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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tanoj Langore Te-Cheng Hsu Yi-Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset.

关键词： Drugs In vivo Neural networks Predictive models Network architecture Feature extraction Protein sequence

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Development and Validation of a Digital Twin for a Commercial Mobile Robot

Development and Validation of a Digital Twin for a Commercia...

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IEEE International Conference on Robotic Computing (IRC)

作者： Wilson Eduardo Barioni Igor Pardal Latini Carina Toledo Scoparo Barioni Flavio Neves L.V.R. Arruda Marco Teixeira RCPGEI - UTFPR Curitiba Parana Brazil Graduate Program in Electrical and Computer Engineering (CPGEI) Universidade Tecnologica Federal do Parana (UTFPR) Curitiba Parana Brazil Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil CPGEI - UTFPR Curitiba Parana Brazil Universidade Positivo Paraná Brasil PPGIa - PUCPR Curitiba Parana Brazil

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

In recent decades, there has been a significant increase in the presence of robots in the daily operations of industries. Automated Guided Vehicles (AGVs) used in industrial environments are gaining prominence due to their numerous applications, ranging from logistic tasks such as material transport to more complex process tasks like replacing conveyor belts in production lines. For a mobile robot to operate in these environments, it must possess skills to perform its functions and ensure safety. In this regard, simulating a computational model of a real autonomous system can contribute to developing new operational strategies. The objective of this work is to explore the use of a Digital Twin for an Automated Guided Vehicle (AGV), aiming to design and validate navigation and obstacle avoidance algorithms, with the intent to optimize the operation of these devices in industrial environments. In the current study, the AGV G3R1000 was used, which was modeled in detail in FUSION 360 software, along with a production line from the AGVS company in Brazil. As validation, the digital robot was used to design the navigation software using the PID control system and obstacle avoidance using Fuzzy control. The algorithm developed in the digital robot was applied to the real robot, and a performance comparison was made. In conclusion, it is possible to use the simulated robot to design algorithms for the real robot, saving resources such as time and equipment.

关键词： Remotely guided vehicles Service robots Navigation Computational modeling Software algorithms Production Software Digital twins Safety Mobile robots

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NG-DTA: Drug-target affinity prediction with n-gram molecular graphs

NG-DTA: Drug-target affinity prediction with n-gram molecula...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Lok-In Tsui Te-Cheng Hsu Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Institute of Communication Engineering National Tsing-Hua University Hsinchu Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

Drug–target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared to a graph representation. In this paper, we present a deep-learning-based DTA prediction method called N-gram Graph DTA (NG-DTA) that takes molecular graphs of drugs and n-gram molecular sub-graphs of proteins as inputs which are then processed by graph neural networks (GNNs). Without using any prediction tool for protein structure, NG-DTA performs better than other methods on two datasets in terms of concordance index (CI) and mean square error (MSE) (CI: 0.905, MSE: 0.196 for the Davis dataset; CI: 0.904, MSE: 0.120 for Kiba dataset). Our results showed that using n-gram molecular sub-graphs of proteins as input improves deep learning models’ performance in DTA prediction.

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

arXiv

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arXiv 2023年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The International Audio Laboratories Erlangen Germany The Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra University of Coimbra Portugal The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica 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). © 2023, CC BY.

关键词： Dynamic programming

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Analysis of Mucilage Levels Build Up in the Sea of Marmara Based on Unsupervised Unmixing of Worldview-3 Data

Analysis of Mucilage Levels Build Up in the Sea of Marmara B...

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： Gö zdenur Keleş oğ lu Alp Ertü rk Esra Erten Institute of Informatics Satellite Communication and Remote Sensing Program Istanbul Technical University Istanbul Turkey Department of Electronics and Telecommunication Engineering Kocaeli University Turkey Department of Geomatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (数字)9781665427951

ISBN: (纸本)9781665427968

Increasing uncontrolled industrialization causing untreated wastewater and marine traffic, coupled with specific weather conditions makes the Marmara Sea face major challenges, such as the mucilage outbreak in the summer of 2021. Cleaning up the Sea of Marmara will continue to be critical to the long term survival of the fishing industry and tourism, and new methods and techniques for monitoring its behaviour will be important for benchmarking. In this specific context, this paper discusses the potential of using unmixing approaches for the analysis of multispectral images in order to understand and map the abundance of mucilage coverage, in connection with its chemical compositions related to varying levels of aggregation. A WorldView-3 multispectral image, acquired on May 25, 2021, was chosen for the study due to its high spatial resolution and VIS/NIR spectral region. Unmixing provides the spectral characteristics of endmembers (i.e., water, ship and different types mucilage), and the abundance associated with each endmember. Preliminary results were discussed and some research aspects for understanding mucilage phenomena using EO were put forward.

关键词： Water Toxicology Sea measurements Cleaning Wastewater Spatial resolution Marine vehicles

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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The global structure of codimension-2 local bifurcations in continuous-time recurrent neural networks

arXiv

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arXiv 2021年

作者： Beer, Randall D. Cognitive Science Program Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

If we are ever to move beyond the study of isolated special cases in theoretical neuroscience, we need to develop more general theories of neural circuits over a given neural model. The present paper considers this challenge in the context of continuous-time recurrent neural networks (CTRNNs), a simple but dynamically-universal model that has been widely utilized in both computational neuroscience and neural networks. Here we extend previous work on the parameter space structure of codimension-1 local bifurcations in CTRNNs to include codimension-2 local bifurcation manifolds. Specifically, we derive the necessary conditions for all generic local codimension-2 bifurcations for general CTRNNs, specialize these conditions to circuits containing from one to four neurons, illustrate in full detail the application of these conditions to example circuits, derive closed-form expressions for these bifurcation manifolds where possible, and demonstrate how this analysis allows us to find and trace several global codimension-1 bifurcation manifolds that originate from the codimension-2 bifurcations. Copyright © 2021, The Authors. All rights reserved.

