Lorsqu'un enseignant veut évaluer le savoir-faire des apprenants à l'aide d'un logiciel, il utilise souvent les systèmes Tutoriels Intelligents (STI). Or, les STI sont difficiles à développer et destinés à un domaine pédagogique très ciblé. Depuis plusieurs années, l'utilisation d'algorithmes de classification par apprentissage supervisé a été proposée pour évaluer le savoir des apprenants. Notre hypothèse est que ces mêmes algorithmes vont aussi nous permettre d'évaluer leur savoir-faire. Notre domaine d'application étant l'électronique numérique, nous proposons une mesure de similarité entre schémas électroniques et une bas d'apprentissage générée automatiquement. cette base d'apprentissage est composées de schémas électroniques pédagogiquement étiquetés "bons" ou "mauvais" avec des informations concernant le degré de simplification des erreurs commises. Finalement, l'utilisation d'un algorithme de classification simple (les k plus proches voisins) nous a permis de faire une évaluation des schémas électroniques dans la majorité des cas. / When a teacher wants to evaluate the know-how of the learners using a software, he often uses Intelligent Tutorial Systems (ITS). However, those systems are difficult to develop and intended for a very targeted educational domain. For several years, the used of supervised classification algorithms was proposed to estimate the learners' knowledge. From this fact, we assume that the same kinf of algorithms can help to adress the learners' know-how evaluation. Our application field being digital system design, we propose a similarity measure between digital circuits and instances issued from an automatically generated database. This database consists of electronic circuits pedagogically labelled "good" or "bad" with information concerning the simplification degrees or made mistakes. Finally, the use of a simple classification algorithm (namely k-nearest neighbours classifier) allowed us to achieve a circuit's evaluation in most cases.

Notre relation avec nos apparentés forme une grande partie de notre monde social; et la façon dont nous reconnaissons et traitons nos apparentés a donné lieu à une importante somme de recherche. Lorsqu'il s'agit de reconnaître un apparenté direct, la similarité faciale est considérée comme un indice d'apparentement. Dans cette thèse, j'étudie si elle joue un rôle comparable lorsqu'il s'agit de reconnaître un apparentement entre des tiers, en menant deux lignes de recherche: les prédictions de comportement népotistiques et les prédictions de préférences de couple, par des tiers, en présence de stimuli faciaux. La catégorisation devant servir l'action, la similarité faciale doit avoir un effet dépendant du contexte sur ces prédictions, susceptible à des changements de valence et de domaine. En l'absence de contexte, les individus semblent pouvoir détecter la similarité faciale et la mettre en relation avec l'apparentement. Nos deux séries d'expériences offrent une conclusion différente. Quand la valence du contexte change et que nous analysons les prédictions des participants en terme de kin selection, leurs choix ne semblent pas mettre en relation similarité faciale et apparentement. / Our relation to our kin shapes much of our social world. It's no surprise then, that how we recognize and react to our own kin has been a widely investigated topic. In particular, when tackling direct kin recognition, facial similarity has emerged as a putative cue of relatedness. In this thesis, I investigate whether or not the same can be said for third party kin recognition. Split between two lines of research, we explore individuals' predictions of nepotistic and mating behavior} in third party scenarios using facial stimuli. These two domains provide the backbone of our research. Categorization must serve action. So, what would strengthen the notion of a presence of third-party kin recognition in humans? Facial similarity \emph{must have} a context-dependent effect on participants predictions, susceptible to valence changes in scenarios and switches from the prosocial and mate choice domains. This is precisely what we set out to do with our two lines of research. Though our literature review revealed that when context is starved participants seem to be able to detect similarity and seemingly connect it to relatedness. Our nepotism and mating series of experiments, by re-inserting context, offers us a different conclusion altogether. Within scenarios in which valence is modified and our participants analysis is bounded by predictions made by kin selection, their choices do no reflect a connection between similarity and relatedness.

Es soll ein mathematisches Modell zur Berechnung von Abweichungen verschiedener Datentypen auf relationalen Datenbanksystemen eingeführt und getestet werden. Basis dieses Modells sind Ähnlichkeitsmessungen für verschiedene Datentypen. Hierbei führen wir zunächst eine Betrachtung der relevanten Datentypen für die Arbeit durch. Danach definieren wir für die für diese Arbeit relevanten Datentypen eine Algebra, welche die Grundlage zur Berechnung des Anonymisierungsgrades θ ist. Das Modell soll zur Messung des Grades der Anonymisierung, vor allem personenbezogener Daten, zwischen Test- und Produktionsdaten angewendet werden. Diese Messung ist im Zuge der Einführung der EU-DSGVO im Mai 2018 sinnvoll, und soll helfen personenbezogene Daten mit einem hohen Ähnlichkeitsgrad zu identifizieren.

