Customer feedback is essential for improving a company's products or services and increasing customer satisfaction. Despite its value, user involvement faces many challenges, such as low user motivation and feedback with missing context information. To increase the chance of collecting user feedback, the process must be fast, easy, and well-integrated into the system or product.  In this thesis, a suitable way of designing a high-fidelity (Hi-Fi) prototype of a reactive feedback feature that improves the ease of submitting end-user feedback and enriching it with sufficient information to streamline the interpretation and handling process has been investigated. This was done by following the Design Thinking method and its five phases. In the first phase of the process, Empathize, information about the end-users' and the product teams' pain points, wants, and needs regarding the current feedback process were gathered through interviews. A literature study and research of existing feedback tools were also conducted to build a broader knowledge about the subject. The collected data was organized and analyzed during the second phase, Define, through affinity diagramming, a customer journey map, and defining user need statements. In the third phase, Ideate, a wide range of design solutions to the identified problems and user need statements were generated during a Crazy 8 workshop and initial sketching. The two final phases, Prototype and Test, consisted of three iterations of creating and evaluating prototypes through an expert review, a usability test, a heuristic evaluation, and a final usability test.  The two major identified pain points were the product team expressed that customer feedback is often difficult to interpret due to missing context information, and that end users have to go through a long and laborious procedure to provide feedback. End-users wanted an easy and fast way to submit a wide range of feedback to have some impact on the product's future improvement, and the product team wanted to collect as much sufficient information as possible to facilitate the interpretation and handling process. The study explores the challenges of including predefined feedback options and a prioritization feature to enhance the interpretation and management of feedback while maintaining ease of use. The prototype includes the possibility to attach annotated screenshots and screen recordings and automatically collects contextual data to address the lack of information and simplify and speed up the feedback process for end-users. The final usability test resulted in positive feedback and showed that the suggested feedback feature enables end-users to provide feedback quickly and easily while providing the product team with the most necessary information. The result also gives a first indication that the feedback feature will increase the end-user involvement in the product's future development.

Cette thèse traite le problème d’extension d’annotation d’images. En effet, la croissance rapide des archives de contenus visuels disponibles a engendré un besoin en techniques d’indexation et de recherche d’information multimédia. L’annotation d’images permet l’indexation et la recherche dans des grandes collections d’images d’une façon facile et rapide. À partir de bases d’images partiellement annotées manuellement, nous souhaitons compléter les annotations de ces bases, grâce à l’annotation automatique, pour pouvoir rendre plus efficaces les méthodes de recherche et/ou classification d’images. Pour l’extension automatique d’annotation d’images, nous avons utilisé les modèles graphiques probabilistes. Le modèle proposé est un mélange de distributions multinomiales et de mélanges de Gaussiennes où nous avons combiné des caractéristiques visuelles et textuelles. Pour réduire le coût de l’annotation manuelle et améliorer la qualité de l’annotation obtenue, nous avons intégré des retours utilisateur dans notre modèle. Les retours utilisateur ont été effectués en utilisant l’apprentissage dans l’apprentissage, l’apprentissage incrémental et l’apprentissage actif. Pour combler le problème du fossé sémantique et enrichir l’annotation d’images, nous avons utilisé une hiérarchie sémantique en modélisant de nombreuses relations sémantiques entre les mots-clés d’annotation. Nous avons donc présenté une méthode semi-automatique pour construire une hiérarchie sémantique à partie d’un ensemble de mots-clés. Après la construction de la hiérarchie, nous l’avons intégré dans notre modèle d’annotation d’images. Le modèle obtenu avec la hiérarchie est un mélange de distributions de Bernoulli et de mélanges de Gaussiennes / This thesis deals the problem of image annotation extension. Indeed, the fast growth of available visual contents has led a need for indexing and searching of multimedia information methods. Image annotation allows indexing and searching in a large collection of images in an easy and fast way. We wish, from partially manually annotated images databases, complete automatically the annotation of these sets, in order to make methods of research and / or classification of images more efficient. For automatic image annotation extension, we use probabilistic graphical models. The proposed model is based on a mixture of multinomial distributions and mixtures of Gaussian where we have combined visual and textual characteristics. To reduce the cost of manual annotation and improve the quality of the annotation obtained, we have incorporated user feedback into our model. User feedback was done using learning in learning, incremental learning and active learning. To reduce the semantic gap problem and to enrich the image annotation, we use a semantic hierarchy by modeling many semantic relationships between keywords. We present a semi-automatic method to build a semantic hierarchy from a set of keywords. After building the hierarchy, we integrate it into our image annotation model. The model obtained with this hierarchy is a mixture of Bernoulli distributions and Gaussian mixtures

