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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Zhang, Hao Zhang, Chizhi Chris Jiang, Jian Chen, Xinan Dong, Jing Liang, Dongfang Changchun Institute of Optics Fine Mechanics and Physics CAS Advanced Computer and Digital Technology Research Center Changchun China University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Beijing China Xi'an Jiaotong-Liverpool University International Business School Suzhou China University of Cambridge Department of Engineering Cambridge United Kingdom

ISBN: (纸本)9798350356083

This study presents a stochastic model for corrosion deterioration in submarine pipelines, a major threat to structural integrity and operational efficiency. Using Bayesian updating, the model dynamically adjusts its parameters with new inspection data, improving predictions of corrosion growth and optimizing maintenance intervals to minimize failures. A numerical example demonstrates the model's effectiveness in forecasting corrosion progression and enhancing maintenance decisions based on reliability and failure probability using the gamma process. © 2024 IEEE.

关键词： Pipeline corrosion

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Biosignal-Based Attention Monitoring for Evaluating Train Driver Safety-Relevant Tasks

SSRN

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SSRN 2024年

作者： Kim, Jung Hwan Kim, Yeongjun Cho, Younggeol Kim, Tae Kyun Jang, Tongil Park, Chanwoo Kang, Seongkeun Advanced Instrumentation & Control Research Section Korea Atomic Energy Research Institute Daejeon Korea Republic of Department of Nuclear & Quantum Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Railroad Electrical & Electronic Engineering Korea National University of Transportation Uiwang Korea Republic of System Safety Research Department Korea Railroad Research Institute Uiwang Korea Republic of

This study explores the impact of phone usage on train driver performance, a critical aspect influencing the safety of railroad operations. The persistent problem of phone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. Leveraging advancements in technology, this study introduces a novel approach combining electroencephalogram (EEG)-based analysis with deep learning algorithms to assess the effects of phone usage on the attention states of train drivers. Utilizing a full-type train simulator to replicate real-life scenarios, this study conducts an experimental design involving scenarios with and without phone engagement, aiming to provide a comprehensive assessment of phone usage on performance in critical situations. The deep learning model employed achieved an impressive classification accuracy of approximately 85.6% in distinguishing attention states among 25 participants, with high precision and recall values indicating its efficacy in detecting phone interaction. Moreover, findings reveal that phone usage leads to a 1.4 times increase in response time to situational awareness, significantly affecting driver performance. These findings highlight the significant impact of phone usage on train driver attention and underscore the potential of EEG-based analysis and deep learning in improving railroad safety measures. This study contributes to the growing body of safety literature by focusing on train drivers, offering fresh insights and practical implications for enhancing the safety and efficiency of railroad operations. © 2024, The Authors. All rights reserved.

关键词： Railroads

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Reparable threshold Paillier encryption scheme for federated learning

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Soft computing 2025年 1-6页

作者： Zhang, Yi Tian, Kun Lu, Yunfan Liu, Fengxia Li, Cheng Gong, Zixian Hu, Zhe Li, Jia Xu, Qun Institute for Mathematical Sciences Renmin University of China Zhongguancun Street Beijing100872 China School of Mathematics Renmin University of China Zhongguancun Street Beijing100872 China Engineering Research Center of Financial Computing and Digital Engineering Ministry of Education Zhongguancun Street Beijing100872 China Artificial Intelligence Research Institute Beihang University Huayuan Lu Beijing266100 China

Threshold Paillier encryption scheme finds extensive application in the context of federated learning. However, the issue of client dropout frequently arises within the context of federated learning, rendering the conventional threshold Paillier encryption scheme ineffective for data decryption. To address this issue, this paper introduces a (N, k, d)-reparable threshold Paillier encryption scheme by combining the reparable Shamir secret-sharing scheme with the Paillier encryption scheme. The proposed scheme has demonstrated several valuable capabilities. Specifically, we initially establish the security of the secret-sharing property of this scheme, namely, any group of k participants can collaboratively recover the private key, while k-1 or fewer participants cannot recover the private key. Moreover, we demonstrate the scheme’s ability to handle the client dropout problem, namely, any group of d normal participants can collectively repair the private key segments of dropout clients. Ultimately, we substantiate that the (N, k, d)-reparable threshold Paillier encryption scheme consistently upholds the precision of decryption processes. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Federated learning

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Exploring Pedestrian Responses to eHMIs in Virtual Reality: Balancing Efficiency and Safety in Interactions with Automated Buses

