Dissertations / Theses on the topic 'Knowledge generation'

Thesis (S.M.)--Massachusetts Institute of Technology, Technology and Policy Program; and, (S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 115-116).
by Abhinav Taneja.
S.B.
S.M.

This dissertation presents a methodology for generation of fault diagnostic knowledge from a description of a device. The generated knowledge is to be used in a computer based learning environment for fault diagnostic tasks. In the past, developers of such environments obtained diagnostic knowledge through a knowledge engineering exercise with human experts. There are a number of drawbacks associated with such an approach. A major bottleneck in the development of such learning environments was capturing human expertise for diagnosis of an application, encoding this knowledge in a suitable form in a computer, and then verifying this knowledge. This exercise also depends on the availability and cooperation of a knowledgeable human expert(s). If more than one expert participates in the knowledge acquisition process, one may obtain contradictory information. Furthermore, most of the diagnostic knowledge is highly application specific thus making it useless for other applications. The starting point of the methodology of this thesis is a description of a device in terms of the components, component behaviors, and interconnections between components (structural knowledge). The end point of the methodology is a set of rules that can be used to diagnose faults in the device. The intermediate points are a behavioral and causal descriptions of the device, and a set of domain independent diagnostic strategies. This methodology has been applied to a counter circuit in the domain of digital electronics to test both the ability of the fault diagnostic system to diagnose faults in a device as well as to test its efficiency. The rules generated were able to successfully detect all the faults that were inserted in the counter application. Furthermore, as more diagnostic strategies were included in the diagnostic rule generation, the efficiency of the diagnostic system improved considerably.

Many expert, or knowledge-based, systems have been constructed in the domain of ecology, several of which are concerned with habitat evaluation. However, these systems have been geared to solving particular problems, with little regard paid to the underlying relationships that exist within a biological system. The implementation of problem-solving methods with little regard to understanding the more primary knowledge of a problem area is referred to in the literature as 'shallow', whilst the representation and utilisation of knowledge of a more fundamental kind is termed 'deep'. This thesis contains the details of a body of research exploring issues that arise from the refinement of traditional expert systems methodologies and theory via the incorporation of depth, along with enhancements in the sophistication of the methods of reasoning (and subsequent effects on the mechanisms of communication between human and computer), and the handling of uncertainty. The approach used to address this research incorporates two distinct aspects. Firstly, the literature of 'depth', expert systems in ecology, uncertainty, and control of reasoning and related user interface issues are critically reviewed, and where inadequacies exist, proposals for improvements are made. Secondly, practical work has taken place involving the construction of two knowledge based systems, one 'traditional', and the other a second generation system. Both systems are primarily geared to the problem of evaluating a pond site with respect to its suitability for the great crested newt (Triturus cristatus). This research indicates that it is possible to build a second-generation knowledge-based system in the domain of ecology, and that construction of the second generation system required a magnitude of effort similar to the firstgeneration system. In addition, it shows that, despite using different architectures and reasoning strategies, such systems may be judged as equally acceptable by endusers, and of similar accuracy in their conclusions. The research also offers guidance concerning the organisation and utilisation of deep knowledge within an expert systems framework, in both ecology and in other domains that have a similar concept-rich nature.

This work focus on the genetic generation of fuzzy systems. One of the main contribution of this work is the proposal of the FCA-BASED method, which generates the genetic search space using the formal concept analysis theory by extracting rules from data. The experimental evaluation results of the FCA-BASED method show its robustness, producing a good trade-off between the accuracy and the interpretability of the generated models. Moreover, the FCA-BASED method presents improvements to the DOC-BASED method, a previously proposed approach, related to the reduction of the computational cost for the generation of the genetic search space. In order to tackle high dimensional datasets, we also propose the FUZZYDT method, a fuzzy version of the classic C4.5 decision tree, a highly scalable method that presents low computational cost and competitive accuracy. Due to these characteristics, FUZZYDT is used in this work as a baseline method for the experimental evaluation and comparisons of other classic and fuzzy classification methods. We also include in this work the use of the FUZZYDT method to a real world problem, the warning of the coffee rust disease in Brazilian crops. Furthermore, this work investigates the task of feature subset selection to address the dimensionality issue of fuzzy systems. To this end, we propose the FUZZYWRAPPER method, a wrapper-based approach that selects features taking the relevant information regarding the fuzzyfication of the attributes into account, in the feature selection process. This work also investigates the automatic design of fuzzy data bases, proposing the FUZZYDBD method, which estimates the number of fuzzy sets defining all the attributes of a dataset and evenly distributing the fuzzy sets in the domains of the attributes. A modified version of the FUZZYDBD method, FUZZYDBD-II, which defines independent numbers of fuzzy sets for each attribute of a dataset, by means of estimation functions, is also proposed in this work
Este trabalho foca na geração genética de sistemas fuzzy. Uma das principais contribuições deste trabalho é a proposta do método FCA-BASED, que gera o espaço de busca genético usando a teoria de análise de conceitos formais por meio da extração de regras dos dados. Os resultados da avaliação experimental do método FCA-BASED demonstram sua robustez. O método FCABASED também produz um bom trade-off entre acurácia e interpretabilidade dos modelos gerados. Além disso, o método FCA-BASED apresenta melhorias em relação ao método DOC-BASED, uma abordagem proposta anteriormente. Essas melhorias estão relacionadas à redução do custo computacional para a geração do espaço de busca genético. Para ser capaz de trabalhar com conjuntos de dados de alta dimensão, foi também proposto o método FUZZYDT, uma versão fuzzy da clássica árvore de decisão C4.5. FUZZYDT é um método altamente escalável que apresenta baixo custo computacional e acurácia competitiva. Devido a essas características, o FUZZYDT é usado nesse trabalho como um método baseline para a avaliação experimental e comparações de outros métodos de classificação, fuzzy e clássicos. Também está incluido nesse trabalho a aplicação do método FUZZYDT em um problema do mundo real, o alerta da doença da ferrugem cafeeira em plantações brasileiras. Além disso, esse trabalho investiga a tarefa de seleção de atributos como forma de atacar o problema da dimensionalidade de sistemas fuzzy. Para esse fim, foi proposto o método FUZZYWRAPPER, uma abordagem baseada em wrapper que seleciona atributos levando em consideração as informações relevantes sobre a fuzificação dos atributos durante o processo de seleção. Esse trabalho também investiga a construção automática de bases de dados fuzzy, incluindo a proposta do método FUZZYDBD, que estima o número de conjuntos fuzzy que define todos os atributos de um conjunto de dados e distribui os conjuntos fuzzy proporcionalmente nos domínios dos atributos. Uma versão modificada do método FUZZYDBD, o método FUZZYDBD-II, também é proposta nesse trabalho. O método FUZZYDBD-II define números independentes de conjuntos fuzzy para cada atributo de um conjunto de dados por meio de funções de estimação

Purpose: The purpose of particular study is to explore individuals’ perceptions of knowledge sharing and its obstacles in a multigenerational engineering company. This study focuses on two groups of individuals: employees and managers. The perception of knowledge sharing and how differently generations perceive it is covered in this study. The obstacles which could occur during knowledge sharing between different generations in the engineering field are explored. The role of management in knowledge sharing processes is also covered. Methodology: To explore individuals’ perceptions of knowledge sharing and its obstacles in a multigenerational engineering company. A qualitative research approach was applied, using single company case study strategy. The case company is one of the leading construction engineering companies in Latvia. The primary data was gathered by conducting 12 semi-structured interviews, 3 interviews per every generation (Millennial, Generation X and Baby boomer) and 3 interviews with managers. The secondary data was obtained from the company, which included internal documents covering the general information about the company, guidelines and policies. By the usage of primary and secondary data triangulation of the study was reached. Findings: The thesis provides analysis of knowledge sharing and its obstacles within the multigenerational workforce of a construction engineering company. The perceptions of three generational cohorts such as Baby Boomers, Generation X and Millennials are presented. The conceptual framework for the study was built on the basis of three dimensions such as organizational. individual and technological obstacles. Based on the conceptual framework, authors brought up several propositions which supported the analysis process. The analysis showed that there are differences in perception of knowledge sharing and obstacles connected with it between engineers of different generations. Originality: There has not been any research about knowledge sharing between different generations in Latvia nor in Northern Europe, in general. The engineering field is a knowledge intensive sector, therefore knowledge sharing is crucial. Labor force still contains three different generations, thus it is important to explore whether engineering companies in Latvia have faced knowledge sharing obstacles, as it is a rapidly developing sector in this particular country.

