This thesis describes a new method of calculating data priority by using adaptive mobile user and device profiles which change with user location, time of the day, available networks and data access history. The profiles are used for data prefetching, selection of most suitable wireless network and cache management on the mobile device in order to optimally utilize the device�s storage capacity and available bandwidth. Some of the inherent characteristics of mobile devices due to user movements are �non-persistent connection, limited bandwidth and storage capacity, changes in mobile device�s geographical location and connection (eg. connection can be from GPRS to WLAN to Bluetooth). New research is being carried out in making mobile devices work more efficiently by reducing and/or eliminating their limitations. The focus of this research is to propose, evaluate and test a new user profiling technique which specifically caters to the needs of the mobile device users who are required to access large amounts of data, possibly more than the device storage capability during the course of the day or week. This work involves the development of an intelligent user profiling system along with mobile device caching system which will first allocate weight (priority) to the different sets and subsets of the total given data based on user�s location, user�s appointment information, user�s preferences, device capabilities and available networks. Then the profile will automatically change the data weights with user movements, history of cached data access and characteristics of available networks. The Adaptive User and Device Profiles were designed to handle broad range of the issues associated with: �Changing network types and conditions �Limited storage capacity and document type support of mobile devices �Changes in user data needs due to their movements at different times of the day Many research areas have been addressed through this research but the primary focus has remained on the following four core areas. The four core areas are : selecting the most suitable wireless network; allocating weights to different datasets & subsets by integrating user�s movements; previously accessed data; time of the day with user appointment information and device capabilities.

This thesis describes a new method of calculating data priority by using adaptive mobile user and device profiles which change with user location, time of the day, available networks and data access history. The profiles are used for data prefetching, selection of most suitable wireless network and cache management on the mobile device in order to optimally utilize the device�s storage capacity and available bandwidth. Some of the inherent characteristics of mobile devices due to user movements are �non-persistent connection, limited bandwidth and storage capacity, changes in mobile device�s geographical location and connection (eg. connection can be from GPRS to WLAN to Bluetooth). New research is being carried out in making mobile devices work more efficiently by reducing and/or eliminating their limitations. The focus of this research is to propose, evaluate and test a new user profiling technique which specifically caters to the needs of the mobile device users who are required to access large amounts of data, possibly more than the device storage capability during the course of the day or week. This work involves the development of an intelligent user profiling system along with mobile device caching system which will first allocate weight (priority) to the different sets and subsets of the total given data based on user�s location, user�s appointment information, user�s preferences, device capabilities and available networks. Then the profile will automatically change the data weights with user movements, history of cached data access and characteristics of available networks. The Adaptive User and Device Profiles were designed to handle broad range of the issues associated with: �Changing network types and conditions �Limited storage capacity and document type support of mobile devices �Changes in user data needs due to their movements at different times of the day Many research areas have been addressed through this research but the primary focus has remained on the following four core areas. The four core areas are : selecting the most suitable wireless network; allocating weights to different datasets & subsets by integrating user�s movements; previously accessed data; time of the day with user appointment information and device capabilities.

