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Immersive Education: Exploring the Combination of New Technology and Virtual Reality in Real-Time Teaching

Immersive Education: Exploring the Combination of New Techno...

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2024 International Conference on Electrical and Computer engineering Researches, ICECER 2024

作者： Muzata, Artwell Regis Singh, Ghanshyam Stepanov, Mikhail Sergeevich Musonda, Innocent University of Johannesburg Faculty of Engineering and the Built Environment Department of Electrical and Engineering Science Johannesburg South Africa Moscow Technical University of Communications and Informatics Department of Network and Communication Systems Moscow Russia Faculty of Engineering and the Built Environment Johannesburg South Africa

ISBN: (纸本)9798331539733

The revolutionary possibilities of combining virtual reality (VR) with the latest innovations like augmented reality (AR), artificial intelligence (AI), and natural language processing (NLP) in the classroom are examined in this paper. It looks at how virtual reality improves immersive learning settings by enabling participants to explore virtual worlds, interact with digital design, and play games, which helps them comprehend and remember complicated topics better. The article also emphasises how technology developments like more compact headgear and haptic feedback systems have democratised virtual reality and made immersive educational experiences more widely available. This paper describes how VR's combination with other technologies is changing classroom instruction through an analysis of present trends, difficulties, and potential developments. It also provides case studies and empirical findings to help guide the adoption of VR-enhanced learning in educational contexts. © 2024 IEEE.

关键词： Virtual environments

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A Survey on Swarm Intelligence Algorithms Employed for Optimizing Machine Learning Techniques Used in Recommendation systems 11

A Survey on Swarm Intelligence Algorithms Employed for Optim...

引用

11th IEEE Conference on systems, Process and Control, ICSPC 2023

作者： Zet, Lim Cher Jasser, Muhammed Basheer Wong, Richard T.K. Chua, Hui Na Issa, Bayan Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350340860

With the exponential growth of digital data, recommendation systems or recommender systems are widely used in various domains assisting users in filtering and decision-making on massive information. Recommendation systems are capable of delivering personalized content to enhance user experience and satisfaction through users' preferences and behaviors. The machine learning algorithms employed in the recommendation systems facilitate the effectiveness of tasks achieved by those systems among which, for example, is providing accurate prediction that matches user preferences. Swarm Intelligence offers robust optimization mechanisms that have been successfully applied in various computational problems including recommendation systems for refining recommendation algorithms. To the best of our knowledge, there is no recent comprehensive survey on swarm intelligence algorithms used for optimizing machine learning techniques when used in recommendation systems. Therefore, this survey presents a survey of the swarm intelligence algorithms used in optimizing the machine learning techniques when employed in recommendation systems. We conducted a literature survey on recommendation systems and swarm intelligence, using relevant keywords and their variants, focusing on recent publications since 2019. Our findings highlight the use of swarm intelligence algorithms primarily in clustering, classification, and feature selection for recommendation systems. Swarm intelligence has significantly enhanced recommendation systems, especially in clustering and classification. However, the balance between computational complexity and processing speed remains a challenge. Future research could focus on refining these algorithms for better efficiency and effectiveness in recommendation systems. © 2023 IEEE.

关键词： Swarm intelligence

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Automatic Deployment to Kubernetes Cluster by Applying a New Learning Tool and Learning Processes 10

Automatic Deployment to Kubernetes Cluster by Applying a New...

引用

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

作者： Golis, Tomáš Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia

The rapid growth and widespread adoption of containerization technologies, such as Docker, and the increasing popularity of Kubernetes as a container orchestration platform have significantly shaped the landscape of modern software development and deployment. Containers have created a paradigm shift in application packaging, offering a consistent and portable environment across diverse infrastructure setups. Meanwhile, Kubernetes has emerged as the dominant choice for managing containerized applications at scale, providing features such as automated scaling, load balancing, and fault tolerance. However, despite the numerous advantages containers and Kubernetes offer, deploying applications to a Kubernetes cluster often poses challenges due to the steep learning curve and complex configuration requirements. To address these challenges, this paper aims to introduce a comprehensive learning tool that automates and simplifies the deployment process on Kubernetes clusters. © 2023 Copyright for this paper by its authors.

关键词： CI/CD Container orchestration Docker image Kubernetes

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Code Based Selected Object-Oriented Mechanisms Identification 10

Code Based Selected Object-Oriented Mechanisms Identificatio...