In the wind resource and wind turbine suitability industry Computational Fluid Dynamics has gained widespread use to model the airflow at proposed wind farm locations. These models typically focus on the neutrally stratified surface layer and ignore physical process such as buoyancy and the Coriolis force. These physical processes are integral to the accurate description of the atmospheric boundary layer and reductions in uncertainties of turbine suitability and power production calculations can be achieved if these processes are included. The present work focuses on atmospheric flows in which atmospheric stability and the Coriolis force are included. The study uses Monin-Obukhov Similarity Theory to analyse time series data output from a proposed wind farm location to determine the prevalence and impact of stability at the location. The output provides the necessary site data required for the CFD model as well as stability-dependent wind profiles from measurements. The results show non-neutral stratification to be the dominant condition onsite with impactful windfield changes between stability conditions. The wind flows considered in this work are classified as high Reynolds number flows and are based on numerical solutions of the Reynolds-Averaged Navier-Stokes equations. A two-equation closure method for turbulence based on the k __ turbulence model is utilized. Modifications are introduced to standard CFD model equations to account for the impact of atmospheric stability and ground roughness effects. The modifications are introduced by User Defined Functions that describe the profiles, source terms and wall functions required for the ABL CFD model. Two MOST models and two wall-function methods are investigated. The modifications are successfully validated using the horizontal homogeneity test in which the modifications are proved to be in equilibrium by the model�s ability to maintain inlet profiles of velocity and turbulence in an empty domain. The ABL model is applied to the complex terrain of the proposed wind farm location used in the data analysis study. The inputs required for the stability modifications are generated using the available measured data. Mesoscale data are used to describe the inlet boundary conditions. The model is successfully validated by cross prediction of the stabilitydependent wind velocity profiles between the two onsite masts. The advantage of the developed model is the applicability into standard wind industry loading and power production calculations using outputs from typical onsite measurement campaigns. The model is tuning-free and the site-specific modifications are input directly into the developed User Defined Functions. In summary, the results show that the implemented modifications and developed methods are applicable and reproduce the main wind flow characteristics in neutral and non-neutral flows over complex wind farm terrains. In additions, the developed method reduce modelling uncertainties compared against models and measurements that neglect non-neutral stratification. / Dissertation (MEng)--University of Pretoria, 2018. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

<p>The fact that every individual has a different sense of humor and it varies greatly from one person to another means that it is a challenge to learn any individual’s humor preferences. Humor is much more than just a source of entertainment; it is an essential tool that aids communication. Understanding humor preferences can lead to improved social interactions and bridge existing social or economic gaps.</p><p> </p><p>In this study, we propose a methodology that aims to develop a recommendation system for jokes by analyzing its text. Various researchers have proposed different theories of humor depending on their area of focus. This exploratory study focuses mainly on Attardo and Raskin’s (1991) General Theory of Verbal Humor and implements the knowledge resources defined by it to annotate the jokes. These annotations contain the characteristics of the jokes and also play an important role in determining how alike these jokes are. We use Lin’s similarity metric (Lin, 1998) to computationally capture this similarity. The jokes are clustered in a hierarchical fashion based on their similarity values used for the recommendation. We also compare our joke recommendations to those obtained by the Eigenstate algorithm (Goldberg, Roeder, Gupta, & Perkins, 2001), an existing joke recommendation system that does not consider the content of the joke in its recommendation.</p>

