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International computer Science and engineering Conference (ICSEC)

作者： Supakorn Trakulmaiphol Krerk Piromsopa Department of Computer Engineering Faculty of Engineering Software Engineering Program Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781665462730

We created a customize DNS operator to address the shortcoming of CoreDNS in Kubernetes. While Kubernetes is gaining popularity in orchestrating containers, many organizations with Virtual Machine (VM) based legacy applications still struggle to containerize. Rehost strategy seems to be a faster method. On Kubernetes, KubeVirt can be used to manage rehosted VM. However, there is a huge difference between the long-term use of the VMs and containers. For example, container IP addresses change over time is problematic for VMs that need a static IP. In addition, some VM-based applications may require multiple network interfaces or a specific domain name for service discovery. These issues prevent some VMs from functioning properly after the migration to KubeVirt. We developed an operator framework to solve network problems that arise in the application layer such as DNS queries. In addition, we provide a guideline on how to use open-source projects like the Multus Container Network Interface, NMState to enable the migration of VMs with applications related to network function properly. Our operator and guideline allow most VMs to function properly with few modifications (to none) after migrating to KubeVirt. This facilitated the adoption of Kubernetes in more organizations.

关键词： computer science Organizations Containers Virtual machining Network interfaces IP networks Guidelines

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Yemeni Paper Currency Recognition System Using Deep Learning Approach 1st

Yemeni Paper Currency Recognition System Using Deep Learning...

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1st International Conference of Advanced Computing and Informatics, ICACIN 2020

作者： Ba Alawi, Abdulfattah E. Saeed, Ahmed Y. A. Al-Zekri, Burhan T. Radman, Borhan M. N. Saif, Alaa A. Alshami, Maged Alshadadi, Ahmed Mohammad, Muneef A. Mohammed, Ala’a M. Saeed, Osama Y. A. Saeed, Adeeb M. Department of Software Engineering Al-Saeed Faculty of Engineering & IT Taiz University Taiz Yemen Department of Communication and Computer Engineering Al-Saeed Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (纸本)9789811560477

The recognition of paper currency with various designs is a difficult task for automated banking machines and people with low visions. Due to the wide use of automated system such as ATM, for getting cash, safe and precise methods for recognizing paper currency are highly demanded. Recently, the use of an efficient automatic classifying system becomes one of the most important needs of current banking services. The main aim of this paper is to introduce an intelligent model that can distinguish between different types of Yemeni paper currencies using convolution neural networks (CNN). The system is based on image processing methods to classify different types of currencies efficiently. Moreover, deep learning techniques are used to perform the classification process effectively. The obtained results in the actual implementation of this model are encouraging. The accuracy rate reaches 92.7% in the testing phases. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reality Exergaming 4

VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reali...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Mambu, Joe Y. Rismayani Sihotang, Jay Idoan Rantung, Vivi Peggy Computer Science Faculty Universitas Klabat Airmadidi Indonesia Dipa Makassar University Dept. of Software Engineering Makassar Indonesia Faculty of Information Technology Universitas Advent Indonesia Bandung Indonesia Faculty of Information System Universitas Negeri Manado Tondano Indonesia

ISBN: (纸本)9781665453950

There are many different types of exergaming, but one of the most engaging one is the one with virtual reality. This type of gaming allows you to immerse yourself in a completely different world, and it can be a lot of fun. Due to Covid-19 pandemic many people resort to their usual exercise activities and opt to do it at home yet only to find a monotonous and boring fitness routine. VR Real Run is aimed to give a solution on how to have fun and to get fit at the same time. By using the latest and front runner in the VR headsets products, the Oculus Quest 2, we developed an exergame that require user to run in place and jump to play. It also features high score to motivate users in perform better in the next session. A black box test has been well performed and a usability test was came out with a 79 SUS score which translate the application as 'good' therefore its usability is acceptable. © 2022 IEEE.

关键词： Virtual reality

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Success Factors of Enterprise Resource Planning: A State-of-the-Art Mini Review

Success Factors of Enterprise Resource Planning: A State-of-...

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Artificial Intelligence, Metaverse and Cybersecurity (ICAMAC), International Conference on

作者： Ahmed BaniMustafa Olla Bulkrock Faculty of Computer Information Science Higher Colleges of Technology Abu Dhabi UAE Software Engineering Department Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9798350353488

ISBN: (纸本)9798350353495

Enterprise Resource Planning Systems (ERP) are vital for today’s businesses. However, the successful implementation of ERP systems faces several challenges, which can determine its success or failure. This paper provides a brief state-of-theart review of the success factors of ERP implementation using various applications that span different contexts. The paper discusses and compares the aims defined and the techniques applied in these studies, as well as the data used and the results obtained from each of these studies. This review paper reviews published work to tackle these challenges, covering the literature published in 2020-2023. A special effort was also made to focus on high-quality papers published in top journals with high-impact factors and indexed in Scopus and Web of Science. The review process is repeatable, as special keywords were used to search the literature using publishers’ databases and three specialized tools. The paper concludes by discussing the future of the successful implementation of ERP systems. The paper argues that ERP systems will continue to have a major impact on the success of today’s businesses. Therefore, they must carefully consider various factors that influence their successful implementation.