Exploring Pedestrian Responses to eHMIs in Virtual Reality: ...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Kosei Ido Yanbin Wu Naohisa Hashimoto Koya Takahashi Satoru Ogino Makoto Itami Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology(AIST) Tsukuba Japan Tokyo University of Science Tokyo Japan Department of Applied Electronics Faculty of Advanced Engineering Tokyo University of Science Tokyo Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Automated buses, which have the potential to enhance public mobility, are being deployed globally. However, due to their non-human driving style and lack of traditional driver communication cues (e.g., eye contact and gestures), these vehicles can pose traffic hazards. This study aims to explore pedestrians' information needs regarding external human-machine interfaces (eHMI) when encountering automated buses in ambiguous situations, such as un-signalized intersections. In a virtual reality (VR) experiment, 32 participants navigated a residential area under four eHMI conditions: (1) no eHMI; (2) eHMI indicating the bus is autonomous; (3) eHMI displaying the bus's intent; and (4) eHMI displaying right of way. Participants' crossing behaviors and responses to questionnaires were collected to assess the impact of eHMI on pedestrian-bus interactions. Results showed that eHMI indicating either the bus's intent or priority reduced pedestrians' hesitation and increased their perceived safety. However, eHMI displaying the bus's intent also fostered pedestrian dependence on the interface, potentially leading to risky behaviors. These findings highlight the need for clear, effective eHMI designs and call for further research to fully assess their impact on pedestrian safety.

关键词： Pedestrians Navigation Human-machine systems Virtual reality Hazards System analysis and design Consumer electronics Vehicles

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Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method 9th

Support Vector Machine for Satellite Images Classification U...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Mansor, Nur Suhaili Awang, Hapini Malami, Sarkin Tudu Shehu Zolkafli, Amirulikhsan Taiye, Mohammed Ahmed Maulana, Hanhan Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Sokoto852101 Nigeria Department of Cultural Sciences Faculty of Arts and Humanities Linnaeus University Vaxjo351 95 Sweden Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Bandung40134 Indonesia

ISBN: (纸本)9789819995882

Machine learning, particularly Support Vector Machines (SVM), has gained popularity in geospatial data processing and image classification. Geospatial data from various sources may contain errors, impacting image classification accuracy. Traditional pixel-based and object-based methods struggle to classify complex land cover classes accurately. Previous studies explored machine learning algorithms like Random Forests, K-Nearest Neighbors, and Neural Networks. Still, they faced challenges capturing intricate relationships within images and required substantial labeled training data, leading to computational expenses. SVM with polynomial kernels was attempted in some studies, but it suffered from potential overfitting and inefficiency for large datasets. To overcome these issues, this study employed SVM with RBF and Linear kernels to classify multispectral satellite images from the SPOT-6 Satellite Imagery dataset in Sungai Kelang, Malaysia. Previous research evaluated each kernel’s performance accuracy compared using a test dataset, utilizing open-source tools like Jupyter Notebooks and Python libraries to explore SVM’s potential as a high-performance satellite image classification technique. The findings revealed that SVM with RBF kernel outperformed SVM with polynomial or linear kernels in classifying satellite images. The RBF kernel’s robustness allowed SVM to model intricate decision boundaries and capture complex patterns in the image data, making it suitable for tasks with non-linearly separable data. The study introduces a new methodology and theoretical contribution to image classification-related literature, shedding light on the efficacy of SVM-RBF for geospatial data processing. It provides an alternative to traditional approaches for complex image classification tasks. Moreover, the research assists in selecting the optimal algorithm for remote sensing and satellite imagery applications. © 2024, The Author(s), under exclusive license to Springer Nature Si

关键词： Image classification

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Semivortex solitons and their excited states in spin-orbit-coupled binary bosonic condensates

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Physical Review E 2024年第6期109卷 064201-064201页

作者： Haiming Deng Jinqing Li Zhaopin Chen Yaohui Liu Dong Liu Chunzhi Jiang Chao Kong Boris A. Malomed School of Physics and Electronic-Electrical Engineering Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Physics Department and Solid-State Institute Department of Physical Electronics School of Electrical Engineering Faculty of Engineering Instituto de Alta Investigación

It is known that two-dimensional two-component fundamental solitons of the semivortex (SV) type, with vorticities (s+,s−)=(0,1) in their components, are stable ground states (GSs) in the spin-orbit-coupled (SOC) binary Bose-Einstein condensate with the contact self-attraction acting in both components, in spite of the possibility of the critical collapse in the system. However, excited states (ESs) of the SV solitons, with the vorticity set (s+,s−)=(S+,S++1) and S+=1,2,3,..., are unstable in the same system. We construct ESs of SV solitons in the SOC system with opposite signs of the self-interaction in the two components. The main finding is stability of the ES-SV solitons, with the extra vorticity (at least) up to S+=6. The threshold value of the norm for the onset of the critical collapse, Nthr, in these excited states is higher than the commonly known critical value, Nc≈5.85, associated with the single-component Townes solitons, Nthr increasing with the growth of S+. A velocity interval for stable motion of the GS-SV solitons is found too. The results suggest a solution for the challenging problem of the creation of stable vortex solitons with high topological charges.

