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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Sama, Andi Gaol, Ford Lumban Harso Supangkat, Suhono Ranti, Benny Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350394559

The Information Technology/Operational Technology convergence towards Industry 4.0 opens the opportunity to leverage recent advancements in Information Technology for Operational Technology, such as Cloud, Internet of Things, and Artificial Intelligence. Meanwhile, cyber-attacks are increasing for Operational Technology. The security aspect in Operational Technology systems has traditionally been a low priority, in contrast with speed. This introduces challenges as Industrial Control Systems such as programmable Logic Controller in Operational Technology have been traditionally optimized for speed rather than security due to limited computing power. As computations need to be as efficient as possible, security in Operational Technology has yet to be managed as robustly as in Information Technology. Operational Technology communication protocols for securing data transfer, for example, have no or just a few security capabilities, even for basic authentication and encryption. Common security algorithms rely on random numbers. However, Random Number Generator is not usually part of standard functions in programmable Logic Controllers, the core control component in Industrial Control Systems. In Industrial Control Systems, the Random Number Generator is mostly implemented as a software-based Pseudo Random Number Generator. This paper shows how to apply a Pseudo Random Number Generator in a Siemens Compact PLC S7-1200 using a modified lightweight XORshift algorithm. The XORshift algorithm can generate better randomness than the system's clock-based implementation in the Siemens Library of Generic Functions. © 2023 IEEE.

关键词： Internet of things

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Immersive Learning in Using the Metaverse of Satellite Communication Systems science 9

Immersive Learning in Using the Metaverse of Satellite Commu...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Akbar, Komputerio Meyliana Warnars, Harco Leslie Hendric Spits Jayadi, Riyanto Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information Systems Management Department Jakarta Indonesia

ISBN: (纸本)9798350383010

Education stands out as one of the most impactful applications of the metaverse, holding immense potential for the future. Within the realm of satellite communication system science education, the integration of immersive learning through the metaverse adds a layer of interest and interactivity to the teaching and learning process. Numerous design and evaluation techniques have been employed in a growing body of research, aiming to understand how immersive learning via the metaverse can effectively contribute to specific educational objectives. Hence, this research is dedicated to delving into the development of an immersive learning metaverse model for the science of satellite communication systems. The research contributes a model that advocates for a holistic learning approach by integrating metaverse technology, VR, the 6E Instructional Process Model, and the MSLQ evaluation instrument. The objective of this model is to enhance the efficiency, effectiveness, and interactivity of learning within the field of satellite communication systems science. © 2024 IEEE.

关键词： Satellite communication systems

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Imagery contents descriptions for People with visual impairments 23

Imagery contents descriptions for People with visual impairm...

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Proceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems

作者： Alessandra Helena Jandrey Duncan Dubugras Alcoba Ruiz Milene Selbach Silveira School of Technology - Graduate Program in Computer Science PUCRS Brazil

ISBN: (纸本)9798400717154

Image descriptions are crucial in assisting individuals without eyesight by providing verbal representations of visual content. While manual and Artificial Intelligence (AI)-generated descriptions exist, automatic description generators have not fully met the needs of visually impaired People. In this study, we have examined the problems related to image descriptions reported in existing literature using the Snowballing technique. Through this method, we have identified thirteen issues, including ethical concerns surrounding physical appearance, gender and identity, race, and disability. Furthermore, we have identified five reasons why sighted Individuals often fail to provide descriptions for visual content, highlighting the necessity for accessibility campaigns that raise awareness about the social significance of descriptive sentences. We conducted interviews with eight low-vision volunteers, in which we analyzed the characteristics of descriptive sentences for 25 indoor images and gathered participants’ expectations regarding image descriptions. As a result, we propose a set of Good Practices for writing descriptive sentences aimed to assist automatic tools and sighted Individuals in generating more satisfactory and high-quality image descriptions. We hope our results will emphasize the societal importance of imagery descriptions and inspire the community to pursue further interdisciplinary research to address the issues identified in our study.

关键词： Good practices Image descriptions Visually impaired People

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AI in EEG-Based BCI for the Diagnosis of Mild Cognitive Impairment: A Mini Review 13

AI in EEG-Based BCI for the Diagnosis of Mild Cognitive Impa...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Zaitsev, Vasilii Wei, Chun-Shu EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798331521929

Mild Cognitive Impairment (MCI) is a condition that often precedes dementia, making early diagnosis critical for delaying cognitive decline. Electroencephalography (EEG) has emerged as a non-invasive, cost-effective tool for monitoring brain activity and detecting MCI. This paper overviews recent advancements in machine learning (ML) and deep learning (DL) models for EEG-based MCI diagnosis. Traditional ML approaches, such as support vector machines (SVM) and K-nearest neighbors (KNN), have been widely used but rely on manually extracted features and face challenges with the complex nature of EEG signals. In contrast, DL models like convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers have shown promise in automatically learning features and capturing temporal and spatial information from EEG data. Despite these advancements, issues such as small dataset sizes and variability in EEG recordings remain barriers to clinical application. This paper discusses these challenges and highlights potential future directions for improving the diagnosis of MCI. © 2025 IEEE.

