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An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

IEEE Access 2023年 11卷 1306-1323页

作者： Oladipupo, Esau Taiwo Abikoye, Oluwakemi Christiana Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Chang, Ting-Yi Lee, Cheng-Chi Do, Dinh-Thuan The Federal Polytechnic Bida Department of Computer Science Bida912211 Nigeria University of Ilorin Faculty of Information and Communication Sciences Department of Computer Science Ilorin240003 Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany National Changhua University of Education Department of Industrial Education and Technology Changhua City500 Taiwan Fu Jen Catholic University Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science New Taipei City24205 Taiwan Asia University Department of Computer Science and Information Engineering Taichung City41354 Taiwan University of Mount Union School of Engineering AllianceOH44601 United States

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage. © 2013 IEEE.

关键词： Electric power utilization

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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FLBC-DSS: Federated Learning-Enabled Blockchain-Based Data Sharing Scheme for IIoT 11

FLBC-DSS: Federated Learning-Enabled Blockchain-Based Data S...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Yao, Yan Li, Guangshun Zhang, Huihui Feng, Shanshan Yan, Biwei Cao, Jian School of Computer Science Qufu Normal University Rizhao China Rizhao-Qufu Normal University Joint Technology Transfer Center Rizhao China School of Computer Engineering Weifang University Weifang China Qilu University of Technology Shandong Academy of Sciences Jinan China School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Scienceand Technology Shandong University Qingdao China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798331531904

This paper introduces the Federated Learning-Empowered Blockchain-based Data Sharing Scheme (FLBC-DSS), a novel framework designed to enhance data security and efficiency in Industrial Internet of Things (IIoT) environments. By integrating federated learning with blockchain technology, FLBC-DSS addresses the critical challenges of data privacy and unauthorized access, promoting secure, decentralized data sharing across various industrial sectors. Utilizing zk-SNARKs, the scheme provides a mechanism for anonymous data verification without compromising data integrity. Performance evaluations conducted using Hyperledger Fabric and SecureBoost demonstrate significant improvements in prediction accuracy and computational efficiency compared to traditional approaches. The implementation of non-cooperative game theory further illustrates the economic benefits of FLBC-DSS, highlighting its potential to increase profitability through strategic data sharing. The results affirm the effectiveness of FLBC-DSS in leveraging advanced cryptographic and machine learning techniques to foster a secure, transparent, and efficient digital ecosystem for IIoT. © 2024 IEEE.

关键词： Federated learning

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Universal Transfer Framework for Urban Spatiotemporal Knowledge Based on Radial Basis Function

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4458-4469页

作者： Chiu, Sheng-Min Liou, Yow-Shin Chen, Yi-Chung Lee, Chiang Shang, Rong-Kang Chang, Tzu-Yin Zimmermann, Roger National University of Singapore School of Computing 119077 Singapore National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan National Chung Hsing University Taichung402202 Taiwan National Science and Technology Center for Disaster Reduction New Taipei City23143 Taiwan

The accurate and rapid transfer of complex urban spatiotemporal data is crucial for urban computing tasks such as urban planning and public transportation deployment for smart-city applications. Existing works consider auxiliary data or propose end-to-end models to process complex spatiotemporal information into more complex deep features. However, the latter is incapable of decoupling spatiotemporal knowledge, which means these end-to-end models lack modularity and substitutability. A general modular framework that can automatically capture simple representations of complex spatiotemporal information is required. In this article, we thus propose a universal framework for the transfer of spatiotemporal knowledge based on a radial basis function (RBF). We termed this approach spatial-temporal RBF transfer framework (STRBF-TF). The proposed STRBF-TF generates simple RBF representations of spatiotemporal flow distribution with an RBF transfer block and also leverages a channel attention mechanism. Moreover, we propose two RBF kernel initializers suitable for the source and the target domains, respectively. The framework retains important spatiotemporal knowledge in simple representations for the reconfiguration of spatiotemporal feature distribution for fast and accurate transfer. We conducted cross-domain learning experiments on a large real-world telecom dataset. The results demonstrate the efficiency and accuracy of the proposed approach, as well as its suitability for real-world applications. © 2020 IEEE.

