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17th INDIACom;2023 10th International Conference on computing for Sustainable Global Development, INDIACom 2023

作者： Tiwari, Shashi Kant Sinha, Shweta Kaur, Karamjit Singh, Ram Sewak Amity School of Engineering and Technology Amity University Haryana Gurugram India Fairfield Inst. of Mgmt. and Technology New Delhi India School of Electrical and Computing Engineering Adama Science and Technology University Adama Ethiopia

ISBN: (纸本)9789380544472

This study proposes a new method for predicting cardiac diseases using spectral, nonlinear techniques, ranking methods, generalized discriminant analysis (GDA), and classification algorithm as a 1-Norm linear extreme learning machine (1-NLELM). The accessible thirty features from heart rate variability (HRV) data were extracted using spectral techniques and ten features from nonlinear techniques. For the classification model of coronary and congestive heart diseases, the top ten attributes acquired from ranking methods were applied to suggested GDA + 1-NLELM model. For all of this, the HRV databases of healthy young (YNG) and Elderly (ELY) coronary artery disease (CAD) and congestive heart failure (CHF) were taken from PhysioNet ATM. The experiments were carried on the grouping of record sets as YNG-ELY, CHF-CAD, YNG-CAD, YNG-CHF, ELY-CAD, and ELY-CHF subjects. The numerical results have shown that ranked nonlinear attributes have 100% test accuracy using the proposed GDA + 1-NLELM binary classifier and do not depend on ranking methods. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning

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A patient-centric doctor referral model based on hyperledger chaincode 5

A patient-centric doctor referral model based on hyperledger...

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5th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2021

作者： Vinodhini, M. Prakash, M. SRM Inst. of Science and Technology School of Computing TamilNadu Kattankulathur India

ISBN: (纸本)9781665406109

In a variety of areas such as the financial sector, the energy sector, the Internet of Things, and most importantly, healthcare, blockchain is evolving and advancing to serve as a secure and reliable platform to facilitate effective and secure data sharing. Electronic Health Data Management privacy issue has been raised when cloud-based data is shared between stakeholders or when records are outsourced to the cloud, resulting in sensitive or private information being disclosed by unauthorized individuals. As a result of the doctor referral management process, stakeholder communications are conducted via emails, faxes, and phone calls, which may result in errors, mistakes, and misunderstandings. This paper proposes the following solutions to the identified problems: To facilitate better sharing of healthcare records between clinicians and health care facilities through an efficient referral mechanism based on advanced smart contracts named as Patient Centric Healthcare Data Management Access Control via Smart contract (PCHDMAC-SC). Doctor referrals are made through the List referral system, which is patient-centric and restricted to doctors and other authorized providers. The results demonstrate that unauthorized access cannot be traced to healthcare records as all healthcare records are encrypted as part of the working model, which keeps personal information secure, protects personal information, makes data more scalable, and allows medical records to be shared and accessed more securely. Because of this, the system of doctor referrals results in scalability, a higher level of trust, and flexibility for all stakeholders involved. © 2021 IEEE.

关键词： Smart contract

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P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving

P-YOLOv8: Efficient and Accurate Real-Time Detection of Dist...

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2024 IEEE High Performance Extreme computing Conference, HPEC 2024

作者： Elshamy, Mohamed R. Emara, Heba M. Shoaib, Mohamed R. Badawy, Abdel-Hameed A. Klipsch School of ECE New Mexico State University Las CrucesNM88003 United States Pyramids Higher Inst. for Engineering & Technology Dept. of Electronics and Communications Engineering Egypt College of Computing and. Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350387131

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained-YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8's versa-tility in both object detection and image classification tasks using the Distracted Driver Detection dataset from state farm, which includes 22,424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1, 451, 098, due to its innovative architecture. It achieves a high accuracy of 99.46% with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging Automatic CAD Annotations for Supervised Learning in 3D Scene Understanding

arXiv

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arXiv 2025年

作者： Rao, Yuchen Ainetter, Stefan Stekovic, Sinisa Lepetit, Vincent Fraundorfer, Friedrich Inst. of Visual Computing Graz Univ. of Technology Austria LIGM École des Ponts et Chaussees IP Paris CNRS France

High-level 3D scene understanding is essential in many applications. However, the challenges of generating accurate 3D annotations make development of deep learning models difficult. We turn to recent advancements in automatic retrieval of synthetic CAD models, and show that data generated by such methods can be used as high-quality ground truth for training supervised deep learning models. More exactly, we employ a pipeline akin to the one previously used to automatically annotate objects in ScanNet scenes with their 9D poses and CAD models. This time, we apply it to the recent ScanNet++ v1 dataset, which previously lacked such annotations. Our findings demonstrate that it is not only possible to train deep learning models on these automatically-obtained annotations but that the resulting models outperform those trained on manually annotated data. We validate this on two distinct tasks: point cloud completion and single-view CAD model retrieval and alignment. Our results underscore the potential of automatic 3D annotations to enhance model performance while significantly reducing annotation costs. To support future research in 3D scene understanding, we will release our annotations, which we call SCANnotate++, along with our trained models. © 2025, CC BY.

