We have never stopped in the pursuit of science. Standing on the shoulders of the giants, we gradually make our path to build a systematic and testable body of knowledge to explain and predict the universe. Emerging from researchers’ interactions and self-organizing behaviors, scientific communities feature intensive collaborative practice. Indeed, the era of lone genius has long gone. Teams have now dominated the production and diffusion of scientific ideas. In order to understand how collaboration shapes and evolves organizations as well as individuals’ careers, this dissertation conducts analyses at both macroscopic and microscopic levels utilizing large-scale scholarly data. As self-organizing behaviors, collaborations boil down to the interactions among researchers. Understanding collaboration at individual level, as a result, is in fact a preliminary and crucial step to better understand the collective outcome at group and organization level. To start, I investigate the role of research collaboration in researchers’ careers by leveraging person-organization fit theory. Specifically, I propose prospective social ties based on faculty candidates’ future collaboration potential with future colleagues, which manifests diminishing returns on the placement quality. Moving forward, I address the question of how individual success can be better understood and accurately predicted utilizing their collaboration experience data. Findings reveal potential regularities in career trajectories for early-stage, mid-career, and senior researchers, highlighting the importance of various aspects of social capital. With large-scale scholarly data, I propose a data-driven analytics approach that leads to a deeper understanding of collaboration for both organizations and individuals. Managerial and policy implications are discussed for organizations to stimulate interdisciplinary research and for individuals to achieve better placement as well as short and long term scientific impact. Additionally, while analyzed in the context of academia, the proposed methods and implications can be generalized to knowledge-intensive industries, where collaboration are key factors to performance such as innovation and creativity.

Being First generation born Mexican American I am looking into the blend of the two cultures and the disparity between them. The border is the core of my investigation; by traveling across the border I have become conscious of the differences among both sides and duality within myself. My identity has developed from a synthesis of these two cultures, and my wok explores these two factions that cannot be one without the other. fusion is apparent in my self-portraits where I dress up with the colors from both sides of the border. But I also take a personal look into understanding the history and identity of each nation. I create a juxtaposition with these two identities that become one and explore the social, cultural, and political issues we face in the everyday. I recreate my “investigation,” by trying to dig deeper, exposing the layers, and facing my own identity crisis in the process.

The availability of large-scale data on the Web motivates the development of automatic algorithms to analyze topics and to identify relationships between topics. Various approaches have been proposed in the literature. Most focus on specific topics, mainly those representing people, with little attention to topics of other kinds. They are also less flexible in how they represent topics. In this thesis we study existing methods as well as describe a different approach, based on profiles, for representing topics. A Topic Profile is analogous to a synopsis of a topic and consists of different types of features. Profiles are flexible to allow different combinations of features to be emphasized and are extensible to support new features to be incorporated without having to change the underlying logic. More generally, topic profiles provide an abstract framework that can be used to create different types of concrete representations for topics. Different options regarding the number of documents considered for a topic or types of features extracted can be decided based on requirements of the problem as well as the characteristics of the data. Topic profiles also provide a framework to explore relationships between topics. We compare different methods for building profiles and evaluate them in terms of their information content and their ability to predict relationships between topics. We contribute new methods in term weighting and for identifying relevant text segments in web documents. In this thesis, we present an application of our profile-based approach to explore social networks of US senators generated from web data and compare with networks generated from voting data. We consider both general networks as well as issue-specific networks. We also apply topic profiles for identifying and ranking experts given topics of interest, as part of the 2007 TREC Expert Search task. Overall, our results show that topic profiles provide a strong foundation for exploring different topics and for mining relationships between topics using web data. Our approach can be applied to a wide range of web knowledge discovery problems, in contrast to existing approaches that are mostly designed for specific problems.

An informational source where teachers and teacher-candidates could gain a functional knowledge of contingency management techniques had not been developed. A programed text was written to provide this source. Five teachers and teacher-candidates were exposed to the text for a period of three days. As a result of this exposure, learning gains, significant at the 0.01 level, were found in the students' abilities (a) to write behavioral prescriptions and (b) to write the principles of contingency management when presented with open-ended questions pertaining to these principles.

