Humans must regularly locate task-relevant objects when interacting with the world around them. Previous research has identified different types of information that the visual system can use to help locate objects in real-world scenes, including low-level image features and scene context. However, previous research using object arrays suggest that there may be another type of information that can guide real-world search: target knowledge. When a participant knows what a target looks like they generate and store a visual representation, or template, of it. This template then facilitates the search process. A complete understanding of real-world search needs to identify how a target template guides search through scenes. Three experiments in Chapter 2 confirmed that a target template facilitates realworld search. By using an eye-tracker target knowledge was found to facilitate both scanning and verification behaviours during search, but not the search initiation process. Within the scanning epoch a target template facilitated gaze directing and shortened fixation durations. These results suggest that target knowledge affects both the activation map, which selects which regions of the scene to fixate, and the evaluation process that compares a fixated object to the internal representation of the target. With the exact behaviours that a target template facilitates now identified, Chapter 3 investigated the role that target colour played in template-guided search. Colour is one of the more interesting target features as it has been shown to be preferred by the visual system over other features when guiding search through object arrays. Two real-world search experiments in Chapter 3 found that colour information had its strongest effect on the gaze directing process, suggesting that the visual system relies heavily on colour information when searching for target-similar regions in the scene percept. Although colour was found to facilitate the evaluation process too, both when rejecting a fixated object as a distracter and accepting it as the target, this behaviour was found to be influenced comparatively less. This suggests that the two main search behaviours – gaze directing and region evaluation – rely on different sets of template features. The gaze directing process relies heavily on colour information, but knowledge of other target features will further facilitate the evaluation process. Chapter 4 investigated how target knowledge combined with other types of information to guide search. This is particularly relevant in real-world search where several sources of guidance information are simultaneously available. A single experiment investigated how target knowledge and scene context combined to facilitate search. Both information types were found to facilitate scanning and verification behaviours. During the scanning epoch both facilitated the eye guidance and object evaluation processes. When both information sources were available to the visual system simultaneously, each search behaviour was facilitated additively. This suggests that the visual system processes target template and scene context information independently. Collectively, the results indicate not only the manner in which a target template facilitates real-world search but also updates our understanding of real-world search and the visual system. These results can help increase the accuracy of future realworld search models by specifying the manner in which our visual system utilises target template information, which target features are predominantly relied upon and how target knowledge combines with other types of guidance information.

There is a continuing demand for higher performance, particularly in the area of scientific and engineering computation. In order to achieve high performance in the context of frequent hardware upgrading, software must be adaptable for portable performance. What is required is an optimising compiler that evolves and adapts itself to environmental change without sacrificing performance. Java has emerged as a dominant programming language widely used in a variety of application areas. However, its architectural independant design means that it is frequently unable to deliver high performance especially when compared to other imperative languages such as Fortran and C/C++. This thesis presents a language- and architecture-independant approach to achieve portable high performance. It uses the mapping notation introduced in the Unified Transformation Framework to specify a large optimisation space. A heuristic random search algorithm is introduced to explore this space in a feedback-directed iterative optimisation manner. It is then extended using a machine learning approach which enables the compiler to learn from its previous optimisations and apply the knowledge when necessary. Both the heuristic random search algorithm and the learning optimisation approach are implemented in a prototype Adaptive Optimisation Framework for Java (AOF-Java). The experimental results show that the heuristic random search algorithm can find, within a relatively small number of atttempts, good points in the large optimisation space. In addition, the learning optimisation approach is capable of finding good transformations for a given program from its prior experience with other programs.

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.

