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International Conference on Computing, engineering, and Design (ICCED)

作者： Media Anugerah Ayu Dimas Erlangga Teddy Mantoro Dini Handayani Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University - Sukabumi Indonesia

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it’s practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953.

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The Robustness of Structural Features in Species Interaction Networks

arXiv

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arXiv 2025年

作者： Fard, Sanaz Hasanzadeh Dolson, Emily Department of Computer Science and Engineering Michigan State University United States Program in Ecology Evolution and Behavior Michigan State University United States

Species interaction networks are a powerful tool for describing ecological communities;they typically contain nodes representing species, and edges representing interactions between those species. For the purposes of drawing abstract inferences about groups of similar networks, ecologists often use graph topology metrics to summarize structural features. However, gathering the data that underlies these networks is challenging, which can lead to some interactions being missed. Thus, it is important to understand how much different structural metrics are affected by missing data. To address this question, we analyzed a database of 148 real-world bipartite networks representing four different types of species interactions (pollination, host-parasite, plant-ant, and seed-dispersal). For each network, we measured six different topological properties: number of connected components, variance in node betweenness, variance in node PageRank, largest Eigenvalue, the number of non-zero Eigenvalues, and community detection as determined by four different algorithms. We then tested how these properties change as additional edges – representing data that may have been missed – are added to the networks. We found substantial variation in how robust different properties were to the missing data. For example, the Clauset-Newman-Moore and Louvain community detection algorithms showed much more gradual change as edges were added than the label propagation and Girvan-Newman algorithms did, suggesting that the former are more robust. Robustness also varied for some metrics based on interaction type. These results provide a foundation for selecting network properties to use when analyzing messy ecological network data. © 2025, CC BY.

关键词： Anthropogenic

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3D geometric shape assembly via efficient point cloud matching 24

3D geometric shape assembly via efficient point cloud matchi...

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Proceedings of the 41st International Conference on Machine Learning

作者： Nahyuk Lee Juhong Min Junha Lee Seungwook Kim Kanghee Lee Jaesik Park Minsu Cho Department of Computer Science and Engineering POSTECH Pohang Korea Graduate School of Artificial Intelligence POSTECH Pohang Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Department of Computer Science and Engineering POSTECH Pohang Korea and Graduate School of Artificial Intelligence POSTECH Pohang Korea

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr.

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A Performance Comparison of Commercial Speech Recognition APIs in Noisy Environments

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Transactions of the Korean Institute of Electrical Engineers 2022年第9期71卷 1266-1273页

作者： Lee, Geonhui Lee, Sanghwa Ji, Suhwan Kim, Auk Im, Hyeonseung Dept. of Computer Science and Engineering Dept. of Convergence Security Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Dept. of Convergence Security Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of

This paper compares the performance of five commercial speech recognition APIs under noisy environments, namely those provided by Amazon AWS, Microsoft Azure, Google, Kakao, and Naver. To this end, we used an open dataset for development and evaluation of multi-channel noise processing technology provided in AI Hub. We tested each API's performance with respect to the speaker's gender and location and the speech content, and measured their error rate using both word error rate (WER) and character error rate (CER). Except for the AWS API, the error rate was higher when tested with female's data than male's one, and when tested with the data recorded from the side than the front. The error rate was also relatively high when the test sentences contained proper nouns such as person's names and local names, and the shorter the sentences, the higher the error rate. Moreover, the Google API outperformed all the others in terms of both WER and CER, with 53% and 18% of error rate, respectively. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Speech recognition

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Enhancing the Performance of E-Mode AlGaN/GaN HEMTs With Recessed Gates Through Low-Damage Neutral Beam Etching and Post-Metallization Annealing

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 150-155页

作者： Chen, Yi-Ho Ohori, Daisuke Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan Tohoku University Institute of Fluid Science Sendai980-8577 Japan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Pioneer Semiconductor Innovation Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan

This study investigated the electrical properties of AlGaN/GaN high-electron-mobility transistors (HEMTs) with varied recess depths under the gate electrode. We demonstrated a recess depth of approximately 6 nm, which was achieved through neutral beam etching (NBE) technique with a low etch rate of 1.8 nm/min, resulting in device enhancement-mode (E-mode) behavior with threshold voltage (Vth) of 0.49 V. The effects of post-metallization annealing (PMA) on the device performance were also examined. The results revealed that PMA treatment improves the DC characteristics of the devices, including maximum drain current (IDMAX), transconductance (gm), subthreshold swing (SS), on-off ratio, and off-state leakage current, with maximum enhancement percentage of 18.3% for IDMAX, 3758% for on-off ratio, and 54.3% for SS. Moreover, this study compared the recess depths of metal-insulator-semiconductor high-electron-mobility transistors (MIS-HEMTs) with the SiN dielectric layer. The results showed that MIS-HEMTs exhibit more negative Vth values, which can be attributed to the controlled surface states achieved through passivation. © 2020 IEEE.

