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Attention-based unsupervised prompt learning for SAM in leaf disease segmentation

Knowledge-Based Systems 2025年 320卷

作者： Tian, Luda Yuan, Yingchun En, Qing Ma, Wei Zhang, Guanghui Liang, Fangfang School of Information Science and Technology Hebei Agricultural University Baoding071001 China Hebei Key Laboratory of Agricultural Big Data Baoding071001 China Hebei Engineering Research Center for Agricultural Remote Sensing Application Baoding071001 China Lab School of Computer Science Carleton University OttawaK1S 5B6 Canada Faculty of Information Technology Beijing University of Technology Beijing100124 China

In modern agriculture, leaf disease segmentation is crucial for crop disease management and yield improvement. Since most deep learning-based segmentation models require extensive annotations, pursuing unsupervised methods becomes a practical solution. Although advanced models like the Segment Anything Model (SAM) can produce precise class-agnostic masks without annotations, their reliance on human-interactive prompts limits their utility to unsupervised disease segmentation tasks. To address this limitation, we present UPLS, a novel three-stage framework that leverages unsupervised prompt learning to enable SAM for leaf disease segmentation. This method employs unsupervised contrastive learning to progressively segment leaf and disease areas and automatically generate disease prompts for SAM. Concretely, for fine and intricate lesions, we utilize an improved high-frequency attention mechanism to extract high-frequency features from the leaf area, and construct contrastive losses between global priors and foreground/background features. Furthermore, we devise a strategy to automatically generate disease-related prompts from the segmented leaf and initial lesion regions, enabling SAM to refine disease boundaries without human intervention. Experiments on three public plant disease segmentation datasets show that UPLS outperforms existing non-fully supervised segmentation methods in accuracy and robustness. Source code is available at https://***/Tianluda/UPLS. © 2025 Elsevier B.V.

关键词： Unsupervised learning

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

arXiv

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Hashgraph-Based Model Parameter Management for Reliable and Secure Deep Learning

Hashgraph-Based Model Parameter Management for Reliable and ...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Eunsung Roh Jinheock Choi Younghoon Park Seung-Woo Seo Big Data Research Center for New Value Creation Sookmyung Women's University Seoul South Korea Department of Electric Engineering Seoul National University Seoul South Korea Division of Computer Science Sookmyung Women's University Seoul South Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Deep learning networks have rapidly developed across various fields, with several high-performing models already being applied in real-world applications. However, even with identical network architectures, variations in parameter values can lead to significantly different outputs, making the security of these parameters crucial for maintaining network performance. Despite their importance, limited research has focused on securing deep learning network parameters. In recent years, distributed ledger technologies, such as blockchain and hashgraph, have been extensively studied. Their applications extend beyond cryptocurrencies to include public services, voting, supply chains, and insurance. This paper proposes a novel approach to enhancing the security of deep learning network parameters by leveraging Hedera Hashgraph, providing a more secure foundation for their deployment and commercialization. With the security offered by hashgraph, model parameters are safely stored, ensuring the reliability and trustworthiness of deep learning systems. Additionally, when these stored parameters are used for further training, such as in transfer learning, the results are expected to be reliable. We demonstrate that our proposed system secures the parameters effectively, guaranteeing both security and reliability in deep learning.

关键词： Deep learning Training Transfer learning Supply chains Insurance Network architecture Security Reliability History Software development management

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology 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 Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks

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Ore Geology Reviews 2025年 182卷

作者： Qiu, Qinjun Jin, Xiangguo Tian, Miao Wu, Qirui Tao, Liufeng Chen, Jianguo Xie, Zhong Technology Innovation Center for Geological Data Intelligent Application Department of Natural Resources of Jiangsu Province 210012 China School of Computer and Science China University of Geosciences Hubei Wuhan430074 China National Engineering Research Center for Geographic Information System Hubei Wuhan430074 China Key Laboratory of Geological Survery and Evaluation of Ministry of Education Hubei Wuhan430074 China School of Future Technology China University of Geosciences Hubei Wuhan430074 China Wuhan University Hubei Wuhan430074 China

Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets. © 2025

关键词： Geochemical surveys

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Anisotropic exchange spin model to investigate the Curie temperature dispersion of L10-FePt magnetic nanoparticles

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Journal of Applied Physics 2025年第18期137卷

作者： Ochiai, Kohei Tsuyama, Tomoyuki Shimizu, Sumera Zhang, Lei Watanabe, Jin Kudo, Fumito Zhu, Jian-Gang Okuno, Yoshishige Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & Development Center 5-1 Yawatakaigan-dori Chiba Ichihara290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We developed an anisotropic exchange spin model that accounts for magnetic anisotropy and evaluated the Curie temperature ( T c ) dispersion due to finite-size effects in L1 0 -FePt nanoparticles. In heat-assisted magnetic recording (HAMR) media, a next-generation magnetic recording technology, high-density recording is achieved by locally heating L1 0 -FePt nanoparticles near their T c and rapidly cooling them. However, variations in T c caused by differences in particle size and shape can compromise recording stability and areal density capacity, making the control of T c dispersion critical. In this study, we constructed atomistic Landau-Lifshitz-Gilbert models to explicitly incorporate the spin exchange anisotropy of L 1 0 -FePt, based on parameters determined by first-principles calculations. Using this model, we evaluated the impact of particle size on T c dispersion. As a result, (1) the T c dispersion critical to the performance of HAMR can be reproduced, whereas it was previously underestimated by isotropic models and (2) approximately 70 % of the experimentally observed T c dispersion can be attributed to particle-size effects. This research highlights the role of exchange anisotropy in amplifying finite-size effects and underscores the importance of size control in HAMR media. © 2025 Author(s).

