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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Uganya, G. Rajalakshmi, D. Vangapandu, Ramarao Dinesh, M. Giri, Jayant Odeh, Mahmoud Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vignan's Institute of Engineering for Women Department of Computer Science and Engineering Vishakhapatnam India Saveetha University Department of Vlsi Microelectronics Saveetha School of Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350377972

The 'Internet of things' is the developing technology to transfer the world into smart innovation. 'IoT' security is the most problematic issue in recent applications due to its properties including 'centralized architecture', 'limited capacity devices', and 'ambiguity'. So, blockchain with 'IoT' applications provides secure data communication compared to traditional 'IoT' applications. Blockchain is a recent growing technology that has features including 'decentralized structure', 'transparency', 'immutable records', 'increased capacity', and 'quick backtracks'. The 'cryptography hash algorithm' is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-DE) to protect the private key message. Then, this proposed algorithm has been analyzed with MD5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm. © 2024 IEEE.

关键词： Encryption algorithms

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A High Temperature Operando Study of Epitaxial Graphene Growth on Cubic Silicon Carbide Using Neutron Reflectometry

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Neutron News 2024年第2期35卷 9-11页

作者： Pradeepkumar, A. Cortie, D. Smyth, E. Le Brun, A.P. Iacopi, F. School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia ARC Centre of Excellence for Transformative Meta-Optical Systems University of Technology Sydney 2007 NSW Australia Australian Centre for Neutron Scattering Australian Nuclear Science and Technology Organization 2234 NSW Australia ARC Centre of Excellence in Future Low-Energy Electronics Technologies Melbourne 3800 VIC Australia

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Model Inversion Attacks Through Target-Specific Conditional Diffusion Models

arXiv

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arXiv 2024年

作者： Li, Ouxiang Hao, Yanbin Wang, Zhicai Zhu, Bin Wang, Shuo Zhang, Zaixi Feng, Fuli School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China School of Computing and Information Systems Singapore Management University Singapore School of Information Science and Technology University of Science and Technology of China Hefei China Department of Computer Science and Engineering University of Science and Technology of China Hefei China

Model inversion attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. To alleviate these issues, leveraging on diffusion models' remarkable synthesis capabilities, we propose Diffusion-based Model Inversion (Diff-MI) attacks. Specifically, we introduce a novel target-specific conditional diffusion model (CDM) to purposely approximate target classifier's private distribution and achieve superior accuracy-fidelity balance. Our method involves a two-step learning paradigm. Step-1 incorporates the target classifier into the entire CDM learning under a pretrain-thenfinetune fashion, with creating pseudo-labels as model conditions in pretraining and adjusting specified layers with image predictions in fine-tuning. Step-2 presents an iterative image reconstruction method, further enhancing the attack performance through a combination of diffusion priors and target knowledge. Additionally, we propose an improved max-margin loss that replaces the hard max with top-k maxes, fully leveraging feature information and soft labels from the target classifier. Extensive experiments demonstrate that Diff-MI significantly improves generative fidelity with an average decrease of 20% in FID while maintaining competitive attack accuracy compared to state-of-the-art methods across various datasets and models. Our code is available at: https://***/Ouxiang-Li/Diff-MI. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Integrating Attribute information into Relational data: A Latent Structure Approach

Integrating Attribute Information into Relational Data: A La...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Kenta Mikawa Manabu Kobayashi Tomoyuki Sasaki Akiko Manada Department of Information Systems Tokyo City University Kanagawa Japan Center for Data Science Waseda University Tokyo Japan Department of Computer Science Tokyo City University Tokyo Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology Niigata Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Various analytical methods for diverse data have been proposed with the evolution of information technology. This study focuses on relational data, comprising information on relationships between objects. Although several studies have focused on the relationships between objects, only a few have utilized attribute information. The conventional study proposed analyzing relational data with attribute information using a latent structure model. However, the model assumes that both attribute information and relational data are generated by a single latent variable. Furthermore the model does not effectively handle missing values in relational data. Therefore, in this study, we modify the conventional latent structure analysis model for relational data with attribute information, which assumes that latent variables have various types of structures, and derived a method for estimating its parameters. In addition, we apply the proposed model to cases when the relational data contain missing values. The effectiveness of our proposed method is demonstrated through simulation experiments on synthetic dataset.

关键词： Analytical models data models information technology Synthetic data

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Image-based Cutting Tool Health Diagnosis in Multi-machine Settings

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Procedia Computer science 2025年 253卷 1412-1421页

作者： Debasish Mishra Krishna R. Pattipati George M. Bollas Data Science and Information Systems Krea University 5655 Central Expressway Sri City 517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs 06269 USA Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs 06269 USA

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices–such as Bhattacharyya distance, Fourier transform, and Cosine similarity–to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators.