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<p>This dissertation explores the complex process of organizational change, applying a behavioral lens to understand change in processes, products, and search behaviors. Chapter 1 examines new practice adoption, exploring factors that predict the extent to which routines are adopted “as designed” within the organization. Using medical record data obtained from the hospital’s Electronic Health Record (EHR) system I develop a novel measure of the “gap” between routine “as designed” and routine “as realized.” I link this to a survey administered to the hospital’s professional staff following the adoption of a new EHR system and find that beliefs about the expected impact of the change shape fidelity of the adopted practice to its design. This relationship is more pronounced in care units with experienced professionals and less pronounced when the care unit includes departmental leadership. This research offers new insights into the determinants of routine change in organizations, in particular suggesting the beliefs held by rank-and-file members of an organization are critical in new routine adoption. Chapter 2 explores changes to products, specifically examining culling behaviors in the mobile device industry. Using a panel of quarterly mobile device sales in Germany from 2004-2009, this chapter suggests that the organization’s response to performance feedback is conditional upon the degree to which decisions are centralized. While much of the research on product exit has pointed to economic drivers or prior experience, these central finding of this chapter—that performance below aspirations decreases the rate of phase-out—suggests that firms seek local solutions when doing poorly, which is consistent with behavioral explanations of organizational action. Chapter 3 uses a novel text analysis approach to examine how the allocation of attention within organizational subunits shapes adaptation in the form of search behaviors in Motorola from 1974-1997. It develops a theory that links organizational attention to search, and the results suggest a trade-off between both attentional specialization and coupling on search scope and depth. Specifically, specialized unit attention to a more narrow set of problems increases search scope but reduces search depth; increased attentional coupling also increases search scope at the cost of depth. This novel approach and these findings help clarify extant research on the behavioral outcomes of attention allocation, which have offered mixed results.</p> / Dissertation

This research contributes to the field of ontology-based semantic matching techniques and also to the field of Instant Messaging (IM) based enhanced presence. It aims to achieve a mutually beneficial development of two fields through interactions in their use of data and their functionality. With respect to semantic matching this research has developed a collaborative and self-evolutionary approach based on user involvement in order to overcome disadvantages of traditional ontology-based approaches. At the same time, enhanced semantic matching algorithms were also explored and developed to achieve better performance when searching and querying through the ontology. In order to realize this automatic, dynamic and collaborative approach, a Jabber-based IM system was built to support its development with specific data and to evaluate its performance. In the prototype of the system, Computer Science area is selected to be the domain of the ontology in order to demonstrate the practicability of the new approach. With respect to enhanced presence an efficient semantic-based contacts search engine which can feature context-based search ranking is provided to support academic researchers. It is especially designed to help new academic researchers to find potential contacts who share a common research interest. It enriches the IM system's presence information, and helps the user to pick the most suitable contacts and conveniently organize meetings or co-operating with others. Consequently, this research improves the efficiency of users' academic researching, and extends users' relationship radius during their academic research careers. The contributions are particularly highlighted by the comprehensive support during the academic user's self-educational process.

Paper [I] models a game, where two temporary work agencies (TWAs) compete to fill a vacancy at a client firm (CF). They simultaneously choose how much effort to expend, based on their expectation of how good their opponent’s best candidate will be. I then show that this will make the TWAs overconfident, as the rational way of judging your own probability of winning is not looking at the opponents expected best, but comparing how much effort your opponent will expend. Paper [II] examines the misaligned incentives in the temporary work agency sector, where we first look at pure recruiting contracts, that either require payment on delivery, or payment on some specified point in time. We then look at the incentives of recruit-and-rent contracts, where the worker is leased to the client firm. We assume that the better the worker, the higher the probability that the client firm is going to want to hire him/her. If that happens then the TWA will no longer get revenues from said worker, incentivizing the TWA to not always deliver the first match it finds, if it is too good. Lastly we look at how competition can dampen this perverse incentive. Paper [III] models the waiting behavior that can occur if a TWA is contracted to find a worker for a specific time far in the future; the TWA will postpone effort. This behavior is modeled for two types of TWAs; one that is rational and plans ahead, and another that does not plan ahead at all, but instead only looks at the immediate future. I find that the one that only looks at the immediate future starts exerting effort earlier than the planner. After looking at optimal contracts under perfect monitoring and hidden action I provide two extensions. I first show that for the principal to want to delegate search to a rational TWA, the agent has to be better than the CF, by some factor, as it has to make up in efficiency what the principal loses in moral hazard, when the agent waits longer than the principal would like it to. Lastly I prove that it is profit maximizing for the principal to contract one agent and give it a deadline earlier than when the principal would need the worker, and then replace that agent with a competitor if the first one has not succeeded by that earlier deadline. Paper [IV] estimates at the effect of family experience on relative transition probability into the temporary work agency sector. Using register data for all of Sweden we run a bias-reduced logistic regression, where we include various factors that affect the probability of young adults (aged 18-34) entering the sector. This paper ties in to the literature on occupational inheritance, as well as the literature on changing social norms. We find that having had a parent, sibling or partner in the TWA sector increases your probability of entering.

