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Movies and Learning: Does Watching Movies Help Students Improve Learn English? 1

9th International Conference on Computational Science and Technology, ICCST 2022

作者： Baharum, Aslina Naim, Nur Faraha Mohd Ismail, Rozita Deris, Farhana Diana Noor, Noorsidi Aizuddin Mat Jasin Campus Melaka Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Bahru Malaysia UTM CRES Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (数字)9789811984068

ISBN: (纸本)9789811984051

Since English is the most widely used language for communication in the world, mastering English has become essential and important in our lives. Many methods are used to improve the English level, including watching movies. This research aims to study whether watching movies helps students improve their English. This study uses a quantitative method to collect the data through an online questionnaire using Google Forms. The online questionnaire consists of 15 questions with the participation of 250 students from one of the universities in Malaysia, of which each faculty has 25 students for random clustering. The results show that most students usually watch action English movies very often. From the findings, there is no result showing that there is a significant relationship between movie genre preference and English learning. However, it can be concluded that watching movies does help students improve learn English. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Application for Selecting Legislative Candidates with Social Media Data that Influences Election Decisions 12

Application for Selecting Legislative Candidates with Social...

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12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Bawono, Marastika Wicaksono Aji Utomo, Stevani Dwi Kristianto, Andika Rozy, Agus Fachrur Utomo, Rio Guntur Nuha, Hilal Hudan School of Computing Telkom University Bandung Indonesia Universitas Kristen Duta Wacana Faculty of Informatics Central Java Indonesia University Airlangga Faculty Science Jakarta Indonesia University Brawijaya Dapertement Statistic Malang Indonesia

ISBN: (纸本)9798350363432

Numerous studies have explored legislative election candidates, yet there remains a significant gap in understanding the impact of social media analysis on election choices. This research investigates the analysis of campaign news disseminated through social media platforms in shaping voter polling trends. A significant challenge encountered in the digital world is the prevalence of fake accounts and bots that can increase the number of followers and distinguish between fake and genuine comments, thereby enhancing the reputation of legislative candidates. Therefore, this requires the implementation of social media analysis reports combined with survey questionnaire results that directly reflect the opinions of supporting respondents. The data obtained from this survey is then visualized in the report. The methodological approach uses analysis from social media news combined with data processing using the pls sem model from questions given to respondents. The primary goal of this research is to provide valuable insights for legislative candidate nominations, particularly in understanding campaign trends and accurately measuring the extent of real-time support on social media platforms. © 2024 IEEE.

关键词： Report for selecting legislative candidates SEM PLS Systematic literature review

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Aspect-Based Subjectivity Analysis Using a BERT-based Approach

Aspect-Based Subjectivity Analysis Using a BERT-based Approa...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Ng, Hu Chong, Wing Kin Chia, Yu Zhang Vun Yap, Timothy Tzen Goh, Vik Tor Wong, Lai Kuan Teck Tan, Ian Kim Cher, Dong Theng Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia School of Mathematical & Computer Sciences Heriot-Watt Univeristy Putrajaya Malaysia SIRIM Berhad Shah Alam Malaysia

ISBN: (纸本)9798350353464

Aspect-based subjectivity analysis stands as an important task in natural language processing, seeking to identify the subjectivity of various aspects or features within a text. A new method for aspect-based subjectivity analysis using BERT is introduced in this paper. BERT has demonstrated impressive performance across various NLP tasks, and its capabilities are utilized to accurately ascertain the subjectivity of specific aspects within a given text. The approach involves fine-tuning BERT on a sizable dataset annotated with aspect-level subjectivity labels, enabling the model to grasp the subtleties of aspect-based subjectivity analysis. Extensive experiments on benchmark datasets are conducted to showcase the effectiveness of this approach and compare it with existing methods. The results reveal that this proposed approach surpasses state-of-the-art techniques in aspect-based subjectivity analysis, underscoring the potential of leveraging BERT for such purposes. ©2024 IEEE.

