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2022 International Conference on Information Technology Research and Innovation, ICITRI 2022

作者： Ramdhani, Yudi Maulidia, Dea Setiadi, Ahmad Alamsyah, Doni Purnama Universitas Adhirajasa Reswara Sanjaya Informatics Engineering Program Bandung Indonesia Universitas Adhirajasa Reswara Sanjaya Computer Science Program Bandung Indonesia Universitas Bina Sarana Informatika Information System Program Jakarta Indonesia Bina Nusantara University Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665461849

ISBN: (纸本)9781665461849

Many pregnant women still do not want to check their pregnancy to recognize the signs of high-risk pregnant women. High-risk pregnancies refer to abundant women with several risky conditions requiring special care during and after delivery. This study aims to predict pregnancy risk levels with early detection efforts. The method used in this research is the Random Forest method for classification and the Genetic Algorithm for optimization. The results obtained from the Maternal Health Risk dataset analysis using the Random Forest method get an accuracy value of 73.37%. After optimizing the features using a genetic algorithm, the accuracy value in the classification of the health risk level of pregnant women has increased significantly to 90.20%. The results of the t-Test in this study showed a value of less than 0.05, and the t-Test value obtained is 0.000218269. That is, the genetic algorithm has excellent performance to improve the performance of the classification algorithm. Based on the results of the study to get a good accuracy value, it can be used by medical personnel to detect the level of health risk in pregnant women. It can be a reference material for further research to implement this research into a program. © 2022 IEEE.

关键词： Genetic algorithms

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Computational strategies for cross-species knowledge transfer and translational biomedicine

arXiv

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arXiv 2024年

作者： Yuan, Hao Mancuso, Christopher A. Johnson, Kayla Braasch, Ingo Krishnan, Arjun Genetics and Genome Science Program Ecology Evolution and Behavior Program Michigan State University United States Department of Biostatistics & Informatics University of Colorado Anschutz Medical Campus United States Department of Biomedical Informatics University of Colorado Anschutz Medical Campus United States Department of Integrative Biology Genetics and Genome Science Program Ecology Evolution and Behavior Program Michigan State University United States

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer. © 2024, CC BY.

关键词： Biotic

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Analysis of Digital Population Services for the Poor in Palembang City Using the Information Technology Infrastructure Library (ITIL) Framework 4

Analysis of Digital Population Services for the Poor in Pale...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Antoni, Darius Septiyanti, Reni Akbar, Muhamad Herdiansyah, Muhammad Izman Department of Informatics Engineering Graduate Program Bina Darma University Palembang Indonesia

ISBN: (纸本)9781665433945

As it is known that the city government of Palembang has implemented e-government, but how much e-government can help the Palembang city government to interact between the government and all levels of society including the poor in Palembang city. In its application, the condition of e-government services is still insufficient for the infrastructure to support e-government services for the poor in Palembang city. To design e-government services such as good digital population services, it is necessary to design IS / IT services for people in Palembang City with the Service Design stage in the 2011 Version of the Information Technology Infrastructure Library (ITIL) Framework so that they can be accepted by all levels of society including the poor in Palembang city. This study uses a qualitative approach. The results of this research are in the form of a digital population service information system design such as administrative and population services, Family Hope program (PKH) services and digital archive storage services based on the Service Design stages in ITIL Version 2011 that have been carried out, namely the Service Catalog Management process and Service Level Management in the form of documents. Service Level Requirements, Service Level Agreement and Operational Level Agreement. © 2021 IEEE

关键词： Information management

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Exploring Parallelism in ZynqNet AI Engine to Accelerate Image Classification in Aerospace

Exploring Parallelism in ZynqNet AI Engine to Accelerate Ima...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Eduardo Marañon Aguilar Fabio Benevenuti Fernanda Lima Kastensmidt Federal University of Rio Grande do Sul (UFRGS) Institute of Informatics Microelectronics Graduate Program (PGMICRO) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This research investigates various configurations of the Zynqnet AI Engine to accelerate image classification tasks using the SAT-6 dataset and a 4-layer CNN. The C++ code for the hardware accelerator was translated into RTL using Vivado HLS and integrated with the Zynq Processing System on a ZedBoard Zynq-7000 ARM/FPGA SoC. The AI engine was configured to work with 32 -bit floating point, 8bit fixed point and 8 -bit integer type operations, with different processing elements for parallelizing CNN tasks. Given a 64-bit data memory bus in communication with the accelerator and an 8-bit integer quantization, the variation in the number of processing elements does not result in a significant performance improvement. To address this, 8 values were grouped for reading and writing in 64-bit parallel mode, and different communication architectures were designed to improve data transfer between the accelerator and DDR memory, with optional enablement of L1D and L2 cache. The research analyzes CNN computation performance and FPGA resource usage (LUTs, FFs, BRAMs, DSPs) across configurations, providing insights into the trade-offs in FPGA-based CNN implementations, aiding the development of efficient, high-performance hardware accelerators for Deep Learning applications.

关键词： Quantization (signal) Writing Table lookup System-on-chip Convolutional neural networks Artificial intelligence Hardware acceleration Engines Field programmable gate arrays Image classification

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A 3D Model Generation from a Single Snapshot in Outdoor Scene

A 3D Model Generation from a Single Snapshot in Outdoor Scen...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Luoxi Zhang Chun Xie Itaru Kitahara Doctoral Program in Empowerment Informatics University of Tsukuba Tsukuba Japan Center for Computational Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

While scene modeling is widely used in various fields such as VR/AR and 3D art asset creation, it usually requires multiple viewpoint images captured by calibrated cameras, which make difficult to realize practical applications. This paper proposes a method for 3D scene reconstruction from a single snapshot based on RGB-D information without camera parameters. Mask-RCNN are used to separate the foreground and background, and pretrained deep neural network is applied to infer the human mesh of the foreground region. Image inpainting, monocular depth estimation using MiDaS, and point cloud processing are combined to generate the 3D background point cloud. Finally, the foreground and background are integrated to achieve modeling the 3D scene.

