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Effect of Using Continuous Integration (CI) and Continuous Delivery (CD) Deployment in DevOps to reduce the Gap between Developer and Operation 23

Effect of Using Continuous Integration (CI) and Continuous D...

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23rd International Arab Conference on information technology, ACIT 2022

作者： Mowad, Abrar Mohammad Fawareh, Hamed Hassan, Mohammad A. Zarqa University Faculty of Information Technology Software Engineering Dept Jordan Zarqa University Faculty of Information Technology Computer Science Dept Jordan

ISBN: (纸本)9798350320244

This paper focuses on how to use continuous integration (CI) and continuous Delivery (CD) methodology in DevOps to reduce the developer-operator gap. It also, shows how CI can be a CD bridge. The paper review DevOps and analyze strategies, methodologies, issues, and processes identified for the adoption and implementation of continuing practices. The result of our case studies shows the benefits, and advantages of using CI/CD in software development. Furthermore, this paper presents DevOps as a new model for reducing the gaps between development (Dev) and operations (ops). The Azure tool is used as DevOps CI/CD for the empowerment of continuous delivery of software to enable rapid and frequent releases, this enables rapid responses to changing customer requirements and thus it may be a decisive competitive advantage. This paper also measures the effectiveness of using CI/CD for reducing the time and effort in software development. We also, focus on the DevOps initiative to benefit of CI/CD and to effect of enhances flexibility in delivering the program with the expected quality on time to determine the areas that bridge the gap between Continuous Integration for Continuous Delivery. The methodology used in this paper by exploring and tracking a project developed by the company using the Azure software development tool. The experiment includes a project performance and evaluation. © 2022 IEEE.

关键词： Integration

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Enhancing SEO Compliance of JavaScript Web Frameworks through Automated Testing inside CI/CD Pipelines 11

Enhancing SEO Compliance of JavaScript Web Frameworks throug...

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11th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2024

作者： Ágh, Ján Makarova, Irina Dakić, Pavle Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 Slovakia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia

This article aims to incorporate search engine optimization validation into the development process of JavaScript-based web applications, thus connecting two sides of optimizations, namely the selection of appropriate practices and their correct implementation. We summarize strategies for achieving search engine optimization (SEO) compliance in single-page applications (SPAs) and propose two distinct testing solutions aimed at addressing SEO concerns in modern web apps. Both approaches are incorporated into CI/CD pipelines, making it possible to enforce existing SEO strategies. The issue of visibility in search engines is especially characteristic for JavaScript-based frameworks. This is because of the way they load and render their content, are mostly unable to achieve satisfactory SEO scores and rankings. Due to the ever-increasing popularity of these frameworks, it is essential to identify and compare available optimization techniques to address this subject adequately. Here, we take a closer look at the SEO-friendly features of *** and *** frameworks and evaluate their capabilities using two mock e-commerce websites as examples. To fulfill our second objective, an approach to continuous SEO testing is presented. We discuss the results of similar CI/CD pipeline setups using distinct technologies and tools, in particular Jenkins and GitHub actions. The results show that by integrating specific tests focused on SEO into CI/CD pipelines, developers can identify crucial shortcomings of their product and eliminate them before they cause harm in the production environment. © 2024 Copyright for this paper by its authors.

关键词： Search engines

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Comparative Analysis of Machine Learning Algorithms for Arabic Sentiment Analysis on Imbalanced Social Media Data

Comparative Analysis of Machine Learning Algorithms for Arab...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Almuhaya, Basheer Saha, Bishal Kaur, Manbir Bazel, Mahmood A. Mohammed, Rehab School of Cse Lovely Professional University Punjab Phagwara144411 India Taiz University Faculty of Engineering and Information Technology Taiz Yemen Taiz University Software Engineering Dept Taiz Yemen

ISBN: (纸本)9798350372229

Sentiment analysis is crucial in understanding public opinions and attitudes on social media platforms. However, dealing with imbalanced datasets, especially in the context of Arabic sentiment analysis, poses significant challenges. In this study, we utilize five well-known machine learning algorithms: support vector machine, logistic regression, random forest, multinomial naive Bayes, and Knearest neighbors, in combination with appropriate preprocessing techniques and pipelines, such as text cleaning, normalization, tokenization, and feature extraction using TFIDF vectorizer. Additionally, we addressed the imbalanced data issue through synthetic minority over-sampling technique and random under-sampling. The evaluation was conducted on the SS2030 Twitter dataset, consisting about 2,436 and 1,816 positive and negative tweets in Arabic. This study provides insights into the performance of various algorithms and the effectiveness of sampling techniques in mitigating the challenges posed by imbalanced data in Arabic sentiment analysis. © 2024 IEEE.

