Thesis discusses about different ways of a communication between components of a distributed system. It describes a communication using a message exchange and at the same time talks about other alternatives. It adds details about various models of a message exchange, various message types and about various specifications as well. Commercial tools ActiveMQ, RabbitMQ and Kafka are presented. Special emphasis is placed on describing the way these tools exchange messages, scalability options and others. The web service is designed according to the described features. Its main purpose is management and monitoring of the tool by user choice and easy replacement of this tool with another one. Designed application is implemented using the Kotlin language for selected tool RabbitMQ. The implemented solution allows a simple exchange of messages through the REST api.

We explore the use a Latent Dirichlet Allocation (LDA) imitating pseudo-topic-model, based on our original relevance metric, as a tool to facilitate distant annotation of short (often one to two sentence or less) documents. Our exploration manifests as annotating tweets for emotions, this being the current use-case of interest to us, but we believe the method could be extended to any multi-class labeling task of documents of similar length. Tweets are gathered via the Twitter API using "track" terms thought likely to capture tweets with a greater chance of exhibiting each emotional class, 3,000 tweets for each of 26 topics anticipated to elicit emotional discourse. Our pseudo-topic-model is used to produce relevance-ranked vocabularies for each corpus of tweets and these are used to distribute emotional annotations to those tweets not manually annotated, magnifying the number of annotated tweets by a factor of 29. The vector labels the annotators produce for the topics are cascaded out to the tweets via three different schemes which are compared for performance by proxy through the competition of bidirectional-LSMTs trained using the tweets labeled at a distance. An SVM and two emotionally annotated vocabularies are also tested on each task to provide context and comparison.

As social interactions increasingly become exclusively online, there is a need for research on the role of identity and social identity in online platforms. Drawing on Symbolic Interactionist approaches to identity, namely Social Identity Theory and Identity Theory, as well as Signaling Theory, this study argues that actors will selectively use religious language to signal their credentials to an audience for the purpose of garnering prosocial behavior in the form of donations to their fundraising campaign. Using latent semantic analysis topic models to analyze the self-presentations of crowdsourcing campaigners on GoFundMe.com, this study found evidence for the presence of signaling to a religious identity online as well as a significant difference in the presentation of need for campaigns originating in areas with high reported religiosity compared to campaigns from areas of low religiosity. In comparison to other campaigns, campaigners engaging in religious signaling were significantly increasing their donations. I suggest that strategically chosen religious topics in online crowdfunding is an example of low-cost identity signaling and provides insight into how signaling happens online and the potential outcomes resulting from this cultural work.

abstract: Social media is a medium that contains rich information which has been shared by many users every second every day. This information can be utilized for various outcomes such as understanding user behaviors, learning the effect of social media on a community, and developing a decision-making system based on the information available. With the growing popularity of social networking sites, people can freely express their opinions and feelings which results in a tremendous amount of user-generated data. The rich amount of social media data has opened the path for researchers to study and understand the users’ behaviors and mental health conditions. Several studies have shown that social media provides a means to capture an individual state of mind. Given the social media data and related work in this field, this work studies the scope of users’ discussion among online mental health communities. In the first part of this dissertation, this work focuses on the role of social media on mental health among sexual abuse community. It employs natural language processing techniques to extract topics of responses, examine how diverse these topics are to answer research questions such as whether responses are limited to emotional support; if not, what other topics are; what the diversity of topics manifests; how online response differs from traditional response found in a physical world. To answer these questions, this work extracts Reddit posts on rape to understand the nature of user responses for this stigmatized topic. In the second part of this dissertation, this work expands to a broader range of online communities. In particular, it investigates the potential roles of social media on mental health among five major communities, i.e., trauma and abuse community, psychosis and anxiety community, compulsive disorders community, coping and therapy community, and mood disorders community. This work studies how people interact with each other in each of these communities and what these online forums provide a resource to users who seek help. To understand users’ behaviors, this work extracts Reddit posts on 52 related subcommunities and analyzes the linguistic behavior of each community. Experiments in this dissertation show that Reddit is a good medium for users with mental health issues to find related helpful resources. Another interesting observation is an interesting topic cluster from users’ posts which shows that discussion and communication among users help individuals to find proper resources for their problem. Moreover, results show that the anonymity of users in Reddit allows them to have discussions about different topics beyond social support such as financial and religious support. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

