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SSRN 2024年

作者： Lee, Hyeokjong Chang, Jooyoung Kim, Jaewon Han, Bohyung Park, Sang Min Department of Biomedical Sciences Seoul National University College of Medicine Seoul03080 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of XAIMED Co. Ltd. Seoul02738 Korea Republic of

Detecting stress is crucial for preventing long-term health issues, and recent research, particularly using the WESAD (WEarable Stress and Affect Detection) dataset, has made significant advances in this area. Deep Neural Networks (DNNs) demonstrate a key advantage by efficiently processing raw signals, unlike traditional machine learning methods that predominantly rely on human-engineered *** our study, we enhance DNN capabilities by integrating both raw signals and human-engineered features altogether. Our model, augmented with attention mechanisms, significantly improves its performance. This approach yields state-of-the-art results, demonstrating high accuracies across various modalities in the stress classification task: 92.89% in Electrodermal Activity (EDA), 92.16% in Photoplethysmography (PPG), and 96.58% in all *** model decisions in biosignals is challenging compared to the image domain, where images are inherently more intuitive. Many Explainable AI (XAI) techniques, optimized for images, face difficulties when applied to biosignals. To address this, we employ human-engineered features for biosignals, grounded in the ‘Human Comprehensible Space’ concept, making explanations intuitively understandable. Furthermore, a survey conducted to statistically validate the comprehensibility of our XAI methods confirms their effectiveness in enhancing the accessibility of explanations. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Low-Cost Air Box Application for Indoor Environment Classification and LSTM Microclimate Prediction

Low-Cost Air Box Application for Indoor Environment Classifi...

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International Conference on Control and Robots (ICCR)

作者： Hu Hsun-Jui Chiu Yu-Hsien Yang Pei-Ching Master Degree Program in Information and Design Chang Jung Christian University Tainan Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Artificial Intelligence Research Center Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering Chang Jung Christian University Tainan Taiwan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

Air pollution is a significant issue because it burdens human health and the environment. Particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) can penetrate the lungs and enter the bloodstream, seriously affecting the cardiovascular system, causing strokes, and impacting the respiratory system. Modern individuals spend 70% to 90% of their time indoors, making indoor air quality even more important than outdoor air quality. To estimate and predict indoor air quality, this study utilizes low-cost, self-assembled air boxes based on the Internet of Things to collect various air quality data. The collected data is then used in an indoor air quality forecasting system that combines Long Short-Term Memory (LSTM) and K-means clustering methods to compare the overall performance of the algorithms. The experimental results are analyzed and statistically described based on each model method prediction accuracy and clustering results. These methods are integrated to develop an indoor air quality forecasting system to help indoor populations prevent issues in advance or optimize energy usage.

关键词： Temperature measurement Temperature sensors Atmospheric modeling Predictive models Air quality Data models Forecasting Long short term memory Monitoring Indoor air quality

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Constructing a Minimized Connected Dominating Set Based on the Decentralized Nomination Algorithm in Mobile Ad Hoc Networks

SSRN

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SSRN 2023年

作者： Hsieh, Hui-Ching Chiang, Mao-Lun Lin, Tzu-Ling Liao, Lu-Wen Chen, Ze-Yu Department of New Media Communication Hsing Wu University New Taipei City Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan Department of Business Management National Taichung University of Science and Technology Taichung404336 Taiwan Department of Intelligent Production Engineering National Taichung University of Science and Technology Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung City Taiwan

Inspired by the backbone architecture of wired networks, in this study, we applied the concept of a virtual backbone to the routing scheme in a mobile ad hoc network (MANET) to develop an effective routing method. Using connected dominating set (CDS)-based methods to construct a virtual backbone network can effectively improve routing performance in a MANET. The CDS virtual backbone network we developed in the present study can respond quickly and identify routing paths through these CDS members as the network topology changes. Meanwhile, the nodes that are not CDS members can enter the power-saving mode to minimize unnecessary power consumption. Wireless nodes in a MANET can move freely within the network;therefore, CDS virtual backbones are easy to deconstruct. Researchers have developed centralized and decentralized CDS algorithms to reconstruct network topologies. When previously developed centralized CDS algorithms are applied, the system requires the information of all nodes in the network to reconstruct the virtual backbone. Therefore, the information exchange costs of such algorithms before refactoring are high. By contrast, decentralized CDS algorithms can use local information to reconstruct virtual backbones quickly. Yet, decentralized algorithms produce larger CDSs than centralized algorithms. To improve upon the performance of previously developed decentralized algorithms, we developed and herein present a new decentralized CDS algorithm called APP-CDS that can quickly construct a virtual backbone with the smallest possible CDS for use in MANET environments. © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