关键词： Tool condition monitoring Precision machining Similarity Machine Learning Unsupervised Clustering

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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Cyber security-based Multikey Management System in Cloud Environment 8

Cyber security-based Multikey Management System in Cloud Env...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Jeyaselvi, M. Jayakumar, C. Sathya, M. Ibrahim, S Jafar Ali Kalyan Chakravarthy, N.S. SRM Institute of Science and Technology Kattankulathur603203 India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Sriperumbudur India AMC Engineering College Dept of Information Science and Engineering Bengaluru Karnataka India Vellore Institute of Technology Dept of Internet of Things TN Vellore632014 India QIS College of Engineering and Technology Director-Data Science and Business Systems AP Ongole523 272 India

ISBN: (纸本)9781665491068

The revolutionary expansion in processing and storing mechanisms through the internet has given rise to affordable and powerful computing features. These qualities have been enhanced as a result of this growth. Computing in the cloud is an emerging technology that provides users with the ability to access data storage facilities as well as application access facilities in an online setting. This approach presents an almost infinite number of chances and difficulties. In light of this, ensuring the safety of one's data and preventing the accumulation of duplicate or otherwise identical information in the cloud are both highly critical concerns that must be handled. As a result, a method known as deduplication was created to cut down on the amount of data in the identical storage system. In this investigation, a unique approach for removing repetitive or duplicate data from cloud servers, which will also help reduce the amount of internet access and storage space needed, has been presented. The results of the experiments show that the suggested system not only offers a higher level of protection for the data that is stored in stored in the cloud but also addresses the primary issues of rent systems. © 2022 IEEE.

关键词： Cloud storage

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Numerical Simulations of Filtered Reciprocal-Kind Zhang Neural Network for Solving Time-Varying QP Problems

Numerical Simulations of Filtered Reciprocal-Kind Zhang Neur...

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Intelligent Robotics and Automatic Control (IRAC), International Conference on

作者： Pengfei Guo Zhiguo Tan Yunong Zhang School of Mathematics and Data Science Zhongkai University of Agriculture and Engineering Guangzhou China School of Information Engineering Guangzhou Panyu Polytechnic Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

In our previous work [1], we introduced the filtered reciprocal-kind Zhang neural network (FRKZNN) model for handling time-varying quadratic programming (QP) problems. In this paper, we conduct two sets of numerical simulations to evaluate the model’s generalization, analyze the influence of its parameters, and perform an ablation study. The results from these simulations demonstrate the effectiveness and superiority of the proposed FRKZNN model.

关键词： Neurodynamics Analytical models Sensitivity Neural networks Numerical simulation Numerical models Quadratic programming Robots Convergence

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Average Performance Analysis of Multi-Class Classification Based on Error-Correcting Output Codes

Average Performance Analysis of Multi-Class Classification B...

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IEEE International Conference on systems, Man and Cybernetics

作者： Manabu Kobayashi Gendo Kumoi Hideki Yagi Shigeichi Hirasawa Center for Data Science Waseda University Tokyo Japan Department of Information and Management Systems Engineering Nagaoka University of Technology Niigata Japan Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for $M$ class classification if the number of binary classifiers is $O(\log M)$ , then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and $\log M$ and the difference between the maximum posterior probability and the second highest posterior probability for the categories.

关键词： Codes Error probability Posterior probability Estimation Machine learning Random variables Performance analysis Cybernetics

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Integrating I4.0 Knowledge Graphs with Large Language Models Beyond SPARQL Endpoints 1

Integrating I4.0 Knowledge Graphs with Large Language Models...

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Joint of the 1st Software Lifecycle Management for Knowledge Graphs Workshop and the 3rd International Workshop on Semantic Industrial information Modelling, SOFLIM2KG-SEMIIM 2024

作者： Wahid, Abdul Yahya, Muhammad Zaman, Farooq Zhou, Baifan Breslin, John G. Intizar, Muhammad Ali Kharlamov, Evgeny Data Science Institute University of Galway IDA Business Park Lower Dangan GalwayH91 AEX4 Ireland Valeo Vision Systems Tuam Galways Ireland Information Technology University Lahore Pakistan Department of Computer Science Oslo Metropolitan University Oslo Norway School of Electronic Engineering Dublin City University Dublin Ireland Bosch Center for AI Renningen Germany

Industry 4.0 (I4.0) knowledge graphs are a common way to represent industrial information models. Conventional SPARQL querying systems require the users to be familiar with the data schema and SPARQL syntax. However, this is often very difficult for many users in industrial production, who have mostly an engineering background, instead of a semantic web. Recent developments in large language models (LLMs) make it possible for non-semantic experts to use natural language to query knowledge graphs (KG). In this work, we present a framework and preliminary results of integrating Industry 4.0 KGs with LLMs to improve how data is represented, reasoned, and processed in manufacturing contexts, facilitating user interaction with KGs and contributing to operational efficiency. Our technique enhances Language Models (LLMs) by utilising the semantic complexity and interdependence of Knowledge Graphs (KGs). This allows us to incorporate domain-specific knowledge. We used the FAISS library and LLaMA2 to optimise the storage and retrieval of vectors, which improved the system’s performance and scalability. This integration allows for advanced fault detection, proactive maintenance, and process optimisation, resulting in decreased periods of inactivity and improved productivity. We introduce the framework’s architecture, implementation strategy, and possible advantages while also discussing the difficulties associated with data integration and scalability. The results of our study show that the integration of KG-LLM surpasses traditional approaches in terms of operational efficiency, as evidenced by enhanced fault detection, proactive maintenance, and process optimisation, thereby opening up possibilities for the advancement of more intelligent and resilient production systems. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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