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arXiv 2025年

作者： Littauer, Richard Wilson, Greg Ainali, Jan AlOmar, Eman Abdullah Arabas, Sylwester Saibene, Yanina Bellini Bubendorfer, Kris Champion, Kaylea Dillon, Clare Helske, Jouni Huybrechts, Pieter Katz, Daniel S. Liao, Chang Lippert, David Liu, Fang Marshall, Pierre McCloy, Daniel R. McInerney, Ian Mkaouer, Mohamed Wiem Ojha, Priyanka Treude, Christoph White, Ethan P. CURIOSS SustainOSS GNOME Foundation Te Herenga Waka Victoria University of Wellington Te Whanganui-a-Tara Aotearoa Wellington New Zealand Third Bit TorontoON Canada Open By Default Italy Department of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States AGH University of Krakow Poland rOpenSci LP Santa Rosa Argentina Te Herenga Waka Victoria University of Wellington Wellington New Zealand Computing and Software Systems University of Washington Bothell BothellWA United States CURIOSS Lero University of Galway Ireland INVEST Research Flagship Centre University of Turku Finland Brussels Belgium University of Illinois Urbana-ChampaignIL United States Pacific Northwest National Laboratory RichlandWA United States Open Source Program Office George Washington University WashingtonDC United States Georgia Institute of Technology GA United States Oxford University United Kingdom Institute for Learning & Brain Sciences University of Washington SeattleWA United States Department of Mechanical Engineering Imperial College London London United Kingdom Department of Computer Science University of Michigan-Flint FlintMI United States Singapore Management University Singapore Department of Wildlife Ecology and Conservation University of Florida GainesvilleFL United States

Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leaving large amounts of research software abandoned. This article presents ten tips to help researchers ensure that the software they have built will continue to be usable after they have left their present job—whether in the course of voluntary career moves or researcher mobility, but particularly in cases of involuntary departure due to political or institutional changes. © 2025, CC BY-SA.

关键词： Human engineering

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Development and Validation of Instruments Adoption FinTech services in Indonesia (Perspective of Trust and Risk)

Development and Validation of Instruments Adoption FinTech s...

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International Conference on Sustainable Information engineering and Technology (SIET)

作者： Erick Fernando Surjandy Meyliana Derist Touriano Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Systems Audit Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Technology Department Faculty of computer science and engineering Adiwangsa Jambi University Jambi Indonesia

ISBN: (纸本)9781538674086;9781538674079

This study explores the influence of trust and risk in the use of FinTech in Indonesian society. This exploration is based on findings from previous studies that suggest that trust and risk are important aspects in considering the use of FinTech. The focus of this research is to develop and validate the research instruments that will be used next. The basic theory of model development uses the theory of Technology Acceptance Model (TAM) developed by Davis. The models and instruments developed will be through pilot studies involving 133 communities as respondents. This Study is quantitative research and data obtained were analyzed using smart pls v3.0. This analysis is conducted to ensure the level of reliability and validity of the instrument. The results of this analysis find 31 instruments that are stated reliable and validity. So it can be used for collecting data from a survey in accordance with the research.

关键词： Instruments Reliability Information systems Business Adaptation models Economics Transportation

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Aperture-Coupled Koch Fractal on-Chip Antennas for 60 GHz ISM Band

Aperture-Coupled Koch Fractal on-Chip Antennas for 60 GHz IS...

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IEEE MTT-S Latin America Microwave Conference (LAMC)

作者： Samuel Medeiros Araújo Morais Alexandre Jean René Serres Ivana Soares Guarany Raimundo Carlos da Silverio Freire Paulo Fernandes da Silva Ewaldo Elder Carvalho Santana Paulo Henrique da Fonseca Silva Post-Graduation Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Post-Graduation Program in Computer Engineering and Systems State University of Maranhão São Luis Brazil Post-Graduation Program in Electrical Engineering Federal Institute of Education Science and Technology of Paraíba João Pessoa Brazil

ISBN: (纸本)9781538673348;9781538673331

This paper presents Koch fractal aperture-coupled patch antennas operating in industrial scientific and medical band at 60 GHz (57 GHz to 64 GHz), for on-chip application. This work is the continuation of the development of aperture-coupled antennas with aperture and patch element built in 2 μm-thick aluminum lying on two layers of silicon with a height of 200 μm. The use of Koch fractal allowed the development of aperture-coupled antennas of greater perimeter with more metal in the patch elements, which promoted an increase in the gain. The observed gain for Koch fractal level 2 was of 10.55 dBi, with a bandwidth of 7.23 GHz, covering the ISM 60 GHz Band, and an increase of less than 10% in total patch dimension. For Koch fractal levels 0 (square), a polarization close to circular was achieved, with an axial ratio less than 6 at resonant frequencies.

关键词： Fractals Patch antennas Resonant frequency Aperture antennas Bandwidth Gain

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用于糖尿病培训的大型语言模型:一项前瞻性研究（英文）

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Science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

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Questions Classification in Online Discussion Towards Smart Learning Management System

Questions Classification in Online Discussion Towards Smart ...