Abstract INDIVIDUAL-TECHNOLOGY FIT: MATCHING INDIVIDUAL CHARACTERISTICS AND FEATURES OF BIOMETRIC INTERFACE TECHNOLOGIES WITH PERFORMANCE By ADRIANE B. RANDOLPH MAY 2007 Committee Chair: Dr. Melody Moore Jackson Major Department: Computer Information Systems The term biometric literally means “to measure the body”, and has recently been associated with physiological measures commonly used for personal verification and security applications. In this work, biometric describes physiological measures that may be used for non-muscularly controlled computer applications, such as brain-computer interfaces. Biometric interface technology is generally targeted for users with severe motor disabilities which may last long-term due to illness or injury or short-term due to temporary environmental conditions. Performance with a biometric interface can vary widely across users depending upon many factors ranging from health to experience. Unfortunately, there is no systematic method for pairing users with biometric interface technologies to achieve the best performance. The current methods to accommodate users through trial-and-error result in the loss of valuable time and resources as users sometimes have diminishing abilities or suffer from terminal illnesses. This dissertation presents a framework and methodology that links user characteristics and features of biometric interface technologies with performance, thus expediting the technology-fit process. The contributions include an outline of the underlying components of capturing and representing individual user characteristics and the impact on the performance of basic interaction tasks using a methodology called biometric user profiling. In addition, this work describes a methodology for objectively measuring an individual’s ability to control a specific biometric interface technology such as one based on measures of galvanic skin response or neural activity. Finally, this work incorporates these concepts into a new individual-technology fit framework for biometric interface technologies stemming from literature on task-technology fit. Key words: user profiles, biometric user profiling, biometric interfaces, fit, individual-technology fit, galvanic skin response, functional near-infrared, brain-computer interface

A oferta de produtos,informação e serviços a partir de perfis de usuários tem tornado os sistemas de recomendação cada vez mais presentes na Web, aumentando a facilidade de escolha e de permanência dos usuários nestes sistemas. Entretanto, existem otimizações a serem feitas principalmente com relação à modelagem do perfil do usuário. Geralmente, suas preferências são modeladas de modo superficial, devido à escassez das informações coletadas,como notas ou comentários, ou devido a informações indutivas que estão suscetíveis a erros. Esta dissertação propõe uma ferramenta de recomendação baseado em interações multimodais, capaz de combinar informações de usuários processadas individualmente por algoritmos de recomendação tradicionais. Nesta ferramenta desenvolveram-se quatro técnicas de combinação afim fornecer aos sistemas de recomendação, subsídios para melhoria na qualidade das predições em diversos domínios. / Providing products, information and services from user profiles has made the recommendation systems to be increasingly present, increasing the ease of selection and retention of users in Webservices. However, there are optimizations to be made in these systems mainly with respect to modeling the user profile. Generally, the preferences are modeled superficially, due to the scarcity of information collected, as notes or comments, or because of inductive information that is susceptible to errors. This work proposes are commendation tool based on multimodal interactions that combines users\' interactions, wich are processed individually by traditional recommendation algorithms. In this tool developed four combination of techniques in order to provide recommendation systems subsidies to improve the quality of predictions.

In this thesis, we present an innovative and novel approach to personalise maps/geo-spatial services for mobile users. With the proposed map personalisation approach, only relevant data will be extracted from detailed maps/geo-spatial services on the fly, based on a user’s current location, preferences and requirements. This would result in dramatic improvements in the legibility of maps on mobile device screens, as well as significant reductions in the amount of data being transmitted; which, in turn, would reduce the download time and cost of transferring the required geo-spatial data across mobile networks. Furthermore, the proposed map personalisation approach has been implemented into a working system, based on a four-tier client server architecture, wherein fully detailed maps/services are stored on the server, and upon a user’s request personalised maps/services, extracted from the fully detailed maps/services based on the user’s current location, preferences, are sent to the user’s mobile device through mobile networks. By using open and standard system development tools, our system is open to everyday mobile devices rather than smart phones and Personal Digital Assistants (PDA) only, as is prevalent in most current map personalisation systems. The proposed map personalisation approach combines content-based information filtering and collaborative information filtering techniques into an algorithmic solution, wherein content-based information filtering is used for regular users having a user profile stored on the system, and collaborative information filtering is used for new/occasional users having no user profile stored on the system. Maps/geo-spatial services are personalised for regular users by analysing the user’s spatial feature preferences automatically collected and stored in their user profile from previous usages, whereas, map personalisation for new/occasional users is achieved through analysing the spatial feature preferences of like-minded users in the system in order to make an inference for the target user. Furthermore, with the use of association rule mining, an advanced inference technique, the spatial features retrieved for new/occasional users through collaborative filtering can be attained. The selection of spatial features through association rule mining is achieved by finding interesting and similar patterns in the spatial features most commonly retrieved by different user groups, based on their past transactions or usage sessions with the system.

