Open online courses provide a unique opportunity to examine learner preferences in an environment that removes several pressures associated with traditional learning. This mixed methods study sought to examine the pathways that learners will create for themselves when given the choice between an instructor-directed modality and learner-directed modality. Study participants were first examined based on their levels of self-regulated learning. Follow-up qualitative interviews were conducted to examine the choices that participants made, the impact of the course design on those choices, and what role self-regulation played in the process. The resulting analysis revealed that participants desired an overall learning experience that was tailored to personal learning preferences, but that technical and design limitations can create barriers in the learning experience. The results from this research can help shape future instructional design efforts that wish to increase learner agency and choice in the educational process.

Le monde de la TV est en cours de transformation de la télévision analogique à la télévision numérique, qui est capable de diffuser du contenu de haute qualité, offrir aux consommateurs davantage de choix, et rendre l'expérience de visualisation plus interactive. IPTV (Internet Protocol TV) présente une révolution dans la télévision numérique dans lequel les services de télévision numérique sont fournis aux utilisateurs en utilisant le protocole Internet (IP) au dessus d’une connexion haut débit. Les progrès de la technologie IPTV permettra donc un nouveau modèle de fourniture de services. Les fonctions offertes aux utilisateurs leur permettent de plus en plus d’autonomie et de plus en plus de choix. Il en est notamment ainsi de services de type ‘nTS’ (pour ‘network Time Shifting’ en anglais) qui permettent à un utilisateur de visionner un programme de télévision en décalage par rapport à sa programmation de diffusion, ou encore des services de type ‘nPVR’ (pour ‘network Personal Video Recorder’ en anglais) qui permettent d’enregistrer au niveau du réseau un contenu numérique pour un utilisateur. D'autre part, l'architecture IMS proposée dans NGN fournit une architecture commune pour les services IPTV. Malgré les progrès rapides de la technologie de télévision interactive (comprenant notamment les technologies IPTV et NGN), la personnalisation de services IPTV en est encore à ses débuts. De nos jours, la personnalisation des services IPTV se limite principalement à la recommandation de contenus et à la publicité ciblée. Ces services ne sont donc pas complètement centrés sur l’utilisateur, alors que choisir manuellement les canaux de diffusion et les publicités désirées peut représenter une gêne pour l’utilisateur. L’adaptation des contenus numériques en fonction de la capacité des réseaux et des dispositifs utilisés n’est pas encore prise en compte dans les implémentations actuelles. Avec le développement des technologies numériques, les utilisateurs sont amenés à regarder la télévision non seulement sur des postes de télévision, mais également sur des smart phones, des tablettes digitales, ou encore des PCs. En conséquence, personnaliser les contenus IPTV en fonction de l’appareil utilisé pour regarder la télévision, en fonction des capacités du réseau et du contexte de l’utilisateur représente un défi important. Cette thèse présente des solutions visant à améliorer la personnalisation de services IPTV à partir de trois aspects: 1) Nouvelle identification et authentification pour services IPTV. 2) Nouvelle architecture IPTV intégrée et comportant un système de sensibilité au contexte pour le service de personnalisation. 3) Nouveau service de recommandation de contenu en fonction des préférences de l’utilisateur et aussi des informations contextes / Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users’ experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users’ devices ‘screen types, size, supported resolution, ‘‘ and networks available network types to be used by the user, available bandwidth, ..’) as well as the context of the service itself (content type and description, available format ‘HD/SD’, available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users’ identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user’s environment (devices and networks status), interpreting user’s requirements and making the user’s interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users’ preferences) which in turn highly improves the users’ Quality of Experience (QoE) and enriching the offers of IPTV services

En ces temps de pandémie Covid19, une énorme quantité de l’activité humaine est modifiée pour se faire à distance, notamment par des moyens électroniques. Cela rend plusieurs personnes et services vulnérables aux cyberattaques, d’où le besoin d’une éducation généralisée ou du moins accessible sur la cybersécurité. De nombreux efforts sont entrepris par les chercheurs, le gouvernement et les entreprises pour protéger et assurer la sécurité des individus contre les pirates et les cybercriminels. En raison du rôle important joué par les systèmes de recommandation dans la vie quotidienne de l'utilisateur, il est intéressant de voir comment nous pouvons combiner les systèmes de cybersécurité et de recommandation en tant que solutions alternatives pour aider les utilisateurs à comprendre les cyberattaques auxquelles ils peuvent être confrontés. Les systèmes de recommandation sont couramment utilisés par le commerce électronique, les réseaux sociaux et les plateformes de voyage, et ils sont basés sur des techniques de systèmes de recommandation traditionnels. Au vu des faits mentionnés ci-dessus, et le besoin de protéger les internautes, il devient important de fournir un système personnalisé, qui permet de partager les problèmes, d'interagir avec un système et de trouver des recommandations. Pour cela, ce travail propose « Cyberhelper », un système de recommandation de cybersécurité personnalisé basé sur des questions pour la sensibilisation à la cybersécurité. De plus, la plateforme proposée est équipée d'un algorithme hybride associé à trois différents algorithmes basés sur la connaissance, les utilisateurs et le contenu qui garantit une recommandation personnalisée optimale en fonction du modèle utilisateur et du contexte. Les résultats expérimentaux montrent que la précision obtenue en appliquant l'algorithme proposé est bien supérieure à la précision obtenue en utilisant d'autres mécanismes de système de recommandation traditionnels. Les résultats suggèrent également qu'en adoptant l'approche proposée, chaque utilisateur peut avoir une expérience utilisateur unique, ce qui peut l'aider à comprendre l'environnement de cybersécurité. / With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. It’s important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals. With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the “one size fits all” solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named “Cyberhelper”, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders. The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts.

