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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Robust Authentication: Leveraging Hardware Fingerprints and AI to Enhance Security Against Spoofing

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IEEE Access 2025年 13卷 86912-86926页

作者： Nimmy, K. Jain, Kurunandan Sachin, S.M. Abekaesh, P.A. Venkitasubramaniam, Parv Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Center for Cybersecurity Systems and Networks Amritapuri India Lehigh University Department of Electrical & Computer Engineering United States

Fingerprint authentication blends biometric precision with user convenience, leveraging unique ridge patterns for fast, reliable, and tamper-resistant identity verification. However, it remains vulnerable to spoofing attacks, where adversaries create synthetic fingerprints using materials like gelatin. To mitigate this, multi-factor authentication (MFA) enhances security by verifying both the fingerprint and the user’s device, restricting access to authorized hardware and complicating attacks. Although Photo Response Non-Uniformity (PRNU) has been explored for device authentication, its reliability diminishes under varying lighting conditions. To address this, we propose an AI-driven approach to generate robust hardware fingerprints from fingerprint scanners, enabling high-accuracy device identification. Our method, trained on 2,000 fingerprint images from 10 scanners, achieves an impressive 98% accuracy in identifying the source scanner. By integrating fingerprint biometrics, hardware fingerprints, and passwords, our novel MFA protocol significantly enhances security against spoofing and forgery attacks. Through security analysis, we demonstrate its effectiveness in preventing unauthorized access. Finally, we validate our authentication framework using an AI-powered binary classifier model, showcasing its practicality for real-world security applications. © 2013 IEEE.

关键词： Authentication Protocol

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Seer: Accelerating Block chain Transaction Execution by Fine-Grained Branch Prediction 51st

Seer: Accelerating Block chain Transaction Execution by Fine...

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51st International Conference on Very Large Data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

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science China Technological sciences 2025年第5期68卷 294-296页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Foshan Institute of Intelligent Equipment Technology Foshan Huashu Robot Co. Ltd. Center on Frontiers of Computing Studies (CFCS) School of Computer SciencePeking University Beijing Academy of Artificial Intelligence Department of Informatics Universit?t Hamburg

Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.

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Hardware-Accelerated Detection of Unauthorized Mining Activities Using YOLOv11 and FPGA

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International Journal of Advanced computer science and Applications 2025年第4期16卷 967-979页

作者： Ghodhbani, Refka Saidani, Taoufik Kachoukh, Amani Elsayed, Mahmoud Salaheldin Said, Yahia Ahmed, Rabie Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Information Systems-Faculty of Computing and Information Technology Northern Border University Saudi Arabia Department of Computer Sciences-Faculty of Computing and Information Technology Northern Border University Saudi Arabia DCenter for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Computer Science-Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Mathematics and Computer Science Department-Faculty of Science Beni-Suef University Beni-Suef Egypt

Illegal mining activities present significant environmental, economic, and safety challenges, particularly in remote and under-monitored regions. Traditional surveillance methods are often inefficient, labor-intensive, and unable to provide real-time insights. To address this issue, this study proposes a computer vision-based solution leveraging the state-of-the-art YOLOv11 Nano and Small models, fine-tuned for the detection of illegal mining activities. A specific dataset comprising aerial and ground-level images of mining sites was curated and annotated to train the models for identifying unauthorized excavation, equipment usage, and human presence in restricted zones. The proposed system integrates the hardware-software design of YOLOv11 on the PynqZ1 FPGA, offering a highperformance, low-latency, and energy-efficient solution suitable for real-time monitoring in resource-constrained environments. This hardware-accelerated approach combines FPGA's parallel processing capabilities with the lightweight deep learning models, enabling efficient deployment for automated illegal mining detection. By providing a scalable, real-time monitoring tool, this work contributes to the development of automated enforcement tools for the mining industry, ensuring better control and surveillance of mining activities. To validate the efficiency of deep learning deployment on edge devices, YOLOv11n was implemented on an FPGA, utilizing 70% of available LUTs, 50% of FFs, and 80% of DSPs, with 8.3 Mbits of on-chip memory. The design achieved 100.33 GOP/s throughput, 18 FPS at 55 ms latency, consuming 4.8 W, and delivering an energy efficiency of 20.90 GOP/s/W. © (2025), (science and Information Organization). All rights reserved.

