Christ and Culture Valued: Test Cases on Fairness

This research engages H. Richard Niebuhr’s work, Christ and Culture. Niebuhr’s book is a seminal work on the historical trends of Christian cultural engagement. This research applies several tests to the paradigm demonstrated in Niebuhr’s work. These tests demonstrate that Christ and Culture presents a paradigm that lacks fairness and does not adequately meet the goals of an explanatory paradigm. Niebuhr’s paradigm has shaped the discussion of Christian cultural engagement for over fifty years, and this research was done to demonstrate the need for new conversation-shaping paradigms in the field of Christian cultural engagement

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

Constraint-Based Ontology Induction From Online Customer Reviews

We present an unsupervised, domain-independent technique for inducing a product-specific ontology of product features based upon online customer reviews. We frame ontology induction as a logical assignment problem and solve it with a bounds consistency constrained logic program. Using shallow natural language processing techniques, reviews are parsed into phrase sequences where each phrase refers to a single concept. Traditional document clustering techniques are adapted to collect phrases into initial concepts. We generate a token graph for each initial concept cluster and find a maximal clique to define the corresponding logical set of concept sub-elements. The logic program assigns tokens to clique sub-elements. We apply the technique to several thousand digital camera customer reviews and evaluate the results by comparing them to the ontologies represented by several prominent online buying guides. Because our results are drawn directly from customer comments, differences between our automatically induced product features and those in extant guides may reflect opportunities for better managing customer-producer relationships rather than errors in the process

Designing concept mapping models with neural architecture search

Artificial neural networks are widely used in all sorts of applications, many of which directly impact the public’s lives. For all of their qualities, these systems have a major flaw: their black-box nature impedes us from interpreting their behavior, which harms public trust and their overall applicability. Explainable AI is a field that focuses on developing interpretable AI systems. However, the current solutions for black-box models do not provide fully accurate or easy-to-understand explanations. Concept mapping, proposed by Sousa Ribeiro and Leite [60], promises to do both. In this method, classifiers - dubbed mapping networks - are used to map a black-box model’s sub-symbolic internal representations into symbolic, human-understandable ontology concepts, opening the way to explainability. However, little investigation was done in the original work on consistently designing quality architectures for concept mapping. In this dissertation, we fill the existing knowledge gaps by conducting extensive empirical evaluation of architectures for concept mapping. We create a custom-made image classification dataset designed to facilitate observing how the black-box model’s task affects concept mapping. Further, we employ a custom adaption of differentiable architecture search (DARTS [33]) to automatically find good architectures. Our adaption of DARTS for concept mapping proves capable of consistently learning exemplary architectures and shows more resilience to context changes than manual trial-and-error.A rede neuronal artificial tem tido vasto uso em todo o tipo de aplicações, muitas das quais têm um impacto direto na vida pública. Apesar de todas as suas qualidades, estes sistemas têm uma fraqueza crucial: a sua natureza opaca impede-nos de interpretar o seu comportamento, algo que tem um impacto negativo na sua aceitação publica e aplicabilidade. Explainable AI é uma área que se foca em desenvolver sistemas de inteligência artificial interpretáveis, mas muitas das soluções atuais para modelos opacos não providenciam justificações acertadas ou fáceis de entender. Mapeamento de conceitos, proposto por Sousa Ribeiro e Leite, promete ambos. Neste método, classificadores adicionais - chamados de redes mapeadoras - são criados para mapear as representações internas subsimbólicas de um modelo em conceitos pertencentes a uma ontologia: simbólicos e passíveis de compreensão humana. Todavia, pouca investigação foi feita no trabalho original sobre as arquitecturas destas peças instrumentais, as redes mapeadoras. Nesta dissertação, preenchemos as atuais brechas de conhecimento realizando extensos testes empíricos sobre arquitecturas para mapeamento de conceitos. Usamos um dataset de classificação de imagens, gerado por nós especificamente para facilitar a observação de como o mapeamento de conceitos é afetado pela tarefa do modelo original. Para além disso, usamos uma versão de procura de arquiteturas diferencial (DARTS [33]), adaptada para aprender automaticamente boas arquitecturas mapeadoras. Essa nossa adaptação prova ser capaz de consistentemente encontrar arquitecturas competentes, e demonstra uma maior resiliência a mudanças de contexto do que o método original de tentativa e erro

