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International Conference on Information Networking

作者： Leandro Loffi Gerson Luiz Camillo Carla Merkle Westphall Carlos Becker Westphall Department of Post-Graduate Program in Computer Science Federal University of Santa Catarina (UFSC) Florianópolis SC Brazil

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The increasing presence of digital evidence in legal, criminal, and civil cases requires adaptations to traditional chain-of-custody processes to ensure the integrity of this evidence. This work proposes the use of blockchain technology, specifically through Hyperledger Fabric, to strengthen the chain of custody for digital evidence. The developed solution employs smart contracts to immutably record each stage of evidence handling, from collection to transfer of custody, ensuring data authenticity, integrity, and traceability. Our implementation demonstrates that blockchain technology can significantly reduce the risk of evidence tampering, improve process efficiency, and facilitate audits, thereby contributing to greater reliability in the presentation of evidence during judicial proceedings.

关键词： Surveys Accuracy Distributed ledger Tracking Forensics Data security Smart contracts Fabrics Blockchains Reliability

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Edge-IoTDistilBERT: Fine-Tuning DistilBERT for Multi-Class Classification of Network Packet Attacks

Edge-IoTDistilBERT: Fine-Tuning DistilBERT for Multi-Class C...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Valentino Setiawan Benfano Soewito Computer Science Department BINUS Graduate Program - Master of Computer Science Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

This exponential proliferation of IoT devices is creating an ever-growing demand for efficient cybersecurity solutions in resource-constrained environments. In this study, we propose Edge-IoTDistilBERT, a fine-tuned DistilBERT model, intended for the multi-class classification of network packet attacks within IoT ecosystems. Our proposed model was trained on the Edge-IIoT dataset, representative of all traffic classes and attack variants with high accuracy of up to 99.99%, outperforming state-of-the-art solutions. The new preprocessing pipeline of PCAP to text would transform the network traffic to a textual form allowing the model to learn the pattern in the dataset and making it able to generalize its content. Our fine-tuned model show a robust result on imbalanced dataset with two diffrent splits namely 80–20 and 70–30. The result shown by Edge-IoTDistilBERT place this model as a feasible effective cybersecurity solution for IoT networks, offering a good trade-off between high performance and resource constraints linked to edge devices.

关键词： Performance evaluation Adaptation models Accuracy Pipelines Transforms Telecommunication traffic Data models Robustness Internet of Things Information systems

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Analyzing the Impacts of Dynamic Game Balancing in a Survival Roguelike Chess Game

Analyzing the Impacts of Dynamic Game Balancing in a Surviva...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Mario Yulyani Arifin Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

This research investigates the novel application of Dynamic Game Balancing (DGB) techniques in the context of a hybrid chess-survival roguelike game, a unique combination of genres not widely explored in previous studies. The study addresses two primary research questions: the identification of suitable DGB techniques for these genres and the enhancement of user experience through DGB. A game prototype was developed using Unity, where players control a chess knight in a turn-based system, facing increasingly challenging enemy pieces. The DGB system dynamically adjusts the game's difficulty based on player performance, utilizing various inputs such as damage received or dealt, health points, powerup usage, and decision speed. Preliminary results from 14 beta-testers show overall positive feedback, with players reporting higher engagement and enjoyment due to the seamless difficulty adjustments, as reflected by the high mean scores from surveys like the User Experience Questionnaire (UEQ). This research fills a gap in the literature by demonstrating the feasibility and benefits of integrating DGB in a chess-survival roguelike setting. These findings highlight the potential for DGB to significantly improve player experience in hybrid game genres, suggesting that its application can create more adaptive and immersive gaming environments in various other genres as well.

关键词： Surveys Video games Electronic learning Prototypes Games Data science User experience Information systems

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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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A pro-inflammatory diet is associated with higher body adiposity in kidney transplant recipients

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Nutrition Research 2025年 138卷 122-134页

作者： Costa, Mariana Silva da Portugal, Mariana Ribeiro Costa Tibaes, Jenneffer Rayane Braga Bianchi, Larissa Santos Maciel de Azevedo Pontes, Karine Scanci da Silva Menna Barreto, Ana Paula Medeiros Caparelli, Lucas Boechat Barreto Silva, Maria Inês Klein, Márcia Regina Simas Torres Post-Graduate Program in Medical Sciences Rio de Janeiro State University Rio de Janeiro Brazil Department of Applied Nutrition Nutrition Institute State University of Rio de Janeiro (UERJ) Rio de Janeiro Brazil Division of Human Nutrition Department of Agricultural Food and Nutritional Science University of Alberta Edmonton AB Canada Post-Graduate Program in Food Nutrition and Health Rio de Janeiro State University (UERJ) Rio de Janeiro Brazil Post-Graduate Program in Clinical and Experimental Pathophysiology State University of Rio de Janeiro (UERJ) Rio de Janeiro Brazil Nutrition Institute - Federal University of Rio de Janeiro Macaé Brazil Nephrology Division Rio de Janeiro State University Rio de Janeiro Brazil

