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16th International Conference on Knowledge Management and Information Systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Kocak, Seyma Pawlowski, Jan University of Jyväskylä Faculty of Information Technology Jyväskylä Finland University of Applied Sciences Ruhr West Institute of Computer Science Bottrop Germany

ISBN: (纸本)9789897587160

The recent development of digitization has significantly influenced various sectors of the economy, and the Industrial Craft Sector is no exception. The transition to digital technologies and processes is inevitable and holds the potential for increasing efficiency and creating competitive advantages. This research used the Design science methodology to develop a Digital Mindset Competence Model. This model comprises eight dimensions specifically tailored to the requirements and challenges of the Industrial Craft Sector. These dimensions aim to promote and strengthen the digital mindset among professionals in the Industrial Craft Sector. To ensure the validity and relevance of this model, experts from the Industrial Craft Sector were involved in a qualitative methodology. The combination of scientific methodology and practical experience ensures a comprehensive perspective and guarantees the applicability of the developed model. The results of this research underscore the importance of digital transformation in the Industrial Craft Sector and the necessity of a digital mindset. The developed Digital Mindset Competence Model provides a targeted approach to promoting digital competencies in the Industrial Craft Sector and guides future developments in this area. It becomes evident that an appropriate digital mindset is essential to optimally leverage the potentials of digitization in the Industrial Craft Sector and successfully navigate continuous change. This scholarly contribution contributes to raising awareness of the significance of a digital mindset in the Industrial Craft Sector. It forms a basis for further investigations and practical applications within digital transformation. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Design science Research Digital Competence Digital Mindset Industrial Craft Sector

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An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility 2

An Empirical Investigation on Employing Machine Learning for...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Khatri, Anshu Mathi, Senthilkumar Ashiq Sheriff, A. Deepthika, R. Naveen Raaghavendran, G. Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Stable Prediction on Graphs with Agnostic Distribution Shifts

Stable Prediction on Graphs with Agnostic Distribution Shift...

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Workshop on Causal Discovery, Prediction and Decision, CDPD 2023 held in conjunction with KDD 2023

作者： Zhang, Shengyu Tong, Yunze Kuang, Kun Feng, Fuli Qiu, Jiezhong Yu, Jin Zhao, Zhou Yang, Hongxia Zhang, Zhongfei Wu, Fei College of Computer Science and Technology Zhejiang University Hangzhou310027 China University of Science and Technology of China Hefei230026 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Alibaba Group Hangzhou310052 China Alibaba Group Beijing100084 China Department of Computer Science Watson School of Engineering and Applied Sciences Binghamton University New York13850 United States College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Most graph neural networks (GNNs) are proposed and evaluated under independent and identically distributed (IID) training and testing data. In real-world applications, however, agnostic distribution shifts from training to testing naturally exist, leading to unstable prediction of traditional GNNs. To bridge the gap, we pursue stable prediction on graphs, i.e., to achieve high average performance and low performance variance (stability) across non-IID testing graphs. The key to stable prediction lies in capturing stable properties that are resilient to distribution shifts. In this light, we aim to identify neighbor nodes (properties) in neighborhood aggregation that are consistently important for prediction under heterogeneous distribution shifts. To achieve this target, we propose a model-agnostic stable learning framework for GNNs. The framework performs biased selection on the observed training graph, resulting in multiple non-IID graph subsets. We train one weight predictor per subset to measure the importance of properties under a particular distribution shift, and multiple predictors could tell the properties that are consistently important. An important property should contribute to high average performance and also stability (low performance variance) across non-IID subsets. In this regard, in training importance predictors, we introduce a globally stable regularizer to reduce the variance of training losses across non-IID graph datasets. Based on the importance weights of properties across non-IID subsets, a locally stable regularizer down-weights unstable properties in prediction. We conduct extensive experiments on several graph benchmarks and a noisy industrial recommendation dataset where distribution shifts exist. The results demonstrate that our method outperforms various state-of-the-art GNNs for stable prediction on graphs with agnostic distribution shifts. © 2022 Author One and Author Two.

