Research by Emerson and Smith (1999) finds that conservative Protestants tend to blame racial inequalities on individual traits like motivation or ability as opposed to structural constraints such as oppression or discrimination. Emerson and Smith have also established that churches tend to be racially homogenous organizations. The purpose of this study is to determine whether or not members of racially-integrated congregations differ from members of racially-homogenous congregations in their explanations for racial inequalities. I am interested in further exploring interracial relations in the context of United States' Protestant churches, particularly how the level of contact with persons of another race might affect whites' perceptions of reasons for racial inequality. I expect to find that individuals who attend racially-homogeneous churches will be less likely to recognize social constraints that may contribute to socioeconomic inequalities between whites and blacks than those who attend racially-integrated churches. In other words, I expect that attending a racially-integrated congregation will have a positive effect on giving structural-level explanations for racial inequality. Using existing data from the 1994 General Social Survey, I analyze the relationship between attendance in a multi-racial congregation and explanations for racial inequalities. The data do not support the hypothesis. / Master of Science

Dental caries have been identified for several centuries as a common pathology within individuals’ mouths, more specifically in the posterior teeth surfaces (Jesudass, Prabhu, Rajajee, & Sudheer, 2014). Although there have been modern procedures and preventative methods developed to help treat dental caries, in ancient times this was not always the case. The persistence of anterior teeth dental caries in juveniles is a unique pathology observed in Túcume that has not been studied yet. Through the analysis of skeletal samples, this research aims to study the specific case of the formation of anterior deciduous dental caries in juveniles located at the archaeological site of Túcume, Peru, while trying to understand the factors that might have caused them. Observational-based analysis was used to gather data, which was then used to test the proposed hypotheses that aimed to identify possible dental pathology (anterior deciduous dental caries) patterns. The sample size consisted of a dental inventory generated from the skeletal remains of 32 juveniles (< 7 years) that were excavated from Túcume. It was discovered that not all juveniles had the presence of anterior deciduous dental caries in Túcume. The discussion of this research explores possible explanations that may have contributed to the formation of anterior deciduous dental caries. Possible explanations that will be discussed include breastfeeding, The Osteological Paradox (DeWitte & Stojanowski, 2015), genetics, stress, socioeconomic levels, biomechanics and evolution of the mandible, maxilla, and teeth, non-dietary objects, and Andean weaning practices. In doing so, this research aims to provide great improvements in understanding oral health care and creating preventative measures to help avoid oral diseases starting at a young age.

Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.

We discuss explanations in Description Logics (DLs), a family of logics used for knowledge representation. Initial work on explaining consequences for DLs had focused on justifications, which are minimal subsets of axioms that entail the consequence. More recently, it was proposed that proofs can provide more detailed information about why a consequence follows. Moreover, several measures have been proposed to estimate the comprehensibility of justifications and proofs, for example, their size or the complexity of logical expressions. In this paper, we analyze the connection between these measures, e.g. whether small justifications necessarily give rise to small proofs. We use a dataset of DL proofs that was constructed last year based on the ontologies of the OWL Reasoner Evaluation 2015. We find that, in general, less complex justifications indeed correspond to less complex proofs, and discuss some exceptions to this rule.