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

作者： Jablonický, Kristián Lang, Ján Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia

This article deals with select mechanisms that are provided by the object-oriented programming language Java. Using these mechanisms is crucial for a proper approach to object-oriented programming. However, this is often difficult for many programmers that are new to writing code in the said language. This paper looks at these mechanisms, defines them by quoting various sources, and proposes a solution that spots these described mechanisms in code. It does so by reading source code from .java files inputted by its user, gathering information about the defined and used classes, interfaces, methods, etc. With this information gained it outputs data which would often require a larger context of the code and good knowledge of the files’ contents. Thanks to the strict definitions and rules, that will be stated later in this paper, the application can consistently spot these mechanisms in code and inform its user about the fact. As a result, the application can help programmers that are new to object-oriented programming languages by ensuring them, that they implemented these mechanisms correctly, or by informing them about their absence. The core mechanisms described in this paper are inheritance, encapsulation, class association, and polymorphism. © 2023 Copyright for this paper by its authors.

关键词： class association encapsulation inheritance Java object-oriented polymorphism programming refactoring

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Approach to Construction of Optimal Tourist Routes Based on the Analysis of Existing Solutions 10

Approach to Construction of Optimal Tourist Routes Based on ...

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

作者： Kataieva, Yevheniia Leontiev, Oliver Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia

The tourism sector is currently witnessing a significant transformation driven by technological automation. As this evolution unfolds, it is crucial to harness intelligent technologies not only to enhance the industry but also to simplify the experience for tourists themselves. One aspect of simplification that would greatly benefit every tourist exploring a new city is a tool capable of suggesting an optimal sequence for visiting the city's best attractions. This tool would take into account the personal preferences of the tourist as well as the spatial context of the city. In this research, we present findings that are pertinent to the development of such a tool and propose an initial approach to provide these functionalities. Our proposed solution involves applying a relevant variant of the Vehicle Routing Problem with Pickup and Delivery (VRPP) or the Traveling Salesman Problem (TSP) algorithm. By leveraging user-supplied preferences and extracting relevant information from external factors, we aim to assist users in planning their optimal viewing experiences. To realize this solution, we envision creating a modern web application utilizing cutting-edge technologies. The primary focus of this application would be to generate optimal tourist routes on a per-city basis. This means that the tool would prioritize suggesting viewing sequences for tourist attractions within the city where the user is staying or traveling through, as well as attractions in close proximity to the city. Our research aims to contribute to the ongoing wave of technological automation in the tourism sector by developing a tool that simplifies the planning process for tourists. By leveraging advanced algorithms and a modern web application, we strive to provide an optimal viewing sequence of attractions based on individual preferences and the spatial context of the city. © 2023 Copyright for this paper by its authors.

关键词： Information and Communication Technology Tourist Trip Design Problem Traveling Salesman Problem Virtual Travel Community

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Performance Evaluation of Intrusion Detection System with SVM 6

Performance Evaluation of Intrusion Detection System with SV...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Diaz, Ricky Aurelius Nurtanto Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia

ISBN: (纸本)9798331539603

Various machine learning techniques have been proposed to improve the effectiveness of Intrusion Detection systems (IDS), where IDS is one of the important parts of the network that functions to maintain network security. With its various capabilities and reputation, Support Vector Machine (SVM) is often applied in various classification cases. However, it has constraints with accuracy and computing time when it comes to data that has large dimensions. Utilizing metaheuristic techniques is one possibility that researchers can use to solve this problem. To overcome this, in this study, we will evaluate the performance of SVM for the IDS case by utilizing the metaheuristic Grey Wolf Optimization (GWO) algorithm. From the experiments with three different iteration models, the best results of the combination of SVM and GWO were found in a model with 10 GWO iterations and 10 populations with an accuracy of 97.47% using 15 features out of 48 original features of the UKM-IDS20 dataset. © 2024 IEEE.

关键词： Network intrusion

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Matrix Based Approach to Structural and Semantic Analysis Supporting Software Product Line Evolution 10

Matrix Based Approach to Structural and Semantic Analysis Su...

