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User Perceptions of Progressive Web App Features: An Analytical Approach and a Systematic Literature Review 9th

User Perceptions of Progressive Web App Features: An Analyti...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Marchetto, Tulio Morandini, Marcelo Univ Sao Paulo BR-03828000 Sao Paulo SP Brazil

ISBN: (纸本)9789819745807;9789819745814

This article presents an in-depth analysis of the usability of progressive web apps (PWAs) across different operating systems, based on a survey that had 226 responses. The study covers five different apps and two operating systems, providing a comprehensive understanding of user experiences and preferences. Key findings include the relative ease of installation across all apps and operating systems, with iOS users reporting lower levels of difficulty. The visibility of app icons on home screens was generally high, underscoring the importance of app accessibility and user engagement. However, there is room for improvement in certain areas. The dataset obtained from the responses also reveals a high level of user engagement and willingness to provide feedback, which is crucial for the continual improvement of apps. The insights gleaned from this survey are invaluable for developers seeking to optimise their PWAs, ensuring they meet user expectations and deliver a superior app experience.

关键词： Progressive web apps Usability User experience Installation difficulty App visibility User engagement Feedback App optimisation

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Cyber Threat Hunting Using Large Language Models 9th

Cyber Threat Hunting Using Large Language Models

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9th International Congress on Information and Communication Technology (ICICT)

作者： Tanksale, Vinayak Ball State Univ Muncie IN 47306 USA

ISBN: (纸本)9789819732883;9789819732890

Cyber threat hunting plays a critical role in proactively identifying and mitigating potential cybersecurity threats. The emergence of large language models (LLMs) has increased interest in the application of LLMs in cyber threat hunting. This research paper investigates the utilization of LLMs for cyber threat hunting and provides an overview of their potential benefits, challenges, and future directions. LLMs possess powerful language understanding capabilities and can process vast amounts of data, enabling them to uncover hidden patterns, detect anomalies, and identify vulnerabilities. By incorporating LLMs into workflows, organizations can enhance their ability to detect and respond to emerging threats in real time. The adoption of LLMs in threat hunting raises challenges related to bias and fairness, privacy, and computational efficiency. This paper discusses these challenges and explores potential solutions. The paper outlines future directions, such as improving LLM training with cybersecurity-specific domains and integrating contextual knowledge into LLM-based threat hunting approaches.

关键词： Large language models Cybersecurity Machine learning Cyber threat hunting Artificial intelligence

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Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals 1

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4th Congress on Intelligent systems (CIS)

作者： Sharma, Aarti Mehta Arumugam, Senthil Kumar Symbiosis Int Deemed Univ Symbiosis Ctr Management Studies Bengaluru Campus Pune Maharashtra India Christ Univ Bangalore 560029 Karnataka India

ISBN: (数字)9789819990375

ISBN: (纸本)9789819990368;9789819990375

The sustainable development goals (SDGs) as specified by the United Nations are a blueprint to make the Earth to be more sustainable by the year 2030. It envisions member nations fighting climate change, achieving gender equality, quality education for all, and access to quality healthcare among the 17 goals laid out. To achieve these goals by the year 2030, member nations have put special schemes in place for citizens while experimenting with newer ways in which a measurable difference can be made. Countries are tapping into ancient wisdom and harnessing newer technologies that use artificial intelligence and machine learning to make the world more liveable. These newer methods would also lower the cost of implementation and hence would be very useful to governments across the world. Of much interest are the applications of machine learning in getting useful information and deploying solutions gained from such information to achieve the goals set by the United Nations for an imperishable future. One such machine learning technique that can be employed is pattern recognition which has applications in various areas that will help in making the environment sustainable, making technology sustainable, and thus, making the Earth a better place to live in. This paper conducts a review of various literature from journals, news articles, and books and examines the way pattern recognition can help in developing sustainably.

关键词： Sustainable development Pattern recognition Artificial intelligence Machine learning SDGs Sustainability Lower costs

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Comprehensive Analysis of Methods Used for Road Safety Performance Evaluation: A Survey 7th

Comprehensive Analysis of Methods Used for Road Safety Perfo...