The dietary inflammatory index (DII) has been associated with obesity and cardiovascular risk factors (CVRF) in the general population. We hypothesized that in kidney transplant recipients (KTR), a positive relationship between DII, body adiposity and CVRF would also be observed. To test this hypothesis, we conducted a cross-sectional study with adult KTR. Body mass index (BMI), body adiposity index (BAI) and waist circumference (WC) were assessed. Total fat mass (FM), trunk FM, and load-capacity index (LCI) were evaluated using dual-energy X-ray absorptiometry. Energy-adjusted DII (E-DII) was estimated based on three 24-h recalls and stratified as anti-inflammatory (E-DII<0) and pro-inflammatory (E-DII>0). CVRF included hypertension, diabetes, dyslipidemia, and metabolic syndrome. A total of 170 KTR, 59% male, with 49.5 (42-57) years and E-DII from -2.89 to 4.78 were evaluated. KTR with E-DII>0, compared to those with E-DII<0, exhibited significantly higher values of BAI, total FM (kg), and LCI. In multiple adjusted linear regression, E-DII was significantly associated with WC, total FM (kg), and trunk FM (kg). Logistic regression analysis indicated that E-DII>0 was significantly associated with obesity, as assessed by BAI. E-DII was not associated with CVRF. The present study suggests that a pro-inflammatory diet is associated with higher total and central body adiposity in KTR. Interventions targeting an anti-inflammatory diet may contribute to reducing excessive body adiposity in this population. © 2025 Elsevier Inc.

关键词： Body fat Cardiovascular disease risk factors Dietary inflammatory index Kidney transplant Obesity

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An Extended Euclidean Algorithm Implementation over Ciphertext Space 2nd

An Extended Euclidean Algorithm Implementation over Ciphert...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Chi, Po-Wen Lin, Yuan-Pang Wang, Ming-Hung Chuang, Yun-Hsin Department of Computer Science and Information Engineering National Taiwan Normal University Taiwan Taipei Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Chiayi Taiwan Engineering Management Graduate Program College of Engineering National Cheng Kung University Taiwan Tainan Taiwan

ISBN: (纸本)9789819777853

Fully homomorphic encryption (FHE) is a useful cryptographic primitive. It makes a third-party manipulate data in the ciphertext space and therefore is a key technology for privacy-preserving outsourcing services. Theoretically, FHE can evaluate any circuits. However, the implementation is not trivial. The reason it lacks lots of basic operations like conditional check and branch control. In this paper, we focus on extended Euclidean algorithm operation over FHE. We introduce the problems we encounter and show how we solve these issues. We implement our design and evaluate its performance on a real computer. Finally, we propose some research directions for our future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ciphertext

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Advances in 3D fusion of multimodal medical images: 3D reconstruction of bone, muscle, and ligament structures under load from radiographs and magnetic resonance imaging

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Research on Biomedical Engineering 2025年第1期41卷 1-35页

作者： da Silva, Daniel S. Astolfi, Rodrigo S. Jagatheesaperumal, Senthil K. Selvaraj, Priyadharsini Russel, Newlin Shebiah de Albuquerque, Victor Hugo C. Post-Graduation Program of Medical Surgical Sciences Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Teleinformatics Engineering Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Electronics and Communication Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India Department of Computer Science and Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India

Purpose: Foot and ankle pathologies are among the most prevalent conditions within the human locomotor system. Imaging examinations are crucial for diagnosing, treating, and achieving satisfactory functional outcomes for these conditions. However, conventional imaging exams often fail to capture all the necessary details for precise analysis, necessitating the use of multiple imaging modalities to evaluate bones, ligaments, cartilage, and other structures. Objective: This research aims to explore computational technologies and algorithms for 3D reconstruction and fusion of foot and ankle images, alongside the analysis, segmentation, classification, and evaluation techniques applied to these images. Methods: The study investigates different computational approaches for 3D reconstruction and multimodal image fusion, integrating X-ray and MRI data. Advanced segmentation and classification techniques are applied to enhance the precision of structural assessment. Additionally, we assess the availability and quality of databases for training and validating these models. Results: Our findings highlight a significant gap in research concerning these specific applications, as well as a shortage of suitable databases for 3D reconstruction and fusion of images from X-rays and MRI. Conclusion: The study emphasizes the need for dedicated datasets and further research to develop effective computational solutions for multimodal foot and ankle imaging. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2025.

关键词： Magnetic resonance imaging

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Seamless High Availability in MySQL Database Systems: An Advanced Implementation of ProxySQL, Orchestrator, and Keepalived

Seamless High Availability in MySQL Database Systems: An Adv...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

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

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

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.

关键词： Costs Databases Scalability Query processing Robustness Servers IP networks Synchronization Stress Testing

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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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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

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

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

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.

关键词： Deep learning Hands Emotion recognition Storms Speech recognition Transformers Data models Mel frequency cepstral coefficient Speech processing Information systems

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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