关键词： Aspect-based NLP Subjectivity Analysis

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Supervised Machine Learning Techniques for Power Consumption Usage Level Prediction 6

Supervised Machine Learning Techniques for Power Consumption...

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6th International Conference on Software Engineering and Computer Science, CSECS 2023

作者： Tan, Yi-Fei Zhao, Ge-Zhi Cheeng, Tze-Hang Ooi, Chee-Pun Tan, Wooi-Haw Cheong, Soon-Nyean Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Technology Faculty of of Engineering Chongqing Chongqing China Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia Multimedia University Faculty of Creative Multimedia Cyberjaya Malaysia

ISBN: (纸本)9798350306378

Electricity is an essential part of modern life and is crucial for many aspects of society and the economy. It is essential for industries to operate and create products, for transportation systems to function, and for communication and information technology to function. As the world continues to become more connected and technologically advanced, the demand for electricity is likely to increase, making it an even more critical resource in the future. Overusing electricity will lead to higher energy bills, which can put a strain on household or business budgets. In this paper, the authors developed a model to predict the level of electricity consumption usage. The model is developed in 2 stages. First stage is to categorize the electricity usage into several levels by using equally divided method. Second stage is to use supervised machine learning techniques to develop a prediction model for the level of electricity consumption. The experimental results indicated that using the Random Forest classifier leads to better performance in terms of accuracy, precision, recall, F1 score, and kappa score. Besides that, the results show that 2-level prediction performed better than 4-level prediction. © 2023 IEEE.

关键词： Forecasting

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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Computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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A Survey on Product Promotion via E-commerce Platforms - Case Study in Malaysia

A Survey on Product Promotion via E-commerce Platforms - Cas...

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2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021

作者： Alomari, Mohammad Ahmed Ahmed Al-Qasem Al-Hadi, Ismail Yusoff, M. Hafiz Sulaiman, Ibrahim Mohammed Universiti Tenaga Nasional Institute of Informatics and Computing in Energy Malaysia Faculty of Ocean Engineering Technology Informatics Malaysia School of Informatics and Computing Malaysia Faculty of Informatics and Computing Malaysia

ISBN: (纸本)9781665403436

A survey by Malaysian Communication and Multimedia Commission (MCMC) has shown that Internet users in Malaysia has increased up to 88.7% in year 2020 compared to 76.9% in year 2016 which is quite high increase in percentage. The pervasive use of smartphones and computers nowadays as well as the availability of high-speed Internet access has brought a new way of promoting products. E-commerce is a buy and sell system that can be accessed globally around the world. This system can provide efficient strategies for promoting products and services. It is widely agreed that even small enhancements in promotion techniques can increase the profitability of any e-commerce system. In this paper, a research has been conducted to study methods to enhance product promotion among Malaysians. The study investigates through a survey the factors affecting users' WTP (willing-to-pay) during performing e-commerce transaction as well as factors what attract/repel them more. To achieve the study aim, a group of 385 respondents throughout Malaysia have been involved in this research through questionnaire study. Detailed analysis has been introduced to clarify the results. The study has identified the top e-commerce platforms that are used by Malaysians to execute product promotion. The results show that promotion through e-commerce platforms could increase profitability and help businesses to expand rapidly. This is due to the fast market penetration of online promoting compared to conventional one. © 2021 IEEE.

关键词： Surveys

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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Computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Juris-informatics: Law for AI and Law of AI

Juris-Informatics: Law for AI and Law of AI

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Satoh, Ken Takeda, Hideaki Goebel, Randy Kano, Yoshinobu Kim, Mi-Young Rabelo, Juliano Yoshioka, Masaharu Center for Juris-Informatics Tokyo Japan University of Alberta Dept. of Computing Science Alberta Machine Intelligence Institute EdmontonAB Canada Shizuoka University Faculty of Informatics Shizuoka Hamamatsu Japan University of Alberta Augustana Faculty Dept. of Science AB Canada Hokkaido University Faculty of Information Science and Technology Hokkaido Sapporo Japan

ISBN: (纸本)9798350362480

This paper presents an outline of our research project developed at our research center for "Juris-informatics". "Juris-informatics"is a research field based on two main topics;"Law by AI"and "Law of AI". "Law by Ai"is a research field where we investigate a support tool by AI for legal activities such as legal reasoning and legal document processing. "Law of AI"is a research field where we conduct research on legal control of AI such as considering the legal responsibility of AI and legal compliance of AI. © 2024 IEEE.

