1,185 research outputs found
Inclusive Intelligent Learning Management System Framework - Application of Data Science in Inclusive Education
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBeing a disabled student the author faced higher education with a handicap which as experience
studying during COVID 19 confinement periods matched the findings in recent research about the
importance of digital accessibility through more e-learning intensive academic experiences. Narrative
and systematic literature reviews enabled providing context in World Health Organization’s
International Classification of Functioning, Disability and Health, legal and standards framework and
information technology and communication state-of-the art. Assessing Portuguese higher education
institutions’ web sites alerted to the fact that only outlying institutions implemented near perfect,
accessibility-wise, websites.
Therefore a gap was identified in how accessible the Portuguese higher education websites are, the
needs of all students, including those with disabilities, and even the accessibility minimum legal
requirements for digital products and the services provided by public or publicly funded organizations.
Having identified a problem in society and exploring the scientific base of knowledge for context and
state of the art was a first stage in the Design Science Research methodology, to which followed
development and validation cycles of an Inclusive Intelligent Learning Management System
Framework. The framework blends various Data Science study fields contributions with accessibility
guidelines compliant interface design and content upload accessibility compliance assessment.
Validation was provided by a focus group whose inputs were considered for the version presented in
this dissertation. Not being the purpose of the research to deliver a complete implementation of the
framework and lacking consistent data to put all the modules interacting with each other, the most
relevant modules were tested with open data as proof of concept.
The rigor cycle of DSR started with the inclusion of the previous thesis on Atlântica University Institute
Scientific Repository and is to be completed with the publication of this thesis and the already started
PhD’s findings in relevant journals and conferences
Kontextsensitivität für den Operationssaal der Zukunft
The operating room of the future is a topic of high interest. In this thesis, which is among the first in the recently defined field of Surgical Data Science, three major topics for automated context awareness in the OR of the future will be examined: improved surgical workflow analysis, the newly developed event impact factors, and as application combining these and other concepts the unified surgical display.Der Operationssaal der Zukunft ist ein Forschungsfeld von großer Bedeutung. In dieser Dissertation, die eine der ersten im kürzlich definierten Bereich „Surgical Data Science“ ist, werden drei Themen für die automatisierte Kontextsensitivität im OP der Zukunft untersucht: verbesserte chirurgische Worflowanalyse, die neuentwickelten „Event Impact Factors“ und als Anwendungsfall, der diese Konzepte mit anderen kombiniert, das vereinheitlichte chirurgische Display
Complexity Reduction in Image-Based Breast Cancer Care
The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device
Socio-Cognitive and Affective Computing
Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing
Kontextsensitivität für den Operationssaal der Zukunft
The operating room of the future is a topic of high interest. In this thesis, which is among the first in the recently defined field of Surgical Data Science, three major topics for automated context awareness in the OR of the future will be examined: improved surgical workflow analysis, the newly developed event impact factors, and as application combining these and other concepts the unified surgical display.Der Operationssaal der Zukunft ist ein Forschungsfeld von großer Bedeutung. In dieser Dissertation, die eine der ersten im kürzlich definierten Bereich „Surgical Data Science“ ist, werden drei Themen für die automatisierte Kontextsensitivität im OP der Zukunft untersucht: verbesserte chirurgische Worflowanalyse, die neuentwickelten „Event Impact Factors“ und als Anwendungsfall, der diese Konzepte mit anderen kombiniert, das vereinheitlichte chirurgische Display
Towards Real-World Federated Learning: Empirical Studies in the Domain of Embedded Systems
Context: Artificial intelligence (AI) has led a new phase of technical revolution and industrial development around the world since the twenty-first century, revolutionizing the way of production. Artificial intelligence (AI), an emerging information technology, is thriving, and AI application technologies are gaining traction, particularly in professional services such as healthcare, education, finance, security, etc. More machine learning technologies have begun to be thoroughly applied to the production stage as big data and cloud computing capabilities have improved. With the increased focus on Machine Learning applications and the rapid growth of distributed edge devices in the industry, we believe that utilizing a large number of edge devices will become increasingly important. The introduction of Federated Learning changes the situation in which data must be centrally uploaded to the cloud for processing and maximizes the use of edge devices\u27 computing and storage capabilities. With local data processing, the learning approach eliminates the need to upload large amounts of local data and reduces data transfer latency. Because Federated Learning does not require centralized data for model training, it is better suited to edge learning scenarios with limited data and privacy concerns. Objective: The purpose of this research is to identify the characteristics and problems of the Federated Learning methods, our new algorithms and frameworks that can assist companies in making the transition to Federated Learning, and empirically validate the proposed approaches. Method: To achieve these objectives, we adopted an empirical research approach with design science being our primary research method. We conducted a literature review, case studies, including semi-structured interviews and simulation experiments in close collaboration with software-intensive companies in the embedded systems