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WavFace: A Multimodal Transformer-Based Model for Depression Screening

IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3632-3641页

作者： Flores, Ricardo Tlachac, M.L. Shrestha, Avantika Rundensteiner, Elke A. Universidad de Concepción Department of Computer Science Concepción Chile Worcester Polytechnic Institute Department of Data Science WorcesterMA01609 United States Bryant University Department of Information Systems and Analytics Center for Health and Behavioral Sciences SmithfieldRI01609 United States

Depression, a prevalent mental health disorder with severe health and economic consequences, can be costly and difficult to detect. To alleviate this burden, recent research has been exploring the depression screening capabilities of deep learning (DL) models trained on videos of clinical interviews conducted by a virtual agent. Such DL models need to consider the challenges of modality representation, alignment, and fusion as well as small sample sizes. To address them, we propose WavFace, a multimodal deep learning model that inputs audio and temporal facial features. WavFace adds an encoder-transformer layer over pre-trained models to improve the unimodal representation. It also applies an explicit alignment method for both modalities and then uses sequential and spatial self-attention over the alignment. Finally, WavFace fuses the sequential and spatial self-attentions among the two modality embeddings, inspired by how mental health professionals simultaneously observe visual and vocal presentation during clinical interviews. By leveraging sequential and spatial self-attention, WavFace outperforms pre-trained unimodal and multimodal models from the literature. With a single interview question, WaveFace screened for depression with a balanced accuracy of 0.81. This presents a valuable modeling approach for audio-visual mental health screening. © 2013 IEEE.

关键词： Electronic health record

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Applying BPMN and Ontology to Measure Digital Maturity in Construction 4.0 - A case study 58

Applying BPMN and Ontology to Measure Digital Maturity in Co...

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Heidenwolf, Orsolya Antal, Kristóf Szabó, Ildikó Corvinus University of Budapest Doctoral School of Economics Business and Informatics Hungary Corvinus University of Budapest Institute of Data Analytics and Information Systems Hungary

ISBN: (纸本)9780998133188

In recent years, Construction 4.0 (C4) has emerged as a pivotal concept representing the digital transformation of the construction industry by integrating advanced technologies such as Building information Modeling (BIM), the Internet of Things (IoT), Artificial Intelligence (AI), robotics, and big data analytics. Despite the potential benefits, construction organizations often encounter difficulties in implementing these technologies systematically due to a lack of digital maturity. This paper presents how to use the ontology developed from our Construction 4.0 Maturity Model (C4MM) for investigating the digital maturity level of a construction organization based on their digital transformation journey described in Business Process Model and Notation (BPMN) 2.0 models. A case study of a Hungarian construction company illustrates the practical application of our developing methodology, highlighting the key phases of digital transformation and the role of BPMN and ontology in measuring digital maturity. The findings emphasize the importance of aligning business and IT strategies, continuous improvement, and strategic innovation management in achieving successful digital transformation. © 2025 IEEE Computer Society. All rights reserved.

关键词： BPMN business process management construction 4.0 digital transformation maturity model maturity model building ontology readiness model

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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Computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

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IEEE Transactions on information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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Exploring digital transformation strategy to achieve SMEs resilience and antifragility: a systematic literature review

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Journal of Small Business - Canada 2025年第3期37卷

作者： Gaffar Hafiz Sagala Dóra Őri Institute of Data Analytics and Information Systems Department of Information Systems Corvinus University of Budapest Budapest Hungary

Nowadays, the business environment has become more dynamic, making survival issues more challenging for small and medium enterprises (SMEs). Academic literature proposes digital transformation as a facilitator for SMEs to generate resilience and antifragility to overcome this challenge. However, SMEs need appropriate strategies to be successful in their digital transformation journey. This study aims to construct a digital transformation strategy framework for SMEs to generate resilience and antifragility. We use systematic literature review (SLR) to capture critical knowledge from the published literature. The primary articles were analyzed using thematic analysis with Wolcott’s (Citation1994) procedure to construct the framework based on the primary studies. We found the critical values SMEs need to achieve successful digital transformation, including dynamic capabilities, digital capability, digital inclusion, leadership orientation, learning and knowledge management, and collaboration. However, SMEs need a deeper level of learning, higher digital capability, flexibility, and agility to be antifragile. Furthermore, we found that dynamic capabilities are the leading theory used to describe and investigate how a firm generates successful transformation. Finally, this study proposes a conceptual framework for digital transformation strategies to generate SME resilience and antifragile by connecting theory and practical concepts. It also suggests future research agendas.RÉSUMÉDe nos jours, l’environnement commercial est devenu plus dynamique, ce qui rend les questions de survie plus ardues pour les petites et moyennes entreprises (PME). La recherche propose la transformation numérique comme un moyen pour les PME de générer de la résilience et de l’anti-fragilité afin de surmonter ce défi. Les PME ont toutefois besoin de stratégies appropriées pour réussir leur parcours de transformation numérique. Cette étude vise à construire un cadre stratégique de transformation n