关键词： Arabic Sentiment Analysis Imbalanced Datasets Machine Learning Algorithms Sampling Techniques Sentiment Analysis Text Preprocessing

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Facial Recognition Utilizing the CLM Model and Supervised Machine Learning: A Comparative Analysis 25

Facial Recognition Utilizing the CLM Model and Supervised Ma...

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25th International Arab Conference on information technology, ACIT 2024

作者： Iqtait, Musab Ababneh, Jafar Abu-Jassar, Amer Al-Refai, Mohammad Dmour, Mohammad Arabiat, Mohammad Binsaddig, Ruaa Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa Jordan Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Amman Arab University University College of of Computer Sciences and Information Amman Jordan Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan University of Business and Technology College of Business Administration Department of Finance Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Facial recognition is a common challenge in artificial intelligence. We extensively utilized this program in our daily life. Face recognition software was implemented on many phones to protect user identities and promptly recognize Facebook users in photographs. Despite the numerous methodologies proposed to date, the actual implementation of facial recognition continues to pose significant challenges. Distinguishing individuals by a fundamental method relies on various factors, including lighting, postural differences, and partial facial obstruction. This study aims to develop a facial recognition technique utilizing a constrained local model (CLM) and a machine learning algorithm. It is evaluated with KNN, SVR, and CCA. Furthermore, it has employed CLM and CCA to achieve a maximum recognition accuracy of 3.98 MAE. © 2024 IEEE.

关键词： Adversarial machine learning

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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

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Materials Today: Proceedings 2023年 80卷 2701-2705页

作者： Qahatan Alsudani, Mustafa Abbdal Reflish, Salah H. Moorthy, Kohbalan Mundher Adnan, Myasar Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals;firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

关键词： Optimization

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Towards a Comprehensive Classification of Dentist-Centered Needs in the Metaverse 10th

Towards a Comprehensive Classification of Dentist-Centered N...

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10th International Conference on information Management, ICIM 2024

作者： Odeh, Yousra Odeh, Rana Software Engineering Department Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman11733 Jordan Department of Pediatric Dentistry and Orthodontics Faculty of Dentistry University of Jordan Amman11942 Jordan

ISBN: (纸本)9783031643583

The integration of the metaverse into dentistry heralds a new era of innovation, revolutionizing the way dental professionals approach education, research, patient care, and collaboration. While the application of metaverse technologies in medical domains is gaining traction, there exists a notable gap in research specifying a comprehensive classification of dentists’ needs within this virtual space. This paper presents an understanding the requirements and preferences of dental professionals as they navigate the metaverse. The classification is described as dentist-centered and phase-based, where dentists’ needs are classified according to their metaverse navigation phase. The work is validated by a dentist and digital health technology expert. The outcomes of this research will contribute to the establishment of a structured framework that classifies dentists’ needs in the metaverse, offering valuable information for dentists, developers, educators, and policymakers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： information management

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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AKQ: A Hybrid Quantum-Classical Image Encryption System

AKQ: A Hybrid Quantum-Classical Image Encryption System

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2023 RIVF International Conference on Computing and Communication Technologies, RIVF 2023

作者： Nguyen, Tran Khanh Thi Thanh Truc, Nguyen Hai, Vu Tuan University of Information Technology VNU-HCM Ho Faculty of Computer Science Chi Minh city Viet Nam University of Information Technology VNU-HCM Ho Faculty of Software Engineering Chi Minh city Viet Nam

ISBN: (纸本)9798350315844

Image encryption is an essential field between computer vision and security which aims to protect the image before future attacks. Nowadays, it is threatened by quantum computers, which can break some classical cryptography. The encryption system has been upgraded from quantum key exchange protocol to post-quantum key-establishment mechanisms, but they are not explicitly designed for the image encryption task. In this paper, we propose a hybrid quantum-classical theoretical framework and its instance AKQ system which is more secure and suitable in the post-quantum era. © 2023 IEEE.

关键词： Quantum computers

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Establishing Pattern Sequences Using Artificial Neural Networks with an Application to Organizational Patterns 10

Establishing Pattern Sequences Using Artificial Neural Netwo...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Matovič, Viktor Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava84216 4 Slovakia

Explicit relationships included in textual description of patterns are insufficient to establish pattern sequences. This article brings a new method of establishing pattern sequences using artificial neural networks. The method is based on extracting implicit relationships between organizational patterns using the softmax regression model by Zhang et al., which are learned by the artificial neural network and encoded in its weights. The implementation of the method is based on the mini-batch stochastic gradient descent method by Zhang et al. and the Keras application programming interface of the TensorFlow framework. To evaluate the method, we conducted four experiments on selected organizational patterns documented by Coplien and Harrison. Two pattern sequences were established in each experiment. For each pattern sequence, a pattern story was created in order to check its meaningfulness. © 2023 Copyright for this paper by its authors.

关键词： artificial neural networks organizational pattern pattern relationships pattern sequence softmax regression

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