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15th International Conference on Open Source Systems and Technologies, ICOSST 2021

作者： Liao, Xuhan Niu, Weina Li, Jing Zhu, Hang Wang, Yanping Zhang, Xiaosong School of Computer Science and Engineering University of Electronic Science and Technology of China China China Blockchain Research Lab of UESTC Chengdu Jiaozi Financial Holding Group Company Ltd China

ISBN: (纸本)9781665413053

Air-ground service systems can effectively dispatch vehicles, thus improving the quality and efficiency of air service and avoiding flight delays. However, the current system lacks oversight and traceability. To be specific, when a dispatching accident happened, it is difficult to find out the root cause of the accident and the one in charge of the accident. Therefore, we construct an airline ground service supervising system based on blockchain named AGSS. In AGSS, each step requires agreements of most organizations. The event and the organizations in charge are recorded in blockchain immutably. It's easy to know what happened and the one responsible when needed. First, AGSS is easy to use with a layered design. Users only need to know the application layer, and other layers are available for the developer to modify low-level code. Then Hyperledger Fabric - a permissioned blockchain framework is adopted by us to implement AGSS. In addition, we carried out experiments about the time cost and throughput. The results show that AGSS can supervise and trace events with low overhead. © 2021 IEEE.

关键词： blockchain

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Emotion Recognition by Learning the Manifold of Fused Multiscale Information of EEG Signals

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IEEE Transactions on Affective Computing 2025年

作者： Li, Cunbo Zhang, Shuhan Mu, Yufeng Yang, Lei Peng, Yueheng Li, Fali Zhang, Yangsong Liang, Zhen Cao, Zehong Wan, Feng Yao, Dezhong Li, Peiyang Xu, Peng University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu610054 China University of Macau Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation China Southwest University of Science and Technology School of Computer Science and Technology Mianyang621010 China Shenzhen University School of Biomedical Engineering Medical School Shenzhen518060 China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China University of South Australia STEM Mawson Lakes Campus AdelaideSA5095 Australia School of Computer Science University of Technology Sydney Australia University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation China Chongqing University of Posts and Telecommunications School of Bioinfomatics Chongqing400065 China

Recent research has consistently indicated that the fusion of electroencephalography (EEG) features from multiple modalities can integrate cognitive state expressions across diverse dimensions, resulting in a substantial increase in emotion recognition accuracy. However, redundant information within the fused multimodal features could lead to the curse of dimensionality and overfitting of the learning model. In this work, we propose a multiscale EEG feature fusion and representation strategy for EEG emotion recognition named manifold of multiscale information fusion (MMIF), in which the optimal manifold of the multiscale fusion of local and global brain activation patterns can be automatically learned to realize an efficient representation of emotional EEG signals. To evaluate the performance, in this work, both off- and online EEG emotion recognition experiments were conducted, and the experimental results consistently verified the effectiveness and feasibility of the MMIF applied in real-time emotion decoding systems. Furthermore, the analytical experiments confirmed the discriminative capabilities and cognitive interpretability of the MMIF. In summary, the proposed MMIF model may provide an efficient avenue for exploring representations and enhancing the discrimination of multimodal fusion features, which may also provide a promising solution for designing online affective braincomputer interaction systems. © 2010-2012 IEEE.

关键词： Information fusion

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BASS: blockchain-Based Asynchronous SignSGD for Robust Collaborative Data Mining

BASS: Blockchain-Based Asynchronous SignSGD for Robust Colla...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Chenhao Xu Youyang Qu Yong Xiang Longxiang Gao David Smith Shui Yu Deakin Blockchain Innovation Lab School of Information Technology Deakin University Geelong Australia Data 61 Australia Commonwealth Scientific and Industrial Research Organization Australia Qilu University of Technology and Shandong Computer Science Center China School of Computer Science University of Technology Sydney Australia

ISBN: (纸本)9781665473316

Federated learning (FL) is a machine learning framework for collaborative data mining in many scenarios (e.g. Internet of Things) due to its privacy-preserving feature. However, various attacks arise security concerns of FL, such as poisoning, backdoor, and DDoS attacks. Several blockchain-based FL schemes strengthen credibility and security without considering the increased communication overhead. Some existing work compresses local updated gradients to sign vectors to lower communication overhead at the expense of model accuracy. To address the above concerns, this paper offers a blockchain-based asynchronous SignSGD (BASS) scheme. A novel asynchronous sign aggregation algorithm is introduced to ensure model accuracy even if the local updated gradients are compressed to sign vectors. Considering the unstable network connection on IoT, a consensus algorithm that elects multiple leader nodes enables reliable global model aggregation. The introduced blockchain improves credibility and security without downgrading efficiency. Empirical studies show that BASS outperforms other schemes in efficiency, model accuracy, and security.

