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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Wan, Ji Hu, Kai Li, Jie Su, Hao Wu, Qingshun Li, Mingyuan Feng, Libo Pan, Yan Beihang University State Key Laboratory of Software Development Environment Beijing China Beihang Yunnan Innovation Institute Yunnan Key Laboratory of Blockchain Application Technology Kunming China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China The First Affiliated Hospital of Zhengzhou University Zhengzhou China Yunnan University National Pilot School of Software Kunming China China Industrial Control Systems Cyber Emergency Response Team Institute of Software Beijing China

ISBN: (纸本)9798350341010

Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain. © 2023 IEEE.

关键词： Smart contract

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SFedXL: Semi-Synchronous Federated Learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年第12期12卷 18692-18707页

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Engineering Research Center of Cyberspace Ministry of Education Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Department of Communication Systems Biot06410 France University of Illinois Urbana-Champaign Institute Zhejiang University Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering Jurong West 639798 Singapore Singapore University of Technology and Design Tampines 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an autonomous aerial vehicle (AAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a AAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by AAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different AAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed semi-synchronous FL with cross-sharpness and layer-freezing (SFedXL), tailored for a AAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a tradeoff of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

arXiv

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arXiv 2022年

作者： Yu, Yang Liu, Qi Wu, Likang Yu, Runlong Yu, Sanshi Lei Zhang, Zaixi Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China

Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes it susceptible to poisoning attacks. Most previous studies focus on the targeted attack to promote certain items, while the untargeted attack that aims to degrade the overall performance of the FedRec system remains less explored. In fact, untargeted attacks can disrupt the user experience and bring severe financial loss to the service provider. However, existing untargeted attack methods are either inapplicable or ineffective against FedRec systems. In this paper, we delve into the untargeted attack and its defense for FedRec systems. (i) We propose ClusterAttack, a novel untargeted attack method. It uploads poisonous gradients that converge the item embeddings into several dense clusters, which make the recommender generate similar scores for these items in the same cluster and perturb the ranking order. (ii) We propose a uniformity-based defense mechanism (UNION) to protect FedRec systems from such attacks. We design a contrastive learning task that regularizes the item embeddings toward a uniform distribution. Then the server filters out these malicious gradients by estimating the uniformity of updated item embeddings. Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models

arXiv

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arXiv 2024年

作者： Zhang, Shenglin P., Zhu M., Ma J., Wang Y., Sun D., Li J., Wang Q., Guo X., Hua L., Zhu D., Pei Nankai University China Microsoft United States Tsinghua University China China Mobile Research Institute China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China chn

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile's daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

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Backdoor Defense via Deconfounded Representation Learning

Backdoor Defense via Deconfounded Representation Learning

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zaixi Zhang Qi Liu Zhicai Wang Zepu Lu Qingyong Hu Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei Anhui China University of Science and Technology of China Hong Kong University of Science and Technology

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear whether a backdoor-free clean model can be directly obtained from poisoned datasets. In this paper, we first construct a causal graph to model the generation process of poisoned data and find that the backdoor attack acts as the confounder, which brings spurious associations between the input images and target labels, making the model predictions less reliable. Inspired by the causal understanding, we propose the Causality-inspired Backdoor Defense (CBD), to learn deconfounded representations for reliable classification. Specifically, a backdoored model is intentionally trained to capture the confounding effects. The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme. Extensive experiments on multiple benchmark datasets against 6 state-of-the-art attacks verify that our proposed defense method is effective in reducing backdoor threats while maintaining high accuracy in predicting benign samples. Further analysis shows that CBD can also resist potential adaptive attacks. The code is available at https://***/zaixizhang/CBD.

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A Tibetan Ancient Uchen Text Line Dataset for OCR

A Tibetan Ancient Uchen Text Line Dataset for OCR

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Zhuome Gongqu Peng Luo Dongzhou Jiayang Jia Cairang Jiacuo Cizhen Dongzhu Renqing The College of Computer Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining China The College of Information Science and Technology Tibet University Lhasa China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Characters written by hand that represent distinct writing styles are referred to as handwritten text; these are especially common in ancient texts. In the field of digitizing ancient literature, Tibetan ancient document image recognition is a focus of both domestic and international research. However, technological advancement in this field has been hampered by the absence of a specific recognition dataset for image recognition systems of Tibetan ancient documents. This paper addresses this by proposing a dataset for Uchen style text line recognition in Tibetan ancient documents. This dataset includes basic synthetic images of multi-style Uchen text lines as well as real-world images of multi-format Tibetan Uchen text lines that have been manually annotated. Cross-Validation and several rounds of annotation by various people guarantee the quality of the data. Additionally, three popular text line recognition models are used to assess the dataset and validate its usability.

关键词： Hands computer vision Image recognition Text recognition Annotations Computational modeling Optical character recognition Machine learning Writing Usability

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MambaRF: A Bi-directional Mamba Structure for Radio Frequency Signal Classification of Unmanned Aerial Vehicle

MambaRF: A Bi-directional Mamba Structure for Radio Frequenc...

