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检索条件"机构=Graduate Program Of Computer Science"

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Enhancing Stroke Detection in Brain CT Images with Residual Attention Networks

Enhancing Stroke Detection in Brain CT Images with Residual ...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Larasati, Sza Sza Amulya Shalsadilla, Shafatyra Reditha Utaminingrum, Fitri Graduate Program of Computer Science Faculty of Computer Science Universitas Brawijaya Indonesia

ISBN: (纸本)9781837242689

Stroke is becoming one of the most common diseases in modern society. Stroke caught in the early phase has a higher potential for recovery as it can be treated before the worsening of the patient's condition. Detection through brain CT images with deep learning has a favorable impact. Residual Attention Network (RAN) architecture is proposed to perform stroke detection. RAN builds upon the regular ResNet architecture by incorporating an attention layer within each residual block to extract meaningful information. RAN combines the advantages of residual connections and attention mechanisms. RAN is able to prevent vanishing gradient problem while focusing on the most important features. RAN yields an accuracy of 96.12% surpassing the regular ResNet with an accuracy of 94.40%. RAN shows great potential, especially in fields that require high accuracy and efficient feature extraction. This paper explores the effectiveness of Residual Attention Networks and their potential applications in areas requiring robust and accurate feature extraction. © The Institution of Engineering & Technology 2024.

关键词： computerized tomography

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

作者： Mario Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

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. © 2025 IEEE.

关键词： Game design

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

作者： Setiawan, Valentino Soewito, Benfano Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

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. © 2025 IEEE.

关键词： Cyber attacks

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NoSQL Injection Detection Using Deep Neural Network and Principal Component Analysis of Injection Feature Vectors 11

NoSQL Injection Detection Using Deep Neural Network and Prin...

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11th International Conference on computer, Control, Informatics and its Applications, IC3INA 2024

作者： Soegiharto, Irwin Santoso Girsang, Abba Suganda Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program - Master of Computer Science Binus Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331542313

NoSQL database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles and resources efficiency. However, security challenges arise along with increasing usage of NoSQL database, and NoSQL database is no exception to injection attacks. Machine learning proved to be an efficient method, as much has been researched. However, in the future there may be an increasing complexity of features that may prove costly to the model’s performance. Therefore, this research aims to utilize principal component analysis as dimensionality reduction and deep neural network as the classification method, to improve the security of NoSQL database. The text query is converted to feature vectors then further processed to reduce the input dimension of the deep neural network using PCA. The features used are based on previous research and various sources, and some are added after analyzing the dataset.10-fold cross validation is also applied to ensure that the model does not overfit the data, attempting to reduce bias to the result. The 10-fold cross validation model accuracy result is in average 97.44% with a standard deviation of 1.7%, and the testing results are 97.5% in accuracy,95.65% in precision, 91.67% in recall, and 93.61% in F1 score. Thus, it can be concluded that the usage of PCA on injection feature vectors can reduce complexity of the model. © 2024 IEEE.

关键词： Dimensionality reduction

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Integrated Framework for IT Asset Physical Security: A Risk Management Approach Using NIST and ISO Standards

Integrated Framework for IT Asset Physical Security: A Risk ...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Alamsyah, Ricky Arifin, Yulyani Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331511470

This study presents a comprehensive approach to IT asset security risk management in physical access control systems, integrating NIST SP 800-116, 800-53, 800-30, ISO 31000, and 27002:2022 standards. Using a mixed-method approach, we conducted interviews, discussion forums, and observations with IT and security professionals in manufacturing and insurance sectors in Indonesia. Our findings reveal seven key factors crucial for physical security of IT assets, including policies, access authorization, and environmental controls. The study proposes a risk assessment model that determines authentication factor levels based on risk severity. Results indicate discrepancies between current practices and recommended security levels, particularly in server rooms and production areas. We provide a set of control recommendations aligned with ISO 27002:2022 to enhance physical security measures. This research contributes to both theory and practice by offering a holistic framework for managing physical security risks in IT environments, particularly relevant for organizations in emerging economies. © 2024 IEEE.

关键词： ISO Standards

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Analysing the Impact of Rhythm Game in Enhancing Typing Performance 10

Analysing the Impact of Rhythm Game in Enhancing Typing Perf...

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

作者： Lancaster, Lemuel Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

The research was centered on adapting and assessing a new, unique game concept. The game onKeys presents a new approach and poses as an alternative method for improving typing skills. However, despite offering a promising avenue, the game still grapples with certain limitations. Utilizing the MDA Framework, the study seeks to integrate additional game components into onKeys and evaluate its effectiveness in improving typing skills. A paired T-test is used to assess the Pre-Test and Post-Test data. The System Usability Scale gathers subjective feedback about the game design's usability. This study found that the onKeys game has little impact on players' typing speed and accuracy, which may be attributed to a higher average typing speed, short period of playtime or a small sample size. However, upon reviewing the record and survey data, particularly the System Usability Score, it becomes evident that the game design still has potential. Further research can delve deeper into this game concept and design. © 2024 IEEE.