关键词： Semi-supervised learning

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Ontology Engineering and the FAIR principles: A Gap Analysis toward a FAIR-by-design methodology

Ontology Engineering and the FAIR principles: A Gap Analysis...

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Poveda-Villalón, María Garijo, Daniel Gonzalez-Beltran, Alejandra N. Jonquet, Clement Le Franc, Yann Universidad Politécnica de Madrid Madrid Spain Scientific Computing Science and Technology Facilities Council United Kingdom MISTEA Univ. of Montpellier INRAE Inst. Agro Montpellier France LIRMM Univ. of Montpellier CNRS Montpellier France e-Science Data Factory Montpellier France

Ontologies and vocabularies play a key role when standardising, organizing and integrating data from heterogeneous data sources into Knowledge Graphs. In order to develop ontologies, different engineering methodologies have been proposed throughout the years, whose application resulted in thousands of semantic artefacts (taxonomies, vocabularies and ontologies) in a wide range of domains. But how to ensure that ontologies follow the Findable, Accessible, Interoperable and Reusable principles (FAIR) from their inception? In this paper, we review: (i) existing guidelines to help make ontologies FAIR and (ii) published FAIRness assessment methodologies and map them to the ontology development lifecycle activities. Our analysis outlines the current gaps, where no guidelines exist for ontologies to become FAIR-by-design. © 2024 Copyright for this paper by its authors.

关键词： Latent semantic analysis

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Language Models Represent Beliefs of Self and Others 41

Language Models Represent Beliefs of Self and Others

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41st International Conference on Machine Learning, ICML 2024

作者： Zhu, Wentao Zhang, Zhining Wang, Yizhou Center on Frontiers of Computing Studies School of Compter Science Peking University China Inst. for Artificial Intelligence Peking University China Nat'l Eng. Research Center of Visual Technology Peking University China Nat'l Key Lab of General Artificial Intelligence Peking University China

Understanding and attributing mental states, known as Theory of Mind (ToM), emerges as a fundamental capability for human social *** Large Language Models (LLMs) appear to possess certain ToM abilities, the mechanisms underlying these capabilities remain *** this study, we discover that it is possible to linearly decode the belief status from the perspectives of various agents through neural activations of language models, indicating the existence of internal representations of self and others' *** manipulating these representations, we observe dramatic changes in the models' ToM performance, underscoring their pivotal role in the social reasoning ***, our findings extend to diverse social reasoning tasks that involve different causal inference patterns, suggesting the potential generalizability of these representations. Copyright 2024 by the author(s)

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Fast Peer Adaptation with Context-aware Exploration 41

Fast Peer Adaptation with Context-aware Exploration

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41st International Conference on Machine Learning, ICML 2024

作者： Ma, Long Wang, Yuanfei Zhong, Fangwei Zhu, Song-Chun Wang, Yizhou Academy for Advanced Interdisciplinary Studies Peking University China Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU China Center on Frontiers of Computing Studies School of Computer Science Peking University China School of Intelligence Science and Technology Peking University China Inst. for Artificial Intelligence Peking University China Nat'l Eng. Research Center of Visual Technology Peking University China

Fast adapting to unknown peers (partners or opponents) with different strategies is a key challenge in multi-agent games. To do so, it is crucial for the agent to probe and identify the peer's strategy efficiently, as this is the prerequisite for carrying out the best response in adaptation. However, exploring the strategies of unknown peers is difficult, especially when the games are partially observable and have a long horizon. In this paper, we propose a peer identification reward, which rewards the learning agent based on how well it can identify the behavior pattern of the peer over the historical context, such as the observation over multiple episodes. This reward motivates the agent to learn a context-aware policy for effective exploration and fast adaptation, i.e., to actively seek and collect informative feedback from peers when uncertain about their policies and to exploit the context to perform the best response when confident. We evaluate our method on diverse testbeds that involve competitive (Kuhn Poker), cooperative (PO-Overcooked), or mixed (Predator-Prey-W) games with peer agents. We demonstrate that our method induces more active exploration behavior, achieving faster adaptation and better outcomes than existing methods1 Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Prediction of Heart Diseases using Nonlinear Techniques and Machine Learning

Prediction of Heart Diseases using Nonlinear Techniques and ...