关键词： Forecasting

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Software Defect Predictor and Classifier Tool Using Machine Learning Techniques

Software Defect Predictor and Classifier Tool Using Machine ...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Shankar, Sahana P. Pushp, Divyansh Makadia, Nemi Dubey, Raghav Nayak, Anusha Shashikant Chaudhari, Shilpa Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Karnataka Bangalore India M S Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

The Software testing is critical for ensuring the quality of software, but it can consume more than half of a software project's total expenses. This research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. To improve the efficiency of software testing, this report presents research on software defect prediction using a hybrid machine learning algorithm that combines supervised and unsupervised learning techniques. The study used publicly available datasets to train and evaluate various machine learning models, including an ensemble learning model. A comparative analysis was conducted among KNN, Random Forest, Adaboost, Multilayer Perceptron, CNN, and Gaussian Naïve Bayes on different datasets. Feature selection was performed using various methods, including Recurrent Feature Elimination (RFE), SelectKBest, and Correlation, implemented by a tool created to select any feature selection algorithm and compare the results for various combinations with the above mentioned classifiers. The results achieved by the model are showed in detail for each of the algorithms on the selected datasets. The main objectives of the research are to create a tool to apply various machine learning techniques to the datasets and determine the most effective ones, as well as to explore different feature selection methods for improving the accuracy of defect prediction. © 2023 IEEE.

关键词： Software testing

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Advancing Industrial Cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks 5

Advancing Industrial Cybersecurity: Machine Learning-Based D...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kumar, S Gouri Kiran Prasad, Raavi Satya College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Dhanekula Institute of Engineering & Technology Department of Computer Science & Engineering A.P. Vijayawada India

ISBN: (纸本)9798331518097

A threatening entry or exploitation of vulnerabilities within an industrial network or system connected to IoT devices is an IIoT (Industrial Internet of Things) attack. Due to manufacturing delays, broken equipment, lost confidential data, and fines from the government, cyberattacks on IIoT systems can cause significant financial losses. Rapid reaction and recovery are made possible by efficient detection systems, which can lessen these losses. Many researchers have applied machine learning (ML) approaches to predict IIoT attacks in advance. Industrial networks can be highly effectively and dynamically protected against various cyber threats by utilizing MLthrough IIoT attack distinguishing. This ML methodology is a vital component of today's cybersecurity toolkit for IIoT contexts owing to its capacity for handling massive info, adjusting for novel threats, & delivering insights in real-time. Industrial networks can be highly effectively and dynamically protected against cyber threats by utilizing ML to IIoT attack identification. ML is a vital component of today's cybersecurity toolkit for IIoT contexts for its capacity to handle massive quantities of info, adjust to emerging risks, & deliver insights in real time. IIoT data can contain noise, missing values, or irrelevant information, negatively affecting model performance. Identifying and extracting relevant features from IIoT data was highly complex and time-consuming. The methodology's effectiveness is heavily related to features with high quality utilized. This paper discusses the various techniques, their limitations, and the gaps studied during the literature survey. Among the machine learning approaches, the model should adapt to the new infrastructure attacks, which is effectively possible only through meta-heuristic learning algorithms. © 2024 IEEE.

关键词： Cyber attacks

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Image Captcha Blended with OTP for Secured Authentication 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Bazeer Ahamed, B. Krishnamoorthy, Murugan University of Technology and Applied Sciences College of Computing and Information Sciences Al Musannah Oman School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

With the recent development in digital currency, the number of online transactions done every day is increasing exponentially. Along with the growing need for online transactions various attacks are developed to mimic an authenticated user, one of which is a phishing attack. The effort to access important materials such as credit card particulars, usernames, and passwords for mischievous reasons. We have proposed a unified model in which image CAPTCHA serves as the input for a hashing algorithm which serves as OTP generation, to avoid phishing attacks. This system is enhanced with more attack-handling mechanisms to intensify security in online application systems with better user authentication. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Phishing

