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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Pratama, Galih Dea Novika, Andien Dwi Ardiyanto, Elshad Ryan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes. © 2024 The Authors.

关键词： 6-11 framework game analysis racing video games video games

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Experimental evaluation of MIMO configurations in indoor visible light communications under the consideration of illumination

Optics Continuum

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Optics Continuum 2025年第4期4卷 789-803页

作者： Esteban Farfán-Guillén, David Camporez, Higor Silva, Jair Adriano Lima Castellano Rodriguez, Juan Camilo Pohl, Alexandre De Almeida Prado CPGEI–Graduate Program in Electrical Engineering and Computer Science Federal University of Technology Paraná Curitiba80230–901 Brazil Graduate Program in Electrical Engineering Federal University of Espírito Santo Vitória29075-960 Brazil

Visible light communications (VLC) enable wireless data transmission using existing light-emitting diode (LED) illumination. This experimental study evaluates the performance of multiple input and multiple output (MIMO) configurations in indoor VLC systems by analyzing their impact on both communication and illumination. Various MIMO setups (2 × 2, 3 × 2, and 4 × 2) are compared against single input single output (SISO), and single input multiple outputs (SIMO) schemes, using commercial LED luminaires as transmitters. While the 4 × 2 MIMO configuration demonstrates superior overall performance, intermediate configurations like 3 × 2 or 2 × 2 MIMO offer an attractive trade-off between system complexity and performance enhancement, particularly in scenarios with space or cost constraints. The results show that MIMO configurations significantly outperform SISO systems, achieving reliable communication (BER ≤ 10−3) at practical indoor distances of up to 2 meters while maintaining illumination levels above 300 lux, where conventional SISO setups do not meet these dual requirements. These findings demonstrate the practical feasibility of MIMO-VLC systems for indoor deployments. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Frequency division multiplexing Modulation techniques Multiple input multiple output Quadrature amplitude modulation Signal processing Visible light communications

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Improvement of Visual Odometry Based on Robust Feature Extraction Considering Semantics 9

Improvement of Visual Odometry Based on Robust Feature Extra...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Adachi, Miho Xue, JunFeng Honda, Kazufumi Wada, Marin Miyamoto, Ryusuke Graduate School of Science and Technology Department of Computer Science Japan School of Science and Technology Meiji University Department of Computer Science Kawasaki Japan

ISBN: (纸本)9798350311402

Visual odometry is a key technology for an autonomous robot to accurately determine its locations on a map accurately if a camera is the main external sensor. If IMU is available, the scale information can also be estimated by combining visual and IMU information, which is called Visual Inertial Odometry (VIO). This research attempts to modify VINS-Mono, a widely used VIO method, to improve the estimation accuracy in symbiotic environments with people in outdoor scenes, where estimation accuracy becomes worsens according to the dynamic obstacles in images. The proposed method replaces a method for feature point extraction in VINS-Mono and adds a process to limit the area for the feature point extraction using semantic information obtained from segmentation results. Experimental results using datasets created from actual environments demonstrate that SuperPoint showed the best accuracy for most scenes and that area limitation improved estimation accuracy when many dynamic obstacles were included in the input images. © 2023 IEEE.

关键词： Semantics

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Applying Burn-in Strategy to Deep Reinforcement Learning with Spiking Neural Networks 13

Applying Burn-in Strategy to Deep Reinforcement Learning wit...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Iwata, Takahiro Yoshioka, Seankein Miki, Daisuke Graduate School of Computer Science Chiba Institute of Technology Chiba Japan Chiba Institute of Technology Department of Computer Science Chiba Japan

