A guide to the mindset needed to perform competent knowledge work.

This submission reports on a cross-disciplinary inquiry into topicality and relevance, involving an in-depth literature analysis and an inductive development of a faceted typology (containing 227 fine-grained topical relevance relationships arrayed in three facets and 33 types of presentation relationships). This inquiry reveals a large variety of topical connections beyond topic matching (the common assumption of topical relevance in the field), renders a closer look into the structure of a topic, and induces a generic topic-oriented information architecture that is meaningful across topics and domain boundaries. The findings from the analysis contribute to the foundation work of information organization, metadata development, intellectual access / information retrieval, and knowledge discovery. The typology of topical relevance relationships is structured with three major facets: * Functional role of a piece of information plays in the overall structure of a topic or an argument; * Mode of reasoning: How information contributes to the userâ s reasoning about a topic; * Semantic relationship: How information connects to a topic semantically. This inquiry demonstrated that topical relevance with its close linkage to thinking and reasoning is central to many disciplines. The multidisciplinary approach allows synthesis and examination from new angles, leading to an integrated scheme of relevance relationships or a system of thinking that informs each individual discipline. The scheme resolving from the synthesis can be used to improve text and image understanding, knowledge organization and retrieval, reasoning, argumentation, and thinking in general, by people and machines.

Energy models are optimisation tools which aid in the formulation of energy policies. Built on mathematics, the strength of these models lie in their ability to process numerical data which in turn allows for the generation of an electricity generation mix that incorporates economic and the environmental aspects. Nevertheless, a comprehensive formulation of an electricity generation mix should include aspects associated with politics and society, an evaluation of which requires the consideration of non-numerical qualitative information. Unfortunately, the use of energy models for optimisation coupled with the evaluation of information other than numerical data is a complicated task. Two prerequisites must be fulfilled for energy models to consider political and societal aspects. First, the information associated with politics and society in the context of energy policies must be identified and defined. Second, a software tool which automatically converts both quantitative and qualitative data into mathematical expressions for optimisation is required. We propose a software framework which uses a semantic representation based on ontologies. Our semantic representation contains both qualitative and quantitative data. The semantic representation is integrated into an Optimisation Modelling System which outputs a model consisting of a set of mathematical expressions. The system uses ontologies, engineering models, logic inference and linear programming. To demonstrate our framework, a Prototype Energy Modelling System which accepts energy policy goals and targets as inputs and outputs an optimised electricity generation mix has been developed. To validate the capabilities of our prototype, a case study has been conducted. This thesis discusses the framework, prototype and case study.

During the early modern period, practitioners in oftentimes unrelated arts and sciences began to experiment with transcribing and disseminating technical information by means of new symbolic notations. Algebra, music, chemistry, dance—whole fields of knowledge were quite literally rewritten with plus signs, treble clefs, affinity tables, and step symbols. “The Art of Signs” examines why early modern people working within and across disciplinary boundaries converged on the idea that developing complex symbolic notations would ultimately be worthwhile by reconstructing the reasons that they gave for doing so. It argues that symbolic notations appealed because they enabled powerful techniques of “visual thinking” that had no analogue in more conventional methods of inquiry. Notations transformed problems of information into problems of visualization whose solutions could then be derived by manipulating the properties of the drawn, two-dimensional plane. Indeed, early modern proponents of notations frequently described them in terms of vision, of being able to “see” things with them that they had not recognized before. However, because established methods of reasoning were predominantly verbal or empirical, symbolic notations and the visual thinking that they entailed necessarily challenged received ideas about how information ought to be represented and how knowledge ought to be discovered. Critics of the new notations argued that, at best, they amounted to a form of intellectual obscurantism that stymied rather than facilitated the circulation of knowledge. At worst, notations harbored disturbing implications for human ingenuity if the generation of new ideas truly could be reduced to the ranging and rearranging of symbols on a piece of paper. All told, “The Art of Signs” argues that early modern debates about the use and abuse of symbolic notations represent an underappreciated component of the epistemological ruptures that characterize the Scientific Revolution. Moreover, by recovering early modern understandings of symbolic notation, this dissertation demonstrates that a historical treatment of early modern semiotic thought can be leveraged to take a fresh look at perennial questions of representation that concern scholars across the humanities.

