No description available.

No description available.

No description available.

Prior work on command recommendations for feature-rich software has relied on data supplied by a large community of users to generate personalized recommendations. In this work, I explored the feasibility of using an alternative data source: web documentation. Specifically, the proposed approach uses QF-Graphs, a previously introduced technique that maps higher-level tasks (i.e., search queries) to commands referenced in online documentation. The proposed approach uses these command-to-task mappings as an automatically generated plan library, enabling our prototype system to make personalized recommendations for task-relevant commands. Through both offline and online evaluations, I explored potential benefits and drawbacks of this approach.

For robotic systems to continue to move towards ubiquity, robots need to be more autonomous. More autonomy dictates that robots need to be able to make better decisions. Control theory and machine learning are fields of robotics that focus on the decision making process. However, each of these fields implements decision making at different levels of abstraction and at different time scales. Control theory defines low-level decisions at high rates, while machine learning defines high-level decision at low rates. The objective of this research is to integrate tools from both machine leaning and control theory to solve higher dimensional, complex problems, and to optimize the decision making process. Throughout this research, multiple algorithms were created that use concepts from both control theory and machine learning, which provide new tools for robots to make better decisions. One algorithm enables a robot to learn how to optimally explore an unknown space, and autonomously decide when to explore for new information or exploit its current information. Another algorithm enables a robot to learn how to locomote with complex dynamics. These algorithms are evaluated both in simulation and on real robots. The results and analysis of these experiments are presented, which demonstrate the utility of the algorithms introduced in this work. Additionally, a new notion of “learnability” is introduced to define and determine when a given dynamical system has the ability to gain knowledge to optimize a given objective function.

Beside all the technical challenges concerning sensor quality and control algorithms one of the main issues related to the introduction of advanced driver assistance systems (ADAS) constitutes the human-machine interaction. This covers not only the physical interface between the driver and the system but also the understanding and cognitive model the driver needs to operate the system. The explorative analysis of a long-term field study of the use of ACC, was aimed at identifying characteristics of the learning process and their potential implications for conceptualising novel displays to increase, particularly in the early phases, usability and safety of the system through the adaptation of information to the drivers. The analysis of the learning aspects derived from drivers’ interaction with the system enabled the identification of learning aims for the usage of an ACC system and an objective classification of observable behaviours from which different levels of skill can be interpreted. It was concluded that by responding to the difficulties met by users in the actual situation and by adapting the information to the drivers’ experience, drivers’ learning progress could be accelerated through better comprehensibility and predictability of the system. To this aim, two innovative help-systems were conceived, implemented and evaluated in terms of drivers driving behaviour and interactions with the ACC system, in the BMW fixed-base driving simulator. A learn-adaptive, multi-modal, on-line tutor system that covered interactions with the system at every level of the driving task (Reichart, 2001) for which learning must be effectuated, was tested with 11 participants. A personalised learning model of the driver was used to relate the drivers’ prior usage of the system and his situational experience, to give the driver additional advice and explanation in order to shorten the learning period. A main effect was found between the experimental groups’ understanding of the system and in participants’ ability to predict when to reclaim control of the system, as measured by the reduction in unnecessary interventions and reduced number of panic reactions. The use of cognitive apprenticeship methods (Cognition and Technology Group at Vanderbilt, 1993) on an online adaptation of feedback showed a positive influence on the learning process, increasing the speed of the learning process towards the acquisition of skill. The second experiment’s objective was to develop an interface that most effectively helped drivers learn to predict the need to reclaim control and the appropriate sensitivity of response in take-over situations. Drivers interactions with a didactic, two-step warning display, based on a time algorithm that was personalised to drivers maximum preferred deceleration level, was tested with 24 participants. Display effects were observed in time-to-collision, reaction times, the number of false