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Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites

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computers in Biology and Medicine 2024年 178卷 108664-108664页

作者： Nguyen, Van-Nui Ho, Trang-Thi Doan, Thu-Dung Le, Nguyen Quoc Khanh University of Information and Communication Technology Thai Nguyen University Thai Nguyen Viet Nam Department of Computer Science and Information Engineering TamKang University New Taipei251301 Taiwan International Degree Program in Animal Vaccine Technology International College National Pingtung University of Science and Technology Pingtung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei110 Taiwan AIBioMed Research Group Taipei Medical University Taipei110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei110 Taiwan

N4-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous investigations have examined the distribution and functional implications of 4mC sites within the Rosaceae genome, focusing on their potential roles in gene expression regulation, environmental adaptation, and evolution. This research aims to improve the accuracy of predicting 4mC sites within the genome of Fragaria vesca, a Rosaceae plant species. Building upon the original 4mc-w2vec method, which combines word embedding processing and a convolutional neural network (CNN), we have incorporated additional feature encoding techniques and leveraged pre-trained natural language processing (NLP) models with different deep learning architectures including different forms of CNN, recurrent neural networks (RNN) and long short-term memory (LSTM). Our assessments have shown that the best model is derived from a CNN model using fastText encoding. This model demonstrates enhanced performance, achieving a sensitivity of 0.909, specificity of 0.77, and accuracy of 0.879 on an independent dataset. Furthermore, our model surpasses previously published works on the same dataset, thus showcasing its superior predictive capabilities. © 2024 Elsevier Ltd

关键词： Embeddings

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ProPILE: Probing Privacy Leakage in Large Language Models

arXiv

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arXiv 2023年

作者： Kim, Siwon Yun, Sangdoo Lee, Hwaran Gubri, Martin Yoon, Sungroh Oh, Seong Joon Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of NAVER AI Lab Korea Republic of University of Luxembourg Luxembourg Parameter Lab Germany Tübingen AI Center University of Tübingen Germany

The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data. This paper presents ProPILE, a novel probing tool designed to empower data subjects, or the owners of the PII, with awareness of potential PII leakage in LLM-based services. ProPILE lets data subjects formulate prompts based on their own PII to evaluate the level of privacy intrusion in LLMs. We demonstrate its application on the OPT-1.3B model trained on the publicly available Pile dataset. We show how hypothetical data subjects may assess the likelihood of their PII being included in the Pile dataset being revealed. ProPILE can also be leveraged by LLM service providers to effectively evaluate their own levels of PII leakage with more powerful prompts specifically tuned for their in-house models. This tool represents a pioneering step towards empowering the data subjects for their awareness and control over their own data on the web. © 2023, CC BY-NC-SA.

关键词： Piles

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GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection

GNN-Based Point Cloud Maps Feature Extraction and Residual F...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei-Hsiang Liao Chieh-Chih Wang Wen-Chieh Lin Graduate Degree Program of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be beneficial to predetermine where targets are more or less likely to appear. Compared with semantic maps, HD maps formed by point clouds provide much richer information from surrounding static objects and scenes. In this work, we construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Our work is based on PVRCNN, the state-of-the-art LiDAR object detection method. With point-wise and voxel-wise features obtained from PVRCNN, residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40–50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.

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Mechatronic Design of an Amphibious Drone

Mechatronic Design of an Amphibious Drone

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International Conference on Autonomous Robots and Agents, ICARA

作者： Nikolaos Evangeliou Dimitris Chaikalis Nikolaos Giakoumidis Anthony Tzes Electrical Engineering Program New York University Abu Dhabi United Arab Emirates Electrical & Computer Engineering Department New York University Brooklyn NY USA Kinesis Lab Core Technology Platform New York University Abu Dhabi United Arab Emirates Center for Artificial Intelligence and Robotics New York University Abu Dhabi United Arab Emirates

This paper is concerned with the mechatronic design of a tricopter drone which can move as a ground vehicle and/or as a vessel. The tricopter has three pairs of coaxial rotors and is capable of providing 100N of thrust with a 15 minute flight time using an 8S LiPo 12.5Ah battery. Three waterproof motors are attached to an isosceles triangular omniwheel configuration for operating as a ground vehicle after landing. The customized wheels allow the vehicle to move with a linear velocity up to 2 m/sec at a tilt slope up to 20°. Two submersible parallel water thrusters provide 24N collective force leading to a maximum vessel velocity of 1 m/s and a turn radius of 1.5 m. The design alternatives are highlighted and prioritized according to their weight while the overall amphibious drone is shown in several flight/omniwheel vehicle/vessel modes.