关键词： Magnetic nanoparticles

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Postoperative intraocular lens tilt from preoperative full crystalline lens geometry using machine learning

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Biomedical Optics Express 2025年第4期16卷 1439-1456页

作者： Martinez-Enriquez, Eduardo Velarde-Rodríguez, Gonzalo Alejandre-Alba, Nicolás Ansah, Derick Kishore, Sindhu de la Peña, Álvaro Natarajan, Ramya Vaddavalli, Pravin Zhao, Yue Okudolo, Joseph O. McBee, Dylan B. Celik, Ugur Cetin, Mujdat Dong, Jen-Li Lim, Yuli Wang, Li Koch, Douglas Donald Macrae, Scott Marcos, Susana Instituto de Optica Daza de Valdes Comunidad de Madrid Madrid Spain Ophthalmology Service Fundación Jiménez Díaz University Hospital Madrid Spain Madrid28040 Spain Flaum Eye Institute University of Rochester RochesterNY United States Center for Visual Science University of Rochester RochesterNY United States Ophthalmic Biophysics LV Prasad Eye Institute Telangana Hyderabad India Cataract and Refractive Surgery Services L V Prasad Eye Institute Telangana Hyderabad India Shantilal Shanghvi Cornea Institute L V Prasad Eye Institute Telangana Hyderabad India Goergen Institute for Data Science and AI University of Rochester RochesterNY United States Department of Ophthalmology Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Institute of Optics University of Rochester RochesterNY United States

In cataract surgery, the opacified crystalline lens is replaced by an artificial intraocular lens (IOL), requiring precise preoperative selection of parameters to optimize postoperative visual quality. Three-dimensional customized eye models, which can be constructed using quantitative data from anterior segment optical coherence tomography, provide a robust platform for virtual surgery. These models enable simulations and predictions of the optical outcomes for specific patients and selected IOLs. A critical step in building these models is estimating the IOL’s tilt and position preoperatively based on the available preoperative geometrical information (ocular parameters). In this study, we present a machine learning model that, for the first time, incorporates the full shape geometry of the crystalline lens as candidate input features to predict the postoperative IOL tilt. Furthermore, we identify the most relevant features for this prediction task. Our model demonstrates statistically significantly lower estimation errors compared to a simple linear correlation method, reducing the estimation error by approximately 6%. These findings highlight the potential of this approach to enhance the accuracy of postoperative predictions. Further work is needed to examine the potential for such postoperative predictions to improve visual outcomes in cataract patients. © 2025 Optica Publishing Group.

关键词： Intraocular lenses

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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Multimodal Lotus Effect Algorithm for engineering Optimization Problems

Engineering Reports

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engineering Reports 2025年第4期7卷

作者： Dalirinia, Elham Yaghoobi, Mahdi Tabatabaee, Hamid Chandna, Swati Jalali, Mehrdad Department of Computer Engineering Ma.C. Islamic Azad University Mashhad Iran Department of Artificial Intelligent and Data Science Intelligent Financial Innovation Research Center Ma.C. Islamic Azad University Mashhad Iran Department of Electrical Engineering Ma.C. Islamic Azad University Mashhad Iran Department of Information SRH University of Applied Sciences Heidelberg Heidelberg Germany Eggenstein-Leopoldshafen Germany

Multimodal optimization problems (MMOPs) are critical in fields like game theory and robotics, where identifying multiple optimal solutions simultaneously is essential, yet challenging due to the need for effective global exploration and precise localization of optima. This study introduces the multimodal lotus effect algorithm (M-LEA), a novel extension of our previously published lotus effect optimization algorithm (LEA), which was designed for single-modal optimization and thus struggled to maintain multiple optima in complex multimodal spaces. M-LEA addresses this limitation by incorporating a roaming technique with independently evolving subpopulations, enabling it to navigate multimodal spaces without requiring parameters such as radius or prior information about the number or distribution of optima. Its robustness is demonstrated through comparisons with five algorithms on the IEEE CEC2013-2015 challenge, where M-LEA consistently outperformed competitors. The algorithm's practical utility is further validated in two applications: identifying Nash equilibrium points in game theory and localizing resources via robotic systems. Results show that M-LEA achieves superior performance and stability, making it well-suited for scenarios demanding high efficiency and precision. These findings highlight M-LEA's potential for diverse domains, paving the way for its application in game theory, robotics, and other fields requiring advanced multimodal optimization techniques. © 2025 The Author(s). engineering Reports published by John Wiley & Sons Ltd.

关键词： evolutionary algorithms glowworm swarm algorithm lotus effect algorithm M-LEA multimodal optimization Nash equilibrium resource identification

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and Technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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