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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Quantum Error Mitigation for Sampling Algorithms

arXiv

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arXiv 2025年

作者： Liu, Kecheng Cai, Zhenyu Center on Frontiers of Computing Studies School of Computer Science Peking University Beijing100871 China Department of Materials University of Oxford OxfordOX1 3PH United Kingdom

Recent experimental breakthroughs have signalled the imminent arrival of the early fault-tolerant era. However, for a considerable period in the foreseeable future, relying solely on quantum error correction for full error suppression will remain extremely challenging due to its substantial hardware overhead. Additional help from quantum error mitigation (QEM) is essential for bridging this gap towards achieving quantum advantage. The application of QEM has so far been restricted to expectation value estimation, leaving its extension to sampling-based algorithms – which is expected to play a pivotal role in the early fault-tolerant era – an unresolved challenge. In this work, we present a framework for applying any QEM techniques to obtain the error-mitigated output distribution, showing that this incurs no greater cost than estimating a single observable. We also devised a way to sample from this distribution and constructed an explicit scheme for applying any QEM methods to quantum phase estimation, which can be generalised to other sampling algorithms. Numerical experiments were conducted to validate the efficacy of these methods. We believe our methods significantly broaden the scope of QEM, extending its applicability to most algorithms of practical interest and forming a crucial step towards realising quantum advantage. Copyright © 2025, The Authors. All rights reserved.

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Deep Learning Classification of Subtypes of Luminal Breast Cancer from Immunohistochemistry Slides 5th

Deep Learning Classification of Subtypes of Luminal Breast C...

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5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Shaji, Vishnu Sharma, Mridul Mathew, Merin Malavika, K. Louis, Dhanya Mary Vijaykumar, Dehannathparambil Kottarathil Nair, Lakshmi Malavika George, Archana Gutjahr, Georg Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Mathematics Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam India Department of Pharmacy Practice Amrita School of Pharmacy Amrita Institute of Medical Sciences And Research Centre Kerala Kochi India Department of Breast Oncology Amrita Institute of Medical Sciences And Research Center Amrita Vishwa Vidyapeetham Kerala Kochi India Department of Pathology Amrita Institute of Medical Sciences And Research Center Amrita Vishwa Vidyapeetham Kerala Kochi India Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam India

ISBN: (纸本)9789819747108

This paper proposes deep-learning models to assist in classifying subtypes of luminal breast cancer from immunohistochemistry slides. The classification has traditionally been done by pathologists who evaluate Ki67 indexes by manually counting positive and negative cells that are dyed in different colors in the immunohistochemistry slides. This manual process is error-prone and time-consuming. Therefore, this paper explores the possibility of automatically classifying immunohistochemistry slides. The performances of 4 pre-trained, deep convolutional networks are compared. Based on the good performance of the models, we conclude that the task of Ki67 classification for determining subtypes of luminal breast cancer is well-suited for deep learning models and that for this reason, manual counting of Ki67-positive cells are neither needed nor desirable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation

arXiv

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arXiv 2025年

作者： Zheng, Hong Chen, Yucheng Mu, Nan Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Department of Cardiology West China Hospital Sichuan University Chengdu610041 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610066 China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu610066 China

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Predictive Modelling of Protest Event Signatures: Analyzing Temporal Dynamics and Digital Activism Discourse Across Global Movements

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Journal of Social computing 2025年第1期6卷 43-50页

作者： Amila Chethana Nanayakkara B. T. G. Samantha Kumara R. M. Kapila Tharanga Rathnayaka Center for Computer Studies Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Data Science Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

This research introduced a predictive modeling framework to analyze the temporal dynamics of social media discourse during three global movements: the Mahsa Amini Protests (2022), South African Unrest (2021), and the Black Lives Matter Movement (2020). By utilizing Twitter data, the study developed a Protest Social Media Archetype to capture the evolution of tweet activity during key protest periods, identifying common patterns and notable variations in public engagement. Techniques such as LOESS regression and correlation analysis were used to model fluctuations in online activity and assess the impact of social media on public mobilization during socio-political unrest. The findings revealed distinct temporal signatures for each movement, showing similarities in initial engagement and differences in sustained activity across various contexts. These insights underscore the role of digital platforms in protest organization and global solidarity, offering a framework for future studies on digital activism and protest dynamics.