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International Conference on Information Management and Technology (ICIMTech)

作者： Bambang Dwi Wijanarko Dina Fitria Murad Yaya Heryadi Lukas Hapnes Toba Widodo Budiharto Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Engineering Univeritas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group (CERG) Jakarta Indonesia

The widespread interest toward online learning at higher education worldwide brings about necessity toward an smart Learning Management System (LMS), LMS with analytic functionalities, to increase learning outcome of the students. In an effort to develop a smart LMS which is able to both guiding students on learning process and giving automated feedback on online learning delivery to online learning management, this paper presents a preliminary and exploratory results toward textual documents from learning material repository and online discussion forum. The dataset for this study obtained from Bina Nusantara Online Learning repository. The study found interesting finding namely: there is a significant discrepancy between cognitive domain Taxonomy level between question raised in discussion forum and its respective level of learning outcomes. This findings were confirmed using Anova with P-value

关键词： Taxonomy computer science Discussion forums Medical services Support vector machines Machine learning Analysis of variance

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Speech Emotion Recognition using Convolutional Neural Network with Audio Word-based Embedding

Speech Emotion Recognition using Convolutional Neural Networ...

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International Symposium on Chinese Spoken Language Processing

作者： Kun-Yi Huang Chung-Hsien Wu Qian-Bei Hong Ming-Hsiang Su Yuan-Rong Zeng Department of Computer Science and Information Engineering National Cheng Kung University Taiwan PhD Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (纸本)9781538656280;9781538656273

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlying relation of emotional speech. In this work, a convolutional neural network (CNN) with audio word-based embedding is proposed for emotion modeling. In this study, vector quantization is first applied to convert the low level features of each speech frame into audio words using k-means algorithm. Word2vec is adopted to convert an input speech utterance into the corresponding audio word vector sequence. Finally, the audio word vector sequences of the training emotional speech data with emotion annotation are used to construct the CNN- based emotion model. The NCKU-ES database, containing seven emotion categories: happiness, boredom, anger, anxiety, sadness, surprise and disgust, was collected and five-fold cross validation was used to evaluate the performance of the proposed CNN-based method for speech emotion recognition. Experimental results show that the proposed method achieved an emotion recognition accuracy of 82.34%, improving by 8.7% compared to the Long Short Term Memory (LSTM)- based method, which faced the challenging issue of long input sequence. Comparing with raw features, the audio word-based embedding achieved an improvement of 3.4% for speech emotion recognition.

关键词： Emotion recognition Feature extraction Speech recognition Acoustics Training Databases Neural networks

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Exploring Macroscopic Fluctuation of Facial Expression for Mood Disorder Classification

Exploring Macroscopic Fluctuation of Facial Expression for M...

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Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Qian-Bei Hong Chung-Hsien Wu Ming-Hsiang Su Kun-Yi Huang Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781538653128

In clinical diagnosis of mood disorder, a large portion of bipolar disorder patients (BDs) are misdiagnosed as unipolar depression (UDs). Clinicians have confirmed that BDs generally show "reduced affect'' during clinical treatment. Thus, it is expected to build an objective and one-time diagnosis system for diagnosis assistance by using machine-learning techniques. In this study, facial expressions of BD, UD and control group (C) elicited by emotional video clips are collected for exploring temporal fluctuation characteristics of intensities of facial muscles expression among the three groups. The differences of facial expressions among mood disorders are investigated by observing macroscopic fluctuations. To deal with these problems, the corresponding methods for feature extraction and modeling are proposed. From the viewpoint of macroscopic facial expression, action unit (AU) is applied for describing the temporal transformation of muscles. Then, modulation spectrum is used for extracting short-term variation of AU. The multilayer perceptron (MLP)-based disorder prediction model is then applied to obtain the prediction results. For evaluation of the proposed method, 12 subjects for three group are included in the K-fold (K=12) cross validation experiments. The experiment results reached 61.1% classification accuracy, and outperformed the other baseline methods.

关键词： Mood Gold Fluctuations Databases Feature extraction Modulation Predictive models

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Syncgan: Synchronize the Latent Spaces of Cross-Modal Generative Adversarial Networks

Syncgan: Synchronize the Latent Spaces of Cross-Modal Genera...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen-Cheng Chen Chien-Wen Chen Min-Chun Hu Dept. of Computer Science and Information Engineering National Cheng Kung University Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one-directional transfer and have achieved preliminary success on text-to-image transfer. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain the latent space of generators in the GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve semi-supervised learning, which makes our model more flexible for practical applications.

关键词： Synchronization Gallium nitride Generators Data models Training Generative adversarial networks Training data

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LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors

LSTM-based Text Emotion Recognition Using Semantic and Emoti...

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Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Ming-Hsiang Su Chung-Hsien Wu Kun-Yi Huang Qian-Bei Hong Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan

ISBN: (纸本)9781538653128

This study proposes a long-short term memory (LSTM)-based approach to text emotion recognition based on semantic word vector and emotional word vector of the input text. For each word in an input text, the semantic word vector is extracted from the word 2vec model. Besides, each lexical word is projected to all the emotional words defined in an affective lexicon to derive an emotional word vector. An autoencoder is then adopted to obtain the bottleneck features from the emotional word vector for dimensionality reduction. The autoencoder bottleneck features are then concatenated with the features in the semantic word vector to form the final textual features for emotion recognition. Finally, given the textual feature sequence of the entire sentence, the LSTM is used for emotion recognition by modeling the contextual emotion evolution of the input text. For evaluation, the NLPCC-MHMC-TE database containing seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, and surprise was constructed and used. Five-fold cross-validation was employed to evaluate the performance of the proposed method. Experimental results show that the proposed LSTM-based method achieved a recognition accuracy of 70.66%, improving 5.33% compared with the CNN-based method. Besides, the proposed method based on integration of the semantic word vector and emotional word vector of the input text outperformed that using the individual feature vector.

关键词： Emotion recognition Databases Logic gates Feature extraction Semantics Speech recognition computer science

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