关键词： Federated learning Collaboration Consensus algorithm Data science Denial-of-service attack Data models Security

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AN ANALYSIS ON LARGE LANGUAGE MODELS IN HEALTHCARE: A CASE STUDY OF BIOBERT

arXiv

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arXiv 2023年

作者： Sharaf, Shyni Anoop, V.S. School of Computer Science and Engineering Kerala University of Digital Sciences-Innovation and Technology Thiruvananthapuram India Applied NLP Research Lab School of Digital Sciences Kerala University of Digital Sciences-Innovation and Technology Thiruvananthapuram India

This paper conducts a comprehensive investigation into applying large language models, particularly on BioBERT, in healthcare. It begins with thoroughly examining previous natural language processing (NLP) approaches in healthcare, shedding light on the limitations and challenges these methods face. Following that, this research explores the path that led to the incorporation of BioBERT into healthcare applications, highlighting its suitability for addressing the specific requirements of tasks related to biomedical text mining. The analysis outlines a systematic methodology for fine-tuning BioBERT to meet the unique needs of the healthcare domain. This approach includes various components, including the gathering of data from a wide range of healthcare sources, data annotation for tasks like identifying medical entities and categorizing them, and the application of specialized preprocessing techniques tailored to handle the complexities found in biomedical texts. Additionally, the paper covers aspects related to model evaluation, with a focus on healthcare benchmarks and functions like processing of natural language in biomedical, question-answering, clinical document classification, and medical entity recognition. It explores techniques to improve the model’s interpretability and validates its performance compared to existing healthcare-focused language models. The paper thoroughly examines ethical considerations, particularly patient privacy and data security. It highlights the benefits of incorporating BioBERT into healthcare contexts, including enhanced clinical decision support and more efficient information retrieval. Nevertheless, it acknowledges the impediments and complexities of this integration, encompassing concerns regarding data privacy, integrity, bias mitigation, transparency, resource-intensive requirements, and the necessity for model customization to align with diverse healthcare domains. © 2023, CC BY.

关键词： Medical informatics

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Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction

Complementing Event Streams and RGB Frames for Hand Mesh Rec...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jianping Jiang Xinyu Zhou Bingxuan Wang Xiaoming Deng Chao Xu Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University AI Innovation Center School of Computer Science Peking University National Key Lab of General AI School of Intelligence Science and Technology Peking University Chinese Academy of Sciences Institute of Software University of Chinese Academy of Sciences

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging es-pecially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties, but it lacks salient texture appearance for hand mesh reconstruction. In this paper, we propose EvRGBHand - the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time, space, and information dimensions, EvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity as well as background overflow issues in event-based HMR. We further propose EvRGBDegrader, which allows our model to generalize effectively in challenging scenes, even when trained solely on standard scenes, thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera. For code, models, and dataset, please refer to https://***/.***/.

关键词： Costs Three-dimensional displays Image color analysis Cameras Sensors Pattern recognition Reliability

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Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication

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computers, Materials & Continua 2022年第11期73卷 3319-3331页

作者： Muhammad Adnan Khan Ahmed Abu-Khadrah Shahan Yamin Siddiqui Taher M.Ghazal Tauqeer Faiz Munir Ahmad Sang-Woong Lee Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea College of Computing&Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Minhaj University LahoreLahore54000Pakistan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan

Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network ***,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one *** of the main requirements in a VANET is to provide self-decision capability to the *** memory,which stores all the previous routes,is used by the vehicles to choose the optimal *** networks,communication is *** cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each *** are allocated in a distributed manner,which is known as Mode 4 *** support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this *** k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 *** results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users.