关键词： Analytical models Social computing Fluctuations Social networking (online) Blogs Refining Organizations Predictive models Real-time systems Mathematics

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(Sub)Exponential Quantum Speedup for Optimization

arXiv

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arXiv 2025年

作者： Leng, Jiaqi Wu, Kewen Wu, Xiaodi Zheng, Yufan Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of EECS University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States

We demonstrate provable (sub)exponential quantum speedups in both discrete and continuous optimization, achieved through simple and natural quantum optimization algorithms, namely the quantum adiabatic algorithm for discrete optimization and quantum Hamiltonian descent for continuous optimization. Our result builds on the Gilyén–Hastings–Vazirani (sub)exponential oracle separation for adiabatic quantum computing. With a sequence of perturbative reductions, we compile their construction into two standalone objective functions, whose oracles can be directly leveraged by the plain adiabatic evolution and Schrödinger operator evolution for discrete and continuous optimization, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum channel

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Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

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IEEE Transactions on Big Data 2025年第3期11卷 1027-1041页

作者： Wu, Zihao Zhang, Lu Cao, Chao Yu, Xiaowei Liu, Zhengliang Zhao, Lin Li, Yiwei Dai, Haixing Ma, Chong Li, Gang Liu, Wei Li, Quanzheng Shen, Dinggang Li, Xiang Zhu, Dajiang Liu, Tianming The School of Computing University of Georgia Athens30602 United States the Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States The School of Automation Northwestern Polytechnical University 710072 China The Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel Hill27599 United States The Department of Radiation Oncology Mayo Clinic Phoenix85054 United States The Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02115 United States The School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain-specific. To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology natural language inference (NLI) task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4’s reasoning ability by introducing varying levels of inference difficulty. Our results show that 1) ChatGPT and GPT-4 outperform other LLMs in the radiology NLI task;2) other specifically fine-tuned Bert-based models require significant amounts of data samples to achieve comparable performance to ChatGPT/GPT-4. These findings not only demonstrate the feasibility and promise of constructing a generic model capable of addressing various tasks across different domains, but also highlight several key factors crucial for developing a unified model, particularly in a medical context, paving the way for future artificial general intelligence (AGI) systems. We release our code and data to the research community‡. ©2015 IEEE.

关键词： Natural language processing systems

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Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation

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Scientific Reports 2025年第1期15卷 1-12页

作者： Hossain, Khondker Fariha Kamran, Sharif Amit Ong, Joshua Tavakkoli, Alireza Department of Computer Science and Engineering University of Nevada Reno Reno 89557 NV United States Department of Ophthalmology and Visual Sciences University of Michigan Kellogg Eye Center Ann Arbor MI United States

The rapid evolution of deep learning has dramatically enhanced the field of medical image segmentation, leading to the development of models with unprecedented accuracy in analyzing complex medical images. Deep learning-based segmentation holds significant promise for advancing clinical care and enhancing the precision of medical interventions. However, these models’ high computational demand and complexity present significant barriers to their application in resource-constrained clinical settings. To address this challenge, we introduce Teach-Former, a novel knowledge distillation (KD) framework that leverages a Transformer backbone to effectively condense the knowledge of multiple teacher models into a single, streamlined student model. Moreover, it excels in the contextual and spatial interpretation of relationships across multimodal images for more accurate and precise segmentation. Teach-Former stands out by harnessing multimodal inputs (CT, PET, MRI) and distilling the final predictions and the intermediate attention maps, ensuring a richer spatial and contextual knowledge transfer. Through this technique, the student model inherits the capacity for fine segmentation while operating with a significantly reduced parameter set and computational footprint. Additionally, introducing a novel training strategy optimizes knowledge transfer, ensuring the student model captures the intricate mapping of features essential for high-fidelity segmentation. The efficacy of Teach-Former has been effectively tested on two extensive multimodal datasets, HECKTOR21 and PI-CAI22, encompassing various image types. The results demonstrate that our KD strategy reduces the model complexity and surpasses existing state-of-the-art methods to achieve superior performance. The findings of this study indicate that the proposed methodology could facilitate efficient segmentation of complex multimodal medical images, supporting clinicians in achieving more precise diagnoses and comprehensiv

