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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Loa, Jeremy Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

High availability (HA) in MySQL databases remains a critical challenge, particularly due to risks such as single point of failure (SPOF) and limited scalability in existing setups. Previous studies have explored various approaches to address these issues. One study implemented ProxySQL and Orchestrator for query routing and failover but relied on a single server instance, creating a SPOF that could disrupt client access during failures. Another study implemented HAProxy to balance traffic and couple database servers with specific web applications. While effective for certain use cases, this approach reduced resource efficiency and scalability, because it relies on HTTP protocol and database server are coupled to each application, making it less suitable for dynamic, shared environments requiring high flexibility. These limitations highlight the need for a more robust and scalable HA solution. This paper proposes an improved HA architecture that eliminates SPOFs and enhances scalability by combining redundant ProxySQL instances synchronized with Keepalived managed Virtual IP (VIP) and automated failover using Orchestrator. The architecture allows shared database resources across multiple applications while decoupling them from specific application servers. Test results confirm the system's ability to maintain seamless failover, consistent query routing, and uninterrupted client access through dynamic Virtual IP reassignment to backup server. By addressing the issues of previous studies, this solution provides a practical and scalable framework to achieve high availability in the MySQL cluster by combing ProxySQL, Orchestrator, and Keepalived, making it suitable for modern enterprise environments. © 2025 IEEE.

关键词： HTTP

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Evaluating Vision Transformers Efficiency in Image Captioning 4

Evaluating Vision Transformers Efficiency in Image Captionin...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Samosir, Bintang Kevin Hizkia Isa, Sani Muhamad Binus University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

This study investigates the performance of Vision Transformer (ViT) variants - the Shifted Window Transformers (SWIN), Distillation with No Labels (DINO), and Data-efficient Image Transformers (DeIT) - in image captioning tasks using the Flickr8K dataset. While ViT architectures have shown promise in image classification, their effectiveness for image captioning, particularly with smaller datasets, remains unclear. The models' performance was evaluated using BLEU metrics, while training efficiency was analyzed through Pareto front analysis of computational time and accuracy. Among the tested variants, SWIN Transformers demonstrated superior performance (BLEU-1: 64.4, BLEU-2: 33.9, BLEU-3: 17.1, BLEU-4: 8.4), followed by DINO (BLEU-1: 63.1, BLEU-2: 32.7, BLEU-3: 16.4, BLEU-4: 7.5), while DeIT showed the weakest performance (BLEU-1: 61.6, BLEU-2: 31.1, BLEU-3: 14.7, BLEU-4: 6.5). SWIN Transformers achieved the shortest training time at 3 minutes 31 seconds per epoch, making it the most efficient model among ViT variants based on Pareto front analysis. While ViT variants achieved competitive BLEU-1 scores comparable to previous top models, they struggled with generating coherent, longer sentences, as evidenced by suboptimal BLEU-4 scores. These findings provide empirical evidence of how the lack of inductive bias in transformer architectures affects their ability to capture complex scene relationships, despite their strong feature detection capabilities, contributing to the understanding of transformer models' limitations in vision-language tasks, especially with limited data. © 2024 IEEE.

关键词： Distribution transformers

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Body Extension by Using Two Mobile Manipulators

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Cyborg and Bionic Systems 2023年第1期4卷 418-426页

作者： Yusuke Hirao Weiwei Wan Dimitrios Kanoulas Kensuke Harada Graduate School of Engineering Science Osaka UniversityOsakaJapan Department of Computer Science University College LondonLondonUK

This paper presents a remotely operated robotic system that includes two mobile manipulators to extend the functional capabilities of a human *** with previous tele-operation or robotic body extension systems,using two mobile manipulators helps with enlarging the workspace and allowing manipulation of large or long *** system comprises a joystick for controlling the mobile base and robotic gripper,and a motion capture system for controlling the arm *** together enable tele-operated dual-arm and large-space *** the experiments,a human tele-operator controls the two mobile robots to perform tasks such as handover,long object manipulation,and cooperative *** results demonstrated the effectiveness of the proposed system,resulting in extending the human body to a large space while keeping the benefits of having two limbs.

关键词： system Extension enable

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Using CNN and Transformer Model for Unimodal Speech Emotion Recognition on MELD and IEMOCAP

Using CNN and Transformer Model for Unimodal Speech Emotion ...

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Aurelio, Clarence Chowanda, Andry Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

Emotion Recognition is one field that is taking the world by storm in this current age. Multimodal emotion recognition has shown promising results however, previous studies shows that recognition using speech is a field has not performed that greatly. Previous research used popular methods such as CNN, RNN, and other machine learning methods has been used to achieved good results with the help of other modality, where if speech were to be used alone the results won't be as good. A factor that contributes to the result of the speech modality not performing as well is the fact of the dataset and the way it was processed. Therefore, this research looks into what makes speech one of the lowest performing modalities by preprocessing the dataset using a different method than previous researches as well as using a CNN model paired with a Transformer model. Results achieved from this research showed an improvement when using IEMOCAP by 8.09% and MELD by 1.06%. A comparison results of using the pre-processed data with previous studies model was also done to compare the results. In conclusion, this research has explored a different preprocessing method and deep learning model that has achieved results better than previous works with the same dataset. © 2025 IEEE.