Indiana University-Purdue University Indianapolis (IUPUI) / Pharmacogenetic testing has the potential to improve pain management through addressing wide interindividual variations in responses to pharmacogenetically actionable opioids, ultimately decreasing costly adverse drug effects and improving responses to these medications. A recent review of pharmacogenomics in the nursing literature highlighted the need for nurses to more fully embrace the burgeoning field of pharmacogenomics in nursing research, clinical practice, and education. Despite the promise of pharmacogenetic testing, significant challenges exist for evaluating outcomes related to its implementation, including oversimplification of medication exposure, the complexity of patients' clinical profiles, and the characteristics of healthcare contexts in which medications are prescribed. A better understanding of these challenges could enhance the assessment and documentation of the benefits of pharmacogenetic testing in guiding opioid therapies. This dissertation is intended to address the challenges of evaluating outcomes of pharmacogenetic testing implementation and the need for nurses to lead pharmacogenomic-related research. The dissertation purpose was to advance the sciences of nursing, pain management, and pharmacogenomics through the development of a typology of common patterns of medication exposure to known pharmacogenetically actionable opioids (codeine & tramadol). A qualitative, person-oriented approach was used to retrospectively analyze six months of electronic health record and pharmacogenotype data in 30 underserved adult patients. An overarching typology with eight groups of patients that had one of five opioid prescription patterns (singular, episodic, switching, sustained, or multiplex) and one of three types of medical emphasis of care (pain, comorbidities, or both) were identified. This typology consisted of a description of multiple common patterns that compare and contrast salient factors of exposure and the emphasis of why individuals were seeking care. Furthermore, in an aggregate descriptive analysis evaluating key clinical profile factors, these patients had complex medical histories, extensive healthcare utilization, and experienced significant polypharmacy. These findings can aid in addressing challenges related to the implementation of pharmacogenetic testing in clinical practice and point to ways in which nurses can take the lead in pharmacogenomics research. Findings also provide a foundation for future studies aimed at developing medication exposure measures to capture its dynamic nature and identifying and tailoring interventions in this population.

Indiana University-Purdue University Indianapolis (IUPUI) / The rapid innovations in biotechnology have led to an exponential growth of data and electronically accessible scientific literature. In this enormous scientific data, knowledge can be exploited, and novel discoveries can be made. In my dissertation, I have focused on the novel molecular mechanism and therapeutic discoveries from big data for complex diseases. It is very evident today that complex diseases have many factors including genetics and environmental effects. The discovery of these factors is challenging and critical in personalized medicine. The increasing cost and time to develop new drugs poses a new challenge in effectively treating complex diseases. In this dissertation, we want to demonstrate that the use of existing data and literature as a potential resource for discovering novel therapies and in repositioning existing drugs. The key to identifying novel knowledge is in integrating information from decades of research across the different scientific disciplines to uncover interactions that are not explicitly stated. This puts critical information at the fingertips of researchers and clinicians who can take advantage of this newly acquired knowledge to make informed decisions. This dissertation utilizes computational biology methods to identify and integrate existing scientific data and literature resources in the discovery of novel molecular targets and drugs that can be repurposed. In chapters 1 of my dissertation, I extensively sifted through scientific literature and identified a novel interaction between Vitamin A and CYP19A1 that could lead to a potential increase in the production of estrogens. Further in chapter 2 by exploring a microarray dataset from an estradiol gene sensitivity study I was able to identify a potential novel anti-estrogenic indication for the commonly used urinary analgesic, phenazopyridine. Both discoveries were experimentally validated in the laboratory. In chapter 3 of my dissertation, through the use of a manually curated corpus and machine learning algorithms, I identified and extracted genes that are essential for cell survival. These results brighten the reality that novel knowledge with potential clinical applications can be discovered from existing data and literature by integrating information across various scientific disciplines.