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International Conference on computing for Sustainable Global Development (INDIACom)

作者： Shashi Kant Tiwari Shweta Sinha Karamjit Kaur Ram Sewak Singh Amity School of Engineering & Technology Amity University Haryana Gurugram India Fairfield Inst. of Mgmt. and Technology New Delhi India School of Electrical and Computing Engineering Adama Science and Technology University Adama Ethiopia

关键词： Heart Solid modeling Cardiac disease Systems architecture Feature extraction Numerical models Heart rate variability

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First Demonstration of Defect Elimination for Cryogenic Ge FinFET CMOS Inverter Showing Steep Subthreshold Slope by Using Ge-on-Insulator Structure

First Demonstration of Defect Elimination for Cryogenic Ge F...

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2023 International Electron Devices Meeting, IEDM 2023

作者： Yu, X.-R. Hsieh, C.-C. Chuang, M.-H. Chiu, M.-Y. Sun, T.-C. Geng, W.-Z. Chang, W.-H. Shih, Y.-J. Lu, W.-H. Chang, W.-C. Lin, Y.-C. Pai, Y.-C. Lai, C.-Y. Chuang, M.-H. Dei, Y. Yang, C.-Y. Lu, H.-Y. Lin, N.-C. Wu, C.-T. Kao, K.-H. Ma, W.C.-Y. Lu, D.D. Lee, Y.-J. Luo, G.-L. Chiang, M.-H. Maeda, T. Wu, W.-F. Li, Y.-M. Hou, T.-H. National Cheng Kung University Dept of Electrical Engineering Tainan Taiwan National Cheng Kung University Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan Taiwan National Yang Ming Chiao Tung University Inst. of Communications Engineering Hsinchu Taiwan National. Sun Yat-Sen University Dept. of Electrical Engineering Kaohsiung Taiwan National Institute of Advanced Industrial Science and Technology Ibaraki Japan National Yang Ming Chiao Tung University Inst. of Artificial Intelligence Innovation Hsinchu Taiwan Taiwan Semiconductor Research Institute Taiwan Intelligent Computing Industrial Program Ming Wu School of Computing National Cheng Kung University Taiwan Inst. of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Inst. of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798350327670

This work presents experimental electrical characteristics and circuit prediction at cryogenic temperatures (down to 10 K) for three different kinds of germanium (Ge)-based FETs with advanced Fin/GAA structures. Among them, the layer transferred Ge-on-Insulator (GeOI) FinFET significantly improves its I-V characteristic during cryogenic measurements, such as a steeper subthreshold swing at 10K and a better Ion. The developed GeOI fabrication method provides an effective way to eliminate the defects originating from misfit dislocations at the Ge/Si substrate during epitaxial growth, which would be treated as the key to device performance enhancement under 10 K. According to the measured IV at 10 K and circuit prediction, GeOI FinFETs with high Ge crystallinity are strong candidates for High-Performance-computing (HPC) applications. © 2023 IEEE.

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Language models represent beliefs of self and others 24

Language models represent beliefs of self and others

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Proceedings of the 41st International Conference on Machine Learning

作者： Wentao Zhu Zhining Zhang Yizhou Wang Center on Frontiers of Computing Studies School of Compter Science Peking University Center on Frontiers of Computing Studies School of Compter Science and Inst. for Artificial Intelligence and Nat'l Eng. Research Center of Visual Technology and Nat'l Key Lab of General Artificial Intelligence Peking University

Understanding and attributing mental states, known as Theory of Mind (ToM), emerges as a fundamental capability for human social reasoning. While Large Language Models (LLMs) appear to possess certain ToM abilities, the mechanisms underlying these capabilities remain elusive. In this study, we discover that it is possible to linearly decode the belief status from the perspectives of various agents through neural activations of language models, indicating the existence of internal representations of self and others' beliefs. By manipulating these representations, we observe dramatic changes in the models' ToM performance, underscoring their pivotal role in the social reasoning process. Additionally, our findings extend to diverse social reasoning tasks that involve different causal inference patterns, suggesting the potential generalizability of these representations.

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