A oferta de produtos,informação e serviços a partir de perfis de usuários tem tornado os sistemas de recomendação cada vez mais presentes na Web, aumentando a facilidade de escolha e de permanência dos usuários nestes sistemas. Entretanto, existem otimizações a serem feitas principalmente com relação à modelagem do perfil do usuário. Geralmente, suas preferências são modeladas de modo superficial, devido à escassez das informações coletadas,como notas ou comentários, ou devido a informações indutivas que estão suscetíveis a erros. Esta dissertação propõe uma ferramenta de recomendação baseado em interações multimodais, capaz de combinar informações de usuários processadas individualmente por algoritmos de recomendação tradicionais. Nesta ferramenta desenvolveram-se quatro técnicas de combinação afim fornecer aos sistemas de recomendação, subsídios para melhoria na qualidade das predições em diversos domínios. / Providing products, information and services from user profiles has made the recommendation systems to be increasingly present, increasing the ease of selection and retention of users in Webservices. However, there are optimizations to be made in these systems mainly with respect to modeling the user profile. Generally, the preferences are modeled superficially, due to the scarcity of information collected, as notes or comments, or because of inductive information that is susceptible to errors. This work proposes are commendation tool based on multimodal interactions that combines users\' interactions, wich are processed individually by traditional recommendation algorithms. In this tool developed four combination of techniques in order to provide recommendation systems subsidies to improve the quality of predictions.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-05-07T13:43:19Z No. of bitstreams: 1 Leonardo Dalmina_.pdf: 3273879 bytes, checksum: df157a5701b423e92352934d75e44473 (MD5) / Made available in DSpace on 2018-05-07T13:43:19Z (GMT). No. of bitstreams: 1 Leonardo Dalmina_.pdf: 3273879 bytes, checksum: df157a5701b423e92352934d75e44473 (MD5) Previous issue date: 2018-03-26 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O uso de elementos de design de jogos em contextos não relacionados a jogos, definido como gamificação, está sendo cada vez mais usado para aumentar a motivação e o engajamento dos usuários quando eles precisam executar uma tarefa em um ambiente não relacionado a jogo, como o local de trabalho, a escola ou uma aplicação de software. No entanto, quando a gamificação precisa ser implementada, um desafio enfrentado pelos desenvolvedores é identificar quais elementos do jogos engajarão efetivamente os usuários de um software com base em seus perfis de usuário e características motivacionais. Frequentemente, muitas pesquisas tendem a não incluir ou apenas apoiar os tipos de usuário e fatores motivacionais mais comuns. Em resposta a este desafio, esta dissertação propõe um modelo de gamificação genérico intitulado GamiProM que permite um desenvolvedor de software criar uma solução gamificada adaptativa para qualquer área fazendo uso de ontologias e regras, visando fornecer representação do conhecimento bem como adicionar um valor semântico à informação gerada pela gamificação e gerenciamento de perfil. O modelo é avaliado com um teste de correlação que identifica a existência de qualquer associação entre as necessidades psicológicas básicas dos usuários e suas motivações coletadas com a aplicação gamificada, desenvolvida para implementar o modelo proposto. Os resultados mostraram que as motivações coletadas dos perfis gamificados dos usuários têm uma correlação acima de 80% com as necessidades psicológicas básicas analisadas. / The use of game design elements in non-game contexts, defined as gamification, is being increasingly used to raise the motivation and engagement of users when they have to execute a task in a non-game environment, such as the workplace, the school or a software application. However, when gamification needs to be implemented, a challenge faced by developers is to identify what game elements will effectively engage the users of a software based on their user profiles and motivational characteristics. Often, many researches tend to not include or only support the most common user types and motivational factors. In response to this challenge, this thesis proposes a generic gamification model entitled GamiProM that allows a software developer to build an adaptive gamified solution for any area by making use of ontologies and rules, aiming to provide knowledge representation as well as add a semantic value to the information generated by gamification and profile management. The model is evaluated with a correlation test that identifies the existence of any association between the basic psychological needs of the users and their motivations collected with the gamified application, developed to implement the proposed model. The results showed that the motivations collected from the gamified profiles of the users have a correlation above 80% with the basic psychological needs analyzed.