Language is an organic construct. It emanates from the need for communication and changes through time, influenced by multiple factors. The resulting language structures are a mix of regular syntactic and morphological constructions together with divergent irregular elements. Linguistics aims at formalizing these structures, providing a rationalization of the underlying phenomena. However, linguistic information alone is not enough to fully characterize the structures in language, as they are intrinsically tied to meaning, which constrains and modulates the applicability of the linguistic phenomena and also to context and domain. Classical machine translation approaches, like rule-based systems, relied completely on the linguistic formalisms. Hundreds of morphological and grammatical rules were wired together to analyze input text and translate it into the target language, trying to take into account the semantic load carried by it. While this kind of processing can satisfactorily address most of the low-level language structures, many of the meaning-dependent structures failed to be analyzed correctly. On the other hand, the dominant neural language processing systems are trained from raw textual data, handling it as a sequence of discrete tokens. These discrete tokens are normally defined looking for reusable word pieces identified statistically from data. In the whole training process, there is no explicit notion of linguistic knowledge: no morphemes, no morphological information, no relationships among words, or hierarchical groupings.This thesis aims at bridging the gap between the neural systems and linguistics-based systems, devising systems that have the flexibility and good results of the former with a base on the linguistic formalisms, with the purposes of improving quality where data alone cannot and forcing human-understandable working dynamics into the otherwise black-box neural systems. For this, we propose techniques to fuse statistical subwords with word-level linguistic information, to remove subwords altogether and rely solely on lemmas and morphological traits of the words, and to drive the text generation process on the ordering defined by syntactic dependencie. The main results of the proposed methods are the improvements in translation quality that can be obtained by injecting morphological information into NMT systems when testing on out-of-domain data for morphologically-rich languages, and the control over the generated text that can be gained by means of linking the generation order to the syntactic structure.
El lenguaje es una construcción orgánica que surge de la necesidad de comunicación, y que cambia a lo largo del tiempo, influenciado por múltiples factores, resultando en estructuras del lenguaje donde se mezclan construcciones morfológicas y sintácticas regulares con otros elementos irregulares. La lingüística tiene como objetivo el formalizar estas estructuras, proponiendo interpretaciones de los fenómenos subyacentes. Sin embargo, la lingüística no es suficiente para caracterizar de manera completa las estructuras del lenguaje, ya que éstas se encuentran intrínsicamente ligadas tanto al significado -al restringir y modular éste la aplicabilidad de los fenómenos lingüísticos- como al contexto y al dominio. Las técnicas de traducción automática clásicas empleadas por los sistemas basados en reglas, se basan en formalismos lingüísticos, haciendo uso de miles de reglas morfológicas y gramaticales para analizar texto del idioma de origen y traducirlo al idioma de destino, intentando mantener la carga semántica original. Aunque este tipo de traducción procesa adecuadamente la estructuras de bajo nivel del lenguaje, muchas estructuras dependientes del significado no son analizadas correctamente. Los sistemas de procesado del lenguaje natural dominantes, en cambio, se entrenan usando texto como datos de entrada. Dicho texto se procesa como una secuencia de elementos discretos, normalmente definidos como trozos de palabras o sub-palabras, que se agrupan en una estructura de diccionario que es confecccionado estadísticamente de modo que se maximice el reuso de sus sub-palabras al codificar el texto de entrenamiento. En todo este proceso, no hay ninguna noción explícita de conocimiento lingüístico, ni morfemas, ni información morfológica, ni relaciones sintácticas entre palabras o grupos jerárquicos. El objetivo de esta tesis es hibridizar los sistemas neuronales y los sistemas basados en reglas lingüísticas, de manera que el resultado pueda mostrar la flexibilidad y buenos resultados de los primeros, pero teniendo una base lingüística que le permita tanto mejorar la calidad del texto generado en los casos en los que simplemente más datos no lo consiguen, como establer unas dinámicas de funcionamiento internas que sean entendibles por humanos, a diferencia de la naturaleza de "caja negra" de los sistemas neuronales normales. Para ello, se proponen técnicas para enriqueces las sub-palabras con información lingüística de nivel de palabra, ténicas para prescindir de las sub-palabras y basarse únicamente en el lema y los rasgos lingüísticos de las palabras, y técnicas para dirigir el orden de generación de texto mediante dependencias sintácticas. Los principales resultados de los métodos propuestos son la mejora en la calidad de traducción en sistemas neuronales a los que les inyectamos información lingüística, especialmente en escenarios de lenguas morfológicamente ricas con texto de distinto dominio, y el control directo del proceso de generación al ligarlo a las estructuras sintácticas del texto.

Scholars in strategy, economics, and sociology of science and technology have studied technology development as a source of firms’ economic gains as well as institutional changes. Drawing on the extant research of technology and innovation strategy, I investigate the problem of knowledge generation and flows in technology development. Specifically, I explore how firms generate novel technology and develop technological breakthroughs; how knowledge flows between firms affect interfirm cooperation in a knowledge network; and how science and technology programs impact the institutions of knowledge production. In Essay 1 (Chapter 2), I examine the antecedents of knowledge recombination and technological breakthroughs. Conceptualizing a firm’s exploration as a combinatory search of prior new-recombination (an original technology component), I investigate the impacts of prior new-recombination and search boundary (local vs. boundary-spanning) on the characteristics of focal invention. In particular, I theorize and juxtapose the contrasting effects of the boundary of technological search of prior new-recombination on the propensities that the focal invention generates new recombination and becomes a technological breakthrough. Specifically, I hypothesize that, when the technological search involves new recombination in prior inventions, 1) the likelihood of generating new recombination in the focal invention is greatest for a boundary spanning search, smallest for a local search, and intermediate for a hybrid search (which involves both types of search); but 2) the likelihood for the focal invention to become a technological breakthrough is greatest for a local search, smallest for a boundary spanning search, and intermediate for a hybrid search. I find supporting evidence from the analysis of U.S. nanotechnology patents granted between 1980 and 2006. The purpose of Essay 2 (Chapter 3) is to determine the effect of knowledge flows on the formation of interfirm cooperation. By distinguishing codified knowledge flows from tacit knowledge flows, this paper demonstrates that antecedents of interfirm cooperation lie in codified knowledge flows that precede interfirm cooperation. Two properties of asymmetry in directional codified knowledge flows, intensity and uncertainty, underpin this paper’s arguments and empirical tests. The main finding in this study is that intense codified knowledge flows weaken the formation of interfirm cooperation. By mapping dyadic firms to a center and a periphery firm within a knowledge network, I theorize that the uncertainty of directional codified knowledge flows induces the center and the periphery firms to pursue interfirm cooperation differently. The results show that while uncertainty caused by distant technology components in knowledge flows hinders a center firm from pursuing interfirm cooperation, uncertainty stimulates a periphery firm to pursue interfirm cooperation. A statistical analysis performed on a sample of enterprise software firms between 1992 and 2009 supports the hypotheses of this paper. In Essay 3 (Chapter 4), I examine how the National Nanotechnology Initiative (NNI), a most recent U.S. government’s science and technology (S&T) program launched in 2000, impacts the nature of university research in nanotechnology. I characterize the NNI as a policy intervention that targets the commercialization of technology and a focused research direction to promote national economic growth. As such, I expect that the NNI has brought about unintended consequences in terms of the direction of university-industry knowledge flows and the characteristics of university research output in nanotechnology. Using the difference-in-differences analysis of the U.S. nanotechnology patents filed between 1996 and 2007, I find that, for the U.S. universities, the NNI has increased knowledge inflows from the industry, diminished the branching-out to novel technologies, reduced the research scope, and decreased the likelihood of technological breakthroughs, as compared to other U.S. and non-U.S. research institutions. The findings suggest that, at least in the case of the NNI, targeted S&T programs of the government may increase the efficiency of university research, but potentially do so at a considerable price.