ISBN: (纸本)9798350373332

In recent years, neural networks in deep reinforce-ment learning (D RL) have achieved success in various tasks, owing to their high expressiveness and flexibility. Neural networks are expected to be increasingly used for robot control. However, large neural networks consume a significant amount of power during training and operation. Given that the power supply that can be installed in robots is limited, challenges remain in the practical application of robot control using DRL, which includes large-scale neural networks. Conversely, spiking neural networks (SNNs) have high power efficiency, which is achieved through dedicated computational elements. Previous studies reported that SNNs can be applied to DRL to learn a robot's walking motion in a virtual environment and reduce energy consumption. Although previous studies have applied SNNs to DRL, they have the drawback of recursive computation, causing output delays. In this study, we propose a method that combines SNNs with a structure that addresses the issues of previous studies using a twin-delayed deep deterministic policy gradient (TD3). In addition, a burn-in strategy was introduced to estimate the action values using the internal states of the SNNs. Experiments in a virtual environment showed that the proposed method achieved higher rewards than the previous methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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R-LIME: Rectangular Constraints and Optimization for Local Interpretable Model-agnostic Explanation Methods 27th

R-LIME: Rectangular Constraints and Optimization for Local I...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ohara, Genji Kimura, Keigo Kudo, Mineichi Division of Computer Science and Information Technology Graduate School of Information Science and Technology Hokkaido University Sapporo060–0814 Japan

ISBN: (纸本)9783031783531

In recent years, complex machine learning models have been widely introduced in various industrial fields due to their high accuracy. However, their increasing complexity has been a major obstacle to their implementation in sensitive decision-making situations. In order to address this problem, various post-hoc explanation methods have been proposed, but they have not been able to achieve interpretability of both the explanation and its scope. We propose R-LIME, a novel method that interprets complex classifiers within an interpretable scope. R-LIME locally and linearly approximates the complex decision boundary of a black-box classifier within a rectangular region and maximizes the region as long as the approximation accuracy exceeds a given threshold. The resulting rectangular region is interpretable for users because it is expressed as a conjunction of feature predicates. Through qualitative and quantitative comparisons on a real-world dataset, we demonstrate that R-LIME provides more reliable and interpretable explanations than existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Vision Intelligence Assisted Lung Function Estimation Based on Transformer Encoder-Decoder Network With Invertible Modeling

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3336-3349页

作者： Chen, Liuyin Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Zhang, Zijun City University of HongKong School of Data Science Hong Kong Nanfang Hospital Southern Medical University Department of Thoracic Surgery Guangzhou510515 China University of Science and Technology Beijing Department of Computer Science and Technology Beijing100083 China University of Science and Technology Beijing Shunde Graduate School Foshan China

Lung function evaluation is important to many medical applications, but conducting pulmonary function tests is constrained by different conditions. This article presents a pioneer study of an integrated invertible deep learning method for lung function estimation via using computed tomography (CT) images. First, the projection method is proposed to flatten the three-dimensional (3-D) image onto a two-dimensional (2-D) plane, with preserving location information in 3-D. Next, the MBConv transformer-based encoder-decoder structure is developed to extract latent features. Finally, we develop an invertible normalizing flow (NF) model to infer lung function based on the extracted features and design two loss functions for two directions. The method enables both estimating the lung function based on CT images and metadata as well as generating the corresponding simulated CT image according to the lung function. Computational studies show that the proposed regression model outperforms all state-of-the-art image regression models. A comprehensive comparative analysis also demonstrates the effectiveness of using generated images and confirms the superiority of the proposed method. To the best of our knowledge, this work is the first of its kind in combining encoder-decoder network with NFs to ensure the effectiveness of the fully invertible framework, especially in lung CT image analysis. © 2020 IEEE.

关键词： Metadata

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Artificial Intelligence-Powered Intent Classification for Indonesian E-Wallet Customer Service Chatbots 4

Artificial Intelligence-Powered Intent Classification for In...

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

作者： Owen, Christopher Suhartono, Derwin Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