Ο παγκόσμιος ιστός αποτελεί μία τεράστια αποθήκη πληροφοριών και αναπτύσσεται με τάχιστους ρυθμούς, ενώ η ανθρώπινη ικανότητα να εντοπίζει, να επεξεργάζεται και να αντιλαμβάνεται τις παρεχόμενες πληροφορίες παραμένει πεπερασμένη. Οι μηχανές αναζήτησης διευκολύνουν την αναζήτηση στον παγκόσμιο ιστό και έχουν γίνει αναπόσπαστο κομμάτι της καθημερινής ζωής των χρηστών του διαδικτύου. Οι χρήστες όμως χαρακτηρίζονται από διαφορετικές ανάγκες, προτιμήσεις, ιδιαιτερότητες και κατά την πλοήγησή τους μπορεί να χάσουν τον στόχο της αναζήτησής τους. Η προσωποποίηση στον παγκόσμιο ιστό, δηλαδή η εξατομίκευση των παρεχόμενων αποτελεσμάτων, αποτελεί μία πολλά υποσχόμενη προσέγγιση για την λύση του πληροφοριακού υπερφόρτου, παρέχοντας κατάλληλα προσαρμοσμένες εμπειρίες πλοήγησης. Στα πλαίσια αυτής της διπλωματικής εργασίας αναπτύχθηκε μία μεθοδολογία για την προσωποποίηση των αποτελεσμάτων μίας μηχανής αναζήτησης ώστε αυτά να ανταποκρίνονται στις προτιμήσεις των χρηστών και στα διαιτολογικά τους χαρακτηριστικά. Η μεθοδολογία αναπτύχθηκε σε δύο μέρη: στο εκτός σύνδεσης τμήμα και στο συνδεδεμένο. Στο πρώτο με την χρησιμοποίηση των αρχείων πρόσβασης μίας μηχανής αναζήτησης και των διαιτολογικών χαρακτηριστικών των χρηστών, έγινε εξαγωγή πληροφορίας για τις προτιμήσεις των τελευταίων. Στην συνέχεια με την χρήση μίας οντολογίας που κατασκευάστηκε για τα πλαίσια της διπλωματικής αυτής εργασίας, έγινε σημασιολογική κατηγοριοποίηση των επιλογών των χρηστών και κατασκευάστηκαν τα προφίλ που τους χαρακτηρίζουν. Έπειτα με την χρήση ενός αλγορίθμου ομαδοποίησης οι χρήστες κατηγοριοποιήθηκαν με βάση τα διαιτολογικά τους χαρακτηριστικά και τις επιλογές τους στην μηχανή αναζήτησης. Στο συνδεδεμένο τμήμα ο αλγόριθμος προσωποποίησης εκμεταλλευόμενος την σημασιολογική αντιστοίχιση των αποτελεσμάτων της μηχανής αναζήτησης και τις ομάδες των χρηστών που δημιουργήθηκαν στο εκτός σύνδεσης τμήμα αναδιοργανώνει τα παρεχόμενα από την μηχανή αναζήτησης αποτελέσματα. Η αναδιοργάνωση γίνεται προωθώντας στις υψηλότερες θέσεις των αποτελεσμάτων της μηχανής αναζήτησης τα αποτελέσματα που ταιριάζουν καλύτερα με τις προτιμήσεις και τα χαρακτηριστικά της ομάδας στην οποία εντάσσεται ο χρήστης. Στο τέλος έγιναν πειράματα και εξακριβώθηκαν τα επιθυμητά αποτελέσματα για την προσωποποίηση σύμφωνα με τις σημασιολογικές ομάδες των χρηστών. / The World Wide Web has become a huge data repository and it keeps growing exponentially, whereas the human capability to find, process and understand the provided content remains constant. Search engines facilitate the search process in the World Wide Web and they have become an integral part of the web users' daily lives. However users who are characterized by different needs, preferences and special characteristics, navigate through large Web structures and may lost their goal of inquiry. Web personalization, i.e. the customization of the search engines’ returned results, is one of the most promising approaches for alleviating information overload providing tailored navigation experiences to Web users. The present dissertation presents the methodology which was implemented in order to personalize a search engine’s results for corresponding users’ preferences and dietary characteristics. This methodology was implemented in two parts: the offline and the online part. The first one uses a search engines’ log files and the dietary characteristics of the users in order to extract information for the latter preferences. Afterwards, with the use of an ontology which was created explicitly for this work, semantic profiling of users’ interests was achieved and their corresponding profiles were formed. Then with the use of a clustering algorithm, users’ categorization was made based on their dietary profiles and their preferences in the search engine. In the online part the methodology re-ranks the search engines’ results, based on the semantic characterization of those results and the users’ clusters which were created at the offline part. Re-ranking is achieved by placing those results which match better the interests and the characteristics of the user’s cluster at the top of the list of the search engines’ returned results. Experimental evaluation of the presented methodology shows that the expected objectives from the semantic users’ clustering in search engines are achievable.