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Backgroun and aims: Pharmacokinetic variability is of paramount importance for sucessfull pharmacotherapy. The main purpose of this work was to study variability of pharmacokinetics in clinical and non-clinical setting with the aim to predict variability in target population. Specifically, three drugs were chosen, sufentanil, with relativelly narrow therapeutic index, and nabumeton and abirateron, both with known high variability. Methods: The study of pharmacokinetic variability of sufentanil was based on clinical samples taken from patients undergoing surgical cardiac procedure, where the sufentanil was used as a part of the drug coctail used during the procedure. New analytical method was necessary to prepare and validate to measure sufentanil concentrations and obtain pharmacokinetic parameters. These were compared between determined genotype groups of MDR1 and OPRM1. Similarly, clinical study was executed with nabumetone, in which nabumetone was administered in a group of 24 subjects on two separate occassions. Plazma samples were obtained and concentrations of nabumetone and its active metabolite, 6-methoxynaphtylacetic acid (6-MNA), were determined. Obtained pharmacokinetic profiles were compared between female and male volunteers, and genotypes for MDR1 and CYP2D6. Finaly for abiraterone,...

While it is difficult for base stations to estimate the millimeter wave (mmWave) channels and find the optimal mmWave beam for user equipments (UEs) quickly, the sub-6 GHz channels which are usually easier to obtain and more robust to blockages could be used to reduce the time before initial access and enhance the reliability of mmWave communication. Considering that the channel information is collected by a massive number of radio base stations and would be sensitive to privacy and security, Federated Learning (FL) is a match for this use case. In practice, the channel vectors are usually subject to Non-Independently Distributed (non-IID) distributions due to the greatly varying wireless communication environments between different radio base stations and their UEs. To achieve satisfying performance for all radio base stations instead of only the majority of them, a useful solution is designing personalized methods for each radio base station. In this thesis, we implement two personalized FL methods including 1) Finetuning FL Model on Private Dataset of Each Client and 2) Adaptive Expert Models for FL to predict the optimal mmWave beamforming vector directly from the non-IID sub-6 GHz channel vectors generated from DeepMIMO. According to our experimental results, Finetuning FL Model on Private Dataset of Each Client achieves higher average mmWave downlink spectral efficiency than the global FL. Besides, in terms of the average Top-1 and Top-3 classification accuracies, its performance improvement over the global FL model even exceeds the improvement of the global FL over the pure local models. / Även om det är svårt för en basstation att uppskatta en kanal för millimetervåg (mmWave) och snabbt hitta den bästa mmWave-strålen för en användarutrustning (UE), kan den dra fördel av kanaler under 6 GHz, som i allmänhet är mer lättillgängliga och mer motståndskraftig mot blockering, för att minska tid för första besök och förbättra tillförlitligheten hos mmWave-kommunikation. Med tanke på att kanalinformation samlas in av ett stort antal radiobasstationer och är känslig för integritet och säkerhet är federated learning (FL) väl lämpat för detta användningsfall. I praktiken, eftersom den trådlösa kommunikationsmiljön varierar mycket mellan olika radiobasstationer och deras UE, följer kanalvektorer vanligtvis en icke-oberoende distribution (icke-IID). För att uppnå tillfredsställande prestanda för alla radiobasstationer, inte bara de flesta radiobasstationer, är en användbar lösning att utforma ett individuellt tillvägagångssätt för varje radiobasstation. I detta dokument implementerar vi två personliga FL-metoder, inklusive 1) finjustering av FL-modellen på varje klients privata datauppsättning och 2) en adaptiv expertmodell av FL för att direkt generera icke-IID sub-6 GHz kanalvektorer förutsäga optimal mmWave beamforming vektorer. Enligt våra experimentella resultat uppnår finjustering av FL-modellen på varje klients privata datauppsättning högre genomsnittlig mmWave-nedlänksspektral effektivitet än global FL. Dessutom överträffar dess prestandaförbättring jämfört med den globala FL-modellen till och med den för den globala FL jämfört med den rent lokala modellen vad gäller genomsnittlig klassificeringsnoggrannhet i topp-1 och topp-3.

Acute leukemias (AL) comprise a heterogeneous group of hematologic malignancies, and individual patient responses to treatment can be difficult to predict. Monitoring of minimal residual disease (MRD) is thus very important and holds great potential for improving treatment strategies. Common MRD targets include immunoglobulin heavy chain or T-cell receptor gene rearrangements, recurrent cytogenetic abnormalities and mutations in important hematological genes. Whereas in the majority of adult acute lymphoblastic leukemia patients a suitable MRD target can be identified, in adult acute myeloid leukemia patients well-characterized targets are found in only half of cases. The identification of new specific molecular markers of leukemic blasts for MRD assessment, particularly in AML patients, is therefore highly desirable. Our aim was to develop a flexible strategy for mapping of cytogenetically identified unique clone-specific abnormalities down to the single nucleotide level and, based on the sequence, design a specific real-time PCR assay for MRD assessment in AL patients without any previously described MRD marker. Using a combination of cytogenetic (chromosome banding, chromosome microdissection), molecular cytogenetic (mFISH, mBAND) and molecular biological (next- generation sequencing, long-range...