The final chapter of Luke 24 exhibits a peculiar interest in the body of Jesus, which has historically led to theologians interpreting the passage as mainly concerned with christological matters. The phrase “body of Christ” can be understood in regard to the Eucharist meal, but also as employed by Paul to speak of the community of believers. Could the focus on the body in Luke 24, and its climactic recognition scene with Jesus breaking the bread, invoke symbolism related to the corporate community? If so, it could serve as an elegant narratological transition onto Acts. This paper presents a narrative–rhetorical analysis of Luke24:1–53 and a semantic–rhetorical analysis of Did 9:3–4, investigating the use of the symbolism and the ideas associated with the bread of the Eucharist as the body of Jesus. Both analyses are concerned with the historically situated author–reader and utilizes Relevance theory from the field of linguistics to help determine whether the suggested symbolism maybe at play in Luke 24. The study concludes that the bread of the Eucharist carries symbolism that communicates how God’s Wisdom is shared among, and existing within, the community of believers, and that this understanding underlies the entire narrative of Luke 24—adding an ecclesiological layer to the passage.

Implicit feedback is a form a relevance feedback that is inferred from how users interact with an information retrieval system such as an online search engine. This degree project report describes a method of using implicit feedback to establish relevance judgments and rank products based on their relevance to a specified attribute. The report contains an overview of the benefits and limitations of implicit feedback, as well as a description on how those limitations can be mitigated. A prototype that interpreted user actions as relevance votes and calculat-ed a fair relevance score based on these votes with the help of an algo-rithm was developed. This system was then tested on a website with real users during a limited period of time. The results from the test period were evaluated and the system was concluded to be far from perfect, but that improvements could be made by making adjustments to the algo-rithm. The system performed better when looking at the algorithm’s pre-cision rather than its sensitivity. / Implicit feedback är en sorts relevansfeedback som sammanställs utifrån användares interaktion med ett informationsökningsssystem. Denna examensarbetesrapport beskriver ett sätt att använda implicit feedback för att skapa en bedömning av en produkts relevans till ett angivet attribut. Rapporten innehåller också en överblick av fördelarna och nackdelarna med implicit feedback, samt en beskrivning av hur dessa nackdelar kan hanteras. En prototyp som översatte användarbeteende till olika relevansröster och beräknade ett relevansvärde baserat på dessa relevansröster med hjälp av en algoritm, utvecklades. Denna prototyp testades sedan på en hemsida med verkliga användare under en begränsad tid. Resultatet från denna testperiod analyserades och gav slutsatsen att prototypen inte var perfekt, men att resultaten kunde förbättras med hjälp av finjusteringar av algoritmen. Prototypens precision, med avseende på vilka produkter algoritmen valde ut som relevanta, var dock bättre än dess sensitivitet.

This work investigates the self-organizing representation of temporal data in prototype-based neural networks. Extensions of the supervised learning vector quantization (LVQ) and the unsupervised self-organizing map (SOM) are considered in detail. The principle of Hebbian learning through prototypes yields compact data models that can be easily interpreted by similarity reasoning. In order to obtain a robust prototype dynamic, LVQ is extended by neighborhood cooperation between neurons to prevent a strong dependence on the initial prototype locations. Additionally, implementations of more general, adaptive metrics are studied with a particular focus on the built-in detection of data attributes involved for a given classifcation task. For unsupervised sequence processing, two modifcations of SOM are pursued: the SOM for structured data (SOMSD) realizing an efficient back-reference to the previous best matching neuron in a triangular low-dimensional neural lattice, and the merge SOM (MSOM) expressing the temporal context as a fractal combination of the previously most active neuron and its context. The first SOMSD extension tackles data dimension reduction and planar visualization, the second MSOM is designed for obtaining higher quantization accuracy. The supplied experiments underline the data modeling quality of the presented methods.