<p>Predmet istraživanja ovog rada je istraživanje i eksploatacija mogućnosti da parametri Gausovih komponenti korišćenih Gaussian mixture modela&nbsp; (GMM) aproksimativno leže na niže dimenzionalnoj površi umetnutoj u konusu pozitivno definitnih matrica. U tu svrhu uvodimo novu, mnogo efikasniju meru sličnosti između GMM-ova projektovanjem LPP-tipa parametara komponenti iz više dimenzionalnog parametarskog originalno konfiguracijskog prostora u prostor značajno niže dimenzionalnosti. Prema tome, nalaženje distance između dva GMM-a iz originalnog prostora se redukuje na nalaženje distance između dva skupa niže dimenzionalnih euklidskih vektora, ponderisanih odgovarajućim težinama. Predložena mera je pogodna za primene koje zahtevaju visoko dimenzionalni prostor obeležja i/ili veliki ukupan broj Gausovih komponenti. Razrađena metodologija je primenjena kako na sintetičkim tako i na realnim eksperimentalnim podacima.</p> / <p>This thesis studies the possibility that the parameters of Gaussian components of a<br />particular Gaussian Mixture Model (GMM) lie approximately on a lower-dimensional<br />surface embedded in the cone of positive definite matrices. For that case, we deliver<br />novel, more efficient similarity measure between GMMs, by LPP-like projecting the<br />components of a particular GMM, from the high dimensional original parameter space,<br />to a much lower dimensional space. Thus, finding the distance between two GMMs in<br />the original space is reduced to finding the distance between sets of lower<br />dimensional euclidian vectors, pondered by corresponding weights. The proposed<br />measure is suitable for applications that utilize high dimensional feature spaces and/or<br />large overall number of Gaussian components. We confirm our results on artificial, as<br />well as real experimental data.</p>

Recommender System is a tool helping users find content and overcome information overload. It predicts interests of users and makes recommendation according to the interest model of users. The original content-based recommender system is the continuation and development of collaborative filtering, which doesn’t need the user’s evaluation for items. Instead, the similarity is calculated based on the information of items that are chose by users, and then make the recommendation accordingly. With the improvement of machine learning, current content-based recommender system can build profile for users and products respectively. Building or updating the profile according to the analysis of items that are bought or visited by users. The system can compare the user and the profile of items and then recommend the most similar products. So this recommender method that compare user and product directly cannot be brought into collaborative filtering model. The foundation of content-based algorithm is acquisition and quantitative analysis of the content. As the research of acquisition and filtering of text information are mature, many current content-based recommender systems make recommendation according to the analysis of text information. This paper introduces content-based recommender system for the movie website of VionLabs. There are a lot of features extracted from the movie, they are diversity and unique, which is also the difference from other recommender systems. We use these features to construct movie model and calculate similarity. We introduce a new approach for setting weight of features, which improves the representative of movies. Finally we evaluate the approach to illustrate the improvement. / Recommender System är ett verktyg som hjälper användarna att hitta innehåll och övervinna informationsöverflöd. Det förutspår användarnas intressen och gör rekommendation enligt räntemodellen användare. Den ursprungliga innehållsbaserade recommender är en fortsättning och utveckling av samarbete filtrering, som inte behöver användarens utvärdering artiklar. Istället är likheten beräknas baserat på informationen objekt som har varit valde av användare, och sedan göra rekommendationen därefter. Med förbättringen av maskininlärning, kan nuvarande innehållsbaserad recommender systemet bygga profil för användare och produkt respektive. Bygga eller uppdatera profilen enligt analysen av objekt som köps eller besöks av användare. Systemet kan jämföra användaren och profilen av artiklar och rekommendera den mest liknande produkt. Så här recommender metod som jämför användaren och produkten direkt kan inte föras in collaborative filtreringsmodell. Grunden för innehållsbaserad algoritm är förvärv och kvantitativ analys av innehållet. Eftersom forskning förvärv och filtrering av textinformation är mogen, många aktuella innehållsbaserade recommender system gör rekommendation enligt analysen av textinformation. Denna uppsats införa innehållsbaserad recommender system för film webbplats VionLabs. Det finns en mängd funktioner som extraherats från en film, är de mångfald och unik, vilket är också skillnaden med andra recommender system. Vi använder dessa funktioner för att konstruera film vektor och beräkna likheter. Vi introducerar en ny metod för att fastställa vikten av funktioner, vilket förbättrar företrädare för filmer. Slutligen utvärderar vi tillvägagångssättet för att illustrera förbättringen.

Der Beitrag beschreibt ein laufendes Projekt zur computergestützten Erschließung und Analyse einer großen Sammlung handschriftlicher Liedblätter mit Volksliedern aus dem deutschsprachigen Raum. Am Beispiel dieses praktischen Projekts werden Chancen und Herausforderungen diskutiert, die der Einsatz von Digital Humanities-Methoden für den Bereich der Musikwissenschaft mit sich bringt. / This article presents an ongoing project for the computer-based transcription and analysis of handwritten music scores from a large collection of German folk tunes. Based on this project, I will discuss the challenges and opportunities that arise when using Digital Humanities methods in musicology.

