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arXiv 2023年

作者： Jiang-Xin, Shi Tong, Wei Zhi, Zhou Jie-Jing, Shao Xin-Yan, Han Yu-Feng, Li 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 © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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TSF-Net:Abnormal Video Behavior Detection Framework Based on Two-Stream Appearance Motion Semantic Fusion 23

TSF-Net:Abnormal Video Behavior Detection Framework Based on...

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Proceedings of the 2023 International Conference on information Education and Artificial Intelligence

作者： Bonan Li Rui Cao Xingang Wang Hong Zhang Jinyan Zhou China Radio and Television Shandong Network Co Ltd China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (纸本)9798400716157

Video anomalous behavior detection aims to detect rare and random anomalous events, which usually deviate from expectations. Existing detection methods do not consider the appearance semantics and motion semantics of the events in conjunction, so they lead to poor detection results. Since the appearance semantics and motion semantics of normal events are highly consistent and correspond, while the semantic consistency of abnormal events is low. Based on this, this paper proposes a two-stream fusion hybrid network for abnormal behavior detection. The appearance semantics and motion semantics of events are recorded separately using a multi-scale fused self-encoder to generate corresponding reconstructed appearance frames and reconstructed motion frames. Afterwards, the two frames are fused to generate the final prediction frame. We introduce temporal attention to enhance the model's ability to represent motion semantics and capture normal behavior with effective regular motion. Experiments on three standard public datasets show that the method proposed in this paper can accurately detect anomalous events with a performance comparable to state-of-the-art methods.

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Resource Allocation for Mutualistic Symbiotic Radio with Hybrid Active-Passive Communications

Resource Allocation for Mutualistic Symbiotic Radio with Hyb...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Hong Guo Yinghui Ye Haijian Sun Liqin Shi Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China School of Electrical and Computer Engineering The University of Georgia Athens GA USA

To address the limitation on the rate of secondary users (SUs) in traditional mutualistic symbiotic radio (SR) systems, this paper proposes a new mutualistic SR with hybrid active-passive communications. Unlike the traditional mutualistic SR, where SUs only perform passive backscatter communications (BC), in the proposed mutualistic SR, each SU conveys information via passive BC and active communications (AC) alternatively. To exploit the potential advantages of the proposed SR, we formulate a non-convex problem to maximize the total rate of all SUs by jointly optimizing the transmit power of the primary user (PU), the reflection coefficient and transmit power of each SU, and the time allocation for each SU to perform passive BC and AC. With proof by contradiction, auxiliary variables and successively convex approximation (SCA), we transform the original problem into a convex one and solve it by proposing an iterative algorithm. The simulation results demonstrate that the proposed iterative algorithm converges quickly. Furthermore, the performance evaluation of the proposed mutualistic SR system demonstrates its superiority over the traditional mutualistic SR in terms of the rate achieved by SUs under the same constraints.

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STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection

STAD: Joint Spatial-Temporal Dimension and Channel Correlati...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingle Zhou Xingli Wang Delong Han Jin Wan Jiachen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately identifying real anomalies and pseudo-anomalies in complex multi-dimensional time series data has been a difficult problem in time series anomaly detection. To solve this problem, this paper proposes a new framework, STAD, that joint temporal and spatial dimensions. This framework guides the model to capture the correlation information between channels It also aims to learn the deep feature representation of sequences by mining potential information in the spatialtemporal dimension. It can effectively distinguish between true and false anomalies by comparing information from spatial and temporal dimensions. STAD identifies and integrates correlated channels by using a correlation aggregation mechanism to join multiple channels and detect *** addition, the KAN mixer designed in this paper can effectively extract features from different spatial locations in the spatial-temporal dimension. Through extensive experiments on several public datasets, STAD demonstrates its superiority in terms of accuracy and robustness.

关键词： Correlation Accuracy Time series analysis Signal processing Feature extraction Robustness Acoustics Speech processing Mixers Anomaly detection

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DLFaaS:Serverless Platform for Data-Intensive Tasks Based on Interval Access Patterns

DLFaaS:Serverless Platform for Data-Intensive Tasks Based on...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yang Cao Wenbin Song Hanqian Wu Shengchao Yuan School of Computer Science and Engineering Southeast University China School of Cyber Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University China

Serverless architecture is a novel paradigm in cloud computing. The increasing trend in applications is shifting towards serverless platforms to achieve on-demand payment and elastic scalability. However, for data-intensive functions, using serverless architectures can result in performance losses due to frequent data transfers, incurring significant time overhead for data movement. Current serverless systems lack optimization for data migration times in tasks involving data-intensive functions. We conducted an analysis of serverless architecture, function invocation patterns, and latency for data-intensive functions. We found that the lack of awareness of access patterns for these functions leads to inefficient cache eviction, resulting in low cache hit rates. Additionally, bursty calls result in repeated remote storage accesses, contributing to time overhead in dataintensive ***, we proposed a serverless platform based on function invocation patterns, optimized for data-intensive tasks. Specifically, we designed a two-tier caching queue, where data objects accessed multiple times for read and write operations were cached based on historical access patterns. These objects were placed in different cache lists according to various time intervals. We employed an eviction strategy based on function access intervals to enhance overall hit rates. Compared to popular existing solutions, we reduced time latency by 32.1% and improved cache hit rates by 12%.