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

作者： Mufeng Yao Chao Liu Lexu Xie Mingmin Chi School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China School of Computer Science Fudan University Zhengzhou Zhongke Institute of Integrated Circuit and System Application Shanghai China Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The rapid development of Unmanned Aerial Vehicle (UAV) technology has facilitated the widespread use of UAVs in daily life, this advancement brings huge regulatory demand for UAVs. Automatic identification of UAVs using radio frequency (RF) signals can effectively reduce regulatory costs. Previous studies first extracts spectrogram features from raw RF signals using short-time Fourier transform, and then use neural networks to classify the spectrograms. These studies are mainly based on local convolution, ignoring the long-range dependence of the spectrograms, and thus are limited in terms of UAV classification accuracy. In addition, these works also focus only on classifying UAV types without identifying flight states (e.g., hovering, flying, and switch on). To this end, we propose MambaRF, which employs a bi-directional selective scanning mechanism to capture long-range information of spectrograms from two different directions. MambaRF also jointly classifies UAV types and flight states by using two decoupled classification heads, which has not been addressed in previous studies. Experiments on two publicly available datasets show that our proposed MambaRF outperforms VMamba and other previous studies in both type classification and flight state classification.

关键词： Accuracy Convolution RF signals Bidirectional control Switches Autonomous aerial vehicles Feature extraction Speech processing Spectrogram Radiofrequency identification

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Reliability of gold wire leads for MEMS gyroscopes under a thermo-mechanical coupling field

Reliability of gold wire leads for MEMS gyroscopes under a t...

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IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

作者： Yingyu Xu Chunhua He Qinwen Huang Guizhen Yan School of Computer Guangdong University of Technology Guangzhou China Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou China National Key Laboratory of Science and Technology on Micro/Nano Fabrication Institute of Microelectronics Peking University Beijing China

ISBN: (数字)9798350359831

ISBN: (纸本)9798350359848

In this study, we investigate the vibration characteristics and failure modes of gold wire leads in MEMS gyroscopes under high-temperature and thermal-shock conditions through simulation and experimental analysis. The simulation results indicate a gradual degradation in the intrinsic frequencies of the leads as the temperature rises from -40 °C to 120 °C. The simulation results are verified by testing the intrinsic frequency with a percentage error of less than 2%. The average temperature drift of the intrinsic frequency is approximately - 8.82 Hz/°C. When comparing the intrinsic frequency changes before and after burn-in and thermal-shock tests, it is evident that the mechanical degradation of the gold wire leads is limited under the severe thermo-mechanical coupling field, with frequency fluctuations ranging from 0.01% to 17%. There is no occurrence of a lead fracture or solder joint failure. The main failure modes are lead collapse and short-circuit failure due to the fatigue of long leads. Minimizing the span and height of the leads can reduce the risk of fatigue and short-circuit failures.

关键词： Micromechanical devices Couplings Gold Simulation Thermomechanical processes Fatigue Gyroscopes

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Research on Waxiang Dialect Recognition Based on Wav2vec2.0 Pre-Training and Transfer Learning

Research on Waxiang Dialect Recognition Based on Wav2vec2.0 ...

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Computing, Robotics and System Sciences (ICRSS), International Conference on

作者： Mixuan Deng Pengju Tang Yanxia Gao Huixuan Li Guodong Zhen Kaifeng Fu Xiaotian Wu College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

As a unique local dialect, Waxiang dialect carries rich regional culture and historical information. However, with the acceleration of modernization and the increase of population mobility, the inheritance and protection of Waxiang dialect are facing severe challenges. By constructing the ‘Xuewatong’ APP- Waxiang dialect digital protection platform, based on cloud storage technology, digital resource display, multimedia integration and other technologies, and providing learning resources through big data analysis of user needs, it can effectively promote the digital recording and preservation of Waxiang dialect language resources, and significantly improve the dissemination and educational effect of Waxiang dialect culture, showing the great potential of digital technology in dialect protection.

关键词： Technological innovation Cloud computing Transfer learning Media Information age Digital recording Global communication Cultural differences Protection Robots

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A Single-Loop Narrow-Band Force Rebalance Control Method with Temperature Self-Compensation for a MEMS Gyroscope

A Single-Loop Narrow-Band Force Rebalance Control Method wit...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Chunhua He Yingyu Xu Heng Wu Qinwen Huang Qiancheng Zhao Guizhen Yan School of Computer Guangdong University of Technology China Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory CEPREI China School of Automation Guangdong University of Technology China School of Integrated Circuits Peking University Beijing China National Key Lab of Micro/Nano Fabrication Technology Beijing China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper presents a novel single-loop narrow-band force rebalance control method with temperature self-compensation for a MEMS gyroscope. The narrow-bandwidth force rebalance control for the sense mode is achieved using a simple bandpass filter (BPF) and without modulation or demodulation. The center frequency of the BPF is adjusted automatically to match the resonant frequency of the sense mode. Meanwhile, its gain is tuned based on the real-time quality factor of the sense mode. Experimental results demonstrate that the bias drift of the gyroscope with self-compensation is reduced from 1320 deg/h to 16 deg/h, while the scale factor drift is reduced from 0.616 mV/deg/s to 0.075 mV/deg/s.

关键词： Micromechanical devices Band-pass filters Temperature sensors Q-factor Force Resonant frequency Real-time systems Gyroscopes Temperature control Phase locked loops

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