关键词： Convolutional neural networks

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Boosting Engagement and Academic Performance Through Gamification: Leveraging Student Profiles and Game Personas for Enhanced Learning 17

Boosting Engagement and Academic Performance Through Gamific...

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17th International Conference on computer Supported Education, CSEDU 2025

作者： Moura, Waldir Siqueira Delbem, Edgar França, Juliana Baptista dos Santos Dias, Angélica Fonseca da Silva Computer Science Graduate Program PPGI Federal University of Rio de Janeiro Térreo Bloco E CCMN/NCE University City Rio de Janeiro68.530 Brazil

ISBN: (纸本)9789897587467

This study examines the integration of personalized gamification as a strategy to increase student engagement and academic performance, based on the analysis of behavioral profiles and game user personas. Using Detroit: Become Human as a tool to identify collaborative and competitive tendencies, the research aims to address the limitations of traditional methods, which often fail to engage students. The application of Game Theory, combined with the personalization of pedagogical interventions according to each student's profile, enabled the creation of more adapted and motivating approaches. The final results indicate a significant improvement in grades and student engagement levels, with 38.6% of students who were initially below average reaching or exceeding expected performance, along with a marked increase in interest in classroom participation. These findings reinforce the potential of gamified and personalized methodologies to transform the educational experience, adapting it to the individual traits and needs of students. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Students

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Pardamean, Bens Suparyanto, Teddy Sinaga, Gokma Sahat Tua Elwirehardja, Gregorius Natanael Firmansyah, Erick Ginting, Candra Mawandha, Hangger Gahara Putra, Dian Pratama Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Faculty of Agriculture Institute of Agriculture STIPER Yogyakarta55282 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

With the high demand for oil palm production, implementations of Machine Learning (ML) technologies to provide accurate predictions and recommendations to assist oil palm plantation management tasks have become beneficial, such as in predicting annual oil palm. However, different geographical and meteorological conditions may result in different scales of influence for each variable. In this research, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) were used in predicting oil palm yield based on data collected in Riau, Indonesia. Pearson’s correlation coefficient was also calculated in selecting the input features for the models, whereas normalization and standardization were used in scaling the data. By setting the minimum absolute correlation threshold to 0.1 and using standardization, both models managed to obtain more than 0.81 R2, with SVR obtaining the overall best performance with 0.8709 R2, 1.372 MAE, and 1.8025 RMSE without hyperparameter fine-tuning. It was also discovered that the oil palm yield in the previous year is the variable with the most influence in estimating oil palm yield in the current year, followed by the number of plants and soil types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry 8

Improving Supplier Evaluation Model using Ensemble Method-Ma...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Asrol, Muhammad Wahyudi, Sofyan Suharjito Harito, Christian Utama, Ditdit N. Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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Gender Classification on Twitter Based on Feeds and User Descriptions Using Xlnet-Fasttext

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第20期48卷 71-80页

作者： Alandeta, Vito Rozaan Suhartono, Derwin Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

This study proposes a gender classification method for Twitter data using a hybrid XLNet-fastText model. The objective is to enhance gender classification accuracy by leveraging the contextual understanding of XLNet and the semantic richness of fastText embeddings. Computational experiments were conducted on a dataset derived from Kaggle, focusing on user account descriptions and Twitter feeds. The proposed method achieved an accuracy of 71.4%, precision of 77%, recall of 60.9%, and F1-score of 68% for gender classification based on Twitter feeds. For user account descriptions, the scores were 72.4% for accuracy, 75.1% for precision, 63.24% for recall, and 68% for F1-score. These results outperform the baseline XLNet model and demonstrate the potential of the XLNet-fastText combination in improving text-based classification tasks. Our approach highlights a viable pathway to enhance gender classification on social media platforms, suggesting further improvements through multi-modal data integration. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Tweets

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Telematics Work Field Review Text Classification Using the Naïve Bayes Method

Telematics Work Field Review Text Classification Using the N...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Maesya, Aries Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Indonesia

ISBN: (纸本)9781665468763

Indonesia will benefit from a demographic boom in 2030 with a higher labor supply than in earlier decades. Then in industrial revolution 4.0 robotics and artificial intelligence will take the place of low-skilled or menial employment that don't require specialized expertise (AI). To aim research is Telematics Work Field Review Text Classification Using the Naïve Bayes Method. The method using Multinomial Naïve Bayes model which is trained to learn from patterns in training data set without being programmed explicitly. Then, based on the Term Frequency - Inverse Document Frequency, consider the weighting of the word used (TF-IDF). The text classification stage is then carried out using the multinominal nave bayes classification method with evaluation using the confusion matrix, following the acquisition of the TF-IDF value. In the study it took data with web crawling techniques on social media sites twitter. The data collected was 936 data consisting of 7,8% negative sentiments, 26,4% positive sentiments, and 65,8% neutrals. The results of accuracy testing using the Confusion Matrix. And from the results of such tests resulted in an accuracy of 66%, precision 73%, and recall 85%. © 2022 IEEE.

关键词： Telematics

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