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Adaptive group personalization for federated mutual transfer learning 24

Adaptive group personalization for federated mutual transfer...

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Proceedings of the 41st International Conference on Machine Learning

作者： Haoqing Xu Dian Shen Meng Wang Beilun Wang School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China College of Design and Innovation Tongji University Shanghai China

Mutual transfer learning aims to improve prediction with knowledge from related domains. Recently, federated learning is applied in this field to address the communication and privacy concerns. However, previous clustered federated learning (CFL) solutions lack theoretical guarantee of learnability recovery and require time-consuming hyper-parameter tuning, while centralized mutual transfer learning methods lack adaptability to concept drifts. In this paper, we propose the Adaptive Group Personalization method (AdaGrP) to overcome these challenges. We adaptively decide the recovery threshold with a non-parametric method, adaptive threshold correction, for tuning-free solution with relaxed condition. Theoretical results guarantee the perfect learnability recovery with the corrected threshold. Empirical results show AdaGrP achieves 16.9% average improvement in learnability structure recovery compared with state-of-the-art CFL baselines.

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Revocable Registered Attribute-Based Encryption With User Deregistration

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IEEE Internet of Things Journal 2025年

作者： Li, Jiguo Chen, Shaobo Lu, Yang Ning, Jianting Shen, Jian Zhang, Yichen Fujian Normal University College of Computer and Cyber Security Fuzhou 350117 China Key Laboratory of Analytical Mathematics and Applications (Ministry of Education) Fuzhou 350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou 350117 China Nanjing Normal University School of Computer and Electronic Information Nanjing 210023 China Fujian Normal University Key Laboratory of Analytical Mathematics and Applications (Ministry of Education) Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou 350117 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou 310018 China

Many businesses are putting their sensitive data in the cloud with the fast growth of cloud computing and storage. To ensure user privacy, it is necessary to keep encrypted data only in the cloud. Attribute-based encryption (ABE) is a popular mean in cloud storage scenarios. ABE is not only faced with key escrow problem but also suffers from user revocation issue when he or she is no longer authorized to access to encrypted data. In order to address these two issues, we propose a revocable registered attribute-based encryption scheme, which not only avoids key escrow problem but also supports precise revocation of a user’s access to a file as well as permanent deregistration of a user from the system. Furthermore, we prove the semantic security of the scheme and conduct a performance experiment to show the efficiency. © 2014 IEEE.

关键词： attribute-based encryption cloud computing composite-order group registration-based encryption revocation

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Variable projection algorithms with sparse constraint for separable nonlinear models

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Control Theory and Technology 2024年第1期22卷 135-146页

作者： Hui-Lang Xu Guang-Yong Chen Si-Qing Cheng Min Gan Jing Chen College of Computer and Data Science Fuzhou UniversityFuzhou350116FujianChina Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350116FujianChina Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou UniversityFuzhou350116FujianChina Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou UniversityFuzhou350116FujianChina School of Science Jiangnan UniversityWuxi214122JiangsuChina

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

关键词： Variable projection(VP) Non-smooth constraint Separable nonlinear models

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Integrated Sensing and Communications for 6G: Prospects and Challenges of Using THz Radios

Integrated Sensing and Communications for 6G: Prospects and ...

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IEEE Conference on Wireless Communications and networking

作者： Tingguang Gan Shuping Dang Xiangcheng Li Zhenrong Zhang Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning China School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The integrated sensing and communication (ISAC), which will power the future sixth generation (6G) of mobile communication, is anticipated to offer smarter and varied services. The functions of communication and sensing can be integrated because they contain some similarities, but conflicts and differences need to be resolved. The terahertz (THz) band, due to its special characteristics, can combine both communication and sensing capabilities. This article delves into the current trends, prospects, and challenges of ISAC utilizing THz radios. We outline the functions and advantages of THz radios for communication and sensing processes in the context of ISAC. Then, a generic system framework of ISAC in the THz band is given, and the integrated evolution of the communication and sensing functions of ISAC is described. Finally, an outlook on ISAC is given with the hope that THz technology will help realize a ubiquitous, smarter, and more reliable ISAC system in the near future.

关键词： 6G mobile communication Systems architecture Bandwidth Market research Communications technology Reliability Internet of Things

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Hey! You Photographed My Screen without Approved: A Practical Screen Content Protection Scheme Based on Visual Cryptography

Hey! You Photographed My Screen without Approved: A Practica...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Liu, Kui Wang, Bin Shi, Ziwei Chen, Jiadong Zhang, Zhiwei School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Network and Information Security Laboratory Hangzhou Hikvision Digital Technology Co. LTD. Hangzhou China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Hangzhou310053 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Nowadays , with the continuous development of digital media, electronic screens have become the most direct tool for people to interact with information. Various styles of electronic screens provide vivid information exchange methods for people's lives, and at the same time make information leakage more likely to happen. Electronic screens are susceptible to stealing photographs when sharing information, which leads to information leakage and security issues. Under the unavoidable situation of such leaks, various protection schemes have been proposed before, but these schemes cannot effectively protect the screen content. Therefore, in this paper, we propose an effective a screen content protection scheme, based on Visual Cryptography(VC) for image encryption, combined with Scale-Invariant Feature Transform(SIFT) to deal with screen stealing scenes. In addition, our experiments show that our scheme is superior to existing schemes in terms of convenience and security. © 2022 IEEE.

关键词： Digital storage

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