ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / In this paper we investigate the different stages that allow us to create a model that would provide a better understanding of what happens on certain parameters that measure physical quantities related to the behavior of both, burst and reaction, unmanned aircraft as well as unmanned helicopters based on a data transmission to land via radio modem.

We have examined the technique of persistent spectral holeburning as a method for analog optical data storage. Two types of materials were examined from a theoretical standpoint, ones which exhibit photochemical holeburning (PHB) and ones which exhibit nonphotochemical holeburning (NPHB). We have presented the conditions under which a PHB material shows a linear relation between hole depth and exposure intensity or exposure time. Also we show that a NPHB material has no such condition. We conclude that a PHB material may be useful for analog optical data storage, while a NPHB material would not. Experiments were conducted with a NPHB material, R' color centers in LiF, to support the NPHB analysis.

A software solution was developed for remote data acquisition applications, i.e., applications where line power is unavailable. The solution was developed in response to a lack of suitable software for environmental measurements using battery powered computers. The software solution is in the form of a program translator that creates programs dedicated to specific remote data acquisition applications from a dialect governed by specific rules. A methodology is presented for defining real-time measurement applications based upon three time components: a scanning interval, an average period, and an averaging interval. The software solution is termed ADAPT, an acronym for 'All-purpose Data Acquisition Program Translator'. ADAPT was written for a Hewlett-Packard hand-held computer, the HP-71, and a Hewlett-Packard data acquisition system, the HP-3421A. The methodology and algorithms may be applied to other computer and data acquisition systems.

Increasingly, workplace information seeking takes place in digital information environments and is reliant upon search systems. Existing systems are designed to retrieve information that is relevant to the query, but are not capable of identifying information that is well-suited to the context and situation of a search. This is a problem for professionals who often are searching for a small amount of useful information that can be applied to a problem or task, and have limited time to browse through large sets of results. This inability of search systems to discriminate between relevant and useful documents is one of the core problems in information retrieval. In this dissertation, I address this problem by studying the role that contextual factors play in determining how a group of professionals searches for and selects information. The central question concerns the nature of the relationships between these contextual factors, specifically between the genres in the document collection and the tasks of the searcher, with an aim to exploit such relationships to improve workplace information retrieval. Research was conducted through multiple studies in three phases, moving from an exploratory study of workplace information behaviour to a controlled experimental user study. Findings confirm that workplace context shapes search behaviour. This relationship is modeled as a set of key contextual factors and sets of context-dependent access constraints, preferred document characteristics, and search strategies. Among the contextual factors identified, work tasks and information tasks were found to be significantly associated with document genres. This task-genre relationship was modeled as a matrix of associations between domain-specific task and genre taxonomies and successfully implemented as a filtering component in a workplace search system. This is the first major study of the relationship between task and genre in information seeking and of its application to information retrieval systems.

Understanding interactions among words is fundamental for natural language applications. However, many statistical NLP methods still ignore this important characteristic of language. For example, information retrieval models still assume word independence. This work focuses on the creation of lexical affinity models and their applications to natural language problems. The thesis develops two approaches for computing lexical affinity. In the first, the co-occurrence frequency is the calculated by point estimation. The second uses parametric models for co-occurrence distances. For the point estimation approach, we study several alternative methods for computing the degree of affinity by making use of point estimates for co-occurrence frequency. We propose two new point estimators for co-occurrence and evaluate the measures and the estimation procedures with synonym questions. In our evaluation, synonyms are checked directly by their co-occurrence and also by comparing them indirectly, using other lexical units as supporting evidence. For the parametric approach, we address the creation of lexical affinity models by using two parametric models for distance co-occurrence: an independence model and an affinity model. The independence model is based on the geometric distribution; the affinity model is based on the gamma distribution. Both fit the data by maximizing likelihood. Two measures of affinity are derived from these parametric models and applied to the synonym questions, resulting in the best absolute performance on these questions by a method not trained to the task. We also explore the use of lexical affinity in information retrieval tasks. A new method to score missing terms by using lexical affinities is proposed. In particular, we adapt two probabilistic scoring functions for information retrieval to allow all query terms to be scored. One is a document retrieval method and the other is a passage retrieval method. Our new method, using replacement terms, shows significant improvement over the original methods.

Effective methods for information access are of the greatest importance for our modern lives – particularly with respect to handheld devices. Personalisation is one such method which models a user’s characteristics to deliver content more focused to the user’s needs. The emerging area of sophisticated mobile computing devices has started to inspire new forms of personalised systems that include aspects of the person’s contextual environment. This thesis seeks to understand the role of personalisation and context, to evaluate the effectiveness of context for content personalisation and to investigate the event and map content domain for mobile usage. The work presented in this thesis has three parts: The first part is a user experiment on context that investigated the contextual attributes of time, location and interest, with respect to participants’ perception of their usefulness. Results show highly dynamic and interconnected effects of context on participants’ usefulness ratings. In the second part, these results were applied to create a predictive model of context that was related to attribution theory and then combined with an information retrieval score to create a weighted personalisation model. In the third part of this work, the personalisation model was applied in a mobile experiment. Participants solved situational search tasks using a (i) non-personalized and a (ii) personalized mobile information system, and rating entertainment events based on usefulness. Results showed that the personalised system delivered about 20% more useful content to the mobile user than the non-personalised system, with some indication for reduced search effort in terms of time and the amount of queries per task. The work presented provides evidence for the promising potential of context to facilitate personalised information delivery to users of mobile devices. Overall, it serves as an example of an investigation into the effectiveness of context from multiple angles and provides a potential link to some of the aspects of psychology as a potential source for a deeper understanding of contextual processes in humans.