关键词： Game design

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Comparison of LSTM and Transformer for Time Series Data Forecasting 7

Comparison of LSTM and Transformer for Time Series Data Fore...

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7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Sonata, Ilvico Heryadi, Yaya Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350375886

Time series data is data that is collected periodically and has certain time intervals. Time series data is widely available in the fields of finance, meteorology, signal processing, health, and economics. Weather data, stock prices, and sales data are examples of time series data. Time series data analysis can be used to predict future conditions based on patterns and values from previous data through a forecasting process. These forecasting results are useful for identifying trends and data patterns for decision making. The rapid development of deep learning models can now be used as a method for forecasting time series data. Deep learning models that are suitable for forecasting include sequence-to-sequence models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Transformer. LSTM and Transformer are sequence-to-sequence models that are widely used for forecasting time series data. Research to compare the accuracy of the two models is still very limited. This article will discuss the use of the LSTM and Transformer models for the time series data forecasting process using Hewlett Packard stock price data from 1962 to 2022. The accuracy results show that the LSTM model outperforms the Transformer model in forecasting Hewlett Packard stock prices. © 2024 IEEE.

关键词： Data accuracy

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Passive Identification of Rogue Industrial Access Points Using a One-Class Machine Learning Model 50

Passive Identification of Rogue Industrial Access Points Usi...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： de Paula, André L.S. Viegas, Eduardo K. Santin, Altair O. Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (纸本)9781665464543

Access point (AP) security has become increasingly important as wireless local area networks (WLANs) proliferate in industrial environments. Rogue APs are often used by attackers to conduct man-in-the-middle (MiTM) attacks. They can redirect users to malicious servers or do eavesdropping and manipulation of their *** this paper, we propose a novel one-class machine learning model to passively identify rogue APs in industrial environments. The implementation of the model is twofold. First, we passively extract the hardware and software characteristics of the evaluated AP according to its generated messages. This results in a comprehensive feature set that captures both low-level and high-level behaviors of the evaluated ***, we apply a one-class machine learning model to identify APs that significantly deviate from the previously known profile of legitimate APs. The combined evaluation of hardware and software behaviors integrated with an outlier detection scheme to effectively identify rogue APs is the insight of our proposal. We demonstrate the feasibility of our model, achieving an F1 score of 0.89 and a true positive rate of 0.9 in experiments conducted on our new publicly available dataset of 357 unique AP behaviors. © 2024 IEEE.

关键词： Adversarial machine learning

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A Multi-View Android Malware Detection Model Through Multi-Objective Optimization 23

A Multi-View Android Malware Detection Model Through Multi-O...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Fransozi, Philipe Geremias, Jhonatan Viegas, Eduardo K. Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (纸本)9798350374889

Over the past few years, several highly accurate Machine Learning (ML) techniques have been proposed for Android malware detection. Unfortunately, proposed schemes are rarely used in production, a situation usually caused by their limited generalization capabilities, leading to low accuracies when deployed. This paper proposes a new multi-view Android malware detection model, implemented in two stages. First, we extract multiple feature sets from an analyzed Android application package. The feature sets provide a complementary Android app behavioral vector for the classification task, enhancing the system's generalization. Secondly, we conduct a multi-objective optimization to select the optimal feature subset from each view for subsequent ensemble-based classification. Our proposal's insight is to proactively select each feature subset that simultaneously improves accuracy and reduces processing requirements in a multi-view setting. Experiments on our new dataset, comprising over 40 thousand Android app samples, demonstrated the feasibility of our proposal. Our scheme can improve true-positive rates by an average of 4.4 while demanding only up to 65% of inference processing costs. © 2024 IEEE.

关键词： Android malware

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Towards a Feasible Palm Vein Verification Scheme Using Deep Autoencoder and Siamese Networks 23

Towards a Feasible Palm Vein Verification Scheme Using Deep ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Nunes, Mateus Viegas, Eduardo K. Santin, Altair O. Pontifical Catholic University of Parana Graduate Program in Computer Science Brazil

ISBN: (纸本)9798350374889

Palm vein pattern recognition offers a unique personal identification feature. Unfortunately, these techniques typically require a Near Infrared (NIR) camera sensor to extract the individual's venous pattern, challenging their wide deployment. This paper proposes a new feasible palm vein verification scheme using a Deep Autoencoder and a Siamese Network, implemented threefold. First, we capture the individual's palm using a traditional visible spectrum camera sensor and perform preprocessing tasks to correct imprecise positioning, easing palm support accessories requirements. Second, we eliminate NIR sensor requirement by fine-tuning a Deep Autoencoder model to convert images from the visible spectrum to their infrared counterparts. Third, generated images are processed by a lightweight Siamese network using a contrastive loss function for individual verification. Experiments conducted on a publicly available dataset with over a hundred individuals confirmed the feasibility of our proposal. Our scheme reaches up to 0.97 of true-negative rate, with only 0.01 decrease compared to traditional NIR-based approaches. In addition, individual identification can be conducted in less than 6 seconds in a resource-constrained environment thanks to our lightweight model's implementation. © 2024 IEEE.

关键词： Palmprint recognition

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