357 research outputs found

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    Higher Instruction Human Resources Management (HRM) Hones and Information Administration Specialist Presence

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    nformation administration has an affect on human asset hones, agreeing to the organizational life cycle hypothesis. By comparing colleges that incorporate information administration in their scholastic educational programs to those that don't , we trust to decide the impact of information administration on the astuteness of Human Asset (HR) hones. Discoveries show that colleges that instruct information administration are way better prepared than those that don't development investigate, instruction, and data absorption through human asset hones. Besides, colleges that did not instruct information administration tend to be considerably more centered on operational issues and troubles in creating the aptitudes and information of their HR work force, and their execution is essentially lower. Research limitations and implications - collecting respondents through purposive sampling has its limitations. It is suggested to increase the number of respondents by broadening the study's geographical scope and extending its duration. Originality/importance - Numerous organizations and universities have conducted extensive research on human resource practices. However, courses in knowledge management that emphasize lecturers as knowledge management agents are still uncommon. This study also incorporates the life cycle theory by examining HR practices in higher education and encouraging institutions to prioritize strategic HR issues in their environment

    Real-time generation and adaptation of social companion robot behaviors

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    Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukünftigen Zuhauses sein. Sie werden uns bei alltäglichen Aufgaben unterstützen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunächst überwunden werden müssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natürliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. Darüber hinaus müssen Roboter auch die individuellen Vorlieben der Benutzer berücksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter führt. Roboter haben jedoch keine menschliche Intuition - sie müssen mit entsprechenden Algorithmen für diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedürfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle über die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis für maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen für die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt. Er bietet eine übergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird für mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen führt, die in Labor- und In-situ-Studien evaluiert werden. Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Volitional Cybersecurity

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    This dissertation introduces the “Volitional Cybersecurity” (VCS) theory as a systematic way to think about adoption and manage long-term adherence to cybersecurity approaches. The validation of VCS has been performed in small- and medium-sized enterprises or businesses (SMEs/SMBs) context. The focus on volitional activities promotes theoretical viewpoints. Also, it aids in demystifying the aspects of cybersecurity behaviour in heterogeneous contexts that have neither been systematically elaborated in prior studies nor embedded in cybersecurity solutions. Abundant literature demonstrates a lack of adoption of manifold cybersecurity remediations. It is still not adequately clear how to select and compose cybersecurity approaches into solutions for meeting the needs of many diverse cybersecurity-adopting organisations. Moreover, the studied theories in this context mainly originated from disciplines other than information systems and cybersecurity. The constructs were developed based on data, for instance, in psychology or criminology, that seem not to fit properly for the cybersecurity context. Consequently, discovering new methods and theories that can be of help in active and volitional forms of cybersecurity behaviour in diverse contexts may be conducive to a better quality of cybersecurity engagement. This leads to the main research question of this dissertation: How can we support volitional forms of behaviour with a self-paced tool to increase the quality of cybersecurity engagement? The main contribution of this dissertation is the VCS theory. VCS is a cybersecurity-focused theory structured around the core concept of volitional cybersecurity behaviour. It suggests that a context can be classified based on the cybersecurity competence of target groups and their distinct requirements. This classification diminishes the complexity of the context and is predictive of improvement needs for each class. Further, the theory explicates that supporting three factors: A) personalisation, B) cybersecurity competence, and C) connectedness to cybersecurity expertise affect the adoption of cybersecurity measures and better quality of cybersecurity engagement across all classes of the context. Therefore, approaches that ignore the personalisation of cybersecurity solutions, the cybersecurity competence of target groups, and the connectedness of recipients to cybersecurity expertise may lead to poorer acceptance of the value or utility of solutions. Subsequently, it can cause a lack of motivation for adopting cybersecurity solutions and adherence to best practices. VCS generates various implications. It has implications for cybersecurity research in heterogeneous contexts to transcend the common cybersecurity compliance approaches. Building on VCS, researchers could develop interventions looking for volitional cybersecurity behaviour change. Also, it provides knowledge that can be useful in the design of self-paced cybersecurity tools. VCS explains why the new self-paced cybersecurity tool needs specific features. The findings of this dissertation have been subsequently applied to the follow-up project design. Further, it has implications for practitioners and service providers to reach out to the potential end-users of their solutions

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

    Understanding Feedforward – Feedback Controller Components In Human Movement Through Optimization-Based Approaches

