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12th International Conference on Advanced computer Information Technologies, ACIT 2022

作者： Dyvak, Mykola Melnyk, Andriy Dostalek, Libor Ostroverkhov, Viktor Honchar, Lyudmyla Romanets, Ihor West Ukrainian National University Department of Computer Science Ternopil Ukraine West Ukrainian National University Faculty of Economics and Management Ternopil Ukraine Czech Technical University in Prague Computer Networks Research Group Department of Computer Systems Prague Czech Republic

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

To build mathematical models based on the obtained interval data, it is necessary to solve the problems of structural and parametric identification. The result of the identification is the interval difference equations of the dynamics of concentrations of harmful emissions of vehicles. Procedures for structural and parametric identification of such models are quite complex. Therefore, in practice, based on the experience gained in modeling these processes, it is advisable to create a repository of interval models that would describe these processes, or easily customized to specific application conditions. This task is considered in the article. The basis for repository management is the ontological model. The features of building an appropriate ontological model, analysis the shortcomings of existing tasks, which are solving with its help also presented in this paper. © 2022 IEEE.

关键词： Knowledge management

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Machine-Learning Based Prediction of Multiple Types of Network Traffic 21st

Machine-Learning Based Prediction of Multiple Types of Netwo...

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21st International Conference on Computational science, ICCS 2021

作者： Knapińska, Aleksandra Lechowicz, Piotr Walkowiak, Krzysztof Department of Systems and Computer Networks Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783030779603

Prior knowledge regarding approximated future traffic requirements allows adjusting suitable network parameters to improve the network’s performance. To this end, various analyses and traffic prediction methods assisted with machine learning techniques are developed. In this paper, we study on-line multiple time series prediction for traffic of various frame sizes. Firstly, we describe the gathered real network traffic data and study their seasonality and correlations between traffic types. Secondly, we propose three machine learning algorithms, namely, linear regression, k nearest neighbours, and random forest, to predict the network data which are compared under various models and input features. To evaluate the prediction quality, we use the root mean squared percentage error (RMSPE). We define three machine learning models, where traffic related to particular frame sizes is predicted based on the historical data of corresponding frame sizes solely, several frame sizes, and all frame sizes. According to the performed numerical experiments on four different datasets, linear regression yields the highest accuracy when compared to the other two algorithms. As the results indicate, the inclusion of historical data regarding all frame sizes to predict summary traffic of a certain frame size increases the algorithm’s accuracy at the cost of longer execution times. However, by appropriate input features selection based on seasonality, it is possible to decrease this time overhead at the almost unnoticeable accuracy decrease. © 2021, Springer Nature Switzerland AG.

关键词： Machine learning

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Clustering and Weighted Scoring Algorithm Based on Estimating the Number of Clusters 21st

Clustering and Weighted Scoring Algorithm Based on Estimatin...

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21st International Conference on Computational science, ICCS 2021

作者： Klikowski, Jakub Burduk, Robert Department of Systems and Computer Networks Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783030779665

Imbalanced datasets are still a big method challenge in data mining and machine learning. Various machine learning methods and their combinations are considered to improve the quality of the classification of imbalanced datasets. This paper presents the approach with the clustering and weighted scoring function based on geometric space are used. In particular, we proposed a significant modification to our earlier algorithm. The proposed change concerns the use of automatic estimating the number of clusters and determining the minimum number of objects in a particular cluster. The proposed algorithm was compared with our earlier proposal and state-of-the-art algorithms using highly imbalanced datasets. The performed experiments show that the proposed modification is statistically better for a larger number of reference classifiers than the original algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Data mining

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DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless networks

arXiv

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arXiv 2024年

作者： Jeong, Eunjeong Kountouris, Marios Department of Computer and Information Science Linköping University Sweden Department of Computer Science and Artificial Intelligence University of Granada Spain Communication Systems Department EURECOM Sophia Antipolis06904 France

Emerging technologies and use cases, such as smart Internet of Things (IoT), Internet of Agents, and Edge AI, have generated significant interest in training neural networks over fully decentralized, serverless networks. A major obstacle in this context is ensuring stable convergence without imposing stringent assumptions, such as identical data distributions across devices or synchronized updates. In this paper, we introduce DRACO, a novel framework for decentralized asynchronous Stochastic Gradient Descent (SGD) over row-stochastic gossip wireless networks. Our approach leverages continuous communication, allowing edge devices to perform local training and exchange model updates along a continuous timeline, thereby eliminating the need for synchronized timing. Additionally, our algorithm decouples communication and computation schedules, enabling complete autonomy for all users while effectively addressing straggler issues. Through a thorough convergence analysis, we show that even without predefined scheduling policies, DRACO achieves high performance in decentralized optimization while maintaining low variance across users. Numerical experiments further validate the effectiveness of our approach, demonstrating that controlling the maximum number of received messages per client significantly reduces redundant communication costs while maintaining robust learning performance. © 2024, CC BY-NC-ND.