Machine learning-based systems are rapidly becoming popular because it has been realized that machines are more efficient and effective than humans at performing certain tasks. Although machine learning algorithms are extremely popular, they are also very literal and undeviating. This has led to a huge research surge in the field of interpretability in machine learning to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process. Moreover, in most real-world problems just making predictions using machine learning algorithms only solves the problem partially. Time series is one of the most popular and important data types because of its dominant presence in the fields of business, economics, and engineering. Despite this, interpretability in time series is still relatively unexplored as compared to tabular, text, and image data. With the growing research in the field of interpretability in machine learning, there is also a pressing need to be able to quantify the quality of explanations produced after interpreting machine learning models. Due to this reason, evaluation of interpretability is extremely important. The evaluation of interpretability for models built on time series seems completely unexplored in research circles. This thesis work focused on achieving and evaluating model agnostic interpretability in a time series forecasting problem.  The use case discussed in this thesis work focused on finding a solution to a problem faced by a digital consultancy company. The digital consultancy wants to take a data-driven approach to understand the effect of various sales related activities in the company on the sales deals closed by the company. The solution involved framing the problem as a time series forecasting problem to predict the sales deals and interpreting the underlying forecasting model. The interpretability was achieved using two novel model agnostic interpretability techniques, Local interpretable model- agnostic explanations (LIME) and Shapley additive explanations (SHAP). The explanations produced after achieving interpretability were evaluated using human evaluation of interpretability. The results of the human evaluation studies clearly indicate that the explanations produced by LIME and SHAP greatly helped lay humans in understanding the predictions made by the machine learning model. The human evaluation study results also indicated that LIME and SHAP explanations were almost equally understandable with LIME performing better but with a very small margin. The work done during this project can easily be extended to any time series forecasting or classification scenario for achieving and evaluating interpretability. Furthermore, this work can offer a very good framework for achieving and evaluating interpretability in any machine learning-based regression or classification problem. / Maskininlärningsbaserade system blir snabbt populära eftersom man har insett att maskiner är effektivare än människor när det gäller att utföra vissa uppgifter. Även om maskininlärningsalgoritmer är extremt populära, är de också mycket bokstavliga. Detta har lett till en enorm forskningsökning inom området tolkbarhet i maskininlärning för att säkerställa att maskininlärningsmodeller är tillförlitliga, rättvisa och kan hållas ansvariga för deras beslutsprocess. Dessutom löser problemet i de flesta verkliga problem bara att göra förutsägelser med maskininlärningsalgoritmer bara delvis. Tidsserier är en av de mest populära och viktiga datatyperna på grund av dess dominerande närvaro inom affärsverksamhet, ekonomi och teknik. Trots detta är tolkningsförmågan i tidsserier fortfarande relativt outforskad jämfört med tabell-, text- och bilddata. Med den växande forskningen inom området tolkbarhet inom maskininlärning finns det också ett stort behov av att kunna kvantifiera kvaliteten på förklaringar som produceras efter tolkning av maskininlärningsmodeller. Av denna anledning är utvärdering av tolkbarhet extremt viktig. Utvärderingen av tolkbarhet för modeller som bygger på tidsserier verkar helt outforskad i forskarkretsar. Detta uppsatsarbete fokuserar på att uppnå och utvärdera agnostisk modelltolkbarhet i ett tidsserieprognosproblem.  Fokus ligger i att hitta lösningen på ett problem som ett digitalt konsultföretag står inför som användningsfall. Det digitala konsultföretaget vill använda en datadriven metod för att förstå effekten av olika försäljningsrelaterade aktiviteter i företaget på de försäljningsavtal som företaget stänger. Lösningen innebar att inrama problemet som ett tidsserieprognosproblem för att förutsäga försäljningsavtalen och tolka den underliggande prognosmodellen. Tolkningsförmågan uppnåddes med hjälp av två nya tekniker för agnostisk tolkbarhet, lokala tolkbara modellagnostiska förklaringar (LIME) och Shapley additiva förklaringar (SHAP). Förklaringarna som producerats efter att ha uppnått tolkbarhet utvärderades med hjälp av mänsklig utvärdering av tolkbarhet. Resultaten av de mänskliga utvärderingsstudierna visar tydligt att de förklaringar som produceras av LIME och SHAP starkt hjälpte människor att förstå förutsägelserna från maskininlärningsmodellen. De mänskliga utvärderingsstudieresultaten visade också att LIME- och SHAP-förklaringar var nästan lika förståeliga med LIME som presterade bättre men med en mycket liten marginal. Arbetet som utförts under detta projekt kan enkelt utvidgas till alla tidsserieprognoser eller klassificeringsscenarier för att uppnå och utvärdera tolkbarhet. Dessutom kan detta arbete erbjuda en mycket bra ram för att uppnå och utvärdera tolkbarhet i alla maskininlärningsbaserade regressions- eller klassificeringsproblem.