Many universities and colleges have a considerable number of students enroll whose entrance examination scores indicate deficiencies in high school and pre-high school mathematics. The purpose of this study was to investigate the effectiveness of programed texts, as compared with conventional procedures, for teaching basic mathematics to remedial mathematics students. The effects of mental ability, study skills and attitudes on achievement through the use of programed texts also carne under investigation. For one quarter an experimental group of 73 subjects used a set of three linear programed texts for their sole mode of instruction in mathematics. A control group of 58 subjects were taught the same content material by traditional lecture-discussion procedures. At the beginning of the 1965 Fall quarter, the subjects were given a mental ability test, a study skills and attitude survey and a standardized mathematics pretest. The subjects' scores on these three measures served as covariates for a covariance analysis of the mathematics posttest scores. Analysis of variance showed no significant differences (NSD) between the means of the experimental and control groups scores on the mathematics posttest. However, analysis of covariance showed a significant mean score difference in favor of the experimental group for the questions pertaining to mathematics fundamentals (computation) and NSD for the questions pertaining to reasoning (problem solving) . The experimental group went from a mathematics pretest mean score grade placement of about 8.5 to a mathematics posttest mean score grade placement of about 10.5. The control group went from a mathematics pretest mean score grade placement of about 9.0 to a mathematics posttest mean score grade placement of about 10.5. The two different teaching methods did not bring about significant differences in the variability of the subjects' mathematics test scores. The correlation between mental ability scores and mathematics test scores was moderate (about .50). As would be expected, the correlation between mathematics pretest and posttest scores was high (about .80). The correlation between study skills scores and mathematics test scores was low (about .26 for the experimental group and about .04 for the control group). Individual rates of progress, made possible by programed texts, enabled a considerable number of students in the experimental group to complete the equivalent of a quarter's study in basic mathematics in less than a quarter's time. A survey questionnaire concerning interest and attitude of the subjects toward mathematics, programed instruction and the remedial mathematics course was given at the end of the quarter in which the study was conducted. Chi-square analysis of the responses to the questions generally showed the subjects' responses were independent of the type of instruction they had received. The subjects were also asked to comment on what they thought were the most favorable characteristics of the course and what they thought were the least favorable characteristics of the course. The favorable comment listed most frequently by control subjects pertained to the slow group pace. They explained that it was the slow pace, coupled with a very understanding instructor, which enabled them to learn mathematics which they had missed in high school. However, it was also the slow group pace which drew the most number of control subjects' responses as to what they liked least about the course. The favorable comment listed most frequently by the subjects who learned from programed texts pertained to the opportunity the programed texts had provided for each student to progress at his own rate. The unfavorable comment listed most frequently by the programed learning group pertained to no teacher-student interaction and no class discussion when programed texts were used.

Performing Named Entity Recognition on ancient documents is a time-consuming, complex and error-prone manual task. It is a prerequisite though to being able to identify related documents and correlate between named entities in distinct sources, helping to precisely recreate historic events. In order to reduce the manual effort, automated classification approaches could be leveraged. Classifying terms in ancient documents in an automated manner poses a difficult task due to the sources’ challenging syntax and poor conservation states. This thesis introduces and evaluates approaches that can cope with complex syntactial environments by using statistical information derived from a term’s context and combining it with domain-specific heuristic knowledge to perform a classification. Furthermore this thesis demonstrates how metadata generated by these approaches can be used as error heuristics to greatly improve the performance of workflow systems for digitizations of early documents. / Die Extraktion von Metadaten aus historischen Dokumenten ist eine zeitintensive, komplexe und höchst fehleranfällige Tätigkeit, die üblicherweise vom menschlichen Experten übernommen werden muss. Sie ist jedoch notwendig, um Bezüge zwischen Dokumenten herzustellen, Suchanfragen zu historischen Ereignissen korrekt zu beantworten oder semantische Verknüpfungen aufzubauen. Um den manuellen Aufwand dieser Aufgabe reduzieren zu können, sollen Verfahren der Named Entity Recognition angewendet werden. Die Klassifikation von Termen in historischen Handschriften stellt jedoch eine große Herausforderung dar, da die Domäne eine hohe Schreibweisenvarianz durch unter anderem nur konventionell vereinbarte Orthographie mit sich bringt. Diese Arbeit stellt Verfahren vor, die auch in komplexen syntaktischen Umgebungen arbeiten können, indem sie auf Informationen aus dem Kontext der zu klassifizierenden Terme zurückgreifen und diese mit domänenspezifischen Heuristiken kombinieren. Weiterhin wird evaluiert, wie die so gewonnenen Metadaten genutzt werden können, um in Workflow-Systemen zur Digitalisierung historischer Handschriften Mehrwerte durch Heuristiken zur Produktionsfehlererkennung zu erzielen.

