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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yazhi Zhang Chuanwen Luo Guijuan Wang Li Zhang Mengjie Lv Jiguo Yu School of Computer Science and Technology Qilu University of Technology Jinan China School of Information Science and Technology Beijing Forestry University Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Nanjing University of Posts and Telecommunications Nanjing China Big Data Research Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length.

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Inter-Participant Transfer Learning with Attention Based Domain Adversarial Training for P300 Detection

SSRN

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SSRN 2024年

作者： Li, Shurui Daly, Ian Guan, Cuntai Cichocki, Andrzej Jin, Jing Center of Intelligent Computing School of Mathematics East China University of Science and Technology Shanghai200237 China Brain-Computer Interfacing Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Computer Science and Engineering Nanyang Technological University 639798 Singapore RIKEN Brain Science Institute Wako351–0198 Japan Torun87-100 Poland Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which require training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source Domain transfer based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for participant-independent ERP classification tasks. We train and validate our proposed model using both a publicly available OpenBMI dataset and a self-collected dataset, employing a leave-one-participant-out cross-validation scheme. Our experimental results demonstrate that our OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowCovNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method. © 2024, The Authors. All rights reserved.

关键词： Brain computer interface

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Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection

arXiv

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arXiv 2022年

作者： Wan, Wei Hu, Shengshan Lu, Jianrong Yu Zhang, Leo Jin, Hai He, Yuanyuan School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab School of Information Technology Deakin University Australia

Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients’ data privacy. However, FL is susceptible to Byzantine attacks from malicious participants. Although the problem has gained significant attention, existing defenses have several flaws: the server irrationally chooses malicious clients for aggregation even after they have been detected in previous rounds;the defenses perform ineffectively against sybil attacks or in the heterogeneous data setting. To overcome these issues, we propose MAB-RFL, a new method for robust aggregation in FL. By modelling the client selection as an extended multi-armed bandit (MAB) problem, we propose an adaptive client selection strategy to choose honest clients that are more likely to contribute high-quality updates. We then propose two approaches to identify malicious updates from sybil and non-sybil attacks, based on which rewards for each client selection decision can be accurately evaluated to discourage malicious behaviors. MAB-RFL achieves a satisfying balance between exploration and exploitation on the potential benign clients. Extensive experimental results show that MAB-RFL outperforms existing defenses in three attack scenarios under different percentages of attackers. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy

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A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation

A Drug Box Recognition System Based on Deep Learning and Clo...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Honglei Zhu Anming Dong Jiguo Yu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor’s prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it’s hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement.

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Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Chun-Wun Zhao, Yining Cheng, Yanqi Montoya, Javier Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica I. Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States Lucerne School of Computer Science and Information Technology Lucerne University of Applied Sciences and Arts United States Yau Mathematical Sciences Center Tsinghua University China

Image segmentation is a fundamental task in both image analysis and medical applications. State-of-the-art methods predominantly rely on encoder-decoder architectures with a U-shaped design, commonly referred to as U-Net. Recent advancements integrating transformers and MLPs improve performance but still face key limitations, such as poor interpretability, difficulty handling intrinsic noise, and constrained expressiveness due to discrete layer structures, often lacking a solid theoretical *** this work, we introduce Implicit U-KAN 2.0, a novel U-Net variant that adopts a two-phase encoder-decoder structure. In the SONO phase, we use a second-order neural ordinary differential equation (NODEs), called the SONO block, for a more efficient, expressive, and theoretically grounded modeling approach. In the SONO-MultiKAN phase, we integrate the second-order NODEs and MultiKAN layer as the core computational block to enhance interpretability and representation power. Our contributions are threefold. First, U-KAN 2.0 is an implicit deep neural network incorporating MultiKAN and second order NODEs, improving interpretability and performance while reducing computational costs. Second, we provide a theoretical analysis demonstrating that the approximation ability of the MultiKAN block is independent of the input dimension. Third, we conduct extensive experiments on a variety of 2D and a single 3D dataset, demonstrating that our model consistently outperforms existing segmentation networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach

MADP-IIME: malware attack detection protocol in IoT-enabled ...

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作者： Pundir, Sumit Obaidat, Mohammad S. Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Rodrigues, Joel J. P. C. Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248 002 India College of Computing and Informatics University of Sharjah UAE with King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan University of Science and Technology Beijing Beijing 100083 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Federal University of Piauí (UFPI) PI Teresina 64049-550 Brazil Instituto de Telecomunicações Covilhã 6201-001 Portugal

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such

关键词： Internet of things

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Double-Channel Multi-layer Information Fusion for Text Matching 12th

Double-Channel Multi-layer Information Fusion for Text Match...