关键词： Mode-4 communication ad-hoc vehicular network CV2X support vector machine

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BackLip: Passphrase-Independent Lip-reading User Authentication with Backscatter Signals

BackLip: Passphrase-Independent Lip-reading User Authenticat...

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International Workshop on Quality of Service

作者： Ye Tian Hao Zhou Haohua Du Chenren Xu Jiahui Hou Dong Ren Xiang-Yang Li School of Computer Science and Technology University of Science and Technology of China CAS Key Lab of Wireless-Optical Communications University of Science and Technology of China China USTC-Deqing Alpha Innovation Research Institute Zhejiang China School of Cyber Science and Technology Beihang University China Department of Computer Science and Technology Peking University China

User authentication is essential for threat defense and data protection. Existed authentication systems have some known limitations, such as spoofing attacks, privacy leakage, and user-unfriendliness. In this paper, we propose BackLip, a novel anti-spoofing authentication system based on lip reading. We employ Wi-Fi backscatter-based technology to recognize users lip reading due to its various advantages, e.g. privacy protection, low power consumption, and low cost. Our system is touch-free and passphrase-independent, making it user-friendly, especially for the elderly and disabled. We first filter out irrelevant interference and enhance the signal-to-noise ratio of lip-reading signals by modulating the backscatter tags and constructing a series of suitable filters. Then, we study the energy distribution and steady-state characteristics of the backscattered signal caused by lip-reading movement at different frequencies to extract passphrase-independen lip-reading fingerprints. We build a theoretical model to analyze and prove the feasibility of our method and design an adaptive correction method to resist the interference caused by distance and angle changes. Additionally, we propose an adaptive segmentation algorithm to label lip-reading motions automatically. Extensive experiments demonstrate that BackLip has an average accuracy of 92.1% and is improved to 96.5% when users use the same passphrases.

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Content Based Automated File Organization Using Machine Learning Approaches

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computers, Materials & Continua 2022年第10期73卷 1927-1942页

作者： Syed Ali Raza Sagheer Abbas Taher M.Ghazal Muhammad Adnan Khan Munir Ahmad Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797UAE Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120GyeonggidoKorea Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

In the world of big data,it’s quite a task to organize different files based on their *** with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer *** of file management related tasks will be solved if the files on any operating system are somehow categorized according to their ***,the browsing process can be performed quickly and *** research aims to design a system to automatically organize files based on their similarities in terms of *** proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer *** results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user *** results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their *** proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows.

关键词： File organization natural language processing machine learning

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HiCoCS: High Concurrency Cross-Sharding on Permissioned blockchains

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IEEE Transactions on computers 2025年第07期74卷 2168-2182页

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

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MultiHGR: Multi-Task Hand Gesture Recognition with Cross-Modal Wrist-Worn Devices

MultiHGR: Multi-Task Hand Gesture Recognition with Cross-Mod...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Mengxia Lyu Hao Zhou Kaiwen Guo Wangqiu Zhou Xingfa Shen Yu Gu LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Hangzhou Dianzi University China University of Electronic Science and Technology of China China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Hand gesture recognition (HGR) is essential for human-machine interaction. Although the existing solutions achieve good performance in specific tasks, they still face challenges when users navigate through different application contexts, i.e., demanding multi-task ability to support newly arrived HGR tasks. In this paper, we propose the first IMU-vision based system hosted on wrist-worn devices to support multi-task HGR, denoted as MultiHGR. The system introduces a novel two-stage training strategy, i.e., task-agnostic stage to align cross-modal features from unlabeled arbitrary gesture through contrastive learning, and task-related stage to learn modality contributions with limited labeled data in specific tasks through self-attention mechanism. Since only the second task-related stage should be executed for each new task, MultiHGR could accommodate multiple tasks with significant reduced training cost and storage requirement. The evaluation results on three HGR tasks demonstrates that MultiHGR reduces 64.92% training time, and 24.04% storage as compared with traditional multimodal single-task models, and MultiHGR outperforms unimodal single-task models with 14.37%, 19.28%, and 31% improvements in these three tasks, respectively. As compared with state-of-the-art multimodal single-task model, MultiHGR achieves average 6.35% accuracy improvement, along with 65.74% training time reduction.

关键词： Training Performance evaluation Human computer interaction Navigation Gesture recognition Contrastive learning Multitasking

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