991 research outputs found

    Machine-Learning-Powered Cyber-Physical Systems

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    In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety of services and applications across multiple domains. Machine learning processes and analyses such data to generate a feedback, which represents a status the environment is in. A feedback given to the user in order to make an informed decision is called an open-loop feedback. Thus, an open-loop CPS is characterized by the lack of an actuation directed at improving the system itself. A feedback used by the system itself to actuate a change aimed at optimizing the system itself is called a closed-loop feedback. Thus, a closed-loop CPS pairs feedback based on sensing data with an actuation that impacts the system directly. In this dissertation, we propose several applications in the context of CPS. We propose open-loop CPSs designed for the early prediction, diagnosis, and persistency detection of Bovine Respiratory Disease (BRD) in dairy calves, and for gait activity recognition in horses.These works use sensor data, such as pedometers and automated feeders, to perform valuable real-field data collection. Data are then processed by a mix of state-of-the-art approaches as well as novel techniques, before being fed to machine learning algorithms for classification, which informs the user on the status of their animals. Our work further evaluates a variety of trade-offs. In the context of BRD, we adopt optimization techniques to explore the trade-offs of using sensor data as opposed to manual examination performed by domain experts. Similarly, we carry out an extensive analysis on the cost-accuracy trade-offs, which farmers can adopt to make informed decisions on their barn investments. In the context of horse gait recognition we evaluate the benefits of lighter classifications algorithms to improve energy and storage usage, and their impact on classification accuracy. With respect to closed-loop CPS we proposes an incentive-based demand response approach for Heating Ventilation and Air Conditioning (HVAC) designed for peak load reduction in the context of smart grids. Specifically, our approach uses machine learning to process power data from smart thermostats deployed in user homes, along with their personal temperature preferences. Our machine learning models predict power savings due to thermostat changes, which are then plugged into our optimization problem that uses auction theory coupled with behavioral science. This framework selects the set of users who fulfill the power saving requirement, while minimizing financial incentives paid to the users, and, as a consequence, their discomfort. Our work on BRD has been published on IEEE DCOSS 2022 and Frontiers in Animal Science. Our work on gait recognition has been published on IEEE SMARTCOMP 2019 and Elsevier PMC 2020, and our work on energy management and energy prediction has been published on IEEE PerCom 2022 and IEEE SMARTCOMP 2022. Several other works are under submission when this thesis was written, and are included in this document as well

    Front-Line Physicians' Satisfaction with Information Systems in Hospitals

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    Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

    Socio-Cognitive and Affective Computing

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    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

    Preface

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    Controllability and explainability in a hybrid social recommender system

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    The growth in artificial intelligence (AI) technology has advanced many human-facing applications. The recommender system is one of the promising sub-domain of AI-driven application, which aims to predict items or ratings based on user preferences. These systems were empowered by large-scale data and automated inference methods that bring useful but puzzling suggestions to the users. That is, the output is usually unpredictable and opaque, which may demonstrate user perceptions of the system that can be confusing, frustrating or even dangerous in many life-changing scenarios. Adding controllability and explainability are two promising approaches to improve human interaction with AI. However, the varying capability of AI-driven applications makes the conventional design principles are less useful. It brings tremendous opportunities as well as challenges for the user interface and interaction design, which has been discussed in the human-computer interaction (HCI) community for over two decades. The goal of this dissertation is to build a framework for AI-driven applications that enables people to interact effectively with the system as well as be able to interpret the output from the system. Specifically, this dissertation presents the exploration of how to bring controllability and explainability to a hybrid social recommender system, included several attempts in designing user-controllable and explainable interfaces that allow the users to fuse multi-dimensional relevance and request explanations of the received recommendations. The works contribute to the HCI fields by providing design implications of enhancing human-AI interaction and gaining transparency of AI-driven applications