关键词： Mining industry

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Making Large Language Models Better Reasoners with Orchestrated Streaming Experiences

arXiv

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arXiv 2025年

作者： Liu, Xiangyang He, Junliang Qiu, Xipeng School of Computer Science Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

Large language models (LLMs) can perform complex reasoning by generating intermediate thoughts under zero-shot or few-shot settings. However, zero-shot prompting always encounters low performance, and the superior performance of few-shot prompting hinges on the manual-crafted demonstrations. In this paper, we present RoSE (Reasoning with Orchestrated Streaming Experiences), a general framework for solving reasoning tasks that can self-improve without complex external efforts. To enable RoSE, we describe an architecture that extends an LLM to store all answered questions and their thoughts in a streaming experience pool then orchestrates helpful questions from the pool to assist in answering new questions. To set up a question-aware orchestration mechanism, RoSE first calculates the similarity of each question in the pool with a new test question. Since the solution to each answered question is not always correct, RoSE will sort the questions according to their similarity with the new question, and then uniformly divide them into multiple buckets. It finally extracts one question from each bucket to make these extracted questions more diverse. To make these extracted questions help RoSE answer new questions as much as possible, we introduce two other attributes of uncertainty and complexity for each question. RoSE will preferentially select the questions with low uncertainty and high complexity from each bucket. We evaluate the versatility of RoSE in various reasoning tasks, LLMs, and CoT methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Complex networks

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FreqRISE: Explaining time series using frequency masking 6

FreqRISE: Explaining time series using frequency masking

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6th Northern Lights Deep Learning Conference, NLDL 2025

作者： Brüsch, Thea Wickstrøm, Kristoffer Knutsen Schmidt, Mikkel N. Alstrøm, Tommy Sonne Jenssen, Robert Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Physics and Technology UiT The Arctic University of Norway Norway Norwegian Computing Center Oslo Norway

Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. Current methods for obtaining saliency maps assumes localized information in the raw input space. In this paper, we argue that the salient information of a number of time series is more likely to be localized in the frequency domain. We propose FreqRISE, which uses masking-based methods to produce explanations in the frequency and time-frequency domain, and outperforms strong baselines across a number of tasks. The source code is available here: https://***/theabrusch/FreqRISE. © NLDL *** rights reserved.

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Exploiting a No-Regret Opponent in Repeated Zero-Sum Games

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Journal of Shanghai Jiaotong University (science) 2025年第2期30卷 385-398页

作者： Li, Kai Huang, Wenhan Li, Chenchen Deng, Xiaotie Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Beijing Jingdong Century Trading Co. Ltd. Beijing100176 China Center on Frontiers of Computing Studies Peking University Beijing100871 China

In repeated zero-sum games, instead of constantly playing an equilibrium strategy of the stage game, learning to exploit the opponent given historical interactions could typically obtain a higher utility. However, when playing against a fully adaptive opponent, one would have difficulty identifying the opponent’s adaptive dynamics and further exploiting its potential weakness. In this paper, we study the problem of optimizing against the adaptive opponent who uses no-regret learning. No-regret learning is a classic and widely-used branch of adaptive learning algorithms. We propose a general framework for online modeling no-regret opponents and exploiting their weakness. With this framework, one could approximate the opponent’s no-regret learning dynamics and then develop a response plan to obtain a significant profit based on the inferences of the opponent’s strategies. We employ two system identification architectures, including the recurrent neural network (RNN) and the nonlinear autoregressive exogenous model, and adopt an efficient greedy response plan within the framework. Theoretically, we prove the approximation capability of our RNN architecture at approximating specific no-regret dynamics. Empirically, we demonstrate that during interactions at a low level of non-stationarity, our architectures could approximate the dynamics with a low error, and the derived policies could exploit the no-regret opponent to obtain a decent utility. © Shanghai Jiao Tong University 2023.

关键词： Dynamical systems

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A Reinforcement Learning Approach for Routing in Marine Communication Network of Fishing Vessels

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SN computer science 2025年第1期6卷 1-12页

作者： Surendran, Simi Montresor, Alberto Vinodini Ramesh, Maneesha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Information Engineering and Computer Science University of Trento Trento Italy Amrita Center for Wireless Networks & Applications (AmritaWNA) Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam India

The lack of affordable communication facilities to the shore remains a fundamental problem for fishermen engaged in deep-sea fishing. The Offshore Communication Network (OCN) is a wireless network of fishing vessels, whose goal is to provide Internet over the ocean. However, the dynamic nature of OCNs characterized by extreme weather, the difficulty of deploying additional infrastructure, wave-induced vessel movements, and high mobility causes significant challenges for traditional routing protocols. This paper proposes OCN-AR, a Q-learning-based adaptive routing strategy for ocean networks. The quality of the learning process relies on the reward function, which has been carefully designed to incorporate the most important features, including real-time forecasts of connectivity quality, path probability, link availability duration, and distance to the destination. The routing performance is evaluated through extensive simulations conducted under diverse conditions, including varying mobility scenarios, transmission rates, vessel rocking intensities, and node densities, and is compared against traditional protocols. The results demonstrate that OCN-AR significantly outperforms existing routing approaches, making it a reliable solution for maritime communication. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fishing vessel network Marine communication network Reinforcement learning Routing protocol

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