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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Shunkichi Hata Kimiyoshi Usami Graduate School of Engineering and Science Department of Computer Science and Engineering Shibaura Institute of Technology Japan

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

PUF (Physically Unclonable Function) utilizing the physical characteristics of semiconductors has been attracting an attention as the individual identification technology of LSI chips. This technology is expected to make it possible to authenticate genuine products and prevent the distribution of counterfeit products. On the other hand, when energy harvesting is used as a power source, the electromotive force of the energy harvester is small and ultra-low voltage operation is required. LRPUF (Leak Racing PUF) was proposed as a leakage based strong PUF composed of digital circuits. However, ultra-low voltage operation has not been reported with a proof of working in real silicon so far. In this paper, in order to improve the quality of LRPUF at ultra-low voltage, we propose a method to make it operate at ultra-low voltage. The voltage for leakage control (VLC) which controls the leakage current in the leakage generation circuit of the LRPUF is used to enable ultra-low voltage operation. The proposed LRPUF was fabricated in a 180nm CMOS bulk process and the measured results demonstrated that it properly operates at 0.4V. Measurements on 9 chips show Inter-HD of 40.1 %, Intra-HD of 2.72%, and Hamming Weight of 66.53%. The mesured energy dissipation per L-bit output is 18.75 [fJ/bit] and the area to one CRP is $\mathbf{ 1.51E-23[} F^2 / \mathbf{ CRP] }$ .

关键词： Weight measurement Resistance Semiconductor device measurement Current measurement Energy dissipation Physical unclonable function Silicon

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Perfomance Improvement of mpdCNN as Ensemble Learning Model on Low Resolution Image-Face Recognition System 8

Perfomance Improvement of mpdCNN as Ensemble Learning Model ...

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8th International Conference on Electronics, Materials Engineering and Nano-technology, IEMENTech 2025

作者： Wangean, Daniel Anando Suhartono, Derwin Bina Nusantara University Master of Computer Science Binus Graduate Program Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331520762

Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric. © 2025 IEEE.

关键词： Face recognition

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Leveraging Social Learning for Improved Cybersecurity Maturity: A Case Study Using the NIST Framework

Leveraging Social Learning for Improved Cybersecurity Maturi...

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2024 International Conference on Information technology and Computing, ICITCOM 2024

作者： Marcel Meyliana Hendric Spits Warnars, Harco Leslie Nugraha Mursitama, Tirta BINUS Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Krida Wacana Christian University Information Systems Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta Indonesia BINUS Business School Bina Nusantara University Management Department Jakarta Indonesia

ISBN: (纸本)9798350379839

This research shows that social learning can be used to increase an organization's cybersecurity maturity level. Using a literature study and case study approach. Literature studies are used to identify social learning's key success factors (KSF) and challenges in general. Then, the case study was performed in the context of assessing the maturity level of information security using the NIST Cybersecurity framework at ABC Software House, a small-scale software development company in Indonesia. Assessment activities are performed through FGDs which then also identify the most relevant social learning' KSF to increase the organization's information security maturity level. Next, referring to the selected KSF, a process of identifying and mapping social learning' challenges was carried out. The integration of social learning can help organizations gain better focus and understanding in nontechnical contexts to increase their maturity level. This research highlights that integrating social learning with the NIST cybersecurity framework can provide organizations with better focus and perspective for formulating non-technical solutions. This study also enriches the existing literature on social learning in cybersecurity and offers practical insights for organizations looking to improve their cybersecurity resilience. © 2024 IEEE.

关键词： case study cybersecurity maturity level NIST social learning

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Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions 38

Promoting Fairness Among Dynamic Agents in Online-Matching M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ma, Will Xu, Pan Graduate School of Business Columbia University New YorkNY10027 United States Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States

Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively with the objective of maximizing the total number of customers served. We instead study the objective of maximizing the minimum matching rate across all online types, which is referred to as long-run (individual) fairness. For Online Matching under long-run Fairness (OM-LF) with a single offline agent, we show that the first-come-first-serve (FCFS) policy is 1-competitive, i.e., matching any optimal clairvoyant. For the general case of OM-LF: We present a sampling algorithm (SAMP) and show that (1) SAMP is of competitiveness of at least 1 − 1/e and (2) it is asymptotically optimal with competitiveness approaching one in different regimes when either all offline agents have a sufficiently large matching capacity, or all online types have a sufficiently large arrival rate, or highly imbalance between the total offline matching capacity and the number of online arrivals. To complement the competitive results, we show the following hardness results for OM-LF: (1) Any non-rejecting policy (matching every arriving online agent if possible) is no more than 1/2-competitive;(2) Any (randomized) policy is no more than (√3 − 1)-competitive;(3) SAMP can be no more than (1 − 1/e)-competitive suggesting the tightness of competitive analysis for SAMP. We stress that all hardness results mentioned here are independent of any benchmarks. We also consider a few extensions of OM-LF by proposing a few variants of fairness metrics, including long-run group-level fairness and short-run fairness, and we devise related algorithms with provable competitive performance. © 2024 Neural information processing systems foundation. All rights reserved.

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A UML based CPS modelling for intelligent manufacturing application by the case of cylinder head deburring process 3

A UML based CPS modelling for intelligent manufacturing appl...

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3rd International Conference on Mechatronics and Mechanical Engineering, ICMME 2024

作者： Wu, Jing Yong, Xie Ma, Xiaoguang Zhang, Haotian Li, Shuai Wu, Jianqing Department of Computer and Information Science Qinghai Institute of Technology Qinghai Xining China Foshan Graduate School Northeastern University Guangdong Foshan China Newpearl Group Guangdong Foshan China

ISBN: (纸本)9781510681651

Intelligent manufacturing refers to the use of advanced information technology and modern manufacturing technology to carry out comprehensive intelligent and automated transformation of the whole process and all links of manufacturing and production, as well as to achieve efficient manufacturing production and service-oriented operation and enhance the competitiveness of the entire industry. In the field of intelligent manufacturing, this paper researches on the application of intelligent manufacturing modelling based on cyber-physical system, which takes the auto industry as the background, selects the engine cylinder head deburring production case in the intelligent equipment system from its manufacturing architecture. After analysis of the cyber-physical system, then use UML (unified modeling language) class diagram mode to model the deburring process, and complete the equipment case construction corresponding to the mode, analyze the results and draw relevant conclusions and application to build the actual system in the architecture in component parts selection and factory pipeline design. © 2024 SPIE.

关键词： Smart manufacturing

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Attention Convolutional Neural Network for Sentiment Analysis on ChatGPT

Attention Convolutional Neural Network for Sentiment Analysi...

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2024 International Conference on Information technology Research and Innovation, ICITRI 2024

作者： Irawan, Dedi Pardede, Hilman Ferdinandus Anton Putri, Sukmawati Anggraeni Mardiana, Tati Nusa Mandiri University Graduate School of Computer Science Jakarta Indonesia National Research and Innovation Agency Bandung Indonesia Nusa Mandiri University Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350376210

Sentiment analysis is a technique that is able to analyze reviews, sentiment, people's behavior and emotions towards entities such as services, products, organizations, events and social media. Several sentiment analysis approaches have been carried out, one of which uses deep learning such as Convolutional Neural Network (CNN). This research implements the Attentive model using 3 models, namely Attention network, Multi-head Attention and Convolutional Block Attention Module (CBAM). Attentive mechanisms are used to capture important words so that the model can understand important information. Combining Attentive and CNN models to find optimal performance for each model can be done by hyperparameters tuning settings. From the results of selecting the best hyperparameters, it proves that the Attentive mechanism model is able to increase the performance of the baseline model, each accuracy value that increases is Attention network 0.37%, Multi-head Attention 0.43%, and CBAM 0.21%. The highest improvement value was obtained by Multi-head Attention. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementation of computer Vision of Jakarta Weather Image Categorization Using ResNet

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Procedia computer science 2023年 227卷 813-822页

作者： Kartika Purwandari Join W.C. Sigalingging Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were.