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7th International Conference on Logistics Operations Management (GOL)

作者： El Khalai, Ibtissam Chorfi, Zoubida Berrado, Abdelaziz Univ Mohammed 5 Ecole Mohammadia Ingn Equipe AMIPS Rabat Morocco

ISBN: (纸本)9783031686276;9783031686283

Evaluating performance is essential in road safety management as it allows decision makers (DMs) to appraise the effectiveness of implemented measures and identify areas requiring improvement. To achieve this, decision makers must rely on robust techniques for an effective monitoring and evaluation of their systems. This study provides a comprehensive synthesis of the large broad of methods used in the evaluation of road safety performance. An analysis of relevant studies in this area has been carried out, revealing that statistical methods and linear programming methods have been the predominant methods employed in this field. However, recent research shows an increasing interest in the use of both linear programming methods and multiple criteria decision analysis (MCDA) methods for performance evaluation..In particular, the theme of "Benchmarking road safety using composite indexes" frequently appears in the selected publications. These findings offer some promising directions for future research and guidance for DMs in the field of road safety.

关键词： Road safety Performance monitoring Performance measurement

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Addressing Class Imbalance Problem in Semantic Segmentation Using Binary Focal Loss 9th

Addressing Class Imbalance Problem in Semantic Segmentation ...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Chopade, Rushikesh Stanam, Aditya Pawar, Shrikant Indian Inst Technol Dept Geol & Geophys Kharagpur 721302 W Bengal India Univ Iowa Iowa City IA 52242 USA Claflin Univ Dept Comp Sci & Biol Orangeburg SC 29115 USA

ISBN: (纸本)9789819735587;9789819735594

Image segmentation is a foundational technique in computer vision with wide-ranging applications, including its critical role in medical imaging for object identification, automatic labeling, and disease diagnosis. Advancements in deep learning have significantly improved the accuracy and efficiency of image segmentation, making it an increasingly valuable tool in various domains. Class imbalanced datasets are a frequent problem experienced when trying to train segmentation networks. Class imbalance occurs when some classes (semantic categories) in the image have significantly more instances (pixels) than others. In semantic segmentation, this often happens because certain object categories are more prevalent in the real world or dataset, while others are rarer. When training a deep learning model for semantic segmentation, this imbalance can lead to several problems. In this article, we have experimented with the class weightage parameters of binary focal loss to address the class imbalance problem in semantic segmentation. By utilizing the CANDID-PTX dataset, we have utilized U-Net architecture containing upsampling (encoder) and a downsampling (decoder) network for comparing binary focal loss rates among different alpha and gamma coefficients class weights. Doing so, we found that the adjustment of class weights in the loss function could notably help in resolving the class imbalance problems.

关键词： Image segmentation Binary focal loss Class imbalance

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A Survey: Network Attack Detection and Mitigation Techniques 8th

A Survey: Network Attack Detection and Mitigation Techniques

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Shinde, Om Kulkarni, Varad Patani, Harsh Rajput, Anagha Jaiswal, R. C. Pune Inst Comp Technol Pune India

ISBN: (纸本)9789819713196;9789819713202

Network attack detection and mitigation systems (NADMS) is developed as defending software to detect malicious cyber-attacks, safeguard critical infrastructure, and protect sensitive data. NADMSs are recently been researched for enhanced version of security systems based on machine learning (ML), Blockchain, and diverse network frameworks. There are numerous network attacks. As technology enhances, even cyber-treat also increases. Hence, networks need to be secured from these increasing and diverse network attacks. The aim of the presented research survey is to study various optimization techniques used to detect and mitigate different types of attacks. The research gaps are identified and future activities, emphasizing the critical need for adaptive, scalable NADMS frameworks in the dynamic cyber-threat context are suggested. The study also discussed the significance of distributed denial-of-service (DDoS) attacks, providing information on efficient detection and mitigation techniques. The survey focused real-world situations and current network security issues. The consolidated comparison represented as a result of survey can be referred to identify and select appropriate detection and mitigation tools and techniques based on the type of network attacks.

关键词： Network attacks Network attack detection and mitigation system Distributed denial-of-service attacks

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Mapping the Evolution of Open Innovation and Digital Transformation: A Bibliometric Analysis 4th

Mapping the Evolution of Open Innovation and Digital Transfo...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Saidi, Hicham Madhat, Fatima Zahra Sidi Mohamed Ben Abdellah Univ Super Sch Technol Fez Ind Technol & Serv Lab LTSI Fes Morocco

ISBN: (纸本)9783031686740;9783031686757

The article evaluates the Scopus indexed international research literature on open innovation and digital transformation from 2017 to 2023 and establishes a horizonal analysis of a new bibliometric landscape in these fields. We used bibliometric methods to study the evolution of the field based on publication trends, author affiliations, and research areas. The results show that Poland, Italy, and the USA are the three most productive countries in this field, and the Silesian University of Technology and the University of Antioquia are the leading research institutions. Arias-Perez and Brodny are the authors who have made important contributions to the area of business, management and technology. We find that emerging trends highlight the synergy between technology and innovation, the impact in digital transformation by artificial intelligence, and the critical role of ecosystems in creating competitive advantage. Despite these efforts, our study is limited to a single database, which affects the representativity of our results, and the exclusion of local articles not indexed in Scopus.