The main objective for this study was to explore Storytel users’ selection of audiobooks to examine what search strategies and relevance aspects emerge in the relation to a digital book streaming service mobile application, and to examine how this application aids and influences its users in the search and selection of audiobooks. With the help of previous research on book selection and the concepts of search strategies and tactics, as well as the user-centered perspective on relevance and the concept of the digital paratext, five interviews with audiobook readers were analysed. The two major search approaches were known item search and browsing. The major difference compared to previous research on book selection was the new, additional component of the performing narrator. The narrator did not provide any new relevance aspects to the already identified in earlier studies, but rather, proved to be a component spread out on the already identified relevance aspects. Compared to previous research, the author of this study would, however, like to emphasis the relevance aspect of variation. The fact emerged that the readers used the book(s) read previously as a reference, but not necessarily as something they wanted more of or something similar to, but as something they liked something different from. In this desire for variation, the Storytel’s browsing and recommendations’ functions seemed to fall short and the context of the social world outside the app broke in as a helpful aspect of the audiobook selection.

Increasing population and water conflicts are making water management one of the most important issues of the present world. It has become absolutely necessary to find ways to manage water more efficiently. Technological advancement has introduced various techniques for data acquisition and analysis, and these tools can be used to address some of the critical issues that challenge water resource management. This research used learning machine techniques and information acquired through remote sensing, to solve problems related to soil moisture estimation and crop identification on large spatial scales. In this dissertation, solutions were proposed in three problem areas that can be important in the decision making process related to water management in irrigated systems. A data assimilation technique was used to build a learning machine model that generated soil moisture estimates commensurate with the scale of the data. The research was taken further by developing a multivariate machine learning algorithm to predict root zone soil moisture both in space and time. Further, a model was developed for supervised classification of multi-spectral reflectance data using a multi-class machine learning algorithm. The procedure was designed for classifying crops but the model is data dependent and can be used with other datasets and hence can be applied to other landcover classification problems. The dissertation compared the performance of relevance vector and the support vector machines in estimating soil moisture. A multivariate relevance vector machine algorithm was tested in the spatio-temporal prediction of soil moisture, and the multi-class relevance vector machine model was used for classifying different crop types. It was concluded that the classification scheme may uncover important data patterns contributing greatly to knowledge bases, and to scientific and medical research. The results for the soil moisture models would give a rough idea to farmers/irrigators about the moisture status of their fields and also about the productivity. The models are part of the framework which is devised in an attempt to provide tools to support irrigation system operational decisions. This information could help in the overall improvement of agricultural water management practices for large irrigation systems. Conclusions were reached based on the performance of these machines in estimating soil moisture using remotely sensed data, forecasting spatial and temporal variation of soil moisture and data classification. These solutions provide a new perspective to problem–solving techniques by introducing new methods that have never been previously attempted.

A crucial decision in the real-time management of today’s irrigation systems involves the coordination of diversions and delivery of water to croplands. Since most irrigation systems experience significant lags between when water is diverted and when it should be delivered, an important technical innovation in the next few years will involve improvements in short-term irrigation demand forecasting. The main objective of the researches presented was the development of these critically important models: (1) potential evapotranspiration forecasting; (2) hydraulic model error correction; and (3) estimation of aggregate water demands. These tools are based on statistical machine learning or data-driven modeling. These, of wide application in several areas of engineering analysis, can be used in irrigation and system management to provide improved and timely information to water managers. The development of such models is based on a Bayesian data-driven algorithm called the Relevance Vector Machine (RVM), and an extension of it, the Multivariate Relevance Vector Machine (MVRVM). The use of these types of learning machines has the advantage of avoidance of model overfitting, high robustness in the presence of unseen data, and uncertainty estimation for the results (error bars). The models were applied in an irrigation system located in the Lower Sevier River Basin near Delta, Utah. For the first model, the proposed method allows for estimation of future crop water demand values up to four days in advance. The model uses only daily air temperatures and the MVRVM as mapping algorithm. The second model minimizes the lumped error occurring in hydraulic simulation models. The RVM is applied as an error modeler, providing estimations of the occurring errors during the simulation runs. The third model provides estimation of future water releases for an entire agricultural area based on local data and satellite imagery up to two days in advance. The results obtained indicate the excellent adequacy in terms of accuracy, robustness, and stability, especially in the presence of unseen data. The comparison provided against another data-driven algorithm, of wide use in engineering, the Multilayer Perceptron, further validates the adequacy of use of the RVM and MVRVM for these types of processes.