Most people need textual or visual interfaces in order to make sense of Semantic Web data. In this thesis, we investigate the problem of generating natural language summaries for structured data encoded as triples using neural networks. We propose an end-to-end trainable architecture that encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on this encoded vector. In order to both train and evaluate the performance of our approach, we explore different methodologies for building the required data-to-text corpora. We initially focus our attention on the generation of biographies. Using methods for both automatic and human evaluation, we demonstrated that our technique is capable of scaling to domains with challenging vocabulary sizes of over 400k words. Given the promising results of our approach in biographies, we explore its applicability in the generation of open-domain Wikipedia summaries in two under-resourced languages, Arabic and Esperanto. We propose an adaptation of our original encoder-decoder architecture that outperforms a set of strong baselines of different nature. Furthermore, we conducted a set of community studies in order to measure the usability of the generated content by Wikipedia readers and editors. The targeted communities ranked our generated text close to the expected standards of Wikipedia. In addition, we found that the editors are likely to reuse a large portion of the generated summaries, thus, emphasizing the usefulness of our approach to the involved communities. Finally, we extend the original model with a pointer mechanism that enables it to jointly learn to verbalise in a different number of ways the content from the triples while retaining the ability to generate regular words from a fixed target vocabulary. We evaluate performance with a dataset encompassing the entirety of English Wikipedia. Results from both automatic and human evaluation highlight the superiority of the latter approach compared to our original encoder-decoder architecture and a set of competitive baselines.

This research study is concerned with the role and influence of knowledge generation and learning processes in the development of Organic Agriculture in Wales. It builds on previous work which suggested that barriers to the generation and exchange of knowledge about organic agriculture between farmers and other actors in the sector were significant in inhibiting development. The thesis is predicated on the view that organic farming demands a complex treatment of knowledge and processes of learning, and that organic agriculture represents a synthesis of knowledge from a wide range of actors, knowledge domains and knowledge forms. The development of knowledge about organic agriculture is considered at the institutional and at the farmer level and interaction between institutions, institutions and farmers, and between farmers are explored. The development of organic agriculture is seen as a process where all actors are engaged in continuous learning, where learning trajectories are defined by historical conditions, local context and physical influences. The study set out to map the ways by which organic farmers in Wales acquired their knowledge about organic farming as they made the decisions to convert, during conversion and subsequently as they became more proficient organic farmers. It was designed to study the ways by which well embedded conventional family farmers went through this process, and how their knowledge-networks are reconfigured during conversion. The farmers in the study are categorised according to a range of characteristics and these categories are considered in exploring farmer associations and social learning activities. They are also related to farmer attitudes toward organic agriculture and farmers are categorised as different types of organic farmers.

Es besteht weitgehend Einigkeit darüber, dass inzidentelles Lernen bewusstes Wissen über eine sequenziell strukturierte Regelhaftigkeit erzeugen kann, auch wenn die zu Grunde liegenden Lernprozesse nur ungenügend verstanden sind. Ob jedoch Sequenzlernen auch „implizit“ oder unbewusst erfolgen kann, ist umstritten. Fortschritte in diese Frage sind von Untersuchungen zu bewusstem und unbewusstem Lernen zu erwarten, die vor dem Hintergrund übergreifender Bewusstseinstheorien erfolgen. Rünger und Frensch (2008a) zeigen, wie „bewusstes Sequenzwissen“ in Rückgriff auf die „global workspace“-Theorie des Bewusstseins definiert und operationalisiert werden kann. Im Rahmen dieser Theorie wird „inferenzielle Promiskuität“ als zentrales funktionales Merkmal bewusster mentaler Repräsentationen betrachtet. Rünger und Frensch (2008b) überprüfen eine zentrale Vorhersage der „unexpected event“-Hypothese, einer Theorie zur Entstehung bewussten Wissens in inzidentellen Lernsituationen. In einer Serie von Experimenten wurden unerwartete Ereignisse durch Unterbrechungen des inzidentellen Lernprozesses experimentell induziert. In Übereinstimmung mit der „unexpected event“-Hypothese fanden die Autoren, dass sich die Verfügbarkeit bewussten Sequenzwissens erhöhte. Rünger, Nagy und Frensch (in Druck) untersuchen schließlich die Funktion bewussten Sequenzwissens im Kontext eines Rekognitionstests. Die empirischen Befunde deuten darauf hin, dass bewusstes Sequenzwissen die epistemische Grundlage für rationale Urteile im Gegensatz zu intuitiven oder heuristischen Urteilen darstellt.
There is a general consensus that incidental learning can produce conscious knowledge about a hidden sequential regularity, even though the underlying learning mechanisms are still poorly understood. By contrast, whether sequence learning can also be “implicit” or nonconscious is a matter of intense debate. Progress can be achieved by grounding research on conscious and nonconscious learning in larger theoretical frameworks of consciousness. Rünger and Frensch (2008a) show how “conscious sequence knowledge” can be defined and operationalized in reference to global workspace theory of consciousness that depicts “inferential promiscuity” as the functional hallmark of conscious mental representations. Rünger and Frensch (2008b) test a central prediction of the unexpected-event hypothesis — a theoretical account of the generation of conscious knowledge in incidental learning situations. In a series of experiments, unexpected events were induced experimentally by disrupting the incidental learning process. In line with the unexpected-event hypothesis, the authors observed an increased availability of conscious sequence knowledge. Finally, Rünger, Nagy, and Frensch (in press) explore the function of conscious sequence knowledge in the context of a sequence recognition test. The empirical results suggest that conscious sequence knowledge provides the epistemic basis for reasoned — as opposed to intuitive or heuristic — judgments.

Thesis (M.S.)--University of Florida, 2001.
Title from title page of source document. Document formatted into pages; contains viii, 73 p.; also contains graphics. Includes vita. Includes bibliographical references.

Abstract Over the past half a decade, new forms of knowledge sharing, collaboration and online participation have emerged. As a result, a new generation of IT tools are being used for the creation and exchange knowledge. This dissertation uses a knowledge management framework known as the 7C model and applies a multi-method approach to deepen the understanding on how new knowledge emerges with these tools. As the benefits of knowledge are realized when it is applied, this dissertation places special emphasis also on the usability of the knowledge. The results indicate that the knowledge creation sub-processes of comprehension and conceptualization need more scientific attention. In addition, the results suggest that comprehension can be supported by helping users to reflect and by utilizing guideline information. Supporting deeper interaction and improved linking with the existing content, allowing users to stay in a state of flow, and using decision aids can help in comprehension. Conceptualization can be supported through knowledge rationale, metaphors and analogues, decision aids, and by helping users to reach common ground and shared understanding. In order for the knowledge to be really usable, the knowledge creation should aim at producing knowledge in explicit and actionable form. Producing knowledge in the form of guidelines was found to be beneficial for the utilization of knowledge. Guidelines support learning-by-doing and reflection-in-action, which are crucial for the emergence of new tacit knowledge. Evidence-based information and decision aid tools can help in choosing the knowledge that is to be applied. Finally, the results suggest that in the era of Web 2.0, many low-cost experiences inducing constant exposure to knowledge might work better than a few high-cost experiences requiring very deep thinking. The reason for this is that contemporary users seem to be so accustomed to the ease-of-use of Web services that they simply will not use more useful but less usable solutions.