关键词： Open Innovation Digital Transformation Bibliometric Analysis Research Trends Publication Landscape

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Disruptive Technologies Through the Lens of Applications in the Automotive Industry 10th

Disruptive Technologies Through the Lens of Applications in ...

引用

International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Drekovic, Edisa Teofilovic, Zaklina Jovanovic, Nenad Univ Nis Fac Econ Trg Kralja Aleksandra Ujedinitelja 11 Nish 18000 Serbia Univ Belgrade Fac Econ Belgrade 11000 Serbia Shenchi Automot Parts Doo Kragujevac 34000 Serbia

ISBN: (纸本)9783031662676;9783031662683

The automotive industry is one of the industries where disruptive technologies are most applied, from product research and development, design, and manufacturing to sales and after-sales services. The market for electric and autonomous vehicles is constantly growing, and automotive companies in the digital transformation process to achieve success have partnered with technology companies to develop virtual factories and deploy the metaverse. This paper explores the applications of various technologies in the automotive industry, including AI/ML, Big Data and Analytics, Cloud Computing and Cloud Manufacturing, Automation and Robotics, Internet of Things (IoT) and Industrial Internet of Things (IIoT), Cyber-Physical systems (CPS), Cyber safety, additive manufacturing (3D printing), VR/AR, and Digital Twins. Also, the benefits of using these technologies and the implementation challenges associated with each of them are identified. The empirical part of this research aimed to determine the extent to which automotive companies in Serbia use modern technologies. For these purposes, a survey questionnaire was used. The study identified the level of technology implementation and the effects of implementation on the operational and financial performance of the company, as well as the performance of employees. In addition, the research highlighted challenges in implementing these technologies and determined the current level of digital investments in these companies. Additionally, research also examined whether and how parent companies support the digital transformation of these companies in the Republic of Serbia.

关键词： automotive industry disruptive technologies implementation benefits challenges automotive industry Serbia

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Impact of Preprocessing Using Substitution on the Performance of Selected NER Models - Methodology 12th

Impact of Preprocessing Using Substitution on the Performanc...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Potocar, Miroslav Kvet, Michal Univ Zilina Zilina Slovakia

ISBN: (纸本)9783031603273;9783031603280

This paper investigates the effect of preprocessing, specifically word substitution by pseudo words, on the performance of selected named entity recognition (NER) models. The study focuses on explaining the methodology used during the experimental process. The paper comprehensively describes the dataset used, the process of word substitution with pseudo words, the process of model training, the process of executing the test scenario, the performance evaluation criteria and the limitations of the experiment. This paper contributes to the evolving area of Natural Language Processing by providing a comprehensive examination of the impact of preprocessing using substitution strategy on the performance of selected NER models.

关键词： named entity recognition preprocessing substitution pseudo words

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A Performance Comparison Between a Digital Shadow and a Digital Twin in a Mixed Model Stochastic System 1

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9th International Congress on Information and Communication Technology (ICICT)

作者： Tshabalala, Philane Kuriakose, Rangith B. Cent Univ Technol Bloemfontein Free State South Africa

ISBN: (数字)9789819735594

ISBN: (纸本)9789819735587;9789819735594

The competition in the current global market has highlighted the importance of having a virtual system that can monitor the entire manufacturing process. Industries face challenges such as shorter lead times, high demand in product variety and a need for a virtual system that can link the physical and the digital world. The virtual systems that are currently available and used for linking the physical system and the digital system, and enable product variety, have certain restrictions. As a result, this article aims to develop a digital shadow and a digital twin as a possible solution. The digital shadow and the digital twin in this article are designed and developed using the MATLAB and SIMULINK platforms. Digital twins and digital shadows are the two leading virtual systems in the ongoing digital transformation era. The aim of this article was to design and develop a digital shadow and a digital twin for a mixed-model stochastic assembly line, to monitor the raw materials, predicts bottlenecks that might occur during the manufacturing process and thereby reduce the cycle time. The two virtual models are given the same customer orders to use as inputs, to run simulations and calculate the cycle time. The models are compared based on their performance per customer order and in overall performance. The results show that the digital twin is more reliable in overall performance than the digital shadow, with a cycle time improvement of 19%.

关键词： Digital twin Digital shadow Mixed model stochastic system Cycle time

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