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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Richard Kimera Daniela N. Rim Joseph Kirabira Ubong Godwin Udomah Heeyoul Choi Department of Advanced Convergence Handong Global University Pohang South Korea School of Computer Science and Electrical Engineering Handong Global University Pohang South Korea Department of Psychiatry Busitema University (Faculty of Health Sciences) Mbale Uganda Department of Psychiatry University of Uyo Teaching Hospital Uyo Akwa Ibom Nigeria

Depression is a global burden and one of the most challenging mental health conditions to control. Using the BDI questionnaire, experts can detect its severity early, administer appropriate medication to patients, and impede its progression. Owing to the fear of potential stigmatization, many patients turn to social media platforms such as Reddit for advice and assistance at various stages of their journey. This research extracts text from Reddit to facilitate the diagnostic process, employs a proposed labelling approach to categorize the text, and subsequently fine-tunes the Longformer model. The model’s performance is compared against the baseline models, including Naive Bayes, Random Forest, Support Vector Machines, and Gradient Boosting. Our findings reveal that the Longformer model outperforms the baseline models in both English (48%) and Luganda (45%) languages on a custom-made dataset.

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Systematizing Modeler Experience (MX) in Model-Driven Engineering Success Stories

arXiv

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arXiv 2024年

作者： Kalantari, Reyhaneh Oertel, Julian Exelmans, Joeri Rukmono, Satrio Adi Amaral, Vasco Tichy, Matthias Juhnke, Katharina Steghöfer, Jan-Philipp Abrahão, Silvia Department of Engineering Design and Teaching Innovation University of Ottawa 800 King Edward Avenue OttawaK1N6N5 Canada Department of Software Engineering University of Rostock Albert-Einstein-Straße Rostock18057 Germany Department of Computer Science University of Antwerp Middelheimlaan 1 Antwerp2020 Belgium Department of Mathematics and Computer Science Eindhoven University of Technology De Zaale Eindhoven5600 MB Netherlands Department of Computer Science NOVA School of Science & Technology Campus de Caparica Caparica2829-516 Portugal Institute of Software Engineering and Languages Ulm University James-Franck-Ring Ulm89081 Germany SMT-EMI6 Carl Zeiss SMT GmbH Rudolf-Eber-Straße 2 Oberkochen73447 Germany XITASO GmbH Austraße 35 Augsburg86153 Germany Instituto Universitario Mixto de Tecnología Informática Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of "modeler experience" (MX). MX includes factors such as user experience, motivation, integration, collaboration & versioning and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling. © 2024, CC BY.

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Learning to Answer Questions in Dynamic Audio-Visual Scenarios

arXiv

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arXiv 2022年

作者： Li, Guangyao Wei, Yake Tian, Yapeng Xu, Chenliang Wen, Ji-Rong Hu, Di Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science University of Rochester Rochester United States

In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to answer questions regarding different visual objects, sounds, and their associations in videos. The problem requires comprehensive multimodal understanding and spatio-temporal reasoning over audio-visual scenes. To benchmark this task and facilitate our study, we introduce a large-scale MUSIC-AVQA dataset, which contains more than 45K question-answer pairs covering 33 different question templates spanning over different modalities and question types. We develop several baselines and introduce a spatio-temporal grounded audio-visual network for the AVQA problem. Our results demonstrate that AVQA benefits from multisensory perception and our model outperforms recent A-, V-, and AVQA approaches. We believe that our built dataset has the potential to serve as testbed for evaluating and promoting progress in audio-visual scene understanding and spatio-temporal reasoning. Code and dataset: http://***/MUSIC-AVQA/ Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Double Transformed Tubal Nuclear Norm Minimization for Tensor Completion

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Transactions of Nanjing University of Aeronautics and Astronautics 2022年第S01期39卷 166-174页

作者： TIAN Jialue ZHU Yulian LIU Jiahui College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China Fundamental Experimental Teaching Department Nanjing University of Aeronautics and Astronautics

Non-convex methods play a critical role in low-rank tensor completion for their approximation to tensor rank is tighter than that of convex *** they usually cost much more time for calculating singular values of large *** this paper,we propose a double transformed tubal nuclear norm(DTTNN)to replace the rank norm penalty in low rank tensor completion(LRTC)*** turns the original non-convex penalty of a large tensor into two convex penalties of much smaller tensors,and it is shown to be an equivalent ***,DTTNN could take advantage of non-convex envelopes while saving *** results on color image and video inpainting tasks verify the effectiveness of DTTNN compared with state-of-the-art methods.