Doctor en Ciencias, Mención Computación / La Web es un gran espacio de información donde muchos recursos como documentos, imágenes u otros contenidos multimediales pueden ser accesados. En este contexto, varias tecnologías de la información han sido desarrolladas para ayudar a los usuarios a satisfacer sus necesidades de búsqueda en la Web, y las más usadas de estas son los motores de búsqueda. Los motores de búsqueda permiten a los usuarios encontrar recursos formulando consultas y revisando una lista de respuestas. Uno de los principales desafíos para la comunidad de la Web es diseñar motores de búsqueda que permitan a los usuarios encontrar recursos semánticamente conectados con sus consultas. El gran tamaño de la Web y la vaguedad de los términos más comúnmente usados en la formulación de consultas es un gran obstáculo para lograr este objetivo. En esta tesis proponemos explorar las selecciones de los usuarios registradas en los logs de los motores de búsqueda para aprender cómo los usuarios buscan y también para diseñar algoritmos que permitan mejorar la precisión de las respuestas recomendadas a los usuarios. Comenzaremos explorando las propiedades de estos datos. Esta exploración nos permitirá determinar la naturaleza dispersa de estos datos. Además presentaremos modelos que nos ayudarán a entender cómo los usuarios buscan en los motores de búsqueda. Luego, exploraremos las selecciones de los usuarios para encontrar asociaciones útiles entre consultas registradas en los logs. Concentraremos los esfuerzos en el diseño de técnicas que permitirán a los usuarios encontrar mejores consultas que la consulta original. Como una aplicación, diseñaremos métodos de reformulación de consultas que ayudarán a los usuarios a encontrar términos más útiles mejorando la representación de sus necesidades. Usando términos de documentos construiremos representaciones vectoriales para consultas. Aplicando técnicas de clustering podremos determinar grupos de consultas similares. Usando estos grupos de consultas, introduciremos métodos para recomendación de consultas y documentos que nos permitirán mejorar la precisión de las recomendaciones. Finalmente, diseñaremos técnicas de clasificación de consultas que nos permitirán encontrar conceptos semánticamente relacionados con la consulta original. Para lograr esto, clasificaremos las consultas de los usuarios en directorios Web. Como una aplicación, introduciremos métodos para la manutención automática de los directorios.

This investigation identifies end-user cognitive heuristics that facilitate judgment and evaluation during information retrieval (IR) system interactions. The study extends previous research surrounding relevance as a key construct for representing the value end-users ascribe to items retrieved from IR systems and the perceived effectiveness of such systems. The Lens Model of user cognition serves as the foundation for design and interpretation of the study; earlier research in problem solving, decision making, and attitude formation also contribute to the model and analysis. A self reporting instrument collected evaluative responses from 32 end-users related to 1432 retrieved items in relation to five characteristics of each item: topical, pertinence, utility, systematic, and motivational levels of relevance. The nominal nature of the data collected led to non-parametric statistical analyses that indicated that end-user evaluation of retrieved items to resolve an information problem at hand is most likely a multi-stage process. That process appears to be a cognitive progression from topic to meaning (pertinence) to functionality (use). Each step in end-user evaluative processing engages a cognitive hierarchy of heuristics that includes consideration (of appropriate cues), differentiation (the positive or negative aspects of those cues considered), and aggregation (the combination of differentiated cue aspects needed to render an evaluative label of the item in relation to the information problem at hand). While individuals may differ in their judgments and evaluations of retrieved items, they appear to make those decisions by using consistent heuristic approaches.

Even though information scientists generally recognize that relevance judgments are multidimensional and dynamic, there is still discussion and debate regarding the degree to which certain internal (cognition, personality) and external (situation, social relationships) factors affect the use of criteria in reaching those judgments. Much of the debate centers on the relationship of those factors to the criteria and reliable methods for measuring those relationships. This study researched the use of relevance criteria to select an information source by undergraduate students whose task it is to create a course schedule for a semester. During registration periods, when creating their semester schedules, students filled out a two-part questionnaire. After completion of the questionnaire the students completed a Myers-Briggs Type Indicator instrument in order to determine their personality type. Data was analyzed using one-way ANOVAS and Chi-Square. A positive correlation exists between personality type as expressed by the MBTI and the information source selected as most important by the subject. A correlation also exists between personality type and relevance criteria use. The correlation is stronger for some criteria than for others. Therefore, one can expect personality type to have an effect on the use of relevance criteria while selecting information sources.

Military aircraft maintenance methods are moving from practices based on hard-time inspection and replacement intervals to one of Condition Based Maintenance (CBM). Benefits of CBM are the minimization of maintenance efforts and component replacement along with an increase in readiness and safety. Goodrich has developed the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS) for the practices of CBM in helicopters. Great benefits have been realized with the IMD-HUMS system in regards to several maintenance practices, readiness, and safety. However, the total potential of the system in regards to these benefits for the multiple components observed by the IMDHUMS is not yet achieved. The IMD-HUMS gathers a great deal of pertinent, important data on the condition of multiple components and systems, but the meaning and full potential of all this data is not yet fully realized. The purpose of this research is to conduct and document a statistical analysis of IMD-HUMS produced data. Statistical applications of data mining, regression and classification trees are explored. The approaches used in the exploration of the IMD-HUMS acquired data sets are based on six electrical generators which displayed degradation or failure and hence required maintenance actions compared with sixty others which did not.