Information security (IS)-related risks affect global public and private organizations on a daily basis. These risks may be introduced through technical or human-based activities, and can include fraud, hacking, malware, insider abuse, physical loss, mobile device misconfiguration or unintended disclosure. Numerous and diverse regulatory and contractual compliance requirements have been mandated to assist organizations proactively prevent these types of risks. Two constants are noted in these requirements. The first constant is requiring organizations to disseminate security policies addressing risk management through secure behavior. The second constant is communicating policies through IS awareness, training and education (ISATE) programs. Compliance requirements direct that these policies provide instruction about making compliant and positive security decisions to reduce risk. Policy-driven and organizationally-relevant ISATE content is understood to be foundational and critical to prevent security risk. The problem identified for investigation is inconsistency of the terms awareness, training and education as found in security-related regulatory, contractual and policy compliance requirements. Organizations are mandated to manage a rapidly increasing portfolio of inconsistent ISATE compliance requirements generated from many sources. Since there is no one set of common guidance for compliance, organizations struggle to meet global, diverse and inconsistent compliance requirements. Inconsistent policy-related content and instructions, generated from differing sources, may cause incorrect security behavior that can present increased security risk. Traditionally, organizations were required to provide only internally-developed programs, with content left to business, regulatory/contractual, and cultural discretion. Updated compliance requirements now require organizations to disseminate externally-developed content in addition to internally-provided content. This real-world business requirement may cause compliance risks due to inconsistent instruction, guidance gaps and lack of organizational relevance. The problem has been experienced by industry practitioners within the last five years due to increased regulatory and contractual compliance requirements. Prior studies have not yet identified specific impacts of multiple and differing compliance requirements on organizations. The need for organizational relevance in ISATE content has been explored in literature, but the amount of organizationally-relevant content has not been examined in balance of newer compliance mandates.The goal of the research project was to develop a standard content definition and framework. Experienced practitioners responsible for ISATE content within their organizations participated in a survey to validate definitions, content, compliance and organizational relevance requirements imposed on their organizations. Fifty-five of 80 practitioners surveyed (68.75% participation rate) provided responses to one or more sections of the survey. This research is believed to be the first to suggest a standardized content definition for ISATE program activities based on literature review, assessment of existing regulatory, contractual, standard and framework definitions and information obtained from specialized practitioner survey data. It is understood to be the first effort to align and synthesize cross-industry compliance requirements, security awareness topics and organizational relevance within information security awareness program content. Findings validated that multiple and varied regulatory and contractual compliance requirements are imposed on organizations. A lower number of organizations were impacted by third party program requirements than was originally expected. Negative and positive impacts of third party compliance requirements were identified. Program titles and content definitions vary in respondent organizations and are documented in a variety of organizational methods. Respondents indicated high acceptance of a standard definition of awareness, less so for training and education. Organizationally-relevant program content is highly important and must contain traditional and contemporary topics. Results are believed to be an original contribution to information/cyber security practitioners, with findings of interest to academic researchers, standards/framework bodies, auditing/risk management practitioners and learning/development specialists.