关键词： Training Machine learning algorithms Quality assurance Reviews Metaverse Project management Organizations Enterprise resource planning Teamwork Stakeholders

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An Efficient Intrusion Detection System using Advanced Machine Learning Techniques in SDN for Healthcare System

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Asif, Muhammad Waseem Aqdus, Aqsa Amin, Rashid Chaudhry, Shehzad Ashraf Alsubaei, Faisal S. Iqbal, Sajid Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan Department of Computer Science University of Chakwal Chakwal48800 Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey The Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,

关键词： Decision trees

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Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Qiu, Houming Zhu, Kun Luong, Nguyen Cong Niyato, Dusit Jiangxi University of Finance and Economics School of Software and IoT Engineering Jiangxi 330032 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded distributed systems. Firstly, an existence of colluding workers who collude results with each other leads to serious privacy leakage issues. Secondly, there are few existing works considering security issues in data transmission of distributed computing systems/or coded distributed machine learning systems. Thirdly, the number of required results for which need to wait increases with the degree of decoding functions. In this paper, we design a secure and private approximated coded distributed computing (SPACDC) scheme that deals with the above-mentioned problems simultaneously. Our SPACDC scheme guarantees data security during the transmission process using a new encryption algorithm based on elliptic curve cryptography. Especially, the SPACDC scheme does not impose strict constraints on the minimum number of results required to be waited for. An extensive performance analysis is conducted to demonstrate the effectiveness of our SPACDC scheme. Furthermore, we present a secure and private distributed learning algorithm based on the SPACDC scheme, which can provide information-theoretic privacy protection for training data. Our experiments show that the SPACDC-based deep learning algorithm achieves a significant speedup over the baseline approaches. © 2013 IEEE.

关键词： Parallel processing systems

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LDA-UNet: A Lightweight UNet with Multi-Dimensional Selective Attention for Efficient Skin Lesion Segmentation

LDA-UNet: A Lightweight UNet with Multi-Dimensional Selectiv...

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Artificial Intelligence and computer engineering (ICAICE), International Conference on

作者： Qingyang Gao Jingjing Ma Faculty of Software and Computer Engineering ITMO University Saint Petersburg Russian Federation College of Science Sichuan Vocational College of Finance and Economics Chengdu China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Different UNet models do well in tasks like semantic segmentation. Yet, these models still do not use detailed features enough and have weak global feature understanding. UNet links the encoder and decoder with skip connections. This lets the decoder get detailed info from the encoder, which helps the model work better. But having skip connections for just one set of encoder and decoder is not enough to use all the multi-level detailed features. Plus, UNet builds the encoder and decoder with convolution methods. Because convolutional operations have a small view of the data, the model's ability to understand the whole set of data is still low. To solve the problems of weak feature use and poor overall modeling, we suggest a simple fix: a light Multi-Dimensional Selective Attention Enhanced UNet (LDA-UNet). To get around the small view of the UNet design, we add the Multi-Dimensional Selective Attention Block. This part boosts the model's global skill and widens the view while using fewer parameters. To fix the issue of low use of multi-level features, we use a multi-level skip connection method. This helps the decoder reach features at different sizes and improves detail capture. We tested the model on the ISIC-2018 dataset, and the results show that, when compared to other methods, this algorithm does a better job at segmentation while using fewer parameters.

关键词： Training Databases Convolution Semantic segmentation Skin Data models Decoding Lesions Artificial intelligence Diseases

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Pipeline Representation Using Generic Parallel Algorithms in Heterogeneous Parallel Computing. A comparative analysis 36

Pipeline Representation Using Generic Parallel Algorithms in...

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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Rossainz-López, Mario Sánchez-Rinza, Bárbara López-Sosa, Zuriel Capel-Tuñon, Manuel Faculty of Computer Science Autonomous University of Puebla Av. San Claudio and 14 Sur Street San Manuel PueblaC.P. 72570 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9791281988026

Using Parallel Objects and Structured Parallel Programming, the parallel representation of the Communication Pattern between Processes called Pipeline is shown, whose implementation is carried out through different models of Generic Parallel Algorithms within Heterogeneous Parallel Computing (HPC): As a Parallel Design Pattern or PDP, as High-Level Parallel Composition or HLPC and as Flow Graph Interfaces or FGI through programming with message passing. An original and effective development methodology for new programmers in parallelism is proposed for each of them. In these three proposals for Generic Parallel Algorithms, the same problem-example is solved as a case study, and a comparative analysis of their models and designs is carried out, as well as the performance obtained in the execution of said proposals and demonstrate their usefulness, programmability, and performance. The objective of this work is to provide the programmer and/or novice user with different multicore programming approaches so that without much effort they can develop their programs according to a sequential programming style, obtaining automatically, easily and the counterpart parallelization of your code with the help of a specific programming environment like the one proposed. © 2024 The Authors.

关键词： Structured programming

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Synthetic Minority Over-sampling Technique for Student Performance Prediction: A Comparative Analysis of Ensemble and Linear Models

Synthetic Minority Over-sampling Technique for Student Perfo...

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International Conference on computer and Information Technology (ICCIT)

作者： Khaled Mahmud Sujon Rohayanti Hassan Nusrat Jahan Department of Software Engineering Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology(CUET) Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This study explores the impact of the Synthetic Minority Oversampling Technique (SMOTE) on predictive accuracy in student performance prediction using both linear (Logistic Regression, LR) and ensemble (Random Forest, RF) models. We compare the performance of these models, using a real-time dataset from Universiti Teknologi Malaysia (UTM). While previous studies have examined these models separately, this research uniquely investigates the simultaneous application of SMOTE to both. Our results show that LR’s accuracy improves from 91% to 95% with SMOTE, while RF maintains a consistent 98% accuracy. These findings demonstrate that SMOTE significantly enhances the performance of linear models in handling imbalanced data, providing useful insights for curriculum development in higher education. This research contributes to the literature by guiding higher education institutions in selecting the most effective predictive models.

关键词： Radio frequency Analytical models Logistic regression Accuracy Computational modeling Education Predictive models Data models Real-time systems Information technology

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