关键词： SME digital transformation digital strategy resilience antifragility information systems small business PME transformation numérique stratégie numérique résilience anti-fragilité systèmes d'information petites entreprises M15 L26 D81

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Eigenvalue-Based Randomness Test for Residual Diagnostics in Panel data Models

arXiv

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arXiv 2025年

作者： Kurbucz, Marcell T. Garrido, Betsabé Pérez Jakovác, Antal Institute for Global Prosperity The Bartlett University College London 149 Tottenham Court Road LondonW1T 7NF United Kingdom Department of Computer Science Institute of Data Analytics and Information Systems Corvinus University of Budapest 8 Fővám Square Budapest1093 Hungary Department of Computational Sciences Institute for Particle and Nuclear Physics HUN-REN Wigner Research Centre for Physics 29-33 Konkoly-Thege Miklós Street Budapest1121 Hungary Department of Statistics Institute of Data Analytics and Information Systems Corvinus University of Budapest 8 Fővám Square Budapest1093 Hungary

This paper introduces the Eigenvalue-Based Randomness (EBR) test—a novel approach rooted in the Tracy-Widom law from random matrix theory—and applies it to the context of residual analysis in panel data models. Unlike traditional methods, which target specific issues like cross-sectional dependence or autocorrelation, the EBR test simultaneously examines multiple assumptions by analyzing the largest eigenvalue of a symmetrized residual matrix. Monte Carlo simulations demonstrate that the EBR test is particularly robust in detecting not only standard violations such as autocorrelation and linear cross-sectional dependence (CSD) but also more intricate non-linear and non-monotonic dependencies, making it a comprehensive and highly flexible tool for enhancing the reliability of panel data *** Codes 62H25, 15B52, 62M10 © 2025, CC BY.

关键词： Eigenvalues and eigenfunctions

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The dynamics of leadership and success in software development teams

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Nature Communications 2025年第1期16卷 1-11页

作者： Betti, Lorenzo Gallo, Luca Wachs, Johannes Battiston, Federico Department of Network and Data Science Central European University Vienna Austria ANETI Lab Corvinus Institute for Advanced Studies (CIAS) Corvinus University of Budapest Budapest Hungary Institute for Data Analytics and Information Systems Corvinus University of Budapest Budapest Hungary Institute of Economics HUN-REN Centre for Economic and Regional Studies Budapest Hungary Complexity Science Hub Vienna Austria

From science to industry, teamwork plays a crucial role in knowledge production and innovation. Most studies consider teams as static groups of individuals, thereby failing to capture how the micro-dynamics of collaborative processes and organizational changes determine team success. Here, we leverage fine-grained temporal data on software development teams from three software ecosystems – Rust, JavaScript, and Python – to gain insights into the dynamics of online collaborative projects. Our analysis reveals an uneven workload distribution in teams, with stronger heterogeneity correlated with higher success, and the early emergence of a lead developer carrying out the majority of work. Moreover, we find that a sizeable fraction of projects experience a change of lead developer, with such a transition being more likely in projects led by inexperienced users. Finally, we show that leadership change is associated with faster success growth. Our work contributes to a deeper understanding of the link between team evolution and success in collaborative processes. © The Author(s) 2025.