Background: Risky security behaviour displayed by end-users has the potential to leave devices vulnerable to compromise, despite the availability of security tools designed to aid users in defending themselves against potential online threats. This indicates a need to modify the behaviour of end-users, allowing them to consider the security implications of their actions online. Previous research has indicated affective feedback may serve as a successful method of educating users about risky security behaviours. Thus, by influencing end-users via affective feedback it may be possible to engage users, improving their security awareness. Aims: Develop and apply knowledge of monitoring techniques and affective feedback, establishing if this changes users’ awareness of risky security behaviour in the context of a browser-based environment. Methodology: The methodology employs the use of log files derived from the monitoring solution, and information provided by users during the experiments. Questionnaire data was compared against log files and information provided during experiments, providing an overall quantitative approach. Results: In the case of the log files and questionnaires, participants were found to have engaged in instances of risky security behaviours, which they were unaware of, and this indicated a low-level of awareness of risky security behaviour. Whilst the results indicate the affective feedback did not make a difference to behaviour during the course of the experiments, participants felt that the affective feedback delivered had an impact, raising their security awareness, encouraging them to learn about online security. Conclusions: This body of research has made a novel contribution to the field of affective feedback and usable security. Whilst the results indicate the affective feedback made no difference to behaviour, users felt it had an impact on them, persuading them to consider their security behaviours online, and encouraging them to increase their knowledge of risky security behaviours. The research highlights the potential application of affective feedback in the field of usable security. Future work seeks to explore different ways in which affective feedback can be positioned on-screen, and how feedback can be tailored to target specific groups, such as children, or elderly people, with the aim of raising security awareness.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / A descoberta de correspondÃncias semÃnticas entre esquemas à uma importante tarefa para diversos domÃnios de aplicaÃÃes, tais como integraÃÃo de dados, data warehouse e mashup de dados. Na maioria dos casos, as fontes de dados envolvidas sÃo heterogÃneas e dinÃmicas, dificultando ainda mais a realizaÃÃo dessa tarefa. Ontologias vÃm sendo utilizadas no intuito de definir vocabulÃrios comuns usados para descrever os elementos dos esquemas envolvidos em uma determinada aplicaÃÃo. O problema de matching entre ontologias, ou ontology matching, consiste na descoberta de correspondÃncias entre os termos dos vocabulÃrios (representados por ontologias) usados entre as diversas aplicaÃÃes. As soluÃÃes propostas na literatura, apesar de serem totalmente automÃticas possuem natureza heurÃstica, podendo produzir resultados nÃo-satisfatÃrios. O problema se intensifica quando se lida com grandes fontes de dados. O objetivo deste trabalho à propor um mÃtodo para geraÃÃo e refinamento incremental de correspondÃncias entre ontologias. A abordagem proposta faz uso de tÃcnicas de filtragem de ontologias, bem como do feedback do usuÃrio para dar suporte à geraÃÃo e ao refinamento dessas correspondÃncias. Para fins de validaÃÃo, uma ferramenta foi desenvolvida e experimentos foram realizados. / The discovery of semantic correspondences between schemas is an important task for different fields of applications such as data integration, data warehousing and data mashup. In most cases, the data sources involved are heterogeneous and dynamic, making it even harder the performance of that task. Ontologies are being used in order to define common vocabulary used to describe the elements of the schemas involved in a particular application. The problem of matching between ontologies, or ontology matching, consists in the discovery of correspondences between terms of vocabularies (represented by ontologies) used between the various applications. The solutions proposed in the literature, despite being fully automatic have heuristic nature, and may produce non-satisfactory results. The problem intensifies when dealing with large data sources. The purpose of this paper is to propose a method for generation and incremental refinement of correspondences between ontologies. The proposed approach makes use of filtering techniques of ontologies, as well as user feedback to support the generation and refining these matches. For validation purposes, a tool was developed and experiments were conducted