关键词： Federated learning

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Fostering Digital Innovation in Indonesia: The Role of STEM-AI Skills in Overcoming Talent Deficits and Gender Stereotypes

Fostering Digital Innovation in Indonesia: The Role of STEM-...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Erwin Halim Amalia Zahra Maryani Lili Ayu Wulandhari Devi Fitrianah Virienia Puspita Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Communication Department BINUS Graduate Program Master of Strategic Marketing Communication Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

This research explores the optimization of digital talent in advanced industries, particularly in the context of rapid digital transformation. Despite the increasing importance of digital talent for gaining a competitive edge, several challenges persist. In Indonesia, for example, there is a significant shortage of digitally skilled workers, with the World Bank reporting a gap of 9 million skilled individuals over the past 15 years. Additionally, artificial intelligence (AI) is expected to impact 9.5 million jobs, further exacerbating the demand for digital skills. Furthermore, the employability of graduates remains low, largely due to inadequate training, poor language proficiency, and limited cultural sensitivity. Many companies face difficulties in finding qualified individuals, quickly upskilling their workforce, and fostering an innovative organizational culture. This study seeks to evaluate the factors influencing digital talent performance in the context of these challenges. It examines seven key variables: digital readiness, digital technology adoption, relevance of AI, digital skills, individual performance, problem-solving ability, and overall digital talent performance. Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data was collected from 378 participants, primarily students with STEM backgrounds, in the Jabodetabek area of Indonesia in November 2024. The findings reveal that all hypotheses are significant, with results indicating that user satisfaction and the perceived value of digital talent in Indonesia have a notable impact on continued usage intentions in digital roles.

关键词： Training Industries Technological innovation Sensitivity Mathematical models Problem-solving Artificial intelligence Optimization Faces Gender issues

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Alternating Minimization Schemes for Computing Rate-Distortion-Perception Functions with f-Divergence Perception Constraints

arXiv

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arXiv 2024年

作者： Serra, Giuseppe Stavrou, Photios A. Kountouris, Marios Communication Systems Department EURECOM Sophia-Antipolis France Department of Computer Science and Artificial Intelligence University of Granada Spain

We study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint that belongs to the family of f -divergences. In this setting, the RDPF forms a convex programming problem for which we characterize the optimal parametric solutions. We employ the developed solutions in an alternating minimization scheme, namely Optimal Alternating Minimization (OAM), for which we provide convergence guarantees. Nevertheless, the OAM scheme does not lead to a direct implementation of a generalized Blahut-Arimoto (BA) type of algorithm due to the presence of implicit equations in the structure of the iteration. To overcome this difficulty, we propose two alternative minimization approaches whose applicability depends on the smoothness of the used perception metric: a Newton-based Alternating Minimization (NAM) scheme, relying on Newton’s root-finding method for the approximation of the optimal iteration solution, and a Relaxed Alternating Minimization (RAM) scheme, based on a relaxation of the OAM iterates. Both schemes are shown, via the derivation of necessary and sufficient conditions, to guarantee convergence to a globally optimal solution. We also provide sufficient conditions on the distortion and the perception constraints which guarantee that the proposed algorithms converge exponentially fast in the number of iteration steps. We corroborate our theoretical results with numerical simulations and draw connections with existing results. © 2024, CC BY.

关键词： Convex optimization

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Public Key Infrastructure Approaches Based on Blockchain

Public Key Infrastructure Approaches Based on Blockchain

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Dena S. Alani Ali M Sagheer College of Education for Women University of Anbar Anbar Iraq Department of Computer Networks Systems College of Computer Science and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Public-key infrastructure or PKI, is the fundamental technology that makes safe online information sharing possible. But PKI is vulnerable to failures of Certificate Authorities (CAs), which could allow illegal certificates to be issued to the end users. The security of the corresponding end users is at danger if a CA is compromised, as seen by numerous recent breaches. Blockchain technology is a new solution that has the ability to address the issues with traditional PKI systems, specifically the removal of single points of failure and the quick response to CA flaws. Blockchain technology facilitates the management and archiving of digital certificates in an unchangeable, publicly accessible ledger, producing a history log that can be completely tracked. In this article, the classical and modern blockchain-based PKI strategies were explained in addition to illustrating the limitations of each strategy. Finally, this article analyzed PKI methods by comparing their features according to a number of parameters.