This study investigated the comparison between captured and revealed Pedagogical Content Knowledge (PCK) about graphs of motion. The aim of the study was to explore PCK when captured in a written format and discussions (captured PCK) and compare it to the PCK that the same teachers revealed in practice (revealed PCK) when teaching the topic. Four Grade 10 Physical sciences teachers were purposively and conveniently selected as participants of the study. Their PCK was captured through Content Representations (CoRes) and interviews. The revealed PCK on the other hand was gathered through lesson observations. The Topic Specific Pedagogical Content Knowledge (TSPCK) model was used as the framework that guided the analysis of the two manifestations of PCK. The focus was on teachers’ competences in the TSPCK components namely; learners’ prior knowledge including misconceptions, curricular saliency, what is difficult to teach, representations including analogies, and conceptual teaching strategies. The results of this study indicated that teachers’ competences in the TSPCK components varied. This was evident in both the captured and the revealed PCK. Thus it suggested that a teacher’s level of competence in one component is not necessarily an indication of his or her competence in the other components that define PCK, and subsequently in his/her overall captured or revealed PCK. Furthermore, the study suggested that the level of competence in a component in the captured PCK is not necessarily an indication of the level of competence within that component that the teacher would reveal during lesson presentation. The level may be the same, slightly different (higher or lower) or even be drastically different in the lesson than suggested by the captured PCK. A concluding remark was then made that teachers’ captured PCK is not necessarily a true reflection of the PCK they reveal during lesson presentation and that different instruments must be used to reflect on and assess teachers’ PCK in a topic. / Dissertation (MEd)--University of Pretoria, 2017. / Science, Mathematics and Technology Education / MEd / Unrestricted

Topic models for focused analysis aim to capture topics within the limiting scope of a targeted aspect (which could be thought of as some inner topic within a certain domain). To serve their analytic purposes, topics are expected to be semantically-coherent and closely aligned with human intuition – this in itself poses a major challenge for the more common topic modeling algorithms which, in a broader sense, perform a full analysis that covers all aspects and themes within a collection of texts. The paper attempts to construct a viable focused-analysis topic model which learns topics from Twitter data written in a closely related group of non-standardized varieties of Arabic widely spoken in the Levant region (i.e Levantine Arabic). Results are compared to a baseline model as well as another targeted topic model designed precisely to serve the purpose of focused analysis. The model is capable of adequately capturing topics containing terms which fall within the scope of the targeted aspect when judged overall. Nevertheless, it fails to produce human-friendly and semantically-coherent topics as several topics contained a number of intruding terms while others contained terms, while still relevant to the targeted aspect, thrown together seemingly at random.

The study's purpose was to assess, through mixed methods, written linguistic features of 20 Spanish-speaking English language learners (ELLs) in middle school. Students came from Mexico, Puerto Rico, and the Dominican Republic. Participants wrote two expository and two narrative formal texts, each in Spanish and English, for a total of eight writing samples each. Additionally, students developed 10 journal entries in their language of choice, and 6 randomly selected, focal participants were interviewed for the qualitative analysis. The quantitative analysis involved scoring formal texts at the lexical, syntactic, and discourse levels. Scores were analyzed using Friedman's 2-way ANOVA by ranks, and resulting ranks were compared across genre-topic and language. A key outcome was that the text topic, rather than genre or language, impacted on rank differences at all levels, possibly due to student engagement or influence of the prompt structure. Performance at the three levels was essentially similar across both languages, revealing that participants were emerging writers in Spanish and English. Similar outcomes in Spanish and English also implied potential cross-language transfer of academic language proficiency. Results further highlighted the interaction of multiple linguistic levels in text composition. Finally, students appeared to apply a knowledge telling strategy to writing, resulting in unsophisticated vocabulary and structures. For the qualitative analysis, focal participants' journals and interview transcripts were analyzed with domain and taxonomic analyses to discern how their language learning experiences shaped their identities as bilinguals. Results showed that 1) Spanish was preferred for all focal participants; 2) students shared the experience of language discrimination; 3) bilingual and monolingual identities resulted in different attitudes toward language learning and varied writing performance; and 4) Mexican and Puerto Rican students had diverse language learning experiences, leading to differences in identities and writing outcomes. Overall, the quantitative and qualitative findings raise two questions: 1) which aspects of academic language proficiency are shared across both languages, and how might these be assessed with bilingual, integrated language measures? 2) How might integrated assessment in L1 and L2 aid in identifying adolescent ELLs with language impairment?

