Chatbots are an increasingly popular technique that has seen recent steps in development thanks to artificial intelligence. There is research conducted about chatbots in different areas, but one area that is overlooked is the presentation of data collected by the chatbot. This study aimed to explore what to think about in terms of visualizations when designing an interface in order to present chatbots results to novice users. Conducting a user study with several iterations of design, the research question was: How do you visualize the results of a chatbot survey for novice users to facilitate the understanding of the data? To answer this question, four design iterations with a total of 17 participants were conducted, resulting in a final prototype. The data gathered was then analyzed using thematic analysis. The results failed to help answering the research question, but did give some suggestions on interface visualization, mainly the importance of using visualizations that are carefully selected to improve the understanding of the data presented. Moreover, this study suggests the importance of focusing on the participant’s deeper level of understanding and their conceptual models.

Background. According to the World Health Organization, over 264 million people suffer from depression. A recent trend to treat and combat depression is e-health applications like Headspace with the help of mindfulness or meditation. The rise of new treatment methods based on these concepts are seen as a promising alternative to traditional methods like cognitive behavioural therapy and medication. Objectives. The objectives of this study is to make a new mobile application, in the form of a mobile e-health prototype. The application, called MindBud, is designed to help the user reduce depressive thoughts. This is done by using a daily schedule to plan your day and in turn, reduce depressive thoughts and procrastination through structure. Then, the study seeks to compare two versions of this application, one version will have gamification elements and one will be without them. The comparison will measure overall user experience through a test called the system usability scale, and in addition measure the effectiveness of the application on depressive thoughts.Methods. Two versions of MindBud were implemented, one basic app and one with gamification elements added to it. The applications were then tested by performing an experiment with sixteen participants. Each of the participants tested both versions of the application, and then answered a questionnaire about the app. The answers of the questionnaire were used to compare test scores between the two versions of the application, to see if gamification had any impact on overall user experience and to see which gamification elements could be used to reduce depressive thoughts through the application. Results. The results show a slight increase in score in regards to overall user experience when comparing the gamified app with the basic one. Most notable increases came in questions about frequency of use, and complexity of the application. Additionally, the gamified application scored significantly better when participants were asked how much they thought the app version would reduce depressive thoughts.Conclusions. The gamification elements added were found to increase overall user experience, and also help reduce depressive thoughts more than the basic version. The used gamification elements were an in-game avatar, a reward system and an experience and level system.

Human gestures, with the spatial-temporal variability, are difficult to be recognized by a generic model or classifier that are applicable for everyone. To address the problem, in this thesis, personalized dynamic gesture recognition approaches are proposed. Specifically, based on Dynamic Time Warping(DTW), a novel concept of Subject Relation Network is introduced to describe the similarity of subjects in performing dynamic gestures, which offers a brand new view for gesture recognition. By clustering or arranging training subjects based on the network, two personalization algorithms are proposed respectively for generative models and discriminative models. Moreover, three basic recognition methods, DTW-based template matching, Hidden Markov Model(HMM) and Fisher Vector combining classification, are compared and integrated into the proposed personalized gesture recognition. The proposed approaches are evaluated on a challenging dynamic hand gesture recognition dataset DHG14/28, which contains the depth images and skeleton coordinates returned by the Intel RealSense depth camera. Experimental results show that the proposed personalized algorithms can significantly improve the performance of basic generative&discriminative models and achieve the state-of-the-art accuracy of 86.2%. / Människliga gester, med spatiala/temporala variationer, är svåra att känna igen med en generisk modell eller klassificeringsmetod. För att komma till rätta med problemet, föreslås personifierade, dynamiska gest igenkänningssätt baserade på Dynamisk Time Warping (DTW) och ett nytt koncept: Subjekt-Relativt Nätverk för att beskriva likheter vid utförande av dynamiska gester, vilket ger en ny syn på gest igenkänning. Genom att klustra eller ordna träningssubjekt baserat på nätverket föreslås två personifieringsalgoritmer för generativa och diskriminerande modeller. Dessutom jämförs och integreras tre grundläggande igenkänningsmetoder, DTW-baserad mall-matchning, Hidden Markov Model (HMM) och Fisher Vector-klassificering i den föreslagna personifierade gestigenkännande ansatsen. De föreslagna tillvägagångssätten utvärderas på ett utmanande, dynamiskt handmanipulerings dataset DHG14/28, som innehåller djupbilderna och skelettkoordinaterna som returneras av Intels RealSense-djupkamera. Experimentella resultat visar att de föreslagna personifierade algoritmerna kan förbättra prestandan i jämfört medgrundläggande generativa och diskriminerande modeller och uppnå den högsta nivån på 86,2%.