关键词： double transformed tubal nuclear norm low tubal-rank non-convex optimization tensor factorization tensor completion

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Parameter Estimation of Multiple Poles by Subspace-Based Method

Parameter Estimation of Multiple Poles by Subspace-Based Met...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Prabhu Vijayan Philippe Dreesen Ivan Markovsky Mariya Ishteva Department of Computer Science ADVISE-NUMA KU Leuven Geel Belgium Department ELEC Vrije Universiteit Brussel Brussel Belgium Department of Advanced Computing Sciences Maastricht University Maastricht The Netherlands Gran Capità International Centre for Numerical Methods in Engineering (CIMNE) Barcelona Spain Catalan Institution for Research and Advanced Studies (ICREA) Barcelona Spain

This work deals with the problem of estimating the parameters (i.e., pole multiplicity structure, pole locations, and complex amplitudes) of exponential polynomials for a given model order. We start from an exponential polynomial signal model and algebraically demonstrate the reasoning behind and the manner in which this model generalizes to the broader class of signals containing multiple poles. The poles and complex amplitudes are estimated by a subspace-based method. With the given model order as prior, a heuristic algorithm is developed to estimate the multiplicity structure of poles. Numerical simulations are conducted to study the performance of the subspace-based method and the sensitivity of poles and multiplicity estimation. Our simulations show that our pole estimation method is accurate for signals with poles of multiplicity greater than one, particularly for signals with lower orders. This allows for successful signal regeneration, producing estimates that closely match the original data. The estimation is sensitive, particularly in the presence of noise, and when the poles are complex and have a higher order of multiplicity.

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MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment

arXiv

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arXiv 2024年

作者： Fan, Wenrui Suvon, Mohammod Naimul Islam Zhou, Shuo Liu, Xianyuan Alabed, Samer Osmani, Venet Swift, Andrew Chen, Chen Lu, Haiping Department of Computer Science University of Sheffield Sheffield United Kingdom Centre of Machine Intelligence University of Sheffield Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield Sheffield United Kingdom Department of Clinical Radiology Sheffield Teaching Hospitals Sheffield United Kingdom INSIGNEO Institute for in Silico Medicine University of Sheffield Sheffield United Kingdom Information School University of Sheffield Sheffield United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computing Imperial College London London United Kingdom

Vision-language pre-training (VLP) models have shown significant advancements in the medical domain. Yet, most VLP models align raw reports to images at a very coarse level, without modeling finegrained relationships between anatomical and pathological concepts outlined in reports and the corresponding semantic counterparts in images. To address this problem, we propose a Medical Dual-Stream Language- Image Pre-training (MeDSLIP) framework. Specifically, MeDSLIP establishes vision-language fine-grained alignments via disentangling visual and textual representations into anatomy-relevant and pathologyrelevant streams. Moreover, a novel vision-language Prototypical Contrastive Learning (ProtoCL) method is adopted in MeDSLIP to enhance the alignment within the anatomical and pathological streams. MeDSLIP further employs cross-stream Intra-image Contrastive Learning (ICL) to ensure the consistent coexistence of paired anatomical and pathological concepts within the same image. Such a cross-stream regularization encourages the model to exploit the synchrony between two streams for a more comprehensive representation learning. MeDSLIP is evaluated under zero-shot and supervised fine-tuning settings on three public datasets: NIH CXR14, RSNA Pneumonia, and SIIM-ACR Pneumothorax. Under these settings, MeDSLIP outperforms six leading CNN-based models on classification, grounding, and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5012962页

作者： Hameed Abdulkareem, Karrar Awad Mutlag, Ammar Musa Dinar, Ahmed Frnda, Jaroslav Abed Mohammed, Mazin Hasan Zayr, Fawzi Lakhan, Abdullah Kadry, Seifedine Ali Khattak, Hasan Nedoma, Jan College of Agriculture Al-Muthanna University Samawah66001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Ministry of Education General Directorate of Curricula Pure Science Department Baghdad Iraq Engineering Department University of Technology- Iraq Baghdad Iraq Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and Communication University of Žilina Žilina Slovakia Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Poruba Ostrava Czech Republic College of Computer Science and Information Technology University of Anbar 11 Ramadi Anbar Iraq Department of Biochemistry College of Medicine University of Wasit Wasit Iraq Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China Noroff University College Kristiansand Norway Islamabad44500 Pakistan

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in

关键词： COVID-19

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Study of Manhattan’s consensus degrees through an extension based on the Uniform distribution

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Procedia computer science 2022年 199卷 643-649页

作者： J.M. Tapia F. Chiclana M.J. del Moral E. Herrera–Viedma Department of Quantitative Methods in Economic and Business University of Granada 18071 Granada Spain DIGITS Department of Informatics Faculty of Technology De Montfort University Leicester LE1 9BH UK University of Granada 18071 Granada Spain Department of Computer Science and A.I University of Granada 18071 Granada Spain

An important aspect to be considered in Group Decision Making problems is the study of consensus. Since in these problems it is desirable that the final decision is widely accepted, improving the consensus degree in a fair way is a very interesting task. This paper analyses the improvement in the consensus degrees -obtained by applying Manhattan distance- when the experts’ preferences are slightly modified using one of the properties of the Uniform distribution. We carry out an experimental study that shows the enhancement in different cases to which Uniform extension has been applied, with different number of both, experts and alternatives.