The rapid expansion of e-wallet services in Indonesia has significantly heightened the need for efficient customer service solutions, making chatbots an essential tool for user support. However, many providers continue to rely on rule-based chatbots, resulting in rigid interactions that struggle to meet diverse user needs or handle the linguistic complexities of the Indonesian language. This study addresses these limitations by developing an AI-powered intent classification model specifically tailored for Indonesian e-wallet customer service, aimed at delivering a more accurate, adaptive, and user-centered experience. A custom dataset was created from user comments on social media associated with Indonesia's top e-wallet providers, followed by data pre-processing and clustering using the BERTopic model. To improve interpretability, OpenAI's GPT-4 was employed for label refinement, resulting in enhanced clarity. Various models were tested, including IndoBERT, RoBERTa, and Convolutional Neural Network (CNN) architectures in both 2D and 3D configurations. The highest-performing model combined IndoBERT embeddings with a 3D CNN classifier, achieving an accuracy of 84.30%, precision of 84.33%, recall of 84.30%, and an F1-score of 84.24%. This study contributes a unique Indonesian-specific dataset and demonstrates the potential of AI to transform customer service interactions in Indonesia's e-wallet sector, offering a clear advancement over traditional rule-based approaches. © 2024 IEEE.

关键词： Sales

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Comparison of Spelling Error Correction Algorithms for the Indonesian Language 11

Comparison of Spelling Error Correction Algorithms for the I...

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11th International Conference on Information and Education technology, ICIET 2023

作者： Yanfi, Yanfi Setiawan, Reina Soeparno, Haryono Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781665465489

Edit distance as a string measurement metric is often used to help detect misspellings in languages. This paper aims to compare two string spelling error correction algorithms for the Indonesian language. The N-gram, Jaro-Winkler distance, and Levenshtein distance algorithms are used to determine whether they can accurately correct typological errors in the Indonesian language. Moreover, this study utilized KNIME tools to process the data from beginning to end. The data was retrieved from news in Indonesia. After the experiment on N from 1 to 12, the results obtained for the comparative analysis proved that Jaro-Winkler distance performed better than Levenshtein distance for comparing smaller strings like words and names. However, Levenshtein distance performs as well as Jaro-Winkler distance started from four strings. Finally, both Jaro-Winkler distance and Levenshtein distance algorithm got the best performance accuracy for eight strings with an accuracy of 99.52 percent. The result of this study is also presented that both algorithms can support word error correction for the Indonesian language. © 2023 IEEE.

关键词： Error correction

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"Nyctophy": Assessing the effectiveness of serious game for nyctophobia based on virtual reality by usability and heart rate monitoring 9th

"Nyctophy": Assessing the effectiveness of serious game for ...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Ardiyanto, Elshad Ryan Alexander, Patrick Putra, Edyth Novian Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Nyctophobia is a phobia of the dark and is common among children but also found in adults. While the phobia itself is commonly known, the diversity of its treatment is still minimal. As technology has reached its highest point, numerous inventions and innovations have emerged in the medical sector. This study aimed to examine the usability of Virtual Reality and Keyboard and Mouse as an interface using the Extended Reality Game Usability Questionnaire (XR-GUQ) in Nyctophobia therapy and to analyze the heart rate response, impact, and implications using t-test Paired/Wilcoxon Signed Rank. Thirty-four Bina Nusantara University students from various backgrounds completed the test using interfaces and a smartwatch. They were divided into two groups, high- and low fear, using the modified Acrophobia Questionnaire (AQ), which resulted in a significant difference. The XR-GUQ result was insignificant in all variables, which indicates similar usability scores on both interfaces. The t-test Paired/Wilcoxon Signed Rank test revealed a significant difference between the high- and low-fear groups when using the Virtual Reality interface compared to the Keyboard and Mouse interface. This significant difference was found within each group regardless of the interface used. This research shows similar scores in terms of usability but a significant difference in heart rate response with the help of a serious game to give a new sensation of phobia therapy. © 2024 The Authors.

关键词： Serious games

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A Defect Detection Method for the Primary Stage of Software Development

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computers, Materials & Continua 2023年第3期74卷 5141-5155页

作者： Qiang Zhi Wanxu Pu Jianguo Ren Zhengshu Zhou School of Computer Science and Technology Jiangsu Normal UniversityXuzhou221116China Graduate School of Informatics Nagoya UniversityNagoya4648601Japan

In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the ***,due to various reasons,there may be a large number of *** generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of *** this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical *** proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in *** verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS *** CBR is a classicmethod of reviewing SRS *** a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements *** experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed ***,the shortcomings and deficiencies of the proposed method are also observed through the ***,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.

关键词： computer science software engineering requirement engineering software quality defect detection

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