Plusieurs auteurs se sont intéressés au développement des conduites explicatives et justificatives (CEJ) chez des enfants typiques. La littérature reporte aussi de plus en plus d’études qui s’intéressent aux compétences pragmatiques et interactionnelles des enfants qui présentent un trouble du développement du langage (TDL) en montrant que ces enfants présentent des spécificités par rapport aux enfants typiques. Dans ce travail, nous avons combiné ces deux pistes de réflexion en investiguant les CEJ produites par des enfants avec TDL (5-7 ans) et des enfants typiques (4-7 ans), ainsi que par leurs mères, dans des interactions mères-enfants italophones et francophones dans deux activités différentes : unjeu symbolique et une lecture conjointe d’un livre en images. Nos résultats montrent que les deux langues présentent des patrons similaires. Cependant, des différences ponctuelles liées à la langue ont pu être mises en évidence. Les mères prennent en charge les activités proposées et l’activité paraît influencer le fonctionnement des interactions et la production des CEJ. Les enfants avec TDL présentent de CEJ moins élevés que leurs pairs typiques dans l’activité de jeu symbolique et ils semblent avoir davantage besoin que ces derniers du support de leur mère pour en produire dans l’activité de lecture conjointe (notamment dans le corpus francophone). Les mères des enfants typiques et des enfants avec TDL présentent des comportements globalement similaires en amont mais elles semblent s’adapter aux besoins de leurs enfants en aval. Compte tenu de l’importance sociale et éducative de ces conduites, nos résultats pourraient avoir des implications cliniques / Several authors have studied the development of explanations and justifications(E/J) in typical developing (TD) children. Similarly, it is possible to find in the literature studiesthat analyze pragmatic and interactional skills of children with specific language impairment(SLI). These studies show that children with SLI present specific behaviour when compared toTD children. In this thesis, we merge these two strands of research by investigating E/Jproduced by children with SLI (aged 5-7) and TD children (aged 4-7), and by their mothers. Todo so, we analyzed mother-child interactions (in Italian and French) during two differentactivities: symbolic play and joint wordless picture-book reading. Similar patterns wereobserved in both languages. Nevertheless, specific differences related to the typology of thelanguage were highlighted. Our results show that the management of the activity is globallyasymmetric, but important interindividual differences were observed. Moreover, activities playa central role in shaping the structure of interactions and E/J. Furthermore, children with SLIshow lower rates of E/J than their typical peers in the symbolic play and seem to need thesupport of their mothers more than TD children in the joint reading activity (particularly in theFrench corpus). Finally, the mothers of TD children and children with SLI show somesimilarities in the way they solicit E/J, but at the same time they fine tune their reactions to E/Jproduced by children according to the children’s needs. Given the social and educationalimportance of explanations and justifications, our results may have some clinical implications.

abstract: Due to large data resources generated by online educational applications, Educational Data Mining (EDM) has improved learning effects in different ways: Students Visualization, Recommendations for students, Students Modeling, Grouping Students, etc. A lot of programming assignments have the features like automating submissions, examining the test cases to verify the correctness, but limited studies compared different statistical techniques with latest frameworks, and interpreted models in a unified approach. In this thesis, several data mining algorithms have been applied to analyze students’ code assignment submission data from a real classroom study. The goal of this work is to explore and predict students’ performances. Multiple machine learning models and the model accuracy were evaluated based on the Shapley Additive Explanation. The Cross-Validation shows the Gradient Boosting Decision Tree has the best precision 85.93% with average 82.90%. Features like Component grade, Due Date, Submission Times have higher impact than others. Baseline model received lower precision due to lack of non-linear fitting. / Dissertation/Thesis / Masters Thesis Computer Science 2019