Despite the vast amount of research on work motivation, theorists still lack consensus regarding what truly motivates employees; yet employee motivation is widely recognized as critical to organizational survival in the global marketplace. In the knowledge economy, knowledge workers are the key to knowledge creation and, thus, organizational survival and success. Thus, organizations that wish to survive in the current economy must find ways to capitalize on the strengths of knowledge workers by developing an understanding of the motivating forces driving knowledge workers. Research suggests differences in employee motivation across age groups and generations. As Traditionalists and Baby Boomers approach retirement and exit the workforce, Generation Y, born between 1981 and 1997, is becoming a major part of the workforce. Thus, organizational leaders must develop an understanding of what motivates knowledge workers from Generation Y to contribute to the organization's goals and objectives, thereby contributing to higher levels of organizational performance; however, scholarly research has yet to address the work motivation experiences of Generation Y knowledge workers. This grounded theory study explored the work motivation experiences of Generation Y knowledge workers through a review of key motivation theories and exploratory, in-depth interviews with Generation Y knowledge workers in the healthcare industry, toward an understanding of Generation Y's intrinsic and extrinsic motivators, demotivators, and process of and approach to work motivation. The data revealed that Generation Y knowledge workers in the healthcare industry have a strong desire to contribute, both to their organizations and to their communities. The desire for purpose, which can be achieved through the attainment of social acceptance and self-worth, served as the primary motivator for the research participants.

Small-scale fisheries are learning contexts of importance for generating, transferring, and updating ecological knowledge of natural environments through everyday work practices. The rich knowledge fishers have of local ecosystems is the result of the intimate relationship fishing communities have had with their natural environments across generations (see e.g. Urquhart and Acott 2013). This relationship develops strong emotional bonds to the physical and social place. For fishing communities and fishers – who depend directly on local ecosystems to maintain their livelihoods – fishing environments are natural places for living, working and defining themselves. Previous research on fishers’ ecological knowledge has mainly been descriptive, i.e., has focused on aspects such as reproduction, nutrition and spatial-temporal distribution and population dynamics, from a traditional view of knowledge that only recognises scientific knowledge as the true knowledge. By doing this, fishers’ ecological knowledge has been investigated separately from the learning contexts in which it is generated, ignoring the influence of social, cultural and historical aspects that characterise fishing communities, and the complex relationships between fishers and the natural environments they live and work in. This thesis investigates ecological knowledge among small-scale fishers living and working in the ecosystems of Lake Vättern and the Blekinge Archipelago (Baltic Sea) in Sweden and explores how ecological knowledge is generated with particular regard to the influences of work and nature on fishers’ knowledge of ecosystems. The aim of this thesis is to contribute to the knowledge and understanding of informal learning processes of ecosystem complexity among small-scale fishers. This knowledge further contributes to the research field of ecological knowledge and sustainable use and management of natural resources. It addresses the particular research questions of what ecological knowledge fishers generate, and how its generation is influenced by their fishing work practices and relationships to nature. The thesis consists of three articles. Article I focuses on the need to address the significant lack of theoretical and methodological frameworks for the investigation of the cognitive aspects involved in the generation of ecological knowledge. Article II deals with the need to develop theoretical, methodological and empirical frameworks that avoid romanticising and idealising users’ ecological knowledge in local (LEK), indigenous (IEK) and traditional (TEK) ecological knowledge research, by rethinking it as being generated through work practices. Article III addresses the lack of studies that explicitly explore theories linking complex relations and knowledge that humans form within and of ecosystems. It also addressed the lack of attention from environmental education researchers to theory and empirical studies of ‘sense of place’ research, with a particular focus on environmental learning. Research into the question of what ecological knowledge fishers generate shows differences in their ways of knowing ecosystem complexity. These differences are explained in terms of the influences of the species being fished, and the sociocultural contexts distinguishing fishers’ connection to the fishing profession (i.e., familial tradition or entrepreneurship) (Article I), but also by the fishing strategies used (Article II). Results answering the research question of how work practices influence fishers’ knowledge of ecosystem complexity show a way of rethinking their ecological knowledge as generated in a continuous process of work (Article II), thus, far from romantic views of knowledge. Results answering the research question of how fishers’ relationships to nature influence their knowledge of ecosystem complexity demonstrate the complex interconnections between psychological processes such as identity construction, proximity maintenance and attachment to natural environments (Article III). Finally, more similarities than differences between fishers’ knowledge were found, despite the variation in cases chosen, with regards to landscape, target species, regulations systems and management strategies, fishing environments scales, as well as cultural and social contexts.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.

 

Ecological knowledge and sustainable resource management: The role of knowledge acquisition in enhancing the adaptive capacity of co-management arrangements

Modern video surveillance systems generate diverse forms of data and to facilitate the effective exchange of these data a methodical approach is required. This thesis proposes the Video Surveillance Content Description Interface (VSCDI), a component of ISO/IEC 23000-10 - Information technology - Multimedia application format (MPEG-A) - Part 10: Video surveillance application format. The interface is designed to describe content associated with and generated by a surveillance system. In particular, a set of descriptors are included for: content-based image retrieval; user-defined Classification Schemes to impose any required description ontology; and to provide consistent descriptions across multiple sources. The VSCDI is evaluated using comparisons with other meta-data frameworks and in terms of the performance of its colour descriptor components. Two new data sets are created of pedestrians in indoor environments with multiple camera views for re-identification experiments. The experiments use a novel application of colour constancy for cross-camera comparisons. Two evaluation measures are used: the Average Normalised Mean Retrieval Rate (ANMRR) for ranked estimates; and the Information Gain metric for probabilistic estimates. Techniques are investigated for using more than one descriptor both to provide the estimate and to represent a person whose image is split into Top and Bottom clothing components. The re-identification of pedestrians is discussed in the context of providing both a coherent description of the overall scene activity and within an embedded system.

