One-Time Ring Signature 3

An Accountable Anonymous Voting Scheme Based on One-Time Rin...

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3rd International Conference on Frontiers of Electronics, information and Computation Technologies, ICFEICT 2023

作者： Gu, Yunqi Shen, Zhuowei Southeast University School of Cyber Science and Engineering China Ministry of Education Key Laboratory of Computer Networks and Information Nanjing China

ISBN: (纸本)9798350302356

Electronic voting plays an increasingly important role in both economic life and social activities. The electronic voting scheme based on one-Time ring signature and blockchain solves the problems of voter anonymity, double voting, and public verification, while achieving the openness and transparency of voting. However, it is not applicable to the multi-round voting scenario where the voting group is relatively fixed, nor does it consider the need for anonymous accountability and differential privacy protection. In this paper, based on one-Time ring signature, stealth address, blacklist and whitelist, an improved accountability anonymous voting scheme is proposed to enhance the practicability of the scheme. The security analysis shows that the scheme confirms to the basic security standards of an electronic voting system. © 2023 IEEE.

关键词： Electronic voting

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Length matters:Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

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Digital Communications and networks 2022年第3期8卷 289-302页

作者： Zihan Chen Guang Cheng Ziheng Xu Shuyi Guo Yuyang Zhou Yuyu Zhao School of Cyber Science and Engineering Southeast UniversityNanjing211189China Key Laboratory of Computer Network and Information Integration Ministry of EducationNanjing211189China Purple Mountain Laboratories Nanjing211111China

As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security ***,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a ***,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version *** this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 *** experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and *** to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.

关键词： Encrypted internet traffic Encrypted traffic service classification Multi PDU length sequence Length sensitive composite deep learning TLS-1.3

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