HORTÊNCIO FILHO, Fernando Wagner Brito. Uma Abordagem Semi-automática para Geração Incremental de Correspondências entre Ontologias. 2011. 81 f. : Dissertação (mestrado) - Universidade Federal do Ceará, Centro de Ciências, Departamento de Computação, Fortaleza-CE, 2011. / Submitted by guaracy araujo (guaraa3355@gmail.com) on 2016-06-27T19:11:59Z No. of bitstreams: 1 2011_dis_fwbhortênciofilho.pdf: 2807164 bytes, checksum: f2d22503112321ee69d172f0ac56d4c8 (MD5) / Approved for entry into archive by guaracy araujo (guaraa3355@gmail.com) on 2016-06-27T19:14:07Z (GMT) No. of bitstreams: 1 2011_dis_fwbhortênciofilho.pdf: 2807164 bytes, checksum: f2d22503112321ee69d172f0ac56d4c8 (MD5) / Made available in DSpace on 2016-06-27T19:14:07Z (GMT). No. of bitstreams: 1 2011_dis_fwbhortênciofilho.pdf: 2807164 bytes, checksum: f2d22503112321ee69d172f0ac56d4c8 (MD5) Previous issue date: 2011 / The discovery of semantic correspondences between schemas is an important task for different fields of applications such as data integration, data warehousing and data mashup. In most cases, the data sources involved are heterogeneous and dynamic, making it even harder the performance of that task. Ontologies are being used in order to define common vocabulary used to describe the elements of the schemas involved in a particular application. The problem of matching between ontologies, or ontology matching, consists in the discovery of correspondences between terms of vocabularies (represented by ontologies) used between the various applications. The solutions proposed in the literature, despite being fully automatic have heuristic nature, and may produce non-satisfactory results. The problem intensifies when dealing with large data sources. The purpose of this paper is to propose a method for generation and incremental refinement of correspondences between ontologies. The proposed approach makes use of filtering techniques of ontologies, as well as user feedback to support the generation and refining these matches. For validation purposes, a tool was developed and experiments were conducted / A descoberta de correspondências semânticas entre esquemas é uma importante tarefa para diversos domínios de aplicações, tais como integração de dados, data warehouse e mashup de dados. Na maioria dos casos, as fontes de dados envolvidas são heterogêneas e dinâmicas, dificultando ainda mais a realização dessa tarefa. Ontologias vêm sendo utilizadas no intuito de definir vocabulários comuns usados para descrever os elementos dos esquemas envolvidos em uma determinada aplicação. O problema de matching entre ontologias, ou ontology matching, consiste na descoberta de correspondências entre os termos dos vocabulários (representados por ontologias) usados entre as diversas aplicações. As soluções propostas na literatura, apesar de serem totalmente automáticas possuem natureza heurística, podendo produzir resultados não-satisfatórios. O problema se intensifica quando se lida com grandes fontes de dados. O objetivo deste trabalho é propor um método para geração e refinamento incremental de correspondências entre ontologias. A abordagem proposta faz uso de técnicas de filtragem de ontologias, bem como do feedback do usuário para dar suporte à geração e ao refinamento dessas correspondências. Para fins de validação, uma ferramenta foi desenvolvida e experimentos foram realizados.

In pay-as-you-go data integration, feedback can inform the regeneration of different aspects of a data integration system, and as a result, helps to improve the system's quality. However, feedback could be expensive as the amount of feedback required to annotate all the possible integration artefacts is potentially big in contexts where the budget can be limited. Also, feedback could be used in different ways. Feedback of different types and in different orders could have different effects in the quality of the integration. Some feedback types could give rise to more benefit than others. There is a need to develop techniques to collect feedback effectively. Previous efforts have explored the benefit of feedback in one aspect of the integration. However, the contributions have not considered the benefit of different feedback types in a single integration task. We have investigated the annotation of mapping results using crowdsourcing, and implementing techniques for reliability. The results indicate that precision estimates derived from crowdsourcing improve rapidly, suggesting that crowdsourcing can be used as a cost-effective source of feedback. We propose an approach to maximize the improvement of data integration systems given a budget for feedback. Our approach takes into account the annotation of schema matchings, mapping results and pairs of candidate record duplicates. We define a feedback plan, which indicates the type of feedback to collect, the amount of feedback to collect and the order in which different types of feedback are collected. We defined a fitness function and a genetic algorithm to search for the most cost-effective feedback plans. We implemented a framework to test the application of feedback plans and measure the improvement of different data integration systems. In the framework, we use a greedy algorithm for the selection of mappings. We designed quality measures to estimate the quality of a dataspace after the application of a feedback plan. For the evaluation of our approach, we propose a method to generate synthetic data scenarios. We evaluate our approach in scenarios with different characteristics. The results showed that the generated feedback plans achieved higher quality values than the randomly generated feedback plans in several scenarios.