[EN] Software Engineering provides a repository of techniques, methods and tools to manage, process, use and exploit information. In recent decades, this corpus has not only been applied to domains that traditionally act as a receivers of software solutions, but also it has been expanded and enriched by contributions from other disciplines and domains with needs related to the information produced. One of the most common needs in these disciplines is the software assistance to experts or domain professionals in performing processes ranging from the analysis of raw gathered data to the generation of new knowledge based on these, thus allowing the continuous advance of the discipline. In order to assist knowledge generation processes through software, it is necessary a deep understanding of the Software Engineering corpus as well as the particularities of the domain assisted and how knowledge is generated inside it.This situation appears too in the Cultural Heritage domain, whose professionals produce and manage large amounts of data about evidences of our past and present, from which they create new knowledge that constitutes the knowledge about heritage of a particular community.Despite their relevance and the regular application of Software Engineering solutions to the Cultural Heritage domain, the knowledge generation process in Cultural Heritage poses a challenge for Software Engineering, mainly due to the low presence of formal studies of the process, making it difficult to assist it through software. The lack of formal studies implies that we do not know which particular processes in Cultural Heritage we must assist and what should be the appropriate assistance in each case. Furthermore, the Cultural Heritage domain and, in general, the humanities, possesses some particular characteristics that are especially difficult to deal with by software, such as the presence of high subjectivity, the fact that much information is uncertain or vague, and the importance of the temporal aspect in the information. In order to address these two challenges from a transdisciplinary perspective, this thesis presents a conceptual framework based on software models for the construction of software solutions to assist to the knowledge generation process in Cultural Heritage. Firstly, the thesis conducts a deep exploration of the knowledge generation processes in Cultural Heritage, whose inputs are mainly textual sources. As a result, the thesis proposes a methodology and a modelling language to use discourse analysis in Software Engineering. By using this approach, it is possible to relate elements of a text with the domain entities that are referenced and the argumentative mechanisms used during the knowledge generation process and captured in the text. Subsequently, the thesis proposes a conceptual framework whose implementation allows to manage the domain particularities mentioned above, providing a software assistance to the Cultural Heritage professionals through information visualization techniques.The proposed conceptual framework has been validated in two complementary ways. On the one hand, we have developed a full case study in the Cultural Heritage domain, for which we have instantiated all the software models proposed as part of the framework to represent a real-world scenario. This case study application has revealed the potential of the framework in terms of conceptual representation, technical support and software-assistance definition mechanisms.On the other hand, the proposed software models have been implemented as a functional iOS application prototype. The prototype has been validated empirically against professionals in Cultural Heritage, comparing the performance of knowledge generation processes using the proposed framework to the conventional ways without software assistance. The empirical validation has revealed how the proposed framework provides a robust solution for implementing software-assistance in Cultural Heritage.
[ES] La ingeniería del software ofrece un repositorio de técnicas, métodos y herramientas como soluciones para el manejo, tratamiento, uso y explotación de información. En las últimas décadas, este corpus no sólo ha sido aplicado a dominios tradicionalmente receptores de soluciones software, sino que se ha expandido y enriquecido con aportaciones de diversas disciplinas y dominios con necesidades relacionadas con la información que producen.Una de las necesidades más habituales es la asistencia a los profesionales de dichas disciplinas durante el proceso evolutivo que realizan desde el análisis de los datos más primarios hasta la generación de conocimiento nuevo que permita avanzar en la disciplina involucrada. Este es el caso del Patrimonio Cultural, cuyos profesionales producen y manejan ingentes cantidades de datos acerca de evidencias sobre nuestro pasado y presente, y desde los cuáles descubren y generan conocimiento nuevo, que supone la herencia cultural propia de una comunidad. Este conocimiento define la comunidad en el presente y es transmitido a las generaciones presentes y futuras. Pese a su relevancia y a la habitual aplicación de determinadas soluciones de ingeniería software en el dominio, el proceso de generación de conocimiento en Patrimonio Cultural representa en sí mismo un reto para la ingeniería del software, debido fundamentalmente a la poca presencia de estudios formales acerca del mismo, lo que dificulta su asistencia mediante software. Esto implica que no sabemos qué tipo de subprocesos debemos asistir mediante software ni cuál es la asistencia más adecuada. Además, el corpus actual en ingeniería del software debe soportar especificidades del dominio patrimonial y, en general, de las humanidades, como son la presencia de una alta subjetividad, el hecho de que mucha información es incierta o vaga, y la importancia del aspecto temporal en los datos. Con el objetivo de abordar estos dos retos desde una perspectiva co-investigadora y transdisciplinar, la presente tesis doctoral presenta un marco conceptual basado en modelos software para la construcción de soluciones software que asistan a la generación de conocimiento en Patrimonio Cultural. La tesis explora a fondo el proceso de generación de conocimiento en Patrimonio Cultural, cuyas fuentes eminentemente textuales han dado lugar a la propuesta de una metodología completa y un lenguaje de modelado para utilizar análisis del discurso en ingeniería del software. Esta propuesta permite que se puedan relacionar elementos de un texto con las entidades del dominio que se referencian, así como los mecanismos argumentativos que se emplean.Posteriormente, la tesis propone un marco conceptual completo cuya implementación permite gestionar las especificidades del dominio antes señaladas, ofreciendo una asistencia mediante técnicas de visualización de información software a los especialistas en Patrimonio Cultural. El marco conceptual propuesto ha sido validado de dos maneras complementarias. Por un lado, se ha desarrollado un caso de estudio patrimonial completo, para el cual se han implementado todos los modelos software del marco conceptual propuesto, representando un escenario de aplicación completo del mundo real. Este caso de estudio ha permitido comprobar la potencia del marco conceptual propuesto en cuanto a representación, soporte y definición de mecanismos de asistencia software. Por otro lado, los modelos software que conforman el marco conceptual propuesto han sido implementados en un prototipo funcional en forma de aplicación iOS. Esto ha permitido contar con una implementación real de asistencia software en Patrimonio Cultural. Dicha solución se ha validado empíricamente con profesionales del dominio, comparándola con los modos de generación de conocimiento habituales sin dicha asistencia.La validación empírica ha permitido comprobar cómo el marco propuesto constituye una solución sólida para la construcci
[CAT] L'enginyeria del programari ofereix un repositori de tècniques, mètodes i eines com a suport per la manipulació, tractament, ús i explotació d'informació. En les darreres dècades, aquest corpus no sols ha sigut aplicat a dominis tradicionalment receptors de solucions de programari, si no que s'han extés i enriquit amb aportacions des de diferents disciplines i dominis amb necessitats relacionades amb l'informació que produeixen. Una de les necessitats més habituals és l'assistència als professionals d'aquestes disciplines durant el procés evolutiu que realitzen des de l'anàlisi de les dades més primàries fins la generació de nou coneixement que permet avançar en la disciplina involucrada. Aquest és el cas del Patrimoni Cultural, el professionals del qual produeixen i manipulen grans quantitats de dades sobre evidències del nostre passat i present, i des de les quals descobreixen i generen nou coneixement, que suposa l'herència cultural pròpia d'una comunitat. A pesar de la seua relevància i a la normal aplicació de determinades solucions d'enginyeria de programari al domini, el procés de generació de coneixement en Patrimoni Cultural representa, en sí mateix, un repte per a l'enginyeria del programari, fonamentalment per la poca presència d'estudis formals sobre aquest domini, cosa que dificulta la seua assistència per programari. Açò implica que no sabem quin tipus de subprocessos hem d'assistir amb programari ni quina és l'assitència més adient. A més a més, el corpus actual en l'enginyeria del programari ha de suportar especifitats del domini patrimonial i, en general, de les humanitats, com són la presència d'una alta subjectivitat, i el fet que molta informació és incerta o imprescisa, o la importància de la dimensió temporal en les dades. Amb l'objectiu d'abordar aquestos dos reptes des d'una perspectiva de recerca colaborativa i transdisciplinar, aquesta tesi doctoral presenta un marc conceptual basat en models de programari per a la construcció de solucions de programari que assistisquen a la generació de coneixement en Patrimoni Cultural. En primer lloc, la tesi explora en profunditat el procés de generació de coneixement en Patrimoni Cultural, les fonts de les quals són majoritàriament textuals han sigut l'orige per a la proposta d'una metodologia completa i un llenguatge de modelatge per emprar anàlisi del discurs en enginyeria de programari. Aquesta proposta permet que es puguen relacionar elements d'un text amb les entitats del domini que es referencien, així com els mecanismes argumentatius que s'empren. Posteriorment, la tesi proposa un marc conceptual complet amb una implementació que permet gestionar les especificitats del domini abans esmentades, oferint una assistència mitjançant tècniques de visualització d'informació de programari als especialistes en Patrimoni Cultural.El marc conceptual proposat ha sigut validat de dues maneres complementàries. Per una banda, s'ha desenvolupat un cas d'estudi patrimonial complet, implementant tots els models de programari del marc conceptual proposat, representant un escenari d'aplicació complet del món real. Aquest cas d'estudi ha permés comprovar la potència del marc conceptual proposat en quant a la representació, suport i definició de mecanismes d'assistència de programari. Per una altra banda, els models de programari que conformen el marc conceptual proposat s'han implementat en un prototipus funcional en forma d'aplicació iOS. Aquest fet ha permés comptar amb una implementació real d'assistència de programari en Patrimoni Cultural. Aquesta solució s'ha validat empíricament amb professionals del domini, comparant-la amb els modes de generació de coneixement habituals sense aquesta assistència. La validació empírica ha permés comprovar com el marc conceptual proposat constitueix una solució sòlida per a la construcció, a partir dels models de programari especificats, dels sistemes de prog
Martín Rodilla, P. (2016). Software-Assisted Knowledge Generation in the Cultural Heritage Domain: A Conceptual Framework [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68496
TESIS

Narrative Intelligence is the ability to craft, tell, understand, and respond appropriately to narratives. It has been proposed as a vital component of machines aiming to understand human activities or to communicate effectively with humans. However, most existing systems purported to demonstrate Narrative Intelligence rely on manually authored knowledge structures that require extensive expert labor. These systems are constrained to operate in a few domains where knowledge has been provided. This dissertation investigates the learning of knowledge structures to support Narrative Intelligence in any domain. I propose and build a system that, from an corpus of simple exemplar stories, learns complex knowledge structures that subsequently enable the creation, telling, and understanding of narratives. The knowledge representation balances the complexity of learning and the richness of narrative applications, so that we can (1) learn the knowledge robustly in the presence of noise, (2) generate a large variety of highly coherent stories, (3) tell them in recognizably different narration styles and (4) understand stories efficiently. The accuracy and effectiveness of the system have been verified by a series of user studies and computational experiments. As a result, the system is able to demonstrate Narrative Intelligence in any domain where we can collect a small number of exemplar stories. This dissertation is the first step toward scaling computational narrative intelligence to meet the challenges of the real world.