    Supporting lay users in privacy decisions when sharing sensitive data

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    The first part of the thesis focuses on assisting users in choosing their privacy settings, by using machine learning to derive the optimal set of privacy settings for the user. In contrast to other work, our approach uses context factors as well as individual factors to provide a personalized set of privacy settings. The second part consists of a set of intelligent user interfaces to assist the users throughout the complete privacy journey, from defining friend groups that allow targeted information sharing; through user interfaces for selecting information recipients, to find possible errors or unusual settings, and to refine them; up to mechanisms to gather in-situ feedback on privacy incidents, and investigating how to use these to improve a user’s privacy in the future. Our studies have shown that including tailoring the privacy settings significantly increases the correctness of the predicted privacy settings; whereas the user interfaces have been shown to significantly decrease the amount of unwanted disclosures.Insbesondere nach den jüngsten Datenschutzskandalen in sozialen Netzwerken wird der Datenschutz für Benutzer immer wichtiger. Obwohl die meisten Benutzer behaupten Wert auf Datenschutz zu legen, verhalten sie sich online allerdings völlig anders: Sie lassen die meisten Datenschutzeinstellungen der online genutzten Dienste, wie z. B. von sozialen Netzwerken oder Diensten zur Standortfreigabe, unberührt und passen sie nicht an ihre Datenschutzanforderungen an. In dieser Arbeit werde ich einen Ansatz zur Lösung dieses Problems vorstellen, der auf zwei verschiedenen Säulen basiert. Der erste Teil konzentriert sich darauf, Benutzer bei der Auswahl ihrer Datenschutzeinstellungen zu unterstützen, indem maschinelles Lernen verwendet wird, um die optimalen Datenschutzeinstellungen für den Benutzer abzuleiten. Im Gegensatz zu anderen Arbeiten verwendet unser Ansatz Kontextfaktoren sowie individuelle Faktoren, um personalisierte Datenschutzeinstellungen zu generieren. Der zweite Teil besteht aus einer Reihe intelligenter Benutzeroberflächen, die die Benutzer in verschiedene Datenschutzszenarien unterstützen. Dies beginnt bei einer Oberfläche zur Definition von Freundesgruppen, die im Anschluss genutzt werden können um einen gezielten Informationsaustausch zu ermöglichen, bspw. in sozialen Netzwerken; über Benutzeroberflächen um die Empfänger von privaten Daten auszuwählen oder mögliche Fehler oder ungewöhnliche Datenschutzeinstellungen zu finden und zu verfeinern; bis hin zu Mechanismen, um In-Situ- Feedback zu Datenschutzverletzungen zum Zeitpunkt ihrer Entstehung zu sammeln und zu untersuchen, wie diese verwendet werden können, um die Privatsphäreeinstellungen eines Benutzers anzupassen. Unsere Studien haben gezeigt, dass die Verwendung von individuellen Faktoren die Korrektheit der vorhergesagten Datenschutzeinstellungen erheblich erhöht. Es hat sich gezeigt, dass die Benutzeroberflächen die Anzahl der Fehler, insbesondere versehentliches Teilen von Daten, erheblich verringern