关键词： Privacy Scalability Public key Information sharing Authentication Blockchains Encryption

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Evaluating Generative Adversarial networks for Virtual Contrast-Enhanced Kidney Segmentation using Res-UNet in Non-Contrast CT Images

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Multimedia Tools and Applications 2025年第18期84卷 20121-20144页

作者： Syamala, Maganti Chandrasekaran, Raja Balamurali, R. Rani, R. Hashmi, Arshad Kiran, Ajmeera Rajaram, A. Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur District Andhra Pradesh Vaddeswaram522502 India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamil Nadu Chennai India Centre for non linear systems Chennai institute of technology Chennai India Department of ECE Vemu Institute of Technology Tirupathi-chittoor Highway P. Kothakota Chittoor District AP Pakala India King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Computer Science and Engineering MLR Institute of Technology Dundigal Telangana Hyderabad500043 India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam611002 India

This study addresses the challenges of accurately segmenting kidneys in non-contrast CT images;where Convolutional neural networks (CNNs) perform best. The main goal is to investigate how well a Generative Adversarial Network (GAN) performs in producing virtual contrast-enhanced (vCECT) images from non-contrast sources. A GAN model is trained to generate simulated contrast-enhanced images that preserve pertinent information throughout translation by utilizing a dataset of 286 paired non-contrast and contrast-enhanced images. Following, 20 pairs of non-contrast and vCECT pictures are used to assess the performance of a unique segmentation model named Res-U-Net. The Res-U-Net architecture, which combines a U-Net design with residual connections, is optimized to efficiently extract detailed information from original and vCECT images. Image preprocessing methods like Hounsfield windowing with histogram equalization are used to improve the quality of the data before it is fed into the deep neural network. Dice similarity coefficient (DSC) is used to measure Res-U-Net's performance and acts as a benchmark against the most advanced kidney segmentation techniques. ResU-Net's performance is evaluated using the DSC. When compared to state-of-the-art methods for kidney segmentation recognition, the ResU-Net systems with intact connections achieved a DSC value of 0.97 percent for organs recognition and 0.83 percent for segmentation approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework

When Computing Power Network Meets Distributed Machine Learn...

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International Workshop on Quality of Service

作者： Xinjing Yuan Lingjun Pu Lei Jiao Xiaofei Wang Meijuan Yang Jingdong Xu Institute of Systems and Networks College of Computer Science Nankai University China Department of Computer Science University of Oregon USA School of Computer Science and Technology Tianjin University China

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maximizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings.

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Towards a Cyber Defense System in Software-Defined Tactical networks

Towards a Cyber Defense System in Software-Defined Tactical ...

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International Conference on Military communications and Information systems (ICMCIS)

作者： Sean Kloth Paulo H.L. Rettore Philipp Zißner Bruno P. Santos Peter Sevenich Institute of Computer Science 4 University of Bonn Bonn Germany Department of Communication Systems Fraunhofer FKIE Bonn Germany Department of Computer Science Federal University of Bahia Salvador Brazil

ISBN: (数字)9798350373196

ISBN: (纸本)9798350373202

This study investigates the robustness of a Software-defined Networking (SDN) controller when confronted with a Distributed Denial-of-Service (DDOS) attack in a tactical environment. A proactive defense mechanism is introduced to detect and respond to a flooding of “packet-in” requests, triggering a response once the network features indicate an anomaly. The methodology consists of two components: the Cyber Defense Agent (CDA), consisting of monitoring, feature engineering, detection, and responses, and the Cyber Attack Agent (CAA), including the preparation, execution, and evaluation of the attack. The CDA monitors all the IP flows from the SDN controller and processes four main features such as the average number of “packet-in” requests, the response time to these requests, the entropy of IP addresses and ports for source and destination, and “packet-in” requests per switch to identify compromised switches. All the components were emulated and tested, collecting quantitative evidence to demonstrate the effectiveness of both agents.

关键词： Switches Feature extraction Denial-of-service attack Control systems Entropy IP networks Time factors

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