The purpose of the research is to develop more efficient frameworks for supporting design model development. The evolution of these frameworks has occurred in two areas: (1) knowledge representation schemes for the design model and (2) procedures for model structure generation. The original representation scheme for the design model was the System Entity Structure (SES). The SES was subsequently enhanced by other representation schemes to create FRASES (Frame & Rules Associated SES). Furthermore, FRASES was further converted into object-oriented representation. The procedures for manipulating the above representation schemes involved search, reasoning, constraint satisfaction, and message passing. Finally, a distributed approach was proposed, indicating a future trend. The methodologies offered by artificial intelligence, simulation modelling, and software engineering were adopted to support the research.

Magister Commercii - MCom
The aim of this study is to understand how knowledge is created, shared and used within the fast moving consumer goods distribution centre in the Western Cape (WC). It also aims to understand knowledge sharing between individuals in the organisation. A literature review was conducted, in order to answer the research questions- this covered the background of knowledge management (KM) and KS and its current status with particular reference to SA’s private sector. The study found that technological KM infrastructure, cultural KM infrastructure and organisational KM infrastructure are important enablers of KS. A conceptual model was developed around these concepts. In order to answer the research questions, the study identified a FMCG DC in the WC, where KS is practiced

This thesis aims to explore a rather simple question in relation to the process of how knowledge is generated within the organisational group. The question posed and pursued using an inductive methodological approach is "how do groups learn?" This deceptively simple and almost child-like question has helped me to engage with a wide range of philosophical and methodological approaches to group learning and knowledge generation. By using such a straightforward notion I have been able to construct a sophisticated research project which posits a range of conclusions and responses to my original question and demonstrates the complexity of organisational learning in general. The conclusions I generated reveal a series of discrete interconnections between the individual, the group and an enmeshed series of psychological processes which both assist and prevent groups from generating knowledge and learning. Along with generating a number of new insights into how groups learn, this simple question has enabled me to construct and propose a new theoretical framework for group learning and knowledge generation. The framework itself sets out to explore some of the most basic taken-for-granted notions of our Western world-view, ideas which form the bedrock of our epistemological certainty and which are hardly ever held up to critical scrutiny.

Most computer game characters are either ``talkers,' i.e., they engage in dialogue with the player, or ``fighters,' i.e., they engage in actions against or with the player, and that may affect the virtual world. The reason for this dichotomy is a corresponding gap in the underlying development technologies used for each kind of character. Using concepts from task modeling and computational linguistics, we have developed a new kind of character-authoring technology which bridges this gap, thereby making it possible to create richer and more interesting characters for computer games.

Introduction: The scarcity of rigorous evidence regarding rare disease therapies contributes to uncertainty for stakeholders who make decisions about the use, prescription, or funding of such therapies. My dissertation objective was to integrate stakeholder perspectives and evidence related to how rare disease therapies are evaluated to better understand drivers of uncertainty in decision making and develop an evaluation framework for future evidence generation, synthesis, and decision support. Methods: To better understand the perceived challenges in generating robust treatment effectiveness evidence, and describe various methods for mitigating these challenges, I used a meta-narrative literature review. I also conducted focus group interviews with key rare disease stakeholders (patients/caregivers, physicians, and policy advisors) to elicit different perspectives on how evidence is generated, evaluated, and synthesized in the context of health care decision making, both at a personal and population level. Finally, I integrated the focus group findings with a targeted literature review to identify characteristics of rare diseases and their candidate therapies that may warrant special consideration in health technology assessment (HTA) and health care decision making. Findings: My dissertation data revealed three fundamental challenges in generating robust treatment effectiveness evidence for rare diseases: limitations in recruiting a sufficient sample; inability to account for clinical heterogeneity; and reliance on outcomes with unclear clinical relevance. Several methodological solutions have been proposed to overcome these challenges. In addition, study participants described different perspectives on how they choose to participate in and use research in their roles as health care users, care providers, and policy advisors. Notably, conventional wisdom that patients/caregivers participate in clinical research studies because of therapeutic misconception was not supported. Finally, focus group and literature review findings identified information that potentially warrants special consideration in future HTA specific to rare diseases, including characteristics of the disease, understanding of causal hypotheses relevant to the therapy, and complexities of cost-effectiveness given the high price of many rare disease therapies. Discussion: Together, the findings from this dissertation support an evaluation framework with eight key principles that aim to mitigate important aspects of uncertainty from various stakeholder perspectives and promote evidence-informed decision making about rare disease therapies.

En el presente trabajo analizo desde un punto de vista crítico el episodio que representa “Gravity Probe B” (GPB) (La Sonda de la Gravedad B) en la historia de la experimentación en la física fundamental. Anunciado como una “Prueba del Universo de Einstein”, GPB fue un experimento para examinar predicciones de la teoría general de la relatividad (TGR) que duró 50 años. GPB nació en Stanford University, en el momento que el principio de la tecnología de satélites lo convirtió en una posibilidad y se convirtió en el proyecto más longevo de la NASA: los resultados finales se publicaron en el año 2011. Siguiendo el diseño original de 1960, GPB pretendió medir el arrastramiento del marco espaciotemporal y el efecto geodésico sobre un giroscopio en órbita alrededor de la tierra en un satélite funcionando en modo “drag-free” (sin resistencia). Siguiendo una órbita puramente gravitacional, junto con el giroscopio y los magnetómetros formados por dispositivos superconductores de interferencia cuántica, la nave espacial contenía un telescopio que rastreaba una estrella guía como punto de referencia. La misión espacial empezó en el 2004 y concluyó en el 2005 con el objetivo de medir el cambio en la orientación del eje de giro de los giroscopios, relativo al inmóvil espacio inercial, con una precisión de 0.5 milésimas de un segundo de arco ($sim 10^{-7}$ grados) a lo largo de un año. Para realizar el experimento fue necesario desarrollar varias tecnologías completamente novedosas, y los sistemas de abordo diseñados establecieron varios récords por ser los sistemas más cerca de la perfección diseñados jamás. (GPB) representa una oportunidad única para analizar cómo funcionan los experimentos científicos extremos y una gran oportunidad de estudiar los esfuerzos para generar conocimiento acerca de la TRG basado en la experimentación. GPB se encontró con serias dificultades durante la ejecución, con importantes anomalías y un ruido excesivo en los datos. El equipo se vio obligado a desarrollar controvertidos métodos nuevos para analizar los datos que se obtuvieron. Inicialmente presento tanto la física relevante a la aproximación específica a la TRG apropiada para analizar la gravitación en el sistema solar (el marco posnewtoniano de parametrización) como la historia de la confirmación de TRG. Después de presentar GPB y sus objetivos, introduzco el marco analítico que adopto para examinar los resultados y conclusiones que el equipo logró. Utilizo el trabajo de James Woodward y Deborah Mayo, combinándolo en una perspectiva basada en tres puntos: los datos observados pueden ser evidencia para fenómenos teóricos subyacentes; la experimentación hace lo posible para rastrear la veracidad de las hipótesis a través de la sensibilidad contrafáctica de los datos a las afirmaciones teóricas; y para que los datos valgan como evidencia a favor de un fenómeno, la prueba que represente el encaje de estos con las predicciones de las hipótesis examinadas debe ser severo, aunque no necesariamente represente un uso novedoso de los datos. Destaco muchas preocupaciones con el análisis de los datos producidos por GPB, pero a través de mi análisis basada en este marco, mi conclusión es que las afirmaciones del equipo de GPB son perfectamente válidas. También indico que este episodio demuestra que puede ser importante que los científicos adopten las perspectivas más sofisticadas propuestas por filósofos en lugar de contar con los más comunes acercamientos epistemológicos. Finalmente, indico que a pesar de la posibilidad de que el conocimiento generado no sea del todo sólido e inmóvil, y que algún día pueda revisarse, cumple con los requisitos más estrictos que la sociedad normalmente pide de las conclusiones de la investigación.
In this thesis, I critically analyse Gravity Probe B (GPB) as an extraordinary episode in the history of experimentation in fundamental physics. Billed as “Testing Einstein’s Universe,” GPB was a 50-year-long experiment to test crucial predictions of the General Theory of Relativity (GTR). GPB started life at Stanford University when satellite technology first made the “Relativity Gyroscope Experiment” feasible and it went on to become the longest running mission in NASA’s history; final results were published in 2011. Following the original design published in 1960, GPB set out to measure frame dragging (also known as the Lense-Thirring effect) and the geodetic (or de Sitter) effect on a superconducting gyroscope orbiting the Earth in a “drag-free” satellite. Essentially executing a purely gravitational orbit, together with the science instrument assembly containing the (multiple) gyroscope(s) and superconducting quantum interference devices used as magnetometers, the spacecraft housed a telescope trained on a reference “guide star”. The mission flew from 2004 to 2005 and aimed to measure the change in the orientation of the spin axis of the gyroscopes, relative to “fixed” inertial space identified using the guide star, to within 0.5 milliarcseconds (~10-7 degrees) over the year-long experiment. The experiment required the development of several completely new technologies before it could be performed and the on-board systems broke numerous records as the most nearly perfect and most sensitive systems created. It represents a unique opportunity to analyse the workings of scientific experimentation taken to the extreme and a rare chance to examine efforts to generate knowledge based on experimental GTR: one of our two current fundamental physics theories. GPB encountered serious problems during execution of the space mission with major anomalies and excessive noise in the data collected. The team was forced to develop controversial new data analysis methods to attempt to salvage meaningful results from the unexpected and unrepeatable dataset they retrieved. I initially present both the physics of GTR in the specific weak gravity approximation appropriate for analysing gravitational effects within the Solar System (the parametrised post-Newtonian framework) and the prior history of confirmation of GTR. After presenting GPB and its aims, I then introduce the analytical framework that I adopt to examine the claims made by the team regarding their data analysis and eventual findings. I draw heavily on work by James Woodward and Deborah Mayo, among others, and combine this into a 3-point approach: observed data can act as evidence for underlying theoretical phenomena; experimentation contrives to track the truth of hypotheses via the counterfactual sensitivity of the data produced by the specific experimental set-up to those theoretical claims; and for data to count as evidence in favour of a phenomenon, the test that the match between them and the predictions of the hypothesis being examined represents must be severe, although not necessarily entail novel use of the data. I highlight many worries with the GPB data analysis, but through analysing it within this framework, I conclude that the claims of the GPB team are valid. I also indicate that the episode shows how it can be important for working scientists to adopt the more sophisticated approaches advocated by some philosophers rather than relying on more typical epistemological attitudes found in 20th century textbooks. I close by noting that although the knowledge gained may not be unshakeably solid and is open to future revision, it fulfils the strictest demands normally placed by society on the conclusions of investigation.