关键词： informatics

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Bringing Culture to Life: A Usability Study of Web-AR Folklore Storytelling

Bringing Culture to Life: A Usability Study of Web-AR Folklo...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Sani, Zaidatol Haslinda Abdullah Nizam, Dinna Nina Mohd Naim, Nur Faraha Mohd Amat, Nur Asmiaty User Experience Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Centre for the Promotion of Knowledge and Language Learning Universiti Malaysia Sabah Sabah Malaysia

ISBN: (纸本)9798331530303

Learning about culture is essential, and expanding the methods of cultural education beyond traditional learning is crucial. Technology offers new opportunities to not only enhance cultural learning but also promote tourism by introducing cultural elements through innovative platforms. This study evaluates the usability of a web-AR app designed to introduce the Bajau Samah culture through folklore storytelling, providing both an educational tool and a tourism promotion method. The app consists of six pages, allowing users to interact with augmented reality (AR) by scanning markers. Users can zoom and rotate 3D models, listen to audio, and read accompanying narratives. A total of 33 participants took part in the usability study, with more than half of them having no prior experience with AR. The System Usability Scale (SUS) score was 83.5%, indicating good usability. While some usability issues were identified and suggestions for improvement were provided, overall, participants enjoyed using the web-AR app and found it effective in educating them about Bajau Samah culture through AR-folklore storytelling. ©2024 IEEE.

关键词： Federated learning

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Parameterized Complexity Classification for Interval Constraints 18

Parameterized Complexity Classification for Interval Constra...

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18th International Symposium on Parameterized and Exact Computation, IPEC 2023

作者： Dabrowski, Konrad K. Jonsson, Peter Ordyniak, Sebastian Osipov, George Pilipczuk, Marcin Sharma, Roohani School of Computing Newcastle University United Kingdom Department of Computer and Information Science Linköping University Sweden School of Computing University of Leeds United Kingdom Faculty of Mathematics Informatics and Mechanics University of Warsaw Poland IT University Copenhagen Denmark Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9783959773058

Constraint satisfaction problems form a nicely behaved class of problems that lends itself to complexity classification results. From the point of view of parameterized complexity, a natural task is to classify the parameterized complexity of MinCSP problems parameterized by the number of unsatisfied constraints. In other words, we ask whether we can delete at most k constraints, where k is the parameter, to get a satisfiable instance. In this work, we take a step towards classifying the parameterized complexity for an important infinite-domain CSP: Allen's interval algebra (IA). This CSP has closed intervals with rational endpoints as domain values and employs a set A of 13 basic comparison relations such as "precedes" or "during" for relating intervals. IA is a highly influential and well-studied formalism within AI and qualitative reasoning that has numerous applications in, for instance, planning, natural language processing and molecular biology. We provide an FPT vs. W[1]-hard dichotomy for MinCSP(Γ) for all Γ ⊆ A. IA is sometimes extended with unions of the relations in A or first-order definable relations over A, but extending our results to these cases would require first solving the parameterized complexity of Directed Symmetric Multicut, which is a notorious open problem. Already in this limited setting, we uncover connections to new variants of graph cut and separation problems. This includes hardness proofs for simultaneous cuts or feedback arc set problems in directed graphs, as well as new tractable cases with algorithms based on the recently introduced flow augmentation technique. Given the intractability of MinCSP(A) in general, we then consider (parameterized) approximation algorithms. We first show that MinCSP(A) cannot be polynomial-time approximated within any constant factor and continue by presenting a factor-2 fpt-approximation algorithm. Once again, this algorithm has its roots in flow augmentation. © 2023 Schloss Dagstuhl- Leibniz-Zentrum f

关键词： Approximation algorithms

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