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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Zhang, Jingyu Teng, Yilong Sun, Yongtao Zhu, Shi Yang, Fangliao Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China Data Operations Center State Grid Hunan Electric Power Information and Communication Corporation Hunan Changsha China

ISBN: (纸本)9789819628292

Blockchain, as an emerging technology, has been widely studied by the researchers from academia and industry. Alliance chain, as an important form of blockchain, is often applied to smart grids, smart city and the Internet of Things to ensure the authenticity and reliability of data. Practical Byzantine Fault Tolerance (PBFT) algorithm is a key technology to ensure data consistency in the alliance chains, and it plays a critical role in improving system performance, security and scalability. However, with the increase of the number of nodes and transactions, the ability of PBFT to process transactions is challenged. In particular, PBFT’s linear transaction processing also limits its ability to process transactions in parallel. In view of the problems existing in PBFT, this paper studies the consensus mechanism optimization, and puts forward a consensus mechanism with better performance. In this paper, in order to solve the problem of weak parallel transaction processing capability of PBFT, we propose a novel Consensus Mechanism based on Dynamic Sharding (CMDS). CMDS firstly constructs a dynamic fragmentation model based on consistent hash algorithm to segment nodes, and then constructs a voting consensus mechanism based on reputation weight to achieve global state consistency. The experimental results show that CMDS can significantly improve the ability of parallel transaction processing and the performance of blockchain system. © IFIP International Federation for Information Processing 2025.

关键词： Blockchain

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NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors 39

NumbOD: A Spatial-Frequency Fusion Attack Against Object Det...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Ziqi Li, Bowen Song, Yufei Yu, Zhifei Hu, Shengshan Wan, Wei Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs’ attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Improve Accuracy in Healthcare data Analysis using Competitive Ensemble Deep Learning Model 18

Improve Accuracy in Healthcare Data Analysis using Competiti...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sravanthi, Jakkula Reddy, Chada Sampath Mahendar, A. Kumar, V. Ravi Buragadda, Swathi Ghantasala, G. S. Pradeep Gupta, Gaurav Vnr Vignana Jyoti Institute of Engineering and Technology Department of Information Technology Telangana India Sree Chaitanya Institute of Technological Sciences Department of Computer Science Engineering Telangana Karimnagar India Telangana Hyderabad India Ace Engineering College Department of Computer Science Engineering Ghatkesar India Lakireddy Balireddy College of Engineering Department of Computer Science Engineering Kakinada India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

This paper discusses the significance of Machine Learning (ML) and Deep Learning (DL) techniques for structured and unstructured healthcare data. As healthcare data is increasing tremendously, it is difficult to identify hidden patterns in huge amounts of data. DL handles a massive amount of clinical data and provides better outcomes. A novel competitive ensemble deep learning model has been proposed to improve the classification performance of structured data. However, dealing with unstructured data, the proposed work highlights a competitive DL model for Twitter sentiment analysis. In addition, this paper discusses the proposed Competitive Ensemble Deep Learning (CEPL) algorithm for text data. The proposed model is compared with a traditional model to evaluate the model's performance in the range of 0.2%-0.5%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Accuracy CEPL Deep Learning Healthcare data Machine learning Methods

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Attention based ConVnet-Recurrent Neural Network for Facial Recognition and Emotion Detection

Attention based ConVnet-Recurrent Neural Network for Facial ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, S. Vinod Sunil, G. Hussein, Ramy Riad Manju Vidhya, S. Sundaram, S. Meenakshi Department of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Excel Engineering College Department of Artificial Intelligence and Data Science Komarapalayam India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350360660

Facial Emotion Recognition (FER) plays a crucial role in analysing and understanding individual's emotions in the form of non-verbal communication. Existing Deep Learning (DL) based FER methods offered diverse contribution in analysing human emotions but several challenges like extracting accurate spatial and temporal features for accurate classification of emotions is a major concern. To address this, a DL based Facial Recognition and Emotion Detection (FRED) system is proposed by using extended Cohn-Kanade (CK+) dataset as the input. The data is pre-processed first where the Histogram Equalization (HE) is applied and then Convolutional Neural Network (CNN) is used to extract the spatial features while reducing the dimensionality. The extracted features are given to Recurrent Neural Network (RNN) to learn long term dependencies from a temporal sequence of data. The final classification is performed where the Attention Mechanism (AM) is integrated in the RNN to determine the higher weights for better outcome. The results of proposed AMConVnet-RNN showed a superior classification accuracy of 99.54%, precision of 98.63%, and f1-score of 97.39% when compared to existing FER methods like Residual Network-50 (ResNet-50) and Channel Based Attention Mechanism (CBAM) based ResNet50. © 2024 IEEE.