关键词： Solitons

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Automated Counting System Based on Watershed Algorithm for Tilapia Fish Egg

Automated Counting System Based on Watershed Algorithm for T...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Mohamad Ghozali Hassan Nor Hazlyna Harun Amiera Syazlin Md Azhar Siti Naquiah Md Pauzi Noor Ashri Ja'afar Nur Suhaili Mansor Disaster Management Institute (DMI) School of Technology Management and Logistics Universiti Utara Malaysia Malaysia Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia Institute for Advanced and Smart Digital Opportunities School of Computing Sintok Malaysia

Aquaculture is a significant component of Malaysia's fishery sector and makes a substantial contribution to the country's economy. It is the source of income and employment for the nation. Tilapia fish is one of the main species cultured in the aquaculture industry, as eggs produced by this fish are considered very valuable both in terms of quantity and quality. A standard egg selection procedure to calculate the fish egg properly has not yet been implemented. The process of fish egg selection plays a vital role in determining the accurate count of fish eggs. This study focuses on developing an automated counting system for tilapia fish eggs using an image processing technique concentrating on the watershed algorithm. This algorithm enables the image of the fish eggs to be clustered in shaded colors, thus enabling the total number of fish eggs to be automatically counted. The results show that the automated counting system for tilapia fish eggs works successfully as the accuracy obtained was above 90%. It is believed that an automated counting system can help fish breeders to perform fish egg counting automatically and more effectively and efficiently.

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Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for Distributed Edge Network 5th

Towards Efficient and Privacy-Preserving Hierarchical Federa...

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： An, Ningyu Liang, Xiao Zhou, Fei Wang, Xiaohui Li, Zihan Feng, Jia Guan, Zhitao State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd Beijing100209 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China State Grid Zhejiang Electric Power Company.Ltd Hangzhou310007 China

ISBN: (纸本)9789819981007

Federated learning is a promising paradigm that utilizes widely distributed devices to jointly train a machine learning model while maintaining privacy. However, when oriented to distributed resource-constrained edge devices, existing federated learning schemes still suffer from heterogeneity challenge. Hierarchical federated learning divides devices into clusters based on their resources to enhance training efficiency. In addition, there are also security concerns such as the risk of privacy leakage during the interactions intra and cross the layers. In this paper, we propose PHFL, a Privacy-Preserving Hierarchical Federated Learning Scheme, which includes a personalized task assignment protocol and a heterogeneous-friendly aggregation scheme, so as to achieve an efficient and low-cost training for resource-constrained edge devices. To address the heterogeneity challenge, we combine synchronous and asynchronous aggregation, and design different aggregation weights for device differences. On top of that, a privacy-preserving scheme based on threshold homomorphic encryption is designed to accommodate the privacy requirements of the proposed hierarchical federated learning scenario. The security analysis and performance evaluation demonstrate that this approach improves model generalizability and learning efficiency with acceptable overhead, while also providing privacy protection. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Cyclic Lattices, Ideal Lattices and Bounds for the Smoothing Parameter

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Journal of Information Security 2022年第4期13卷 272-293页

作者： Zhiyong Zheng Fengxia Liu Yunfan Lu Kun Tian Engineering Research Center of Ministry of Education for Financial Computing and Digital Engineering Renmin University of China Beijing China Artificial Intelligence Research Institute Beihang University of China Beijing China

In this article, we introduce the discrete subgroup in ℝn as preliminaries first. Then we provide some theories of cyclic lattices and ideal lattices. By regarding the cyclic lattices and ideal lattices as ... 详细信息

In this article, we introduce the discrete subgroup in ℝⁿ as preliminaries first. Then we provide some theories of cyclic lattices and ideal lattices. By regarding the cyclic lattices and ideal lattices as the correspondences of finitely generated R-modules, we prove our main theorem, i.e. the correspondence between cyclic lattices in ℝⁿ and finitely generated R-modules is one-to-one. Finally, we give an explicit and countable upper bound for the smoothing parameter of cyclic lattices.

关键词： Cyclic Lattice Ideal Lattice Finitely Generated R-Module Smoothing Parameter

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