1,693 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Lux junior 2023: 16. Internationales Forum für den lichttechnischen Nachwuchs, 23. – 25. Juni 2023, Ilmenau : Tagungsband

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    Während des 16. Internationales Forums für den lichttechnischen Nachwuchs präsentieren Studenten, Doktoranden und junge Absolventen ihre Forschungs- und Entwicklungsergebnisse aus allen Bereichen der Lichttechnik. Die Themen bewegen sich dabei von Beleuchtungsanwendungen in verschiedensten Bereichen über Lichtmesstechnik, Kraftfahrzeugbeleuchung, LED-Anwendung bis zu nichtvisuellen Lichtwirkungen. Das Forum ist speziell für Studierende und junge Absolventen des Lichtbereiches konzipiert. Es bietet neben den Vorträgen und Postern die Möglichkeit zu Diskussionen und individuellem Austausch. In den 30 Jahren ihres Bestehens entwickelte sich die zweijährig stattfindende Tagung zu eine Traditionsveranstaltung, die das Fachgebiet Lichttechnik der TU Ilmenau gemeinsam mit der Bezirksgruppe Thüringen-Nordhessen der Deutschen Lichttechnischen Gesellschaft LiTG e. V. durchführt

    Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

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    Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)

    Modeling and Simulation in Engineering

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    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

    A survey, review, and future trends of skin lesion segmentation and classification

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    The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis

    Abordagens multimodais com utilização de deep learning e unimodais com aprendizado de máquina no reconhecimento de emoções em músicas

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    Orientadora: Profa. Dra. Denise Fukumi TsunodaCoorientadora: Profa. Dra. Marília Nunes SilvaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Programa de Pós-Graduação em Gestão da Informação. Defesa : Curitiba, 30/08/2023Inclui referênciasResumo: Esta pesquisa foi realizada com base na compreensão da relevância da relação entre música e emoção na vida humana, abrangendo desde o lazer até estudos científicos. Embora a organização emocional da música seja intrínseca à natureza humana, o reconhecimento automático de emoções musicais enfrenta desafios, configurando-se como um tema complexo na recuperação de informações musicais. Nesse contexto, o propósito central desta tese foi investigar se a adoção de abordagens multimodais, envolvendo informações de diferentes fontes e arquiteturas de deep learning, pode superar o desempenho das abordagens unimodais baseadas em algoritmos de aprendizado de máquina. Essa indagação emergiu da carência de estratégias multimodais na área e da perspectiva de melhoria nos resultados de classificação reportados em pesquisas correlatas. Com cinco objetivos específicos, esta pesquisa abordou a identificação de um modelo cognitivo de emoções, definição de modalidades, construção de bases de dados multimodais, comparação de arquiteturas de deep learning e avaliação comparativa das abordagens multimodais com abordagens unimodais utilizando algoritmos tradicionais de aprendizado de máquina. A análise dos resultados demonstrou que as abordagens multimodais alcançaram desempenho superior em diversos cenários de classificação, comparadas às estratégias unimodais. Tais resultados contribuem positivamente para a compreensão da eficácia das abordagens multimodais e das arquiteturas de deep learning no reconhecimento de emoções em músicas. Adicionalmente, a pesquisa ressalta a necessidade de atenção aos modelos emocionais e metadados em plataformas online, visando evitar vieses e ruídos. Esta tese oferece contribuições relevantes na área de reconhecimento de emoções em músicas, particularmente no desenvolvimento de bases de dados multimodais, avaliação de arquiteturas de deep learning para problemas tabulares, protocolos de experimentos e abordagens voltadas à cognição musical. A comparação sistemática entre abordagens multimodais e unimodais evidencia as vantagens das primeiras, incentivando novas pesquisas nesse campoAbstract: This research was conducted based on the understanding of the significance of the relationship between music and emotion in human life, spanning from leisure to scientific studies. Although the emotional organization of music is intrinsic to human nature, the automatic recognition of musical emotions faces challenges, manifesting as a complex theme in the retrieval of musical information. Within this context, the central purpose of this thesis was to investigate whether the adoption of multimodal approaches, involving information from different sources and deep learning architectures, can outperform unimodal approaches based on machine learning algorithms. This inquiry arose from the lack of multimodal strategies in the field and the prospect of improvement in classification results reported in related research. With five specific objectives, this research addressed the identification of a cognitive model of emotions, definition of modalities, construction of multimodal databases, comparison of deep learning architectures, and comparative evaluation of multimodal approaches with unimodal approaches using traditional machine learning algorithms. The analysis of results demonstrated that multimodal approaches achieved superior performance in various classification scenarios, compared to unimodal strategies. These findings positively contribute to the understanding of the effectiveness of multimodal approaches and deep learning architectures in the recognition of emotions in music. Additionally, the research emphasizes the need for attention to emotional models and metadata in online platforms, aiming to avoid biases and noise. This thesis offers relevant contributions to the field of music emotion recognition, particularly in the development of multimodal databases, evaluation of deep learning architectures for tabular problems, experimental protocols, and approaches focused on musical cognition. The systematic comparison between multimodal and unimodal approaches highlights the advantages of the former, encouraging new research in this fiel
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