关键词： Computation theory

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Extending the Core Ontology on Decision Making according to Behavioral Economics 14

Extending the Core Ontology on Decision Making according to ...

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14th Seminar on Ontology Research in Brazil, ONTOBRAS 2021 and 5th Doctoral and Masters Consortium on Ontologies, WTDO 2021

作者： Ramos, Eduardo C. Campos, Maria Luiza M. Baião, Fernanda Guizzardi, Renata Graduate Program in Informatics Universidade Federal do Rio de Janeiro RJ Rio de Janeiro Brazil RJ Rio de Janeiro Brazil Department of Industrial Engineering and Business Information Systems University of Twente Enschede Netherlands

Decisions are constantly made and some of them may be extremely critical. However, most decision makers are unaware that their decisions may be biased. We used Behavioral Economics as a foundation to propose an ontology that shows how decision makers’ preferences are set and the situation in which they occur in an intuitive decision under risk and uncertainty. Our aim is that this ontology be used to improve their decisions by allowing them a better understanding of how they decide. In addition, this ontology can be used as part of a strategy to reduce biases. © 2021 Copyright for this paper by its authors.

关键词： Ontology

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Exploring ERP User Behaviour in Developing Countries: Integrating Self-efficacy and UTAUT Perspectives 5

Exploring ERP User Behaviour in Developing Countries: Integr...

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5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023

作者： Syamsuar, Dedy Oktaharisetia, Muhammad Angga Witarsyah, Deden Luthfi, Ahmad Dell, Peter Permana, Yudistira Sira Antoni, Darius Bina Nusantara University School of Information Systems Information Systems Department Indonesia Universitas Bina Darma Program Pasca Sarjana Palembang Indonesia Telkom University School of Industrial Engineering Bandung Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Indonesia Curtin University School of Management and Marketing Faculty of Business and Law Australia Politeknik Sekayu Teknik Informatika Sekayu Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350303414

ERP stands for enterprise resource planning. It is an information system that is all rolled into one, is very flexible and adaptable, and optimizes business operations while also centralizing all of the company's data. This study aims to empirically investigate the elements that affect users' intention and usage of enterprise resource planning (ERP). This research incorporates self-efficacy into the unified theory of acceptance and use of technology (UTAUT2). A quantitative technique was taken, and as a result, 143 replies that might be used were effectively collected. After that, the information was put through a structural equation modelling partial least square (SEM-PLS) analysis. According to the findings of this research, self-efficacy appears to have a significant influence on all endogenous variables (EE, BI, and UB), as indicated by t-values of 3.951, 7.573, and 5.492, respectively. In addition, the analysis also indicated that PE (4.317), EE (2.397), and HM (3.084) are likewise statistically and substantively significant to BI. The result shows that the R2 for BI and UB indicate a moderate impact of 0.603 and 0.521, respectively. It is suggested that the model has valuable applications in academic and real-world contexts. © 2023 IEEE.

关键词： Enterprise resource planning

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Deep learning-based real time detection for cardiac objects with fetal ultrasound video

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informatics in Medicine Unlocked 2023年 36卷

作者： Iriani Sapitri, Ade Nurmaini, Siti Naufal Rachmatullah, Muhammad Tutuko, Bambang Darmawahyuni, Annisa Firdaus, Firdaus Rini, Dian Palupi Islami, Anggun Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang 30139 Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia

Fetal cardiac anatomical structure interpretation by ultrasound (US) is a key part of prenatal assessment. Unfortunately, the numerous speckles in US video, the small size of fetal cardiac structures, and unfixed fetal positions make manual detection processes difficult. To alleviate such problems, a deep learning model was developed to fully automate the processing of fetal echocardiographs with 2-dimensional cross-sectional images. The weakness of such an approach is that a physician immediately interprets US video with 3-dimensional objects to diagnose patients in clinical practice. To improve fetal cardiac anatomical structure interpretation via US for accurate and real time diagnosis, this paper proposes a real-time fetal cardiac substructure detection using US video with the You Only Look Once (YOLO) framework. In YOLO, an end-to-end neural network makes predictions for cardiac substructure objects, boxes, and class probabilities simultaneously. To achieve reliable performance, 40 fetal echocardiography videos were trained with the new YOLOv7 architecture and fine-tuned to work optimally and run efficiently. We conducted with nine fetal cardiac substructure objects such as the left atrium, right atrium, left ventricle, right ventricle, tricuspid valve, pulmonary valve, mitral valve, aortic valve, and aorta. The results yielded the highest mean average precision of 82.10%, reaching 17 frames per second (FPS) for nine cardiac substructure objects in 0.3 ms. The main finding of our study is that with even a limited number of US videos, YOLOv7 can detect fetal cardiac substructure objects in real-time. Such a network can work efficiently to detect small fetal cardiac objects automatically in a rapid phase with the help of proper fine-tuning. This work mainly assists medical experts in the fetal cardiac anatomy diagnostic process. © 2022 The Authors

关键词： Computer vision Fetal echocardiography Object detection YOLOv7

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