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    Despite many studies in human motion analysis using optimal control theory to understand how movement is generated, less attention is focused on the structure of the optimal controller. The majority of existing studies assume that the person is using a feedforward controller to accomplish the desired task. However, during perturbed motions a feedback controller becomes active, and enables the person to react to unforeseen perturbations and adapt their motions to the environment. As such, understanding controller structure becomes important to analyze human motion more accurately. Three key contributions will be elaborated in this thesis to enable analyzing the human feedforward and feedback controller components. The first key contribution is the formulation of an inverse optimization problem for trajectories generated by feedforward-feedback controllers for nonlinear systems and feedback controllers for linear systems. We adapt the recovery matrix inverse optimal control approach, originally developed for recovering the cost matrices from trajectories observed under feedforward control, and apply it to analyze trajectories observed from systems controlled in the feedback form plus additional feedforward term. This method also estimates the feedback gain for linear systems where inverse linear quadratic regulator approaches are dependent on the given feedback gain assumption. The perturbation in this study is added as a zero mean Gaussian noise at the state output. The second key contribution is an algorithm to decompose the controller components for tracking problems. This algorithm uses Bellman optimality condition to form an optimization problem to detect whether the system was disturbed or not. We formulate a constrained optimization problem to estimate the control signal. The identification of controller components is made based on the estimated and the reference experimental trajectories. The proposed approach is tested in simulation, where a perturbation is applied on different nonlinear systems in a continuous form. The third key contribution is to combine the first two contributions to analyze feedforward and feedback controller components of human movement. First, squat motion is identified as a suitable motion for analyzing human perturbed motion and controller structure. We collected both unperturbed and perturbed squat motions for this study. The perturbation is applied during a short time in a continuous form through a push stick. Then the human body is modeled as a three degrees of freedom system, and the task is modeled by an optimal control problem. By modifying the decomposition algorithm, the trials are classified and the controller components are identified by the inverse optimal control

    Stochastic modelling and inference for evolution in ageing and infectious diseases

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    Counting processes are an important class of stochastic processes with numerous interdisciplinary applications, including to evolving biological systems. Counting processes can be the basis for forward generative models, which can help develop a mechanistic understanding of a phenomenon, or for inferential methods. In this thesis we develop a bottom-up microscopic model of a phenomenon in mitochondrial biology and ageing. Mitochondria are organelles that possess their own DNA, involved in crucial physiological functions. A typical eukaryotic cell hosts a population of mitochondrial DNA molecules, where mutations can expand and cause dysfunctions. With age, skeletal muscle suffers a reduction in strength and functionality; in mammals, this has been connected to the clonal expansion of mitochondrial deletion mutations. The mechanism driving this phenomenon remains poorly understood despite intense research. We develop a stochastic population dynamics model corresponding to a novel evolutionary mechanism, termed stochastic survival of the densest, and we show that it can account for the expansion of mitochondrial mutations in skeletal muscle through a literature-parameterised model. We predict that a species can invade a system in a wave-like fashion, without having an explicit replicative advantage and even if preferentially eliminated. We establish that this mechanism is driven by the combined effect of stochasticity, differences in carrying capacity (or density) and spatial structure. Part of the work for this thesis coincided with the COVID-19 pandemic. We look at another application of counting processes, modelling data collected within the SIREN study, part of the national response to the pandemic. We estimate parameters of counting processes modelling SARS‑CoV‑2 infection events, that correspond to the reduction in rate of infection associated with COVID-19 vaccination (vaccine effectiveness), with a previous infection, or both (hybrid protection). We highlight short-term vaccine effectiveness that subsequently wanes, and confirm the immune escape of the Omicron variant.Open Acces

    University of Maine Undergraduate Catalog, 2022-2023

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    The University of Maine undergraduate catalog for the 2022-2023 academic year includes an introduction, the academic calendars, general information about the university, and sections on attending, facilities and centers, and colleges and academic programs including the Colleges of Business, Public Policy and Health, Education and Development, Engineering, Liberal Arts and Sciences, and Natural Sciences, Forestry and Agriculture

    Building bridges for better machines : from machine ethics to machine explainability and back

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    Be it nursing robots in Japan, self-driving buses in Germany or automated hiring systems in the USA, complex artificial computing systems have become an indispensable part of our everyday lives. Two major challenges arise from this development: machine ethics and machine explainability. Machine ethics deals with behavioral constraints on systems to ensure restricted, morally acceptable behavior; machine explainability affords the means to satisfactorily explain the actions and decisions of systems so that human users can understand these systems and, thus, be assured of their socially beneficial effects. Machine ethics and explainability prove to be particularly efficient only in symbiosis. In this context, this thesis will demonstrate how machine ethics requires machine explainability and how machine explainability includes machine ethics. We develop these two facets using examples from the scenarios above. Based on these examples, we argue for a specific view of machine ethics and suggest how it can be formalized in a theoretical framework. In terms of machine explainability, we will outline how our proposed framework, by using an argumentation-based approach for decision making, can provide a foundation for machine explanations. Beyond the framework, we will also clarify the notion of machine explainability as a research area, charting its diverse and often confusing literature. To this end, we will outline what, exactly, machine explainability research aims to accomplish. Finally, we will use all these considerations as a starting point for developing evaluation criteria for good explanations, such as comprehensibility, assessability, and fidelity. Evaluating our framework using these criteria shows that it is a promising approach and augurs to outperform many other explainability approaches that have been developed so far.DFG: CRC 248: Center for Perspicuous Computing; VolkswagenStiftung: Explainable Intelligent System
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