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Thermal-Aware Communication

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IEEE Transactions on Information Theory 2025年第6期71卷 4145-4155页

作者： Chee, Yeow Meng Etzion, Tuvi Immink, Kees A. Schouhamer Nguyen, Tuan Thanh Vu, Van Khu Weber, Jos H. Yaakobi, Eitan Singapore University of Technology and Design Singapore Technion — Israel Institute of Technology Department of Computer Science Haifa3200003 Israel National University of Singapore Department of Industrial Systems Engineering and Management Singapore Turing Machines Inc Rotterdam3016 DK Netherlands Singapore University of Technology and Design Science Mathematics and Technology Cluster 487372 Singapore Delft University of Technology Department of Applied Mathematics Delft2628 CD Netherlands

Temperature control is of utmost importance in transmission systems. In this paper, a binary channel model is considered in which the transmission of a one causes a temperature increase while communicating a zero causes a temperature drop. By putting constraints on the input sequences, it is guaranteed that the channel temperature will not exceed a certain pre-determined maximum. In the asymptotic regime, the capacity of such a channel is studied. For the non-asymptotic regime, fixed-length codes are presented, with the property that codewords can be freely cascaded without violating the temperature constraint. Optimization of the code size is investigated and codewords are enumerated using generating functions. © 1963-2012 IEEE.

关键词： Channel capacity

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A new method for human activity identification using convolutional neural networks

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International Journal of Cloud Computing 2023年第2-4期12卷 191-200页

作者： Prakash, P.S. Balakrishnan, S. Venkatachalam, K. Balasubramanian, Saravana Balaji Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tiruchengode637205 India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Applied Cybernetics Faculty of Science University of Hradec Králové Hradec Králové50003 Czech Republic Department of Information Technology Lebanese French University Erbil Iraq

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Reseed Skipping of Linear Congruential Generator for Multi-level Image Steganography Security of Financial Data

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SN computer science 2023年第2期4卷 124页

作者： Danso, Juliana Mantebea Missah, Yaw Marfo Gyamfi, Enoch Opanin Dankwa, Stephen Kwabena, Sarpong Department of Computer Science Kwame Nkrumah University of Science and Technology (KNUST) Kumasi Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Upper East Region Navrongo Ghana School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This paper proposed a multi-level image steganography model that integrates with image and plaintext encryption schemes. The model starts by partitioning the cover-image pixel positions into two regions of respective purposes, one is reserved to hold the data, and through the implementation of the dynamic difference expansion principle, the other sloughs some parts to the reserved region in responsive to the size of the data to embed. Continuously reseeding and initializing the parameter values of a Linear Congruential Generator (LCG), generate a set of random pixel positions as members of the reserved region. Before data embedding, the region reserved to hold the data as well as the data bits to embed, are then encrypted using AES with respective keys. Encryption keys are randomly generated using a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG), the Fortuna algorithm. Multiplex security nature of using a randomization generator to reserve image region for encryption and encrypting plaintext data before performing steganography still ensures stability in computational complexities better than a logarithmic O(n log n). A visual inspection by evaluators who made 3000 choices to correctly differentiate stego images from its cover-images shows that only 47 choices were correct, 1884 choices were wrong, while 1069 choices were uncertain. An experiment shows an average Embedding Rates (ERs) of 0.00109 bpp and an average PSNR of 72. This concludes, as our integrated steganography and image encryption which implements the dynamic difference expansion principle, outputs encrypted stego mages that can deceive the naked human eyes into identifying them as rather cover-images, its embedding rate is faster (i.e. ER < 0.1), suitably good, and ensures better imperceptibility of cover-image alterations. When comparing these experimental results with existing methods, our proposed model shows significant competitiveness. We even show real-life scenarios on how this

关键词： AES encryption scheme Dynamic difference expansion principle Linear congruential generator Pseudo-random number generator Reserving room before encryption Steganography

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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