domain. Results: We present four major findings in this paper. First, we present a state-of-the-art review of the empirical results reported in the existing Federated Learning literature. We then categorize those Federated Learning implementations into different application domains, identify their challenges, and propose six open research questions based on the problems identified in the literature. Second, we conduct a case study to explain why companies anticipate Federated Learning as a potential solution to the challenges they encountered when implementing machine learning components. We summarize the services that a comprehensive Federated Learning system must enable in industrial settings. Furthermore, we identify the primary barriers that companies must overcome in order to embrace and transition to Federated Learning. Based on our empirical findings, we propose five requirements for companies implementing reliable Federated Learning systems. Third, we develop and evaluate four architecture alternatives for a Federated Learning system, including centralized, hierarchical, regional, and decentralized architectures. We investigate the trade-o between communication latency, model evolution time, and model classification performance, which is critical for applying our findings to real-world industrial systems. Fourth, we introduce techniques and asynchronous frameworks for end-to-end on-device Federated Learning. The method is validated using a steering wheel angle prediction case. The local models of each edge vehicle can be continuously trained and shared with other vehicles to improve their local model prediction accuracy. Furthermore, we combine the asynchronous Federated Learning approach with Deep Neural Decision Forests and validate our method using important industry use cases in the automotive domain. Our findings show that Federated Learning can improve model training speed while lowering communication overhead without sacrificing accuracy, demonstrating that this technique has significant benefits to a wide range of real-world embedded systems. Future Work: In the future, we plan to test our approach in other use cases and look into more sophisticated neural networks integrated with our approach. In order to improve model training performance on resource-constrained edge devices in real-world embedded systems, we intend to design more appropriate aggregation methods and protocols. Furthermore, we intend to use the Federated Learning and Reinforcement Learning methods to assist the edge in evolving themselves autonomously and fully utilizing the computation capabilities of the edge devices
Integrating passive ubiquitous surfaces into human-computer interaction
Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll
Robust Audio and WiFi Sensing via Domain Adaptation and Knowledge Sharing From External Domains
Recent advancements in machine learning have initiated a revolution in embedded sensing and inference systems. Acoustic and WiFi-based sensing and inference systems have enabled a wide variety of applications ranging from home activity detection to health vitals monitoring. While many existing solutions paved the way for acoustic event recognition and WiFi-based activity detection, the diverse characteristics in sensors, systems, and environments used for data capture cause a shift in the distribution of data and thus results in sub-optimal classification performance when the sensor and environment discrepancy occurs between training and inference stage. Moreover, large-scale acoustic and WiFi data collection is non-trivial and cumbersome. Therefore, current acoustic and WiFi-based sensing systems suffer when there is a lack of labeled samples as they only rely on the provided training data. In this thesis, we aim to address the performance loss of machine learning-based classifiers for acoustic and WiFi-based sensing systems due to sensor and environment heterogeneity and lack of labeled examples. We show that discovering latent domains (sensor type, environment, etc.) and removing domain bias from machine learning classifiers make acoustic and WiFi-based sensing robust and generalized. We also propose a few-shot domain adaptation method that requires only one labeled sample for a new domain that relieves the users and developers from the painstaking task of data collection at each new domain. Furthermore, to address the lack of labeled examples, we propose to exploit the information or learned knowledge from sources where available data already exists in volumes, such as textual descriptions and visual domain. We implemented our algorithms in mobile and embedded platforms and collected data from participants to evaluate our proposed algorithms and frameworks in an extensive manner.Doctor of Philosoph
Serious Games and Mixed Reality Applications for Healthcare
Virtual reality (VR) and augmented reality (AR) have long histories in the healthcare sector, offering the opportunity to develop a wide range of tools and applications aimed at improving the quality of care and efficiency of services for professionals and patients alike. The best-known examples of VR–AR applications in the healthcare domain include surgical planning and medical training by means of simulation technologies. Techniques used in surgical simulation have also been applied to cognitive and motor rehabilitation, pain management, and patient and professional education. Serious games are ones in which the main goal is not entertainment, but a crucial purpose, ranging from the acquisition of knowledge to interactive training.These games are attracting growing attention in healthcare because of their several benefits: motivation, interactivity, adaptation to user competence level, flexibility in time, repeatability, and continuous feedback. Recently, healthcare has also become one of the biggest adopters of mixed reality (MR), which merges real and virtual content to generate novel environments, where physical and digital objects not only coexist, but are also capable of interacting with each other in real time, encompassing both VR and AR applications.This Special Issue aims to gather and publish original scientific contributions exploring opportunities and addressing challenges in both the theoretical and applied aspects of VR–AR and MR applications in healthcare
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