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CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition

arXiv

引用

arXiv 2025年

作者： Alyami, Sarah Luqman, Hamzah Information and Computer Science Department King Fahd University of Petroleum and Minerals Saudi Arabia Computing Department Imam Abdulrahman Bin Faisal University Saudi Arabia SDAIA–KFUPM Joint Research Center for Artificial Intelligence Saudi Arabia

Continuous sign language recognition (CSLR) focuses on interpreting and transcribing sequences of sign language gestures in videos. In this work, we propose CLIP sign language adaptation (CLIP-SLA), a novel CSLR framework that leverages the powerful pre-trained visual encoder from the CLIP model to sign language tasks through parameter-efficient fine-tuning (PEFT). We introduce two variants, SLA-Adapter and SLA-LoRA, which integrate PEFT modules into the CLIP visual encoder, enabling fine-tuning with minimal trainable parameters. The effectiveness of the proposed frameworks is validated on four datasets: Phoenix2014, Phoenix2014-T, CSL-Daily, and Isharah-500, where both CLIP-SLA variants outperformed several SOTA models with fewer trainable parameters. Extensive ablation studies emphasize the effectiveness and flexibility of the proposed methods with different vision-language models for CSLR. These findings showcase the potential of adapting large-scale pre-trained models for scalable and efficient CSLR, which pave the way for future advancements in sign language understanding. Code is available at https://***/snalyami/CLIP-SLA. © 2025, CC BY.

关键词： visual languages

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MediGuard: A Survey on Security Attacks in Blockchain-IoT Ecosystems for e-Healthcare Applications

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computers, Materials and Continua 2025年第3期83卷 3975-4029页

作者： Sutradhar, Shrabani Bose, Rajesh Majumder, Sudipta Khan, Arfat Ahmad Roy, Sandip Ullah, Fasee Prashar, Deepak Institute of Engineering and Technology Dibrugarh University Assam Dibrugarh 786004 India Departments of Computational Sciences Brainware University West Bengal Kolkata 700125 India Department of Computer Science & Engineering JIS University West Bengal Kolkata 700109 India Department of Computer Science College of Computing Khon Kaen University Khon Kaen 40002 Thailand Department of Computer and Information Sciences Universiti Teknologi PETRONAS Perak Darul Ridzuan Seri Iskandar 32610 Malaysia Department of Computer Science and Engineering (CSE) Lovely Professional University Punjab Phagwara 144411 India Jadara University Research Center Jadara University Irbid 21110 Jordan

Cloud-based setups are intertwined with the Internet of Things and advanced, and technologies such as blockchain revolutionize conventional healthcare infrastructure. This digitization has major advantages, mainly enhancing the security barriers of the green tree infrastructure. In this study, we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems, security vulnerabilities, cyberattacks, and system limitations. In addition, we considered several solutions proposed by thousands of researchers worldwide. Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management, transmission, and processing. Here, we describe 17 security threats that violate the privacy and data integrity of a system, over 21 cyber-attacks on security and QoS, and some system implementation problems such as node compromise, scalability, efficiency, regulatory issues, computation speed, and power consumption. We propose a multi-layered architecture for the future healthcare infrastructure. Second, we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these contingencies. Our thorough theoretical examination of several performance criteria—including confidentiality, access control, interoperability problems, and energy efficiency—as well as mathematical verifications establishes the superiority of security, privacy maintenance, reliability, and efficiency over conventional systems. We conducted in-depth comparative studies on different interoperability parameters in the blockchain models. Our research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising from laws and ethics. Determining the theoretical aspects, their scope, and future expectations encourages us to design reliable, secure, and privacy-prese

关键词： Blockchain cloud infrastructure cyber-attacks internet of medical things privacy issues

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