关键词： Etching

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The Impact of Different Modes of Augmented Reality Information in Assisted Aircraft Cable Assembly 1

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

作者： Ariansyah, Dedy Xi, Khaizuo Erkoyuncu, John Ahmet Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia School of Aerospace Transport and Manufacturing Cranfield University BedfordMK43 0AL United Kingdom Centre of Digital Engineering and Manufacturing School of Aerospace Transport and Manufacturing Cranfield University BedfordMK43 0AL United Kingdom Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Aircraft avionics systems are complicated systems which involves high number of components and complex cable assembly procedure. To deal with this challenge, Augmented Reality (AR) has been proposed to be an effective method to improve assembly efficiency and to reduce error rates. In this paper, the authors developed AR-based guidance cable assembly and compare the impact of different modes of AR information. The results show that although AR outperformed the paper document assembly guidance, the design of AR information has distinct influence on the successful completion of the cables assembly process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Augmented reality

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Sentiment Analysis With N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language

Sentiment Analysis With N-Gram Preprocessing for Online-Shop...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Jelita Asian Oktavianus Tegar N.K Putra Media Anugerah Ayu Teddy Mantoro Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers’ opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users’ opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, $\mathrm{N}=2$ for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with $K=11$ as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms $(N=2)$ with Split Validation (8:2), which is $\mathbf{97.26\%}$.

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Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Hidayaturrahman Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusatara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusatara University Jakarta Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation.

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Development of 5G Cyber-Physical Production System

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International Journal of Networked and Distributed Computing 2023年第1期11卷 9-19页

作者： Huang, Hsuan-Chao Tsai, Chun-Hsien Lin, Hsiung-Cheng Department of Computer Science and Information Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan PhD program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan Department of Electronic Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan

The cyber-physical production system (CPPS) was developed for the interconnection between operational technology (OT) and information and communication technology (ICT) among the machines and decentralized production control. However, OT and ICT are now getting more complex in the modern industry. To enhance industrial management and production process, the CPPS based on the 5G architecture under production environments can provide a more comprehensive solution in the context of Industry 4.0. For this purpose, a 5G CPPS model to improve the interoperability of CPPS in ICT and OT is proposed to build a virtualized Industry 4.0 asset management shell system. First, real-time task artifacts and 5G communication modules are bound in a certain form of virtualization. Second, task artifacts are released to the production line in a real-time scheduling. Third, an order is placed on a production line and split into multiple tasks for production process. Single-machine single-workpiece and multi-machine and multi-workpiece processing cases are used for testing in the production line. Experimental results show that the proposed 5G CPPS can efficiently achieve multi-machine work piece processing. © 2022, The Author(s).

关键词： 5G Cyber-physical production system (CPPS) Digital twin (DT) Industry 4.0 Information technology Operational technology

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Thermal simulation of microwave kiln based on multiphysics

Thermal simulation of microwave kiln based on multiphysics

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Jansaengsuk, Thodsaphon Pattanapichai, Sorathon Poopanya, Piyawong Phimphakan, Nonthawat Thongsri, Jatuporn College of Advanced Manufacturing Innovation King Mongkut's Institute of Technology Ladkrabang Computer Simulation in Engineering Research Group Bangkok10520 Thailand Ubon Ratchathani Rajabhat University Program of Physics Faculty of Science Ubonratchatani34000 Thailand

ISBN: (纸本)9798350326413

A microwave kiln, made of silicon carbide and ceramic fiber, commonly employs in a household glassware production process. In this process, when the kiln was in a microwave oven, a microwave transmitted to the kiln generating a high temperature to fuse the glass inside. This article presents a thermal simulation to investigate the temperature inside the kiln based on multiphysics consisting of a high-frequency structure simulator (HFSS) and computational fluid dynamics (CFD). The multiphysics results revealed the temperature inside the kiln in a transient state, consistent with the experimental results. As expected, the temperature increased with the increasing time of the process. Significantly, the silicon carbide had higher temperatures than the ceramic fiber;therefore, silicon carbide is a crucial material for generating heat inside the kiln. In addition, the temperature-increasing rate (TIR) inside the kiln depended on the kiln's thickness (Th). As a result, the thinner Th provided better TIR. The research findings can be applied to develop a high-efficacy household glassware production process. © 2023 IEEE.

关键词： Multiphysics

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