A travers la présente thèse, nous nous intéressons à la prévention des accidents d’avalanche survenant à l’occasion de la pratique du hors-piste. Les jeunes pratiquants du hors-piste (15-30 ans) sont repérés comme les principales victimes des avalanches par les données statistiques de l’Association Nationale pour l’Etude de la Neige et des Avalanches. L’objectif principal de la présente thèse est de parvenir à une meilleure connaissance de ce public adepte de sports de glisse. Pour cela, nous proposons d’investiguer leurs habitudes et motivations de pratique du hors-piste, leurs perceptions des risques liés à cette pratique, les explications qu’ils donnent spontanément pour les accidents d’avalanche en hors-piste, ainsi que les comportements qu’ils adoptent en hors-piste et les critères qu’ils utilisent pour prendre la décision de sortir des pistes. Les données recueillies devraient contribuer à l’enrichissement des stratégies de prévention des accidents d’avalanche en hors-piste. Elles permettent de concevoir des stratégies et des messages ciblés et adaptés à cette population de jeunes pratiquants. En effet, les travaux sur la perception des risques (Kouabenan, 2006) et sur l’explication naïve des accidents (Kouabenan, 1999) suggèrent que si l’on souhaite que les individus adhèrent aux messages de prévention, il est nécessaire de connaître la façon dont ils appréhendent les risques, et pour ce qui nous concerne les risques en hors-piste, notamment le risque d’avalanche. Il importe aussi de connaître les causes qu’ils invoquent pour expliquer la production des accidents d’avalanche. Ce travail de thèse comprend quatre études dont la méthodologie repose sur deux questionnaires. L’élaboration des questionnaires s’appuie sur des entretiens préalables menés avec des pratiquants aux profils divers, et sur l’exploitation de la littérature sur les travaux empiriques empruntant le même cadre théorique ainsi que sur les rapports sur les accidents d’avalanche. Concernant les études 1, 2 et 4, nous avons interrogé 304 jeunes pratiquants de sports de glisse. Pour l’étude 4, nous avons questionné 238 pratiquants. Pour le recueil des données concernant les quatre études, nous avons rencontré les participants sur le terrain, dans des stations de sports d’hiver en Isère, en Savoie et en Haute-Savoie, ainsi que sur le campus universitaire et lors de manifestations sportives. Le recueil des données s’est fait par entretien en face à face avec le participant. / Through this thesis, we examine the avalanche accident prevention occurring in out-of-bounds (OB) practice. Young out-of-bounds practitioners (15-30 years-old) are identified as the core of avalanche victims. The main aim of this thesis is to get better knowledge about these boardsports enthusiasts. To do this, we suggest to investigate their habits and motivations relative to OB practice, their perception of the risks associated to this practice, the spontaneous explanations they give for avalanche accidents in OB, as well as the behaviours they adopt in OB, and the criteria they use to decide to go or not to go OB. The data collected would contribute to improve prevention strategies for avalanche accidents in OB, by targeting and adapting these strategies to young practitioners. Indeed, research on risk perception (Kouabenan, 2006) and naive causal explanations of accidents (Kouabenan, 1999) propose that if we want people to adhere to prevention messages, it is necessary to know the way they understand risks, for our case OB risks, particularly avalanche hazard. It is also essential to know the causes they invoke to explain avalanche accidents. This thesis work consists of four studies for which the methodology relies on two questionnaires. The questionnaires were carried out from preliminary interviews conducted with practitioners of varied profiles, and from literature and empirical works of the same theoretical framework, as well as accidents reports. Concerning the studies 1, 2 and 4, we interviewed 304 young boardsports practitioners. For study 3, we questioned 238 practitioners. For the data collect of the four studies, we met participants in the field, in winter sport resorts in Isère, Savoie and Haute-Savoie (France), as well as at the university campus and during sport events. Data collection was done with face-to-face interview with the participant.

This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown.

When interviewing school students for what constitutes a good mathematics teacher, the first characteristic usually listed is the ability to explain well. Besides well-founded content knowledge most important for classroom episodes of teacher explanations is knowledge about how to present mathematical concepts in a comprehensible way to students. This encompasses competencies in the area of verbal communication as well as the conscious use of means for illustrating and visualising mathematical ideas. We report about an analysis of explanatory processes in math lessons and about an analysis of prospective teachers\'' explanatory competencies. As a result we identify improvements in teacher education at university.