Mobile banking is an application of mobile computing which provides customers with the support needed to be able to bank anywhere, anytime using a mobile handheld device and a mobile service such as text messaging (SMS). Mobile banking removes space and time limitations from banking activities such as checking account balances, or transferring money from one account to another. In recent research and studies it was found that while mobile banking and more specifically SMS-based mobile banking applications have become popular in some countries and regions, they were still not widely used. This study identifies and investigates the factors which influence customers’ decision to use a specific form of mobile banking, and specifically focuses on the evaluation of SMS-based mobile banking in the context of New Zealand. The research model includes the basic concepts of the Technology Acceptance Model (TAM), as well as some constructs derived through a focus group discussion. The model is tested to determine its predictive power with respect to individual’s behaviour when considering the use of SMS-based mobile banking. A survey questionnaire was developed and employed to collect data from 250 AUT university students in New Zealand. The results of the data analysis contributes to the body of knowledge in the area by demonstrating that context specific factors such as service quality and service awareness are influencing user perceptions about the usefulness of SMS mobile banking which in turn affect intention to use and adoption. Secondly, the study demonstrates, on the example of SMS-based mobile banking, how a hybrid approach involving qualitative data collection and a subsequent quantitative survey can help investigate how user perceptions about usefulness and ease of use are formed. Although the study has its limitations, the implications of the results allow providing practical recommendations to the banking industry, and directions for further work.

The research examined the relationships between the morphological structures and features of Arabic and English verbs. An examination of the corpus in the research, which compiled 2000 verbs and their translation from various texts, showed that correlation between verb aspects are minimal. Therefore it is admissible to say that there is no correlation between morphological aspects and categories of Arabic and English verbs. Through analysis of Arabic verb patterns, the research demonstrated the primacy of a morpheme as one of the significant linguistic structural units that incorporates semantic and syntactic features, and also as a pivotal translation unit. The data analysis also signified that genre variation in Arabic is well established and can be clearly identified through morphological aspects of the verb and their distribution in text types. / Master of Arts (Hons)

The thesis Mapping the Self-Portrait: Navigating Identity and Autobiography in Visual Art is a practical project. It explores the relationship between autobiography and self- portraiture, and how these notions of the self can be represented in visual art. The exhibition 360 Potential Truisms forms the major component in this thesis, and is accompanied by a written exegesis. This exegesis explores notions of the self-portrait and autobiography in relation to identity, with focus on a post-structural approach to fragmentation and movement. Artworks have been developed to reflect a shift towards an idea of the fragmented self, involving drawing, photography, and text to allow a constantly changing interpretation of self-portraiture.

All practical text search systems use inverted indexes to quickly resolve user queries. Offline index construction algorithms, where queries are not accepted during construction, have been the subject of much prior research. As a result, current techniques can invert virtually unlimited amounts of text in limited main memory, making efficient use of both time and disk space. However, these algorithms assume that the collection does not change during the use of the index. This thesis examines the task of index maintenance, the problem of adapting an inverted index to reflect changes in the collection it describes. Existing approaches to index maintenance are discussed, including proposed optimisations. We present analysis and empirical evidence suggesting that existing maintenance algorithms either scale poorly to large collections, or significantly degrade query resolution speed. In addition, we propose a new strategy for index maintenance that trades a strictly controlled amount of querying efficiency for greatly increased maintenance speed and scalability. Analysis and empirical results are presented that show that this new algorithm is a useful trade-off between indexing and querying efficiency. In scenarios described in Chapter 7, the use of the new maintenance algorithm reduces the time required to construct an index to under one sixth of the time taken by algorithms that maintain contiguous inverted lists. In addition to work on index maintenance, we present a new technique for accumulator pruning during ranked query evaluation, as well as providing evidence that existing approaches are unsatisfactory for collections of large size. Accumulator pruning is a key problem in both querying efficiency and overall text search system efficiency. Existing approaches either fail to bound the memory footprint required for query evaluation, or suffer loss of retrieval accuracy. In contrast, the new pruning algorithm can be used to limit the memory footprint of ranked query evaluation, and in our experiments gives retrieval accuracy not worse than previous alternatives. The results presented in this thesis are validated with robust experiments, which utilise collections of significant size, containing real data, and tested using appropriate numbers of real queries. The techniques presented in this thesis allow information retrieval applications to efficiently index and search changing collections, a task that has been historically problematic.