关键词： Point cloud compression Solid modeling Three-dimensional displays Computational modeling Artificial neural networks Cameras Hardware Image reconstruction Consumer electronics Load modeling

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Drivers of the Sustainability Performance of Induction Stove Conversion program in Indonesia

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International Journal of Sustainable Energy Planning and Management 2025年 43卷 5-30页

作者： Damayanti, Retno Wulan Safira, Silvia Dhea Laksono, Pringgo Widyo Setiadi, Haryono Entifar, Nisa Aqilla Ellenahaya Industrial Engineering Study Program Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Center of Technology Development and Industrial Collaboration Institute for Research and Community Service Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Informatics and Science Data Study Program Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Smart Green Technology Engineering Department Pukyong National University 45 Yongso-ro Nam-Gu Busan Korea Republic of

In 2022, Indonesia initiated a pilot project to transition from liquid petroleum gas (LPG) stoves to induction stoves, targeting 8.2 million households by 2025. While LPG is considered a clean cooking fuel by the World Health Organization due to its low PM2.5 emissions, this program aims to further reduce CO2 emissions from cooking activities, aligning with Indonesia’s clean energy transition and 2060 Net Zero Emission goals. Throughout the program, doubts and criticisms arose among various sectors of society, particularly concerning the sustainability of this program. This research aims to determine and analyze the driving factors that support the sustainability of the induction stove transition program. Five sustainability aspects, namely, economic, social, environmental, technical, and institutional, were analyzed. A total of 315 Indonesian citizens participated as respondents in this research survey. The Partial Least Squares-Structural Equation Model (PLS–SEM) method was utilized as the analytical approach for the study. The results revealed that nine factors significantly influenced program sustainability, with the strongest impacts from technology costs (path coefficient = 0.859), contextual factors (0.803), electrical infrastructure (0.792), and community perception (0.773). Economic sustainability was driven by technology costs and business opportunities (0.643), while environmental sustainability was strongly influenced by electrical infrastructure. Post-installation support (0.307) and financial management (0.391) significantly impacted technical and institutional sustainability respectively. These empirical findings improve previous research on the sustainability of an energy transition program, which provides insights for researchers and stakeholders in the energy management field, especially in developing countries, for policy determination and decision-making. © 2025, Aalborg University Press. All rights reserved.

关键词： Clean energy

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Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles 5

Improving the Transferability of Clinical Note Section Class...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Zhou, Weipeng Afshar, Majid Gao, Yanjun Dligach, Dmitriy Miller, Timothy A. Department of Biomedical Informatics and Medical Education School of Medicine University of Washington United States Department of Medicine School of Medicine and Public Health University of Wisconsin United States Department of Computer Science Loyola University ChicagoIL United States Computational Health Informatics Program Boston Children's Hospital Harvard Medical School United States

ISBN: (纸本)9781959429883

Text in electronic health records is organized into sections, and classifying those sections into section categories is useful for downstream tasks. In this work, we attempt to improve the transferability of section classification models by combining the dataset-specific knowledge in supervised learning models with the world knowledge inside large language models (LLMs). Surprisingly, we find that zero-shot LLMs out-perform supervised BERT-based models applied to out-of-domain data. We also find that their strengths are synergistic, so that a simple ensemble technique leads to additional performance gains. © 2023 Association for Computational Linguistics.

关键词： Large dataset

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From Data to Action: How Urban Heat Mapping Campaigns Can Expose Vulnerabilities and Inform Local Heat Policy

From Data to Action: How Urban Heat Mapping Campaigns Can Ex...

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作者： Fuhrmann, Christopher Robinson, Andrew Konrad, Charles Bhatia, Abhishek NOAA/Southeast Regional Climate Center Department of Geography and Environment University of North Carolina at Chapel Hill Chapel HillNC United States Carolina Health Informatics Program University of North Carolina at Chapel Hill Chapel HillNC United States

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Asynchronous Data Communication Architecture on Real-Time Face Recognition for Hybrid Laboratory System 17

Asynchronous Data Communication Architecture on Real-Time Fa...

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Lestari, Nina Purnama, Trisna Risnanto, Slamet Fitriadi, Budi Yusuf, Ach Maulana Habibi Prihatmanto, Ary Setijadi Institut Teknologi Bandung Doctoral Program of School of Electrical Engineering and Informatics Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Electrical Engineering Department Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Bandung Indonesia Universitas Komputer Indonesia Bandung Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350309164

The Internet of Things (IoT) and Artificial Intelligence (AI) have reshaped educational technology, increasing the chances of developing more effective and engaging learning environments. This study proposes an architecture for asynchronous data communication with real-Time face recognition, a virtual and remote labs confluence. The architecture uses IoT hardware and software to comprise critical components such as IoT cameras, message brokers, FTP servers, and AI processing servers. The aim is to optimize resource usage and maximize response time efficiency. The architecture employs message queuing to manage high volumes of face image data, facilitating immediate verification and rapid access. Our findings demonstrate that the system can achieve face recognition with 91.7% accuracy and an average system communication latency of 2.631 seconds, effectively leveraging asynchronous communication to optimize resource usage and computational efficiency. The research comprehensively measures the data communication metrics and face recognition metrics. It also explains how asynchronous data communication can be strategically deployed in a hybrid lab environment to enhance educational and operational efficacy. © 2023 IEEE.

关键词： Computational efficiency

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