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Todays youth faces difficulties when navigating on the World Wide Web where they constantly face an infinite amount of information. Therefore it has never been as important to equip the adults of tomorrow with the information literacy skills they need to be able to navigate and successfully evaluate the information.This study makes the attempt to chart the evaluation strategies amongst upper secondary highschool students by sending out a web-based survey consisting of 21 questions regarding their tendencies of searching and evaluating relevance and credibility of information. The result indicates that students efficiently find the information they are looking for on the Internet, but that the methods of evaluation needs further development. Additionally, the students use the qualities of presented information as the caret of the relevance of information, and the majority evaluate the relevance prior to the credibility of the information they face.The majority of the students only aspect of evaluating the credibility is to confirm it with a second source, without taking into account any underlaying purposes if the information or whether there is any bias between the two sources. This concludes that students needs to work more consciously with evaluation strategies to raise their awareness of how they evaluate information. This can be made by, in school context, make them account for how they do so, to make the analytical method of evaluating credibility a part of their metacognitive information literacy skill.

På uppdrag av sportmediakoncernen ESMG undersöker detta arbete två frågeställningar: Hur kan man utveckla och driftsätta ett rekommendationssystem för nyhetsartiklar? Vilka föroch nackdelar finns med ett eget system jämfört med tredjepartssystem? Arbetet använder Polyas fyra steg som undersökningsmetod, där de fyra stegen anpassas och appliceras på detta projekt. För att kunna besvara den första frågeställningen, skapas initialt en kravspecifikation, som ligger till grund för rekommendationssystemets funktionella och icke-funktionella krav. Utifrån kravspecifikationen, görs en initial fallstudie av Amazon Web Services (AWS), där lämpliga verktyg och tjänster väljs, följt av utformning av en arkitektur för rekommendationssystemet. I en fallstudie av Hockeysveriges webbplats, implementeras sedan arkitekturen med hjälp av AWS och några andra verktyg, som Google Tag Manager och Numeri. Slutligen utvärderas arbetet för kravuppfyllnad. För att kunna besvara den andra frågeställningen, görs en summativ utvärdering av ett antal olika tredjepartssystem för rekommendationer. Genom analys av tredjepartssystemens respektive webbplatser, tas listor på föroch nackdelar fram, ackompanjerat med korta beskrivningar av tjänsterna. Resultaten av den första frågeställningen är en lösning, som visar hur man i praktiken kan utveckla och driftsätta ett rekommendationssystem för nyhetsartiklar. Genom en detaljerad beskrivning alla delar av utvecklingen, fungerar resultaten som en konkret guide i skapande av rekommendationssystem med moderna verktyg. Med avseende på arbetets andra frågeställning, visar resultaten att den stora skillnaden mellan ett egenbyggt system och tredjepartssystem ligger i flexibiliteten, men att ett eget system kommer med mer ansvar, fler beroenden och utan annan funktionalitet som statistik, vilket ofta ingår i tredjepartssystem. / On behalf of the corporate group ESMG, this thesis examines two research questions: How can one develop and deploy a custom recommender system for news articles? What are the pros and cons of having a custom system, compared to third-party systems? The thesis utilizes Polya's four steps as its research method, where the four steps are adapted and applied to this particular project. In order to answer the first research question, an initial requirements specification is created, which provides the basis for the recommender system's functional and non-functional requirements. Based on the requirement specification, an initial case study of Amazon Web Services (AWS) is performed, where appropriate tools and services are selected, followed by the design of an architecture for the recommender system. In a case study of ESMG:s website Hockeysverige, the architecture is then implemented, using AWS and some other necessary tools, such as Google Tag Manager and Numeri. Finally, the implementation is evaluated with respect to requirement compliance. To answer the second research question, a summative evaluation of a number of different third-party recommender systems is performed. By analyzing the third-party systems' websites, a list of pros and cons is presented, accompanied by a brief description of the service. The results of the first research question, is a solution which illustrates how one can, in practice, implement a news recommender system. Through a detailed description of all aspects of development, the results function as a guide in creating recommendation systems using modern tools. With regard to the second research question, the results show that the major difference between a custom system and third-party systems, lies in the flexibility, but a custom system brings more responsibility, more dependencies, and no other functionality, such as statistics, which is often part of third-party systems.