As our society becomes more digitalized, the need for programmers is increasing. There are multiple schools world wide who have started implementing programming classes at early ages for their students. These classes are mostly taught using visual programming.Having the correct tools to teach programming in an introductory manner is of utmost importance, which visual programming can provide for the students.When novices start learning programming, the initial struggle for them is to master the aspects of syntax. Visual programming removes the aspect of syntax and focuses on teaching the students core concepts of programming. This is the problem that this project focuses on; The ability for novices to learn programming through visual blocks instead of syntax. The problem was approached with user testing of a game created, where the users solve puzzles using visual programming. Using the data from the user tests, evaluations and conclusions are drawn to whether visual programming is more beneficial for learning core programming concepts than syntax programming is.

This thesis contributes to a better understanding of social media designs for more civil conversations online. I first demonstrated that people disengage from social media interactions when they encounter uncivil behavior from friends. To find alternative designs for social media that are more civil, I evaluated novel social interaction techniques. To do this, I designed a six-phase framework for prototyping social interactions called piggyback prototyping; and an algorithmic probe study methodology to include participants in the development of social curation algorithms. I built a piggyback prototype that modifies the civility on Facebook by highlighting positive posts in green and hiding impolite posts, and I deployed it as an algorithmic probe with 20 participants. I uncovered ways to improve the algorithm, and I found that participants responded most favorably to having civil posts highlighted. These findings open avenues for future research in designing pro-social platforms.

Uncertainty is inherent in many real-world settings; for example, in a combat situation, darkness may prevent a soldier from classifying approaching troops as friendly or hostile. In an environment plagued with uncertainty, decision-support systems, such as sensor-based networks, may make faulty assumptions about field conditions, especially when information is incomplete, or sensor operations are disrupted. Displaying the factors that contribute to uncertainty informs the decision-making process for a human operator, but at the expense of limited cognitive resources, such as attention, memory, and workload. This research applied principles of perceptual cognition to human-computer interface design to introduce uncertainty visualizations in an adaptive approach that improved the operator's decision-making process, without unduly burdening the operator's cognitive load. An adaptive approach to uncertainty visualization considers the cognitive burden of all visualizations, and reduces the visualizations according to relevancy as the user's cognitive load increases. Experiments were performed using 24 volunteer participants using a simulated environment that featured both intrinsic load, and characteristics of uncertainty. The experiments conclusively demonstrated that adaptive uncertainty visualization reduced the cognitive burden on the operator's attention, memory, and workload, resulting in increased accuracy rates, faster response times, and a higher degree of user satisfaction. This research adds to the body of knowledge regarding the use of uncertainty visualization in the context of cognitive load. Existing research has not identified techniques to support uncertainty visualization, without further burdening cognitive load. This research identified principles, such as goal-oriented visualization, and salience, which promote the use of uncertainty visualization for improved decision-making without increasing cognitive load. This research has extensive significance in fields where both uncertainty and cognitive load factors can reduce the effectiveness of decision-makers, such as sensor-based systems used in the military, or in first-responder situations.

The aim of this thesis is to locate current problems concerning the process of finding common ground between a creative producer and its respective client, furthermore it aims to propose a solution to this problem in the context of collaborative video editing. The paper starts off by exploring research related to the topic. After that, it establishes, through interviews with experts within fields of video editing and graphic design, what the currently existing problems are concerning communication within their line of work. As a solution to these problems, a collaborative software is proposed with the idea of bridging the understanding between the video editor and its client. The paper ends with some conclusions surrounding the current state of the topic and proposes a way forward for both practitioners and researchers.

published_or_final_version / abstract / Architecture / Doctoral / Doctor of Philosophy

Pas de résumé

Cette thèse porte sur 1'ínversion acoustique-articulatoire, c'est-à-dire la récupération des mouvements des articulateurs de la parole à partir du signal sonore. Nous présentons dans ce mémoire une évolution importante des méthodes de tabulation à codebooks utilisant une table de correspondants acoustique-articulatoire précalculée à l'aide d'un modèle de synthèse acoustique. En dehors de la méthode d'inversion proprement dite, nous présentons également l'introduction de deux types de contraintes : des contraintes phonétiques génériques, issues de l'analyse par des experts humains de l'invariance articulatoire des voyelles, et des contraintes visuelles, c'est-à-dire des contraintes obtenues automatiquement à partir de l'enregistrement et l'analyse d'images en stéréovision du locuteur.