The rapid development of mobile technologies has facilitated users to generate and store files on mobile devices. However, it has become a challenging issue for users to search efficiently and effectively for files of interest in a mobile environment that involves a large number of mobile nodes. In this thesis, file management and retrieval alternatives have been investigated to propose a feasible framework that can be employed on resource-limited devices without altering their operating systems. The file annotation and retrieval framework (FARM) proposed in the thesis automatically annotates the files with their basic file attributes by extracting them from the underlying operating system of the device. The framework is implemented in the JME platform as a case study. This framework provides a variety of features for managing the metadata and file search features on the device itself and on other devices in a networked environment. FARM not only automates the file-search process but also provides accurate results as demonstrated by the experimental analysis. In order to facilitate a file search and take advantage of the Semantic Web Technologies, the SemFARM framework is proposed which utilizes the knowledge of a generic ontology. The generic ontology defines the most common keywords that can be used as the metadata of stored files. This provides semantic-based file search capabilities on low-end devices where the search keywords are enriched with additional knowledge extracted from the defined ontology. The existing frameworks annotate image files only, while SemFARM can be used to annotate all types of files. Semantic heterogeneity is a challenging issue and necessitates extensive research to accomplish the aim of a semantic web. For this reason, significant research efforts have been made in recent years by proposing an enormous number of ontology alignment systems to deal with ontology heterogeneities. In the process of aligning different ontologies, it is essential to encompass their semantic, structural or any system-specific measures in mapping decisions to produce more accurate alignments. The proposed solution, in this thesis, for ontology alignment presents a structural matcher, which computes the similarity between the super-classes, sub-classes and properties of two entities from different ontologies that require aligning. The proposed alignment system (OARS) uses Rough Sets to aggregate the results obtained from various matchers in order to deal with uncertainties during the mapping process of entities. The OARS uses a combinational approach by using a string-based and linguistic-based matcher, in addition to structural-matcher for computing the overall similarity between two entities. The performance of the OARS is evaluated in comparison with existing state of the art alignment systems in terms of precision and recall. The performance tests are performed by using benchmark ontologies and the results show significant improvements, specifically in terms of recall on all groups of test ontologies. There is no such existing framework, which can use alignments for file search on mobile devices. The ontology alignment paradigm is integrated in the SemFARM to further enhance the file search features of the framework as it utilises the knowledge of more than one ontology in order to perform a search query. The experimental evaluations show that it performs better in terms of precision and recall where more than one ontology is available when searching for a required file.

Title: Search problems and search for collisions in hash functions Author: Samuel Čarnoký Department: The Department of Algebra Supervisor: prof. RNDr. Jan Krajíček, DrSc. Supervisor's e-mail address: krajicek@karlin.mff.cuni.cz Abstract: Central points of this work are NP search problems and the existence of reductions amog them in the relativised world. Absolute separation would separate N from NP. In particular, we talk about the problem of finding collisions in hash functions that must exist due to the famous pigeonhole principle. We present a brief introduction into the topic, we define various NP search problems and recall reductions and separations. Reduction of weak version of PHP to a problem of finding a homogeneous subgraph is described and our own results are presented in the form of reduction of another variant of PHP to a problem related to finding paths in a graph. We talk about reducing the task of finding collisions in multiple functions into finding a collision in one function. Keywords: NP search, reductions, pigeonhole principle, oracles

Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.