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Rapid Automated Mapping of Clouds on Titan With Instance Segmentation

arXiv

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arXiv 2025年

作者： Yahn, Zachary Trent, Douglas M. Duncan, Ethan Seignovert, Benoît Santerre, John Nixon, Conor A. School of Computer Science Georgia Institute of Technology AtlantaGA United States Planetary Systems Laboratory NASA Goddard Space Flight Center GreenbeltMD United States Informatio Data & Analytics Services NASA Langley Research Center HamptonVA United States School of Information University of California Berkeley BerkeleyCA United States Observatoire des Sciences de l'Univers Nantes Atlantique Osuna UAR Nantes3281 France

Despite widespread adoption of deep learning models to address a variety of computer vision tasks, planetary science has yet to see extensive utilization of such tools to address its unique problems. On Titan, the largest moon of Saturn, tracking seasonal trends and weather patterns of clouds provides crucial insights into one of the most complex climates in the Solar System, yet much of the available image data are still analyzed in a conventional way. In this work, we apply a Mask R-CNN trained via transfer learning to perform instance segmentation of clouds in Titan images acquired by the Cassini spacecraft-a previously unexplored approach to a "big data" problem in planetary science. We demonstrate that an automated technique can provide quantitative measures for clouds, such as areas and centroids, that may otherwise be prohibitively time‐intensive to produce by human mapping. Furthermore, despite Titan‐specific challenges, our approach yields accuracy comparable to contemporary cloud identification studies on Earth and other worlds. We compare the efficiencies of human‐driven versus algorithmic approaches, showing that transfer learning provides speed‐ups that may open new horizons for data investigation for Titan. Moreover, we suggest that such approaches have broad potential for application to similar problems in planetary science where they are currently under‐utilized. Future planned missions to the planets and remote sensing initiatives for the Earth promise to provide a deluge of image data in the coming years that will benefit strongly from leveraging machine learning approaches to perform the analysis. © 2025, CC BY.

关键词： Image segmentation

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Mapping Digital Readiness Model into Ontology 15

Mapping Digital Readiness Model into Ontology

引用

15th International Conference on Software, Knowledge, information Management and Applications, SKIMA 2023

作者： Szabo, Ildiko Borbasne Ternai, Katalin Institute of Data Analytics and Information Systems Corvinus University of Budapest Department of Information Systems Budapest Hungary

ISBN: (纸本)9798350316551

Companies need to be aware how Industry 4.0 technologies fit to their working and effect their business processes, what modifications are required before implementing them. Digital maturity and readiness models have been created to meet these requirements. This paper shows how one part of the model developed in the IPAMS project can be mapped into ontology and how ontology development can contribute to automatize the evaluation of company digital readiness. © 2023 IEEE.

关键词： Industry 4.0

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Exploring Policy and Regulations of Clinical AI systems: View from Patients with Chronic Diseases

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Health Policy and Technology 2025年

作者： Bijun Wang Onur Asan Turki Alelyani Department of Business Analytics Data Science Florida Polytechnic University Lakeland FL USA Systems Engineering Stevens Institute of Technology Hoboken NJ USA College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Background Artificial Intelligence (AI) has become a transformative force in healthcare, offering opportunities to enhance patient care, improve efficiency, and reduce costs. However, patients' perspectives, which greatly influence the acceptance and implementation of AI technologies, remain under-researched. Objective This study explores patients with chronic conditions’ perspectives on clinical AI systems, focusing on their concerns, government involvement, accountability for potential AI error, and preferences between AI and doctor recommendations. These insights are crucial for tailoring AI technologies to meet patients' needs and expectations and better engage patients in adopting new technologies. Method This study conducted an online open-ended survey with valid responses from 140 patients with chronic conditions, exploring four aspects of clinical AI perspectives. The data was systematically coded and analyzed using an inductive thematic analysis approach to identify emergent themes. Result The majority of participants expressed concerns about the implementation of AI in healthcare (92.86%), with the top worries including lack of human touch (22.86%), potential AI bias and fairness (16.43%), and over-dependence on AI (16.43%). Regarding responsibility for potential treatment damages, 37.14% of participants believed that physicians should bear the responsibility, 16.43% considered AI developers accountable, and 1.42% viewed the government as the responsible party. Furthermore, 44.57% suggested that responsibility should be shared among stakeholders. In terms of government role, 51.43% saw regulation and monitoring as key responsibilities, while 8.57% perceived no government role in AI healthcare. Finally, around 80% of patients preferred treatment recommendations from care providers over AI. Conclusion The findings suggest patients are looking for a balanced approach between technology and human involvement, with clear accountability and proper regulation. Th

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