关键词： Mechatronics Navigation Force Wheels Rotors Prototypes Land vehicles

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Development of Support Vector Machine Method to Forecast Dengue-Endemic Areas

Development of Support Vector Machine Method to Forecast Den...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Hetty Meileni Ermatita Abdiansah Nyayu Latifah Husni Desmawati Doctoral Program in Engineering Science Universitas Sriwijaya (Politeknik Negeri Sriwijaya) Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Artificial Intelligence Research Development (AIRD) Universitas Sriwijaya Palembang Indonesia Department of Electrical Engineering Politeknik Negeri Sriwijaya Palembang Palembang Indonesia Faculty of Health Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

The cases of dengue hemorrhagic fever (DHF) in Indonesia have increased significantly since 2020. Data shown by the Central Statistics Agency, for example, in South Sumatra Province, there were 6,348 cases of DHF during that period. To suppress the case number of DHF, forecasting areas that are susceptible to this disease is urgently needed. This research aims to develop a machine learning method Support Vector Machine (SVM). The data used include area data, population density data, weather data, and dengue fever case data. The development of the SVM model is carried out using a machine learning method that combines regional data, population density data, weather data, and dengue case data. In the end, using the SVM method can be used to forecast dengue-endemic areas, so that the government and society can take further action to reduce the negative impact of DHF.

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Application of Pose Estimation on Webcam for Fall Risk Assessment

Application of Pose Estimation on Webcam for Fall Risk Asses...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Surapong Uttama Patsakorn Chumpoo Worasak Rueangsirarak Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer Science Program School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building School of Information Technology Mae Fah Luang University Chiang Rai Thailand

Fall risk assessment is an effective and simple measure to evaluate the balance of people especially the elderly who are likely to have balance disorder. Lose of body balance increases a fall risk which could lead to severe damage to aging persons. In this paper, we propose to apply pose estimation technique based on PoseNet method to detect body joints from webcam's video for two types of fall risk assessment: Five Times Sit to Stand Test (FTSTS) and 30-Second Chair Stand Test (30CST). The experiments were performed with 17 volunteers concurrently with the measures of a healthcare expert. The results revealed that our proposed technique corresponds well with the measure of the expert evaluating by a Pearson correlation coefficient which equals to 0.903 and 0.980 for FTSTS and 30CST respectively.

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Intrinsic Parameter Fluctuation and Process Variation Effect of Vertically Stacked Silicon Nanosheet Complementary Field-Effect Transistors

Intrinsic Parameter Fluctuation and Process Variation Effect...

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IEEE International Symposium on Quality Electronic Design

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Parallel and Scientific Computing Laboratory Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program Hsinchu Taiwan Institute of Communications Engineering Hsinchu Taiwan Institute of Biomedical Engineering Hsinchu Taiwan Institute of Pioneer Semiconductor Innovation Hsinchu Taiwan Institute of Artificial Intelligence Innovation Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan

We study the variability of vertically stacked gate-all-around silicon nanosheet (GAA Si NS) complementary field-effect transistors (CFETs). The process variation effect (PVE), the work function fluctuation (WKF), and the random dopant fluctuation (RDF) of CFETs are statistically estimated using an experimentally validated device simulation technique. Among five factors of PVE, the channel thickness (T Nch /T Pch ), the channel width (W ch ), and the gate length (L G ) are significant. Owing to superior GAA channel control and increased effective gate area, both WKF and RDF are suppressed. Notably, the PVE on both N-/P-FETs of GAA Si CFET induce the largest off-state current fluctuations of 80% and 278%, respectively, because the device characteristic is very sensitive to the layer thickness and width of channel.

关键词： Fluctuations Gallium arsenide Logic gates Electric variables Silicon Resource description framework Semiconductor process modeling

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