关键词： Contrastive Learning

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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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Identification of Clustering BRISK Keypoint Feature in Grocery Product Using the Elbow Method

Identification of Clustering BRISK Keypoint Feature in Groce...

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

作者： Rahmania, Rissa Anggreainy, Maria Susan Kartowisastro, Iman Herwidiana Budiharto, Widodo Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

In smart retail, the problem in grocery product image classification is fine-grained image representation. Product variants are characterized by differences in color, text, and size. However, while the process of image classification involves quite complex stages, there is still a misclassification of grocery products. This is because there are many areas of the image being calculated, which makes the models not optimally calculate the most important area. This paper aims to identify a number of patch image formations as an important area of the product by involving Binary Robust Invariant Scalable Keypoints (BRISK) as a feature descriptor, K-Means Clustering, and the elbow method. First, we experiment with the BRISK keypoint feature for grocery products with different categories of brands. Second, the keypoint features are clustered using K-Means clustering. Lastly, the number of clusters is analyzed using the elbow method to find the optimal number. The result shows that the number of clusters from 8 products has variations of 5, 5, and 6 clusters. Finally, each cluster represents an important area in forming patches to support fine-grained grocery product image classification. ©2024 IEEE.

关键词： K-means clustering

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Parallelization of LSTM-GRU Architectures for Multivariate Prediction of Stock Prices 9

Parallelization of LSTM-GRU Architectures for Multivariate P...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Tarigan, Gabriel Asael Hermawan, Eric Girsang, Abba Suganda Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Anticipating stock market movements is challenging, as hasty prediction risks large financial losses. Stock price prediction is complex due to the high volatility and many influencing external factors. Recurrent Neural Network (RNN) models are made to learn time series data making it ideal for stock prediction based on records. Well-known RNN architectures for forecasting problems are Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). However, past studies only explored standalone models and have not generated convincing performance results. By integrating the efficiency of GRU to effectively handle shorter data sequences with LSTM's ability to capture long-term dependencies, our model aims to improve stock price forecasting accuracy by collaborating the strength between both architectures. The proposed model was used to forecast multivariate stock prices and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). We found that parallelization of LSTM-GRU offers better prediction, up to 9.1% improvement compared to sequential LSTM-GRU and Bidirectional LSTM (Bi-LSTM). © 2024 IEEE.

关键词： Mean square error

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Object Driven Fuzzy Decision Model for Evaluating the Cryptocurrency's Performance 11

Object Driven Fuzzy Decision Model for Evaluating the Crypto...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Utama, Ditdit Nugeraha Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

The assessment of cryptocurrency performance has been subject to criticism due to the lack of a standardized approach, leaving the evaluation process without clear guidance. In this research, an innovative object-driven decision model based on fuzzy logic (OFDM) conception was developed to systematically analyze cryptocurrency performance. Object-oriented operated as the main method in analyzing and designing the model. In this context, the model elements deeply observed and functioned to become fundamental thinking for constructing the model. Then, fuzzy logic with curve membership function form was also used as the primary method to avoid the bias value of three key parameters;trend line (and then used to see the slope distance), price consistency, and transaction volume consistency. The constructed model is a novel decision model that was successfully constructed for assessing the determined cryptocurrency performance. Utilizing six months of real data from three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin), the model conclusively demonstrates that Bitcoin exhibits the strongest performance, scoring 36.39 performance points. © 2024 IEEE.

关键词： Fuzzy logic

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The Learning Outcomes Achievement and User Experience Analysis of a Cryptography Education Web Game 10

The Learning Outcomes Achievement and User Experience Analys...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Susanto, Helmi Yusuf Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

The rising threat of cybercrimes in Indonesia, including a significant breach of the national data center, reveals the urgent need for effective cybersecurity education. Cryptography, a key component of cybersecurity, is often difficult for beginners to grasp. To address this, we developed an interactive web game that blends puzzle-solving with a visual novel to make cryptographic concepts more accessible. This study analyzes the game's user experience (UX) using the ARCS model - Attention, Relevance, Confidence, Satisfaction - and assesses learning outcomes by comparing pre-test and post-test results. The findings show significant improvements in participants' understanding and high levels of engagement, demonstrating a strong correlation between positive user experience and enhanced learning outcomes. However, some aspects of the educational content still need refinement. Future updates will offer multilingual support and personalized learning paths, with pre-and post-tests targeting specific cryptography and cybersecurity sub concepts, and analysis of UX features through targeted surveys and in-game analytics to enhance learning experiences. © 2024 IEEE.

关键词： ARCS Model cryptography cybersecurity educational games puzzle games user experience Visual Novels web games

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