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12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022

作者： Zhang, Guoxi Dong, Yongquan Chen, Huafeng School of Computer Science and Technology Jiangsu Normal University No. 101 Shanghai Road Tongshan District Jiangsu Xuzhou221006 China Xuzhou Engineering Research Center of Cloud Computing No. 101 Shanghai Road Tongshan District Jiangsu Xuzhou221006 China

ISBN: (纸本)9783031039478

Text matching is one of the fundamental tasks in natural language processing. Most of the existing models focus only on encoding the text itself but ignore other semantic information which may further improve matching accuracy. In this paper, we propose a novel model for text matching with double-channel multi-layer information fusion. It treats text and part-of-speech information of words in a sentence as double-channel information which is fused by multi-layer interactions. Meanwhile, our model uses a Siamese network structure to learn common and unique features of two sentences, which can improve its ability to learn the relationship between two sentences while reducing the parameter size and complexity. Experimental results on SNLI dataset show that our model can achieve better performance than baseline methods. © 2022, IFIP International Federation for Information Processing.

关键词： Information fusion

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Predicting Ejection Fraction from Electrocardiogram Signals using a Multi-task Learning Model

Predicting Ejection Fraction from Electrocardiogram Signals ...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Gaoyan Zhong Yueyi Wang Sen Liu Xintao Deng Aiguo Wang Cuiwei Yang Center for Biomedical Enginering School of Information Science and Technology Fudan Shanghai China Department of Cardiology Xinghua City People’s Hospital Jiangsu China Center for Biomedical Enginering School of Information Science and Technology Fudan The Key Laboratory of Medical Imaging Computing and Computer Assisted Intercention of Shanghai Shanghai China

The aim of this study was to investigate the feasibility and effectiveness of using electrocardiogram (ECG) signals to predict ejection fraction (EF) in an extremely imbalanced dataset. We collected ECG signals from 9365 patients and conducted a correlation analysis with EF. After collecting and preprocessing the ECG signal, we developed a deep learning-based multi-task learning model designed to extract features from ECG signals and perform predictions. Our study employed a model based on Transformer, multi-scale convolutional neural networks (CNN), channel attention, and pre-trained ResNet to predict EF (EF > 50 or EF ≤ 50) and EF value. The experimental results demonstrate that the proposed model exhibits excellent predictive performance, with an AUC of 0.825 and MAE of 4.855 for predicting EF. It outperforms other models and shows better results in comparison. Our study validates the feasibility and effectiveness of using ECG signals to predict EF and provides strong support for early diagnosis and treatment of cardiovascular diseases.

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Empowering Donors: How Blockchain technology Can Help Ensure Their Contributions Reach the Right Recipients

Empowering Donors: How Blockchain Technology Can Help Ensure...

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Business Analytics for technology and Security (ICBATS), International Conference on

作者： Sahar Ajmal Muteeb Bin Muzammil Muhammad Shoaib Muhammad Hassaan Mehmood Department of Software Engineering National University of Computer and Emmerging Sciences Chiniot-Faisalabad Pakistan College of Computing Riphah International University Faisalabad Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman Jordan

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential.

关键词： Decentralized autonomous organization Smart contracts Transforms Blockchains Consensus protocol Internet of Things Security

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Security boundaries of an optical-power limiter for protecting quantum-key-distribution systems

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Physical Review Applied 2024年第1期21卷 014026-014026页

作者： Qingquan Peng Binwu Gao Konstantin Zaitsev Dongyang Wang Jiangfang Ding Yingwen Liu Qin Liao Ying Guo Anqi Huang Junjie Wu Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China School of Automation Central South University Changsha 410083 China China Greatwall Research Institute China Greatwall Technology Group Co. Ltd. Shenzhen 518057 China Russian Quantum Center Skolkovo Moscow 121205 Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Vigo Quantum Communication Center University of Vigo 36310 Vigo Spain Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo 36310 Vigo Spain atlanTTic Research Center University of Vigo 36310 Vigo Spain College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China School of Computer Science Beijing University of Posts and Telecommunications Beijing 100876 China

Unauthorized light injection has always been a vital threat to the practical security of a quantum-key-distribution (QKD) system. An optical-power limiter (OPL) based on the thermo-optical defocusing effect has been proposed and implemented, limiting the injected hacking light. As a hardware countermeasure, the performance of the OPL under various light-injection attacks will be tested to clarify the security boundary before it is widely deployed. To investigate the security boundary of the OPL in quantum cryptography, we comprehensively test and analyze the behavior of the OPL under continuous-wave (cw) light-injection attacks and pulse-illumination attacks with pulse repetition rates of 0.5 Hz, 40 MHz, and 1 GHz. The test results illuminate the security boundary of the OPL, which allows one to properly employ the OPL in use cases. The methodology of testing and analysis proposed here is applicable to other power-limitation components in a QKD system.

关键词： Optical quantum information processing Photonics Quantum communication Quantum cryptography Devices

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