    Adoption Factors of Artificial intelligence in Human Resource Management

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    Tesis por compendio[ES] El mundo es testigo de nuevos avances tecnológicos que afectan significativamente a las organizaciones en diferentes departamentos. La inteligencia artificial (IA) es uno de estos avances, visto como una tecnología revolucionaria en la gestión de recursos humanos (RRHH). Profesionales y académicos han discutido el brillante papel de la IA en RRHH. Sin embargo, el análisis profundo de esta tecnología en el proceso de RRHH es aún escaso. Con todo ello, el objetivo principal de esta tesis es investigar el estado de la IA en RRHH y así identificar factores clave de implementación concretos. Primero, construyendo un marco académico para la IA en RRHH; segundo, analizar las aplicaciones de IA más utilizada en los procesos de RRHH; tercero, identificar las formas óptimas de transferir el conocimiento en los procesos de implementación de IA. La metodología utilizada para la investigación combina la revisión sistemática de la literatura y técnicas de investigación cualitativa. Como base y medida preparatoria para abordar las preguntas de investigación, se llevó a cabo un extenso análisis de la literatura en el campo AI-RRHH, con un enfoque particular en las publicaciones de algoritmos de IA en HRM, análisis de HR-Big data, aplicaciones/soluciones de IA en HRM e implementación de IA. En la misma línea, el autor publicó artículos en varias conferencias que contribuyeron a mejorar la madurez de las preguntas de investigación. Con base en este conocimiento, los estudios publicados ilustraron la brecha entre la promesa y la realidad de la IA en RRHH, teniendo en cuenta los requisitos técnicos de la implementación de la IA, así como las aplicaciones y limitaciones. Posteriormente, se entrevistó a expertos en recursos humanos y consultores de IA que ya habían adquirido experiencia de primera mano con los procesos de recursos humanos en un entorno de IA para descubrir la verdad de la aplicación de la IA dominante en el proceso de RRHH. Los principales hallazgos de esta tesis incluyen la derivación de una definición completa de IA en RRHH, así como el estado de las estrategias de adopción de aplicaciones de IA en RRHH. Como resultado adicional, se explora la utilidad y las limitaciones de los chatbots en el proceso de contratación en la India. Además, factores clave para transferir el conocimiento del proceso de implementación de IA a los gerentes y empleados de recursos humanos. Finalmente, se concluye identificando desafíos asociados con la implementación de IA en el proceso de recursos humanos y el impacto de COVID-19 en la implementación de IA.[CA] El món és testimoni de nous avanços tecnològics, que afecten significativament les organitzacions en diferents departaments. La intel·ligència artificial (IA) és un d'aquests avanços que s'anuncia àmpliament com una tecnologia revolucionària en la gestió de recursos humans (HRM). Professionals i acadèmics han discutit el brillant paper de la IA en HRM. No obstant això, encara és escàs l'anàlisi profund d'aquesta tecnologia en el procés de HRM. Per tant, l'objectiu principal d'aquesta tesi és investigar l'estat de la IA en HRM i derivar factors clau d'implementació concrets. Primer, construint un marc acadèmic per a la IA en HRM; segon, analitzar l'aplicació de IA més utilitzada en el procés de recursos humans; tercer, identificar les formes òptimes de transferir el coneixement dels processos d'implementació de IA. La metodologia utilitzada per a la investigació es combina entre una revisió sistemàtica de la literatura i una tècnica d'investigació qualitativa. Com a base i mesura preparatòria per a abordar les preguntes d'investigació, es va dur a terme una extensa anàlisi de la literatura en el camp IA-HRM, amb un enfocament particular en les publicacions d'algorismes de IA en HRM, anàlisis de HR-Big data, aplicacions/soluciones de IA en HRM i implementació de IA. En la mateixa línia, l'autor va publicar articles en diverses conferències que van procedir a millorar la maduresa de les preguntes d'investigació. Amb base en aquest coneixement, els estudis publicats van illustrar la bretxa entre la promesa i la realitat de la IA en HRM, tenint en compte els requisits tècnics de la implementació de la IA, així com les aplicacions i limitacions. Posteriorment, es va entrevistar experts en recursos humans i consultors de IA que ja havien adquirit experiència de primera mà amb els processos de recursos humans en un entorn de IA per a descobrir la veritat de l'aplicació de la IA dominant en el procés de recursos humans. Les principals troballes d'aquesta tesi són la derivació d'una definició completa de IA en HRM, així com l'estat de les estratègies d'adopció d'aplicacions de IA en HRM. Com a resultat addicional, explore la utilitat i les limitacions dels chatbots en el procés de contractació a l'Índia. A més, factors clau per a transferir el coneixement del procés d'implementació de IA als gerents i empleats de recursos humans. També es van concloure els desafiaments associats amb la implementació de IA en el procés de recursos humans i l'impacte de COVID-19 en la implementació de IA.[EN] The world is witnessing new technological advancements, which significantly impacts organizations across different departments. Artificial intelligence (AI) is one of these advancements that is widely heralded as a revolutionary technology in Human Resource Management (HRM). Professionals and scholars have discussed the bright role of AI in HRM. However, deep analysis of this technology in the HR process is still scarce. Therefore, the main goal of this thesis is to investigate the status of AI in HRM and derive concrete implementation key factors. Through, first, building an academic framework for AI in HRM; second, analyzing the most commonly used AI applications in HR process; third, identifying the optimal ways to transfer the knowledge of AI implementation processes. The methodology used for the investigation combines a systematic literature review and a qualitative research technique. As a basis and preparatory measure to address the research questions, an extensive literature analysis in the AI-HRM field was carried out, with a particular focus on publications of AI in HRM, HR-Big data analysis, AI applications/solutions in HRM and AI implementation. Along similar lines, the author published papers in several conference proceedings to improve the maturity of research questions. Based on this work, the published studies illustrate the gap between the promise and reality of AI in HRM, taking into account the requirements of AI implementation as well as the applications and limitations. Subsequently, HR experts and AI consultants, who had already gained first-hand experience with HR processes in an AI environment, were interviewed to find out the truth of the dominant AI's application in HR process. The main findings of this thesis are the derivation of a complete definition of AI in HRM as well as the status of the adoption strategies of AI applications in HRM. As a further result, it explores the usefulness and limitations of chatbots in the recruitment processes in India. In addition, derived the key factors to transfer the knowledge of AI implementation process to HR managers and employees. Challenges associated with AI implementation in the HR process and the impact of COVID-19 on AI implementation were also concluded.Tuffaha, M. (2022). Adoption Factors of Artificial intelligence in Human Resource Management [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185909Compendi