This Ph.D. dissertation focuses on a comprehensive study of seismic waveform similarity aiming at two themes: (1) reliable identification of repeating earthquakes (repeaters) and (2) investigation of the detailed source process of induced seismicity through the three-dimensional spatiotemporal evolution of mainly neighbouring earthquakes. Theme 1: Reliable identification of repeaters. Repeaters, occurring repeatedly on the same fault patch with nearly identical waveforms, are usually identified with the match-filtering (MF) method which essentially measures the degree of waveform similarity between an earthquake pair through the corresponding cross-correlation coefficient (CC). However, the performance of the MF method can be severely affected by the length of the cross‐correlation window, the frequency band of the applied digital filter, and the presence of a large‐amplitude wave train. To optimize the performance of MF, I first examine the effects of different operational parameters and determine generic rules for selecting the window length and the optimal frequency passband. To minimize the impact of a large‐amplitude wave train, I then develop a new method, named the match-filtering with multisegment cross-correlation (MFMC) method. By equally incorporating the contributions from various segments of the waveforms, the new method is much more effective in capturing the minor waveform discrepancy between an event pair due to location difference and hence is more reliable in detecting potential repeaters and discriminating non-repeaters with large inter-event separation. With both synthetic and borehole array waveform data, I further reveal that waveform similarity is controlled by not only the inter-event separation but also many other factors, including station azimuth, epicentral distance, velocity structure, etc. Therefore, in contrast to the traditional view, the results indicate that waveform similarity alone is insufficient to unambiguously identify true repeaters. For reliable repeater identification, we should rely on a physics-based approach considering both the overlapped source area and magnitude difference. Specifically, I define an event pair to be true repeaters if their inter-event separation is smaller than the rupture radius of the larger event and their magnitude difference is no more than 1. For the precise estimation of inter-event distance in cases of limited data, I develop the differential traveltime double-difference (DTDD) method which relies on the relative S-P differential traveltime. The findings of this study imply that previously identified repeaters and their interpretations/hypotheses potentially can be biased and hence may need a systematic reexamination. Theme 2: Investigation of the detailed source process of induced seismicity. Earthquakes induced by hydraulic fracturing (HF), especially those with large magnitudes, are often observed to have occurred near/after well completion. The delayed triggering of induced seismicity with respect to injection commencement poses serious challenges for risk mitigation and hazard assessment. By performing waveform cross-correlation and hierarchical clustering analysis, I reveal a high-resolution three-dimensional source migration process with mainshock delayed triggering that is probably controlled by local hydrogeological conditions. The results suggest that poroelastic effects might contribute to induced seismicity but are likely insufficient to activate a non-critically stressed fault of sufficient size. My analysis shows that the rapid pore-pressure build-up from HF can be very localized and capable of producing large, felt earthquakes on non-critically stressed fault segments. I further infer that the number of critically stressed, large intraplate faults should be very limited, and that reactivation of such faults may require sufficient pore-pressure accumulation. The findings of this study may also explain why so few fluid injections are seismogenic. / Graduate

Generative Artificial Intelligence is a field of artificial intelligence where systems can learn underlying patterns in previously seen content and generate new content. This thesis explores a generative artificial intelligence technique used for image-toimage translations called Cycle-consistent Adversarial network (CycleGAN), which can translate images from one domain into another. The CycleGAN is a stateof-the-art technique for doing unsupervised image-to-image translations. It uses the concept of cycle-consistency to learn a mapping between image distributions, where the Mean Absolute Error function is used to compare images and thereby learn an underlying mapping between the two image distributions. In this work, we propose to use the Structural Similarity Index Measure (SSIM) as an alternative to the Mean Absolute Error function. The SSIM is a metric inspired by human perception, which measures the difference in two images by comparing the difference in, contrast, luminance, and structure. We examine if using the SSIM as the cycle-consistency loss in the CycleGAN will improve the image quality of generated images as measured by the Inception Score and Fréchet Inception Distance. The inception Score and Fréchet Inception Distance are both metrics that have been proposed as methods for evaluating the quality of images generated by generative adversarial networks (GAN). We conduct a controlled experiment to collect the quantitative metrics. Our results suggest that using the SSIM in the CycleGAN as the cycle-consistency loss will, in most cases, improve the image quality of generated images as measured Inception Score and Fréchet Inception Distance.