关键词： Convolutional neural networks

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TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment 29th

TX-Phase: Secure Phasing of Private Genomes in a Trusted ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Dokmai, Natnatee Zhu, Kaiyuan Sahinalp, S. Cenk Cho, Hyunghoon Department of Biomedical Informatics and Data Science Yale School of Medicine New HavenCT United States Department of Computer Science and Engineering UC San Diego La JollaCA United States Cancer Data Science Laboratory National Cancer Institute National Institutes of Health BethesdaMD United States Department of Computer Science Yale University New HavenCT United States

ISBN: (纸本)9783031902512

Genotype imputation servers enable researchers with limited resources to extract valuable insights from their data with enhanced accuracy and ease. However, the utility of these services is limited for those with sensitive study cohorts or those in restrictive regulatory environments due to data privacy concerns. Although privacy-preserving analysis tools have been developed to broaden access to these servers, none of the existing methods support haplotype phasing, a critical component of the imputation workflow. The complexity of phasing algorithms poses a significant challenge in maintaining practical performance under privacy constraints. Here, we introduce TX-Phase, a secure haplotype phasing method based on the framework of Trusted Execution Environments (TEEs). TX-Phase allows users’ private genomic data to be phased while ensuring data confidentiality and integrity of the computation. We introduce novel data-oblivious algorithmic techniques based on compressed reference panels and dynamic fixed-point arithmetic that comprehensively mitigate side-channel leakages in TEEs to provide robust protection of users’ genomic data throughout the analysis. Our experiments on a range of datasets from the UK Biobank and Haplotype Reference Consortium demonstrate the state-of-the-art phasing accuracy and practical runtimes of TX-Phase. Our work enables secure phasing of private genomes, opening access to large reference genomic datasets for a broader scientific community. Our open-source software is available at https://***/hcholab/txphase. The full manuscript is available at https://***/10.1101/2024.09.16.613301. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sensitive data

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Intelligent Electrocardiogram Analysis in Medicine:data,Methods,and Applications

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Chinese Medical sciences Journal 2023年第1期38卷 38-48页

作者： Yu-Xia Guan Ying An Feng-Yi Guo Wei-Bai Pan Jian-Xin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive *** can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire ***,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric *** recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis *** we systematically summarize and analyze the ECGbased automatic analysis methods and ***,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing *** we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these ***,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

关键词： Electrocardiogram database preprocessing machine learning medical big data analysis

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ASCM: Analysis of a Sequential and Collaborative Model for Recommendations

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SN computer science 2024年第6期5卷 788页

作者： Tandon, Righa Verma, Ajay Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan 173 234 India Department of Data Science and Engineering School of Computing Mohan Babu University AP Tirupati 517 102 India

The recommender system can predict future lists of items based on the user’s sentiments and interactions. As the data is ubiquitous, we have a number of options available to make a selection. This is the scenario where the recommender system plays its role by narrowing the available options and making the selection process easy and effective. In this paper, we have proposed a sequential recommender system that considers the user interactions along with the items in sequential order and maps the sequences dynamically, in which the weight-age is given to current preferences and recent choices as explored by the user of the system. We have provided a detailed comparison of the proposed sequential recommender system with the commonly used matrix factorization model. The proposed recommender system is based on sequential dependencies and responds equally well to the cold start problem. We have obtained results using Movielens dataset, which reveal that the proposed recommender system is much faster with higher mean reciprocal rank (MRR) value. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Collaborative filtering Machine learning Matrix factorization Recommender systems Sequential model Singular value decomposition

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Secured Provisioning of Multimedia Contents in Cloud Platform Using Cryptographic Approaches

Secured Provisioning of Multimedia Contents in Cloud Platfor...

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computer science and Emerging Technologies (CSET), International Conference on

作者： S.A. Sahaaya Arul Mary Akshaj Nevgi Rhea Ranjith Nair Jehan Sharukh Bhathena Angad Chaudhry S. Sharon Jessika School of Computer Science and Engineering VIT Computer Science and Engineering Computer Science with specialization in Data Science

Cloud computing is a technology that allows the utilisation of a vast network of computers that are distributed and run in parallel with one another. The management of the multimedia files presents challenges and the significant of which is the prevalence of attackers and users with malicious intent. This article presents the conceptual foundation for a four-tiered data security paradigm that can be implemented in cloud computing. The proposed framework employs a DNA-computing-based password or secret key with a length of 1024 bits for the purpose of encrypting the user private multimedia file or correspondence. The DNABMS algorithm is applied in the cryptographic generation of digital identities as well as pseudorandom number generators. The results of the testing that was done on the model have validated both its reliability and its efficacy, demonstrating that the model that was developed is both reliable and effective. Experiments have demonstrated that the developed method is superior to other well-known existing strategies in terms of both effectiveness and efficiency.

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Extended Finite State Machine based Fault Tolerance in WSN 7

Extended Finite State Machine based Fault Tolerance in WSN

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Lekha, D. Ahsan Shariff, M. Lakshmi, S. Venkata Anwar Basha, H. Department of Computer Science and Engineering RMK College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Aalim Muhammed Salegh College of Engineering Tamil Nadu Chennai India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

ISBN: (纸本)9781665464086

Wireless Sensor Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. The main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. The simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN. © 2023 IEEE.

关键词： Finite automata

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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