关键词： Radial basis function networks

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ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification

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European Physical Journal Plus 2024年第5期139卷 1-13页

作者： Hameed, Madiha Zameer, Aneela Khan, Saddam Hussain Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Department of Computer System Engineering The University of Engineering and Applied Science (UEAS) Swat Pakistan Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

Brain tumor detection in medical image processing presents a formidable challenge due to the complex behavior exhibited by these tumors. Their intricate nature arises from a variety of shapes and textures within brain tumor images, exacerbated by their diverse cellular origins. This complexity is further compounded by the presence of noisy images, which add an additional layer of difficulty. The overlapping image intensities in tumor and non-tumor areas pose a significant hurdle in extracting meaningful insights from raw data using predictive models. In response to these challenges, we introduce ARiViT, a novel framework based on residual learning. ARiViT merges vision transformers, convolutions, and adversarial learning to tackle the complexities of brain tumor detection. ARiViT’s core generator integrates RiT blocks, merging residual convolutions and transformers, with residual connections, channel compression, and strategic weight sharing for enhanced capabilities and computational efficiency. ARiViT demonstrates exceptional robustness in handling noisy images and adapts well to various modality setups. Our systematic approach involves dataset splitting into training, testing, and validation sets, maintaining distributions of 80:20 and subsequently 70:30, ensuring rigorous evaluation. Additionally, our data preprocessing strategies are tailored to handle noisy and low-quality images, as well as those with unusual tumor shapes. The GAN architecture embedded in ARiViT effectively manages the complexities inherent in such image variations, ensuring reliable tumor detection even in challenging scenarios. Through exhaustive comparative evaluations, ARiViT proves its superiority over existing CNN and transformer techniques, as evidenced by both qualitative and quantitative analyses. Our efforts culminate in remarkable achievements, including an F1-score of 98.09%, establishing a pioneering solution in the demanding field of brain tumor detection. © The Author(s), under

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Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

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computers, Materials & Continua 2023年第12期77卷 2899-2914页

作者： Vinay Kukreja Asha Abraham K.Kalaiselvi K.Deepa Thilak Shanmugasundaram Hariharan Shih-Yu Chen Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Networking and Communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and *** data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw *** fusion is the process of integrating multiple data *** filtering means examining a dataset to exclude,rearrange,or apportion data according to the *** sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional *** advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional ***,many open issues still exist as machine learning algorithms are used for data *** the literature,nine issues have been identified irrespective of any *** decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these *** helps to get the weights for each corresponding issue and rank issues based on these calculated *** most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted *** least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

关键词： Signal level fusion feature level fusion decision level fusion fuzzy hierarchical process machine learning

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Experimental Demonstration of Input-Output Indefiniteness in a Single quantum Device

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Physical Review Letters 2024年第16期132卷 160201-160201页

作者： Yu Guo Zixuan Liu Hao Tang Xiao-Min Hu Bi-Heng Liu Yun-Feng Huang Chuan-Feng Li Guang-Can Guo Giulio Chiribella CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei 230026 China Hefei National Laboratory University of Science and Technology of China Hefei 230088 China QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong HKU-Oxford Joint Laboratory for Quantum Information and Computation Department of Computer Science University of Oxford Wolfson Building Parks Road Oxford United Kingdom Perimeter Institute for Theoretical Physics 31 Caroline Street North Waterloo Ontario Canada

quantum theory allows information to flow through a single device in a coherent superposition of two opposite directions, resulting into situations where the input-output direction is indefinite. Here we introduce a theoretical method to witness input-output indefiniteness in a single quantum device, and we experimentally demonstrate it by constructing a photonic setup that exhibits input-output indefiniteness with a statistical significance exceeding 69 standard deviations. Our results provide a way to characterize input-output indefiniteness as a resource for quantum information and photonic quantum technologies and enable tabletop simulations of hypothetical scenarios exhibiting quantum indefiniteness in the direction of time.