    Sensor fusion in smart camera networks for ambient intelligence

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    This short report introduces the topics of PhD research that was conducted on 2008-2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion experiments performed with four distinct applications of Ambient Intelligence

    Towards Scalable, Private and Practical Deep Learning

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    Deep Learning (DL) models have drastically improved the performance of Artificial Intelligence (AI) tasks such as image recognition, word prediction, translation, among many others, on which traditional Machine Learning (ML) models fall short. However, DL models are costly to design, train, and deploy due to their computing and memory demands. Designing DL models usually requires extensive expertise and significant manual tuning efforts. Even with the latest accelerators such as Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU), training DL models can take prohibitively long time, therefore training large DL models in a distributed manner is a norm. Massive amount of data is made available thanks to the prevalence of mobile and internet-of-things (IoT) devices. However, regulations such as HIPAA and GDPR limit the access and transmission of personal data to protect security and privacy. Therefore, enabling DL model training in a decentralized but private fashion is urgent and critical. Deploying trained DL models in a real world environment usually requires meeting Quality of Service (QoS) standards, which makes adaptability of DL models an important yet challenging matter.  In this dissertation, we aim to address the above challenges to make a step towards scalable, private, and practical deep learning. To simplify DL model design, we propose Efficient Progressive Neural-Architecture Search (EPNAS) and FedCust to automatically design model architectures and tune hyperparameters, respectively. To provide efficient and robust distributed training while preserving privacy, we design LEASGD, TiFL, and HDFL. We further conduct a study on the security aspect of distributed learning by focusing on how data heterogeneity affects backdoor attacks and how to mitigate such threats. Finally, we use super resolution (SR) as an example application to explore model adaptability for cross platform deployment and dynamic runtime environment. Specifically, we propose DySR and AdaSR frameworks which enable SR models to meet QoS by dynamically adapting to available resources instantly and seamlessly without excessive memory overheads
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