In den vergangenen Jahren hat die Topic-Maps-Technologie eine zunehmende Bedeutung unter den Datenintegrationstechnologien gewonnen. Für die direkte Abfrage von Informationen auseiner Topic Map existiert mit der Topic-Maps-Abfragesprache TMQL ein mächtiges Werkzeug. Um diese nutzen zu können, muss der Benutzer jedoch sowohl über Kenntnisse der Abfragesprache verfügen als auch das Schema der Topic Map kennen. Deshalb wird eine Suchmaschine benötigt, mit der auch unerfahrene Benutzer die Topic-Maps-Datenbasis durchsuchen können. Nach einer Einführung in die relevanten Topic-Maps-Grundlagen werden zunächst verschiedene auf Topic-Maps-Daten spezialisierte Indexierungsalgorithmen untersucht. Einen Spezialfall stellt dabei die Indexierung virtuell zusammengeführter Topic Maps dar. Zu diesem Problem werden verschiedene Lösungsmöglichkeiten untersucht. Auf Basis der Suchmaschinenbibliothek Lucene wird eine semantische Suchmaschine entwickelt, welche die Topic-Maps-immanenten Elemente mit expliziter als auch mit impliziter Bedeutung sowohl bei der Indexierung als auch bei der Gewichtung der Suchergebnisse nutzt. Darüber hinaus wird ein allgemeines Modell zur Beschreibung von Topic-Maps-basierten Facetten vorgestellt. Darauf aufbauend werden Möglichkeiten der Erstellung generischer Facetten untersucht. Weiterhin wird mit Hilfe der Topic-Maps-Abfragesprache TMQL eine Methode zur Definition von domänen-spezifischen Facetten entworfen und erläutert. Mit der prototypischen Implementierung einer Schnittstelle, mit der die entstandene Suchmaschine in Topic-Maps-basiertenWebapplikationen genutzt werden kann, wird die einfache Integration der entwickelten Suchmaschine in bestehende Web-Applikationen demonstriert. Dies wird durchdie Schaffung einesneuen Pakets für die Middleware RTM ermöglicht.

The topic of this thesis concerns the interdisciplinary approach in teaching chemistry and biology at Czech grammar schools. Especially with two main questions: the determination of the level of integration that secondary school teachers prefer and how to promote an interdisciplinary approach at grammar schools. The following main objectives were established: find out which level of integration of science subjects is preferred by Czech teachers for secondary schools level, identify the main obstacles of implementation of integration, find out the factors which can help with the integration of knowledge, create support for interdisciplinary approach at Czech grammar schools. In order to achieve these objectives, a questionnaire survey among secondary school teachers and interviews with the academic staff of the Faculty of Science of Charles University were carried out. The results of the questionnaire survey showed that respondents prefer to preserve separate subjects with an emphasis on interdisciplinary relationships. The main restriction of integration is that similar topics in chemistry and biology are not taught in the same period, insufficient knowledge of the second subject or lack of learning materials. The main factor which can help with more frequent integration is learning materials. As...

In automatically categorizing massive corpora of text, various topic models have been applied with good success. Much work has been done on applying machine learning and NLP methods on Internet media, such as Twitter, to survey online discussion. However, less focus has been placed on studying how geographical locations discussed in online fora evolve over time, and even less on associating such location trends with topics. Can online discussions be geographically tracked over time? This thesis attempts to answer this question by evaluating a geo-aware Streaming Latent Dirichlet Allocation (SLDA) implementation which can recognize location terms in text. We show how the model can predict time-dependent locations of the 2016 American primaries by automatic discovery of election topics in various Twitter corpora, and deduce locations over time.