关键词： quantum correlations, foundations & formalism quantum formalism quantum foundations

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GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification 31

GADFA: Generator-Assisted Decision-Focused Approach for Opin...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Chung-Chi Takamura, Hiroya Kobayashi, Ichiro Miyao, Yusuke Chen, Hsin-Hsi Artificial Intelligence Research Center AIST Japan Ochanomizu University Japan University of Tokyo Japan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9798891761964

The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not continuously generate text or voice their opinions. For instance, consumers pen product reviews after weighing the merits and demerits of a product, and professional analysts issue reports following significant news releases. In essence, opinion expression is typically prompted by particular reasons or signals. Despite long-standing developments in opinion mining, the appropriate timing for expressing an opinion remains largely unexplored. To address this deficit, our study introduces an innovative task - the identification of news-triggered opinion expressing timing. We ground this task in the actions of professional stock analysts and develop a novel dataset for investigation. Our Generator-Assisted Decision-Focused Approach (GADFA) is decision-focused, leveraging text generation models to steer the classification model, thus enhancing overall performance. Our experimental findings demonstrate that the text generated by our model contributes fresh insights from various angles, effectively aiding in identifying the optimal timing for opinion expression. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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A Novel Approach Deep Learning Framework for Automatic Detection of Diseases in Retinal Fundus Images

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computer Modeling in Engineering & sciences 2025年第5期143卷 1485-1517页

作者： Kachi Anvesh Bharati M.Reshm Shanmugasundaram Hariharan H.Venkateshwara Reddy Murugaperumal Krishnamoorthy Vinay Kukreja Shih-Yu Chen Department of Information Technology Vardhaman Cllege of EngineeringShamshabadHyderabad501218India Department of Computer Science and Engineering Visvesvaraya Technoogical UniversityBelagav 5908India Department of Artificial Intlligence and Machine Learning Basaveshwar Engineering CollegeBagalkote587102India Department of Artificial Intlligence and Data Science Vardhaman College of EngineeringHyderabad501218India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India Department of Eletrical and Eletronics Engineering Vardhaman Cllege ofEngineeringHyderabad52India Centre for Research Impact&Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intlligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Automated classification of retinal fundus images is essential for identifying eye diseases,though there is earlier research on applying deep learning models designed especially for detecting tessellation in retinal fundus *** study classifies 4 classes of retinal fundus images with 3 diseased fundus images and 1 normal fundus image,by creating a refined VGG16 model to categorize fundus pictures into tessellated,normal,myopia,and choroidal neovascularization *** approach utilizes a VGG16 architecture that has been altered with unique fully connected layers and regularization using dropouts,along with data augmentation techniques(rotation,flip,and rescale)on a dataset of 302 *** involves class weighting and critical callbacks(early halting,learning rate reduction,checkpointing)to maximize *** in accuracy(93.42%training,77.5%validation)and improved class-specific F1 scores are ***-CAM’s Explainable AI(XAI)highlights areas of the images that are important for each categorization,making it interpretable for better understanding of medical *** results highlight the model’s potential as a helpful diagnostic tool in ophthalmology,providing a clear and practical method for the early identification and categorization of retinal disorders,especially in cases such as tessellated fundus images.

关键词： Deep Learning choroidal neovascularization myopia tessellation deep learning overfitting

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Efficient Streaming Algorithms for Graphlet Sampling 38

Efficient Streaming Algorithms for Graphlet Sampling

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Bourreau, Yann Bressan, Marco Chan, T.-H. Hubert Kuang, Qipeng Sozio, Mauro Cispa Helmholtz Center for Information Security Saarland University Saarbrücken Germany Department of Computer Science University of Milan Milan Italy Department of Computer Science The University of Hong Kong Hong Kong Institut Polytechnique de Paris Télécom Paris Palaiseau France

Given a graph G and a positive integer k, the Graphlet Sampling problem asks to sample a connected induced k-vertex subgraph of G uniformly at random. Graphlet sampling enhances machine learning applications by transforming graph structures into feature vectors for tasks such as graph classification and subgraph identification, boosting neural network performance, and supporting clustered federated learning by capturing local structures and relationships. A recent work has shown that the problem admits an algorithm that preprocesses G in time O(nk2 log k + m), and draws one sample in expected time kO(k) log n, where n = |V (G)| and m = |E(G)|. Such an algorithm relies on the assumption that the input graph fits into main memory and it does not seem to be straightforward to adapt it to very large graphs. We consider Graphlet Sampling in the semi-streaming setting, where we have a memory of M = Ω(n log n) words, and G can be only read through sequential passes over the edge list. We develop a semi-streaming algorithm that preprocesses G in p = O(log n) passes and samples Θ(Mk-O(k)) independent uniform k-graphlets in O(k) passes. For constant k, both phases run in time O((n + m) log n). We also show that the tradeoff between memory and number of passes of our algorithms is near-optimal. Our extensive evaluation on very large graphs shows the effectiveness of our algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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