<div>Following a natural disaster, one of the most important facet that influence a persons chances of survival/being found out is the time with which they are rescued. Traditional means of search operations involving dogs, ground robots, humanitarian intervention; are time intensive and can be a major bottleneck in search operations. The main aim of these operations is to rescue victims without critical delay in the shortest time possible which can be realized in real-time by using UAVs. With advancements in computational devices and the ability to learn from complex data, deep learning can be leveraged in real time environment for purpose of search and rescue operations. This research aims to solve the traditional means of search operation using the concept of deep learning for real time object detection and Photogrammetry for precise geo-location mapping of the objects(person,car) in real time. In order to do so, various pre-trained algorithms like Mask-RCNN, SSD300, YOLOv3 and trained algorithms like YOLOv3 have been deployed with their results compared with means of addressing the search operation in</div><div>real time.</div><div><br></div>

Causal reconstruction is the task of reading a written causal description of a physical behavior, forming an internal model of the described activity, and demonstrating comprehension through question answering. T his task is difficult because written d escriptions often do not specify exactly how r eferenced events fit together. This article (1) ch aracterizes the causal reconstruction problem, (2) presents a representation called transition space, which portrays events in terms of "transitions,'' or collections of changes expressible in everyday language, and (3) describes a program called PATHFINDER, which uses the transition space representation to perform causal reconstruction on simplified English descriptions of physical activity.

The goal of the Programmer's Apprentice project is to develop a theory of how expert programmers analyze, synthesize, modify, explain, specify, verify, and document programs. This research goal overlaps both artificial intelligence and software engineering. From the viewpoint of artificial intelligence, we have chosen programming as a domain in which to study fundamental issues of knowledge representation and reasoning. From the viewpoint of software engineering, we seek to automate the programming process by applying techniques from artificial intelligence.

The interest in extensions of the logic programming paradigm beyond the class of normal logic programs is motivated by the need of an adequate representation and processing of knowledge. One of the most difficult problems in this area is to find an adequate declarative semantics for logic programs. In the present paper a general preference criterion is proposed that selects the ‘intended’ partial models of generalized logic programs which is a conservative extension of the stationary semantics for normal logic programs of [Prz91]. The presented preference criterion defines a partial model of a generalized logic program as intended if it is generated by a stationary chain. It turns out that the stationary generated models coincide with the stationary models on the class of normal logic programs. The general wellfounded semantics of such a program is defined as the set-theoretical intersection of its stationary generated models. For normal logic programs the general wellfounded semantics equals the wellfounded semantics.

Thesis (Ph. D.)--University of Kentucky, 2001. / Title from document title page. Document formatted into pages; contains vii, 75 p. : ill. Includes abstract. Includes bibliographical references (p. 70-72).

The overall purpose of this study is to investigate lexical semantic representation in bilinguals who speak typologically different languages, specifically, Mandarin and English. Three questions are posed about semantic representation: 1) Do bilingual speakers demonstrate greater heterogeneity in semantic knowledge than monolingual speakers; 2) To what extent do bilingual speakers use paradigmatic and syntagmatic relations to organize their semantic knowledge; and 3) What is the cross- linguistic overlap in bilingual speakers' semantic representation. Thirty Mandarin- English bilingual adults and 30 monolingual English-speaking adults participated in a repeated word association task and generated three associations to each of 36 stimuli. The bilingual speakers completed the same task in their two languages on two different days whereas the monolingual speakers responded to the same 36 stimuli on two different days. Results indicated that 1) the bilingual speakers produced a more heterogeneous set of responses in English than monolingual speakers; heterogeneity was greater in English than Mandarin among the bilingual speakers; 2) the bilingual speakers produced more paradigmatic associations (e.g., happy-sad, spoon-chopsticks, catch-throw) and fewer syntagmatic associations (e.g., happy-smile, spoon-eat, catch-ball) than the monolingual speakers; and 3) approximately 48% of the bilingual speakers' responses were cross- linguistic synonyms, whereas approximately 76% of the monolingual speakers' responses were identical from session 1 to session 2. These findings suggest that late bilinguals (second language learners) use categorical relations to organize their semantic knowledge to a greater extent than monolingual speakers and that reduced experience with a second language can lead to greater heterogeneity in semantic knowledge in that language. The findings also suggest that bilingual speakers have more distributed semantic representations than monolingual speakers. Additional research is needed to explore the areas of heterogeneity, categorical organization, and cross-linguistic overlap in order to further our understanding of bilingual speakers' semantic knowledge representation. / text