The effective and efficient management of personal finances is critical for everyone, particularly in a world where uncertainties prevail. Owing to continuous change, new financial challenges frequently confront individuals that culminate ultimately in uncertainties concerning individuals’ financial position and future. Having low levels of debt, an active savings and retirement plan, as well as following an expenditure plan, will lead to financial wellness, which demonstrates an active state of financial wealth. A comprehensive financial plan makes individuals attentive when dealing with financial issues, and acts as a guide when making financial decisions. Owing to insufficient financial literacy and skills, personal financial management is challenging and often results in erroneous financial decisions. Financial knowledge forms the basis for financial skills and competence, which are influenced by personal attitudes in both spending and saving. Therefore, in order to plan effectively, and control and manage financial risks and opportunities in the future, financial skills and abilities are essential. Adequate financial knowledge and skills lead to effective personal financial management and sound financial decisions in the short-term as well as in the long-term. Planning for financial independence should start as early as possible during the financial life cycle, usually at 18 years of age. Students are a rewarding market for financial institutions such as banks, insurance companies, pension funds and brokerage companies, potentially leading the way forward to establish brand-loyalty throughout adulthood. However, the lack of financial management and planning experience, as well as financial literacy and financial skills, make students particularly susceptible to the aggressive marketing tactics of financial institutions, which may be harmful to students’ financial freedom. As such, financial institutions and professionals have to gauge effective ways to convey financial knowledge and product information to a target market to deliver improved financial service as well as understand the relevant consumer behavioural aspects of a target market when developing marketing strategies. Published literature on the South African Generation Y consumer behaviour is limited and none that is focused specifically on attitudes towards personal financial planning, financial literacy and perceived personal financial management skills of the significantly sized black Generation Y cohort. This cohort is defined as individuals born between 1986 and 2005. In South Africa, Generation Y individuals accounted for 38 present of the South African population, with the black Generation Y individuals representing 83 present of this generational cohort. Additionally, the black Generation Y cohort of South Africa account for approximately 32 present of the total population, resulting in a highly salient market segment. Of particular interest to marketers and professionals, including financial institutions and those involved in financial management, especially financial planning, are those individuals attaining tertiary qualifications, and as such they are likely to enjoy higher earnings and a higher social standing, which together is likely to make them opinion leaders and trendsetters amongst their peers. The primary objective of this study was to investigate black Generation Y students’ knowledge of and attitudes towards personal financial management within the South African context. The target population, relevant to this study, was defined as full-time undergraduate black Generation Y students, aged between 18 and 24 years, enrolled at South African registered public higher education institutions (HEIs). From the sampling frame, comprising 23 registered South African public HEIs, one traditional university and one university of technology located in the Gauteng province, were selected using a judgement sampling method. A convenience sample of 400 full-time black Generation Y students, who were enrolled at these two South African HEIs during 2013, was drawn for this study. To conduct this study, a structured format was applied where lecturers of the applicable classes were contacted and permission was requested to carry out the survey. Thereafter, during the scheduled class times of the full-time undergraduate students, hand delivered self-administered questionnaires were distributed for completion, which were collected thereafter. The students’ attitudes towards personal financial planning were measured on a six-point Likert scale, whereby participants were requested to indicate the extent of their agreement/disagreement with items pertaining to personal financial planning. The students’ financial literacy was measured, using multiple-choice questions, whereby the students were asked to choose one of the four alternatives provided. The students’ perceived personal financial management skills were measured on a six-point Likert scale, whereby the participants were requested to indicate the extent of their agreement/disagreement with items pertaining to personal financial management skills. Additionally, certain demographical data were requested from the participants. The findings of this study indicate that South African black Generation Y students exhibit a positive attitude towards personal financial planning, have low levels of financial literacy and perceive themselves as being equipped with having the necessary personal financial management skills. More specifically, students’ attitudes towards estate planning were ranked the highest, whereas attitudes towards the financial planning process were raked the lowest. In terms of financial literacy, students scored the highest in general financial knowledge and the lowest in spending related financial literacy questions. Students’ perceptions towards decision-making skills were rated the highest, whereas stress management skills were rated the lowest. Insights gained from this study will help academics, government, financial institutions and other economic role players understand current black Generation Y consumers’ attitudes towards personal financial planning, their level of financial literacy and their perceived personal financial management skills.
MCom (Business Management), North-West University, Vaal Triangle Campus, 2014

Participatory approaches in knowledge generation have become increasingly important in understanding our environments and integrating human and natural systems. Such approaches have been used to discover new species, address environmental injustices, and develop land management practices. However, frameworks and models used to explore participatory approaches tend to be oversimplified or focus on a specific component. Here, I present an integrated multi-tiered framework to gain insight into how project context and design interact to create outcomes that shape the socio-ecological system. The framework accounts for the nested scales, i.e. ecological, societal, and individual, of both the context and the outcomes. I then demonstrate the utility of the framework by applying it to two case studies in Ecuador: 1.) a climate change monitoring network and 2.) Andean bear para-biologists. Using this framework, it was evident that in both projects, gendered landscapes and how participants engaged were primary factors in shaping outcomes.