Measures of Nonclassical Correlations and Quantum-Enhanced Interferometry

In the first part of this dissertation a framework for categorizing entropic measures of nonclassical correlations in bipartite quantum states is presented. The measures are based on the difference between a quantum entropic quantity and the corresponding classical quantity obtained from measurements on the two systems. Three types of entropic quantities are used, and three different measurement strategies are applied to these quantities. Many of the resulting measures of nonclassical correlations have been proposed previously. Properties of the various measures are explored, and results of evaluating the measures for two-qubit quantum states are presented. To demonstrate how these measures differ from entanglement we move to the set of Bell-diagonal states for two qubits, which can be depicted as a tetrahedron in three dimensions. We consider the level surfaces of entanglement and of the correlation measures from our framework for Bell-diagonal states. This provides a complete picture of the structure of entanglement and discord for this simple case and, in particular, of their nonanalytic behavior under decoherence. The pictorial approach also indicates how to show that all of the proposed correlation measures are neither convex nor concave. In the second part we look at two practical interferometric setups that use nonclassical states of light to enhance their performance. First we consider an interferometer powered by laser light (a coherent state) into one input port and ask the following question: what is the best state to inject into the second input port, given a constraint on the mean number of photons this state can carry, in order to optimize the interferometer\u27s phase sensitivity? This question is the practical question for high-sensitivity interferometry. We answer the question by considering the quantum Cram\\u27er-Rao bound for such a setup. The answer is squeezed vacuum. Then we analyze the ultimate bounds on the phase sensitivity of an interferometer, given the constraint that the state input to the interferometer\u27s initial 50:50 beam splitter B is a product state of the two input modes. Requiring a product state is a natural restriction: if one were allowed to input an arbitrary, entangled two-mode state $|\Xi \rangle$ to the beam splitter, one could generally just as easily input the state $B|\Xi \rangle$ directly into the two modes after the beam splitter, thus rendering the beam splitter unnecessary. We find optimal states for a fixed photon number and for a fixed mean photon number. Our results indicate that entanglement is not a crucial resource for quantum-enhanced interferometry. Initially the analysis for both of these setups is performed for the idealized case of a lossless interferometer. Then the analysis is extended to the more realistic scenario where the interferometer suffers from photon losses

AN ANALYSIS OF PHRASAL VERB IN MOVIE SCRIPT “FOXTROT SIX” BY RANDY KOROMPIS

ABSTRACT The objectives of this research were to find out the type of phrasal verb and find the implied meaning of the phrasal verb in the movie script Foxtrot Six by Randy Korompis. A phrasal verb is a verb formed from two (or sometimes three) parts: verb and particle or adverbial particle. Phrasal verbs also have a particular area of difficulty. Phrasal verbs are part of English materials, which learners of English should master. Translating phrasal verbs is essential because they are frequently use in daily communication. The research used qualitative descriptive research design because it investigates the utterances of the phrasal verb used by Angga and Sari as the main character in the movie script foxtrot six by using a human as the instrument. In data analysis, the researcher watched the original movie, categorizing the movie script by the main characters Angga and Sari, identifying the movie which contains the type of phrasal verb, determining the implied meaning of the phrasal verb, categorizing the data to the next step—analyzing the data type of phrasal verb based on Larsen-Freeman and Celce-Murcia theory: literal phrasal verb, aspectual phrasal verb, and idiomatic phrasal verb, and implied meaning by Leech: Conceptual Meaning, Connotative meaning, Social Meaning, Affected Meaning, Reflected Meaning, Collocative Meaning, and Thematic Meaning. From the data analysis, the result of this research types of phrasal verbs showed that: (1) there are two types of the phrasal verb used by Angga and Sari as the main character in Movie Script Foxtrot Six consists of a Literal and Aspectual phrasal verb (2) there are five implied meaning of phrasal verb used by Angga and Sari as the main character in Movie Script Foxtrot Six consisted Conceptual, Connotative, Social, and Affected. Keywords: Phrasal Verb, Movie Script,Qualitative Research, Foxtrot Six by Randy Korompi