Although spoken dialogue systems have greatly improved, they still cannot handle communications involving unknown topics. One of the problems is, that they experience difficulties when they should pronounce unknown words. We will investigate methods that can improve spoken dialogue systems by correcting the pronunciation of unknown words. This is a crucial step to provide a better user experience, since for example mispronounced proper nouns are highly undesirable. Incorrect pronunciation is caused by imperfect phonetic representation of the word. We aim to detect incorrectly pronounced words, use knowledge about the pronunciation and user's feedback and correct the transcriptions accordingly. Furthermore, the learned phonetic transcriptions can be added to the speech recognition module's vocabulary. Thus extracting correct pronunciations benefits both speech recognition and text-to-speech components of the dialogue systems.

In web forms, feedback can be provided to users at different points in time. This study investigates these three ways of providing feedback to find which results in the shortest completion time, which results in the lowest number of gaze revisits to input fields, and which type of feedback the users prefer. This was investigated through development of prototypes that were tested with 30 participants in a within-group design after which they were interviewed about their experiences. Providing feedback instantly or after form submission resulted in significantly shorter completion times than providing feedback after users left a field. Providing feedback instantly also resulted in significantly fewer revisits to input fields compared to providing feedback after leaving a field. Through a thematic analysis, users’ experiences were shown to be the most negative when given feedback after form submission, while the most positive experiences occurred when users were given feedback immediately. The results indicate that providing feedback immediately may be an equally good or better alternative to earlier research recommendations to provide feedback after form submission and that revisits to areas of interest may, with further research, be a measurement of optimal scanpath disruption.

En trend i den teknologiska utvecklingen på senare tid har varit att skapa en ökad rörlighet hos användaren. En bidragande faktor till detta är den snabba förbättringstakten hos de smartphones som i större utsträckning har funktionalitet som tidigare endast datorer hade. Detta har i sin tur förändrat de förväntningar slutanvändarna har på tjänsters tillgänglighet, vilket har lett till att produktiva mobila applikationer, appar, har funnit sin plats i vardagen. Denna studie är genomförd på Scanias Fleet Management-avdelning som erbjuder en webbaserad tjänst där åkerier kan administrera, övervaka och följa upp sina fordonsflottor. Syftet med studien var att undersöka om en förlängning av Scanias existerande webbtjänst till den mobila sfären är rätt steg att ta och vilka organisatoriska och tekniska utmaningar en ingång inom apputveckling ger upphov till. För att bemöta denna problematik har en omfattande kvalitativ undersökning genomförts där tre huvudgrupper av respondenter har intervjuats: jämförbara företag som har ställts inför liknande utmaningar, apputvecklingsföretag som har förståelse för appområdet samt Fleet Management-tjänstens existerande användare. Dessutom har en undersökning av potentiella tekniska utmaningar vid apputveckling gjorts genom framtagning av en prototyp i form av en app. Den genomförda undersökningen har, i kombination med studiens teoretiska referensram, lett till rekommendationer om hur man bör bemöta steget till de mobila enheterna. Denna studie visar på att det är lämpligt att anamma denna teknologiska trend då de positiva effekterna av en app väger upp kostnaderna. Apputveckling kräver ett tillvägagångssätt som skiljer sig från klassisk mjukvaruutveckling då slutanvändarens roll är mycket viktig under hela utvecklingsprocessen samt att en teknisk specialistkompetens behövs. En starkt iterativ utvecklingsprocess förutsätts och en proaktiv utvecklingsfilosofi krävs då tekniska förändringar inom smartphone-världen sker med stormsteg. / A recent trend in the technological evolution has been to increase the mobility of the end-user. A contributing factor is the fast rate of improvement in the smartphone area. This, in turn, has changed the expectations end-users have on the availability of services, which has allowed mobile applications, apps, to find their place in everyday life. This study has been conducted at Scania’s Fleet Management department that offers a web-based service where haulage contractors can administer, monitor and follow up on their vehicle fleets. The purpose of this study was to investigate if Scania’s existing web service should be extended into the mobile area, and if so, what organizational and technical challenges app development imposes. To address this problem, a qualitative investigation was carried out with three main groups of respondents: comparable companies that have faced similar challenges, app development companies and the current users of the Fleet Management service. Additionally, a prototype application was developed to investigate the potential technical challenges that may occur. Combined with a theoretical framework the conducted research has led to recommendations on how to best take the step into the field of mobile devices. This study shows that it is advisable to adopt the mobile technological trend since the positive effects of an app outweigh the potential costs. The technical changes within the field of smartphones occur at a rapid rate, which requires special technical skills. The development also requires iterative development methods where the end-user is included throughout the whole process.