关键词： Consensus Group Decision Making Manhattan distance Uniform distribution

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Professional practice profile, treatment preferences, and the bases for clinical, educational, and research among Nigerian physiotherapists

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Bulletin of Faculty of Physical Therapy 2023年第1期28卷 1-12页

作者： Mbada, Chidozie Emmanuel Onigbinde, Omotola Adam Oyewole, Olufemi Oyeleye Binuyo, Overcomer Temiloluwa Gebrye, Tadesse Egwu, Michael Ogbonnia Karstens, Sven Fatoye, Francis Department of Health Professions Faculty of Health and Education Manchester Metropolitan University Manchester UK Department of Medical Rehabilitation College of Health Sciences Obafemi Awolowo University Ile-Ife Nigeria Department of Physiotherapy Olabisi Onabanjo University Teaching Hospital Sagamu Nigeria College of Health Sciences University of KwaZulu-Natal Durban South Africa Therapeutic Sciences Department of Computer Science Trier University of Applied Sciences Trier Germany

Physiotherapy education and practice have country-specific peculiarities which may limit globalization in health care. This study aimed to characterize physiotherapy practice and treatment preferences, educational qualifications, and research in Nigeria, with a view of providing vital information for transnational integration and collaboration. A cross-sectional survey of 104 Nigerian physiotherapists was conducted. The Physical Therapy Practice Questionnaire and a self-developed proforma were used as survey tools. The mean age of respondents was 33.5 ± 9.4 years. About two-fifth of all respondents (39.4%) had an MSc and mostly practice as clinicians (51.0%) in teaching hospitals (34.6%). The respondents were mostly involved in general practice (50.0%), with a caseload of 1–10 patients per day (67.3%). Soft tissue mobilization (83%), proprioceptive neuromuscular facilitation (76%), breathing exercises (77%), and transcutaneous electrical neuromuscular stimulation (83%) were commonly used. Respondents were familiar databases and evidence-based resources (81.2%) and mostly utilize PubMed (73.3%). Regular case conferences with professional colleagues (47.6%) and treatment planning of between 11 and 30 min (40.6%) were common. Educators spend 1–3 h planning educational work (91.8%). Clinical decision-making is mostly based on professional experience, while journals are the primary resource for educational information. Physiotherapy practice in Nigeria is degree based and requires registration board’s licensure. Practitioners deal with a high caseload and utilize a wide range of techniques and modalities and have tendencies to utilize personal experience and research in making clinical decisions. The parity in education and practice with advanced climes inadvertently gives physiotherapy practice in Nigeria a global purview.

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Machine Learning for Energy Load Prediction and its Interpretation

Machine Learning for Energy Load Prediction and its Interpre...

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International IEEE Conference on Intelligent Systems, IS

作者： Małgorzata Charytanowicz Anna Olwert Weronika Radziszewska Jolanta Jarnicka Krzysztof Gajowniczek Tomasz Ząbkowski Jacek Brożyna Grzegorz Mentel Grzegorz Matejko Center for Methods of Data Analysis Systems Research Institute PAS Warsaw Poland Department of Computer Science Systems Research Institute PAS Warsaw Poland Center for Computer Modelling Systems Research Institute PAS Warsaw Poland Institute of Information Technology Warsaw University of Life Sciences Warsaw Poland Department of Quantitative Methods Rzeszow University of Technology Rzeszów Poland Polskie Towarzystwo Cyfrowe Lublin Poland

ISBN: (纸本)9781665492768

This paper refers to real-life data on energy consumption from three companies, connected to a medium voltage grid. The companies use single-, two-, or three-zone tariffs and the general characteristic of their activity is comparable. The input dataset contains hourly electricity consumption recorded from January 2020 to December 2020. The independent variables used for the study include weather, time-dependent factors, and aggregated energy factors. We perform the prediction of consumption using three methods: naive, multiple regression, and extreme gradient boosting regression. The performance of the methods is compared based on the MSE, RMSE, MAE, and MAPE metrics. The study shows that the scores achieved for the extreme gradient boosting method are higher than for the multiple regression. To gain the interpretability of the energy load forecasting and feature analysis the Shapley additive explanations method is used for the best model. As a result, a ranking of the most important factors is created. It shows the importance of aggregated energy factors and weather conditions while placing daylight and season at the bottom of the ranking.

关键词： Measurement Additives Load forecasting Tariffs Companies Medium voltage Predictive models

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