<p>När Windows Vista introducerades förändrades mappstrukturen för hur användarprofilen sparas jämfört med tidigare Windowsversioner såsom Windows XP. Denna rapport undersöker kompatibilitetsproblem med Roaming User Profiles mellan äldre och nyare versioner av operativsystemet. Syftet med rapporten var att utreda vilka problem som kan uppstå, vad det gäller Roaming User Profiles, vid en övergång från Windows XP till Windows Vista eller 7. Undersökningen genomfördes med hjälp av laborationer, litteratur och tester för att hitta problemen såväl som lösningar till dem. Resultatet visar att profiler skapade med Windows XP inte följer med till Windows Vista eller 7 utan det skapas sammanlagt två olika profiler, en för det äldre och en för det nyare operativsystemet. Ett skript som kopierar en användares filer från Windows XP till Vista/7 utformades därför. Slutligen presenterades en handlingsplan med olika alternativ vid en övergång från Windows XP till den nyare generationen av operativsystemen i Windowsfamiljen. Folder Redirection är en väl fungerande lösning dock går funktionaliteten som Roaming User Profiles erbjuder förlorad eftersom man endast får en uppmappning av bl.a. ”Mina Dokument” och ”Start Menu”.</p>

Na última década observou-se grande interesse pra o desenvolvimento de técnicas para Recuperação de Imagens Baseada em Conteúdo devido à explosão na quantidade de imagens capturadas e à necessidade de armazenamento e recuperação dessas imagens. A área médica especificamente é um exemplo que gera um grande fluxo de informações, principalmente imagens digitais para a realização de diagnósticos. Porém um problema ainda permanecia sem solução que tratava-se de como atingir a similaridade baseada na percepção do usuário, uma vez que para que se consiga uma recuperação eficaz, deve-se caracterizar e quantificar o melhor possível tal similaridade. Nesse contexto, o presente trabalho de Doutorado visou trazer novas contribuições para a área de recuperação de imagens por contúdo. Dessa forma, almejou ampliar o alcance de consultas por similaridade que atendam às expectativas do usuário. Tal abordagem deve permitir ao sistema CBIR a manutenção da semântica da consulta desejada pelo usuário. Assim, foram desenvolvidos três métodos principais. O primeiro método visou a seleção de características por demanda baseada na intenção do usuário, possibilitando dessa forma agregação de semântica ao processo de seleção de características. Já o segundo método culminou no desenvolvimento de abordagens para coleta e agragação de perfis de usuário, bem como novas formulações para quantificar a similaridade perceptual dos usuários, permitindo definir dinamicamente a função de distância que melhor se adapta à percepção de um determinado usuário. O terceiro método teve por objetivo a modificação dinâmica de funções de distância em diferentes ciclos de realimentação. Para tanto foram definidas políticas para realizar tal modificação as quais foram baseadas na junção de informações a priori da base de imagens, bem como, na percepção do usuário no processo das consultas por similaridade. Os experimentos realizados mostraram que os métodos propostos contribuíram de maneira efetiva para caracterizar e quantificar a similaridade baseada na percepção do usuário, melhorando consideravelmente a busca por conteúdo segundo as expectativas dos usuários / In the last decade techniques for content-based image retrieval (CBIR) have been intensively explored due to the increase in the amount of capttured images and the need of fast retrieval of them. The medical field is a specific example that generates a large flow of information, especially digital images employed for diagnosing. One issue that still remains unsolved deals with how to reach the perceptual similarity. That is, to achieve an effectivs retrieval, one must characterize and quantify the perceptual similarity regarding the specialist in the field. Therefore, the present thesis was conceived tofill in this gap creating a consistent support to perform similarity queries over images, maintaining the semantics of a given query desired by tyhe user, bringing new contribuitions to the content-based retrieval area. To do so, three main methods were developed. The first methods applies a novel retrieval approach that integrates techniques of feature selection and relevance feedback to preform demand-driven feature selection guided by perceptual similarity, tuning the mining process on the fly, according to the user´s intention. The second method culminated in the development of approaches for harvesting and surveillance of user profiles, as well as new formulations to quantify the perceptual similarity of users , allowing to dynamically set the distance function that best fits the perception of a given user. The third method introduces a novel approach to enhance the retrieval process through user feedback and profiling, modifying the distance function in each feedback cycle choosing the best one for each cycle according to the user expectation. The experiments showed that the proposed metods effectively contributed to capture the perceptual similarity, improving in a great extent the image retrieval according to users´expectations