关键词： computer vision weather image categorization data augmentation deep transfer learning residual network

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Audio-guided implicit neural representation for local imagestylization

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Computational Visual Media 2024年第6期10卷 1185-1204页

作者： Seung Hyun Lee Sieun Kim Wonmin Byeon Gyeongrok Oh Sumin In Hyeongcheol Park Sang Ho Yoon Sung-Hee Hong Jinkyu Kim Sangpil Kim Department of Artificial Intelligence Korea UniversitySeoul 02841Republic of Korea NVIDIA Research Nvidia CorporationSanta ClaraCA 95051USA Graduate School of Culture Technology KAISTSeoul 34141Republic of Korea Hologram Research Center Korea Electronics Technology InstituteSeoul 03924Republic of Korea Department of Computer Science and Engineering Korea UniversitySeoul 02841Republic of Korea

We present a novel framework for audio-guided localized image *** often provides information about the specific context of a scene and is closely related to a certain part of the scene or ***,existing image stylization works have focused on stylizing the entire image using an image or text *** a particular part of the image based on audio input is natural but *** work proposes a framework in which a user provides an audio input to localize the target in the input image and another to locally stylize the target object or *** first produce a fine localization map using an audio-visual localization network leveraging CLIP embedding *** then utilize an implicit neural representation(INR)along with the predicted localization map to stylize the target based on sound *** INR manipulates local pixel values to be semantically consistent with the provided audio *** experiments show that the proposed framework outperforms other audio-guided stylization ***,we observe that our method constructs concise localization maps and naturally manipulates the target object or scene in accordance with the given audio input.

关键词： audio guidance image style transfer implicit neural representations(INR)

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Fully Explicit Dynamic Gaussian Splatting

arXiv

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arXiv 2024年

作者： Lee, Junoh Won, Changyeon Jung, Hyunjun Bae, Inhwan Jeon, Hae-Gon School of Electrical Engineering and Computer Science AI Graduate School Gwangju Institute of Science and Technology Korea Republic of

3D Gaussian Splatting has shown fast and high-quality rendering results in static scenes by leveraging dense 3D prior and explicit representations. Unfortunately, the benefits of the prior and representation do not involve novel view synthesis for dynamic motions. Ironically, this is because the main barrier is the reliance on them, which requires increasing training and rendering times to account for dynamic motions. In this paper, we design a Explicit 4D Gaussian Splatting(Ex4DGS). Our key idea is to firstly separate static and dynamic Gaussians during training, and to explicitly sample positions and rotations of the dynamic Gaussians at sparse timestamps. The sampled positions and rotations are then interpolated to represent both spatially and temporally continuous motions of objects in dynamic scenes as well as reducing computational cost. Additionally, we introduce a progressive training scheme and a point-backtracking technique that improves Ex4DGS’s convergence. We initially train Ex4DGS using short timestamps and progressively extend timestamps, which makes it work well with a few point clouds. The point-backtracking is used to quantify the cumulative error of each Gaussian over time, enabling the detection and removal of erroneous Gaussians in dynamic scenes. Comprehensive experiments on various scenes demonstrate the state-of-the-art rendering quality from our method, achieving fast rendering of 62 fps on a single 2080Ti GPU. © 2024, CC BY-NC-ND.

关键词： Gaussian distribution

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BERT and RoBERTa Models for Enhanced Detection of Depression in Social Media Text

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Procedia computer science 2024年 245卷 202-209页

作者： Felix Indra Kurniadi Ni Luh Putu Satyaning Pradnya Paramita Erna Fransisca Angela Sihotang Maria Susan Anggreainy Rongfang Zhang Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Statistic Department School of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Department of Rehabilitation and Health Service University of North Texas

Depression is a disease that affects everyone, both young and old. This mental illness not only affects the surrounding environment but everyone. Depression is characterized by deep sadness, behavioral changes and many other actions that are risky for people. In this research we try to solve the problem of detecting depression using Natural Language Processing (NLP) approaches, these two methods are Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa), where these two methods are used to detect posts made in reddit. The dataset is taken from Kaggle. The results obtained found that the average use of BERT and RoBERTa resulted in a high accuracy value of around 98% and with a well balanced precision, recall and F1-Score ratio. This research shows that there is a possibility of using BERT and RoBERTa in depression detection.

关键词： depression natural language processing BERT RoBERTa

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