Generation Y intar arbetsmarknaden med särskiljande egenskaper, förväntningar och behov, vilket resulterat i ett mer frekvent byte av arbetsplats än vad tidigare generationer gjort. Chefer och ledare måste således utveckla ny förståelse för hur de skall arbeta med att behålla de yngre medarbetarna, för att undgå risken att förlora företagets viktigaste tillgång, kunskapen.Det finns sedan tidigare omfattande forskning kring hur organisationer arbetar med kunskapsbevaring och dessutom mycket forskning kring den nya generationens egenskaper. Vi upplever däremot att det saknas forskning kring hur organisationer skall ta tillvara på den kunskap som riskerar att försvinna när den nya generationen träder in på arbetsmarknaden. Studien syftar till att söka förståelse i vad det är som gör att medarbetare från generation Y är villiga att stanna kvar inom den studerade organisationen tillräckligt länge för att organisationen skall få möjlighet att ta tillvara på kunskapen.Utförandet av studien har skett genom en abduktiv ansats, där teori och empiri samlats allt efter studiens gång. Insamling av empiri har skett i form av en fallstudie, där semistrukturerade intervjuer genomförts på ett multinationellt, tidigare svenskägt, teknik- och produktionsföretag, verksamma i bilindustrin, placerat i Göteborgsområdet. Trots att det visade sig att organisationen i dagsläget är relativt duktig på att ta tillvara på och motivera de yngre medarbetarna, kan de inte ta det förgivet. Vi har kommit fram till ett par viktiga slutsatser som vägledning inför framtiden; det första är att utvecklingsmöjligheter, stimulerande arbetsuppgifter och en balans mellan arbetsliv och privatliv är de primära incitamenten som motiverar medarbetare från generation Y i den studerade organisationen; för det andra skiljer sig generation Ys tidsuppfattning från övriga generationer. Slutligen behöver chefer och ledare identifiera de arbetsuppgifter som är inre motiverade för de yngre medarbetarna, för att på bästa sätt hålla dem tillfredsställda.
Generation Y enters the labor market with distinctive characteristics, expectations and needs, resulting in the tendency to change workplaces more frequently than previous generations. Managers and leaders must thus develop a new understanding of how they should work to keep younger employees in order to avoid the risk of losing the company's most important asset, knowledge.There has been extensive research in the past about how organizations work with knowledge retention. There is also a lot of research about the new generation's characteristics. We find, however, that there is lack of research in how organizations will utilize the knowledge that is likely to disappear when the new generation enters the labor market. The study aims therefore to seek explanation of the factors that can facilitate the conditions within the studied organization to retain the younger generation's employees, so that the company is undergoing the risk of losing essential knowledge.The study has been conducted through an abductive approach, where theory and empirics were collected according to the course of the study. Empirics has been collected in the form of a case study, in which semistructured interviews were conducted at a multinational, former Swedish-owned technology and production company, active in the automotive industry, located in the Gothenburg area. Even though the organization turned out to be relatively good at utilizing and motivating the younger employees at this time, should it not be taken for granted. We have developed a few important conclusions as guidance for the future; Firstly, the primary incentives that motivate employees of generation Y in the study organization are development opportunities, stimulating tasks, a fun work environment and a balance between working life and privacy; And secondly, generation Y's perception of time differs with the other generations; Finally, managers and leaders need to identify the tasks that are motivated internally for the younger employees, to best satisfy them.This thesis is written in Swedish.

The purpose of this thesis project is to bring the application of Cognitive Discovery to an informal type of knowledge. Cognitive Discovery is a term coined by IBM Research to indicate a series of Information Extraction (IE) processes in order to build a knowledge graph capable of representing knowledge from highly unstructured data such as text. Cognitive Discovery is typically applied to a type of formal knowledge, i.e. of the documented text such as academic papers, business reports, patents, etc. While informal knowledge is provided, for example, by recording a conversation within a meeting or through a Power Point presentation, therefore a type of knowledge not formally defined. The idea behind the project is the same as that of the original Cognitive Discovery project, that is the processing of natural language in order to build a knowledge graph that can be interrogated in different ways. This knowledge graph will have an architecture that will depend on the use case, but tends to be a network of entity nodes connected to each other through a certain semantic relationship and to a certain type of nodes containing structural data such as a paragraph, an image or a slide from a presentation. The creation of this graph requires a series of steps, a data processing pipeline that starting from the raw data (in the specific case of the prototype the audio file of the conversation) a series of features are extracted and processed such as entities, semantic relationships between entities, main concepts etc. Once the graph has been created, it is necessary to define an engine for querying and / or generating insights from the knowledge graph; in general the graph database infrastructure also provides a language for querying the graph, however to make the application usable even for those who do not have the technical knowledge necessary to learn the query language, a component has been defined to process the natural language query to query the graph.

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.

Purpose: To identify how young women of generation Y define what apparel quality means for them based on their personal wardrobe content by further considering their knowledge in the field and values in their upbringing. Methodology: The research was conducted through nine semi-structured face to face interviews with young women. The interviews took place in the respondents’ homes as their wardrobe content was required for the purpose of the study. Empirical data: The data was collected through a purposive sampling of Gothenburg-based young women using a recruitment instrument with specific respondent criteria. Conclusion: The findings of this study support various aspects of existing literature on consumers’ quality definition and assessment, however, also highlights the importance of experience with garments as a quality indicator when there is a knowledge gap about garment construction. In accordance with that, durability was the most important quality indicator of wardrobe items, while fabric blend and sensory feel were most useful when assessing quality before purchase. Respondents based their knowledge primarily on own experiences and information gained through media and their mothers as main socialization agents. The relevance of sustainability in quality assessments had previously not been investigated but was revealed in this study.

A new era of wireless networks is evolving, thanks to the significant advances in communications and networking technologies. In parallel, wireless services are witnessing a tremendous change due to increasingly heterogeneous and stringent demands, whose quality of service requirements are expanding in several dimensions, putting pressure on mobile networks. Examples of those services are augmented and virtual reality, as well as self-driving cars. Furthermore, many physical systems are witnessing a dramatic shift into autonomy by enabling the devices of those systems to communicate and transfer control and data information among themselves. Examples of those systems are microgrids, vehicles, etc. As such, the mobile network indeed requires a revolutionary shift in the way radio resources are assigned to those services, i.e., RRM. In RRM, radio resources such as spectrum and power are assigned to users of the network according to various metrics such as throughput, latency, and reliability. Several methods have been adopted for RRM such as optimization-based methods, heuristics and so on. However, these methods are facing several challenges such as complexity, scalability, optimality, ability to learn dynamic environments. In particular, a common problem in conventional RRM methods is the failure to adapt to the changing situations. For example, optimization-based methods perform well under static network conditions, where an optimal solution is obtained for a snapshot of the network. This leads to higher complexity as the network is required to solve the optimization at every time slot. Machine learning constitutes a promising tool for RRM with the aim to address the conflicting objectives, i.e., KPIs, complexity, scalability, etc. In this thesis, we study the use of reinforcement learning and its derivatives for improving network KPIs. We highlight the advantages of each reinforcement learning method under the studied network scenarios. In addition, we highlight the gains and trade-offs among the proposed learning techniques as well as the baseline methods that rely on either optimization or heuristics. Finally, we present the challenges facing the application of reinforcement learning to wireless networks and propose some future directions and open problems toward an autonomous wireless network. The contributions of this thesis can be summarized as follows. First, reinforcement learning methods, and in particular model-free Q-learning, experience large convergence time due to the large state-action space. As such, deep reinforcement learning was employed to improve generalization and speed up the convergence. Second, the design of the state and reward functions impact the performance of the wireless network. Despite the simplicity of this observation, it turns out to be a key one for designing autonomous wireless systems. In particular, in order to facilitate autonomy, agents need to have the ability to learn/adjust their goals. In this thesis, we propose transfer in reinforcement learning to address this point, where knowledge is transferred between expert and learner agents with simple and complex tasks, respectively. As such, the learner agent aims to learn a more complex task using the knowledge transferred from an expert performing a simpler (partial) task.