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

作者： Perdek, Jakub Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia

The evolution of product families is complex due to the exponential growth of new products with a modified codebase. It is caused by the introduction of new such important or restrictive features that allow transforming an existing solution into a new product as the basis for another one. Specifically, both the structure of resulting components and semantic information need to be taken into account to provide in-depth supporting materials by capturing feature interactions and their capabilities. Additionally, data from heterogeneous applications usually differ and their handling needs account for different scoring. We tackled these problems by creating various supporting views based on structural and semantic information. Related applications are modeled as graphs, also instances of their components are optionally included, and various matrix based algorithms based on similarity metrics are integrated. The final integrated and automated approach is efficient because it runs in polynomial time, is extensively focused on dependencies/connections between nodes, can be adapted to big data, and is extendable and enhanced to support different metrics. Its capability to organize analyzed parts into hierarchies by applying hierarchic clustering helps to specify the context to support comprehension or find a related position of features based on their interaction, especially their coupling. With regards to possible design issues, each updated product should be checked by an expert or more autonomously against performed predictions, especially its complexity and coupling between components. Additionally, extendability can be measured from the chosen sequence of such updates. An approach including an automated matrix based feature recognition process is presented in the analysis of modular Angular applications. Our future work will be more focused on semantic enhancements, and its applications on big data by generating and analyzing various types of fractals including extracte

关键词： feature modeling feature trees graph comparison hierarchical clustering matrix clustering

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Towards Utilizing Unlabeled Data for Flood Forecasting with Weakly Supervised Adaptive Learning

Towards Utilizing Unlabeled Data for Flood Forecasting with ...

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2023 Innovations in Intelligent systems and Applications Conference, ASYU 2023

作者： Abdulla, Nawaf Demirci, Mehmet Ozdemir, Suat Gazi University Graduate School of Informatics Department of Information Systems Ankara Turkey Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey Hacettepe University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350306590

The importance of accurate flood forecasting is rising as climate change makes it more difficult to predict when and where floods will occur. Flood predictions use supervised machine learning models, and flood levels are being tracked via the Internet of Things (IoT). Despite advancements in data-driven hydrological forecasting, producing reliable flood forecasts remains challenging. This research paper explores the possibility of utilizing adaptive deep learning with weak supervision to predict flooding in coastal cities that are equipped with Internet of Things technology. This approach uses weakly supervised learning for data labeling and enhancement, a bidirectional long-short-term memory (LSTM) RNN for time-series prediction and classification, and adaptive learning to handle seasonality. The results of the proposed system show that flood forecasting accuracy could be improved by 40% compared to the baseline models. © 2023 IEEE.

关键词： Floods

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ROS Integration and Evaluation for Robot Navigation in a Narrow-Path Indoor Environment 14

ROS Integration and Evaluation for Robot Navigation in a Nar...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Ung, Lilian Wei Fang Wong, Richard T.K. Eu, Kok Seng Jasser, Muhammed Basheer Issa, Bayan Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia Taylor's University Selangor Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350364507

This project explores the implementation of ROS for robot navigation in an indoor environment with narrow pathways typically found in a grocery shop or library. The study discusses the design of the integration of different components including hardware such as sensors and actuators, and algorithms in ROS. The system uses LiDAR to first map the real-world environment to determine the static components of the environment. The purpose of the project is to determine the optimal system integration to achieve obstacle detection and avoidance specifically on dynamic obstacles in environments with narrow pathways. The results show 100% collision-free navigation either through avoiding obstacles or re-routing to an obstacle-free course. © 2024 IEEE.

关键词： Integration

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A Comparative Study on Embedding Models for Keyword Extraction Using KeyBERT Method 13

A Comparative Study on Embedding Models for Keyword Extracti...

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Issa, Bayan Jasser, Muhammed Basheer Chua, Hui Na Hamzah, Muzaffar University of Aleppo Faculty of Informatics Engineering Syria Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350340891

KeyBERT is a method for keywords/keyphrases extraction, which has three steps. The first step is selecting candidate keywords from a text using sklearn library, the second step is the embedding operation of the text and its candidate keywords;this operation is done by BERT to get a numerical representation that represents the meanings. The third step is calculating the cosine similarity between individual keywords vectors and document vector. In this paper, we focus on the second step of KeyBERT (embedding step). Although KeyBERT has a lot of supported models for the embedding operation, there are no extensive previous comparative studies to analyze and study the effect of using different supported models in KeyBERT. We introduce a comparative study of two commonly used groups of models;the first group is sentence-transformers pretrained models, supported via the sentence-transformers library, and the second group includes the Longformer model, supported via the Hugginface Transformers library. We conduct the comparative study of models on benchmark datasets, which contain English text documents of multi-domains with different text lengths. Based on the study, we found that the Paraphrase-mpnet-base-v2 model provides the best results among all other models in keyword extraction in terms of effectiveness (f1-score, recall, precision, MAP) on all datasets, with higher efficiency (time) on short text compared with using it on long text;accordingly, we recommend using it in that context. On the other hand, the Longformer model is the most efficient/fastest in keyword extraction among all other models on all datasets and this superiority has been evident, especially in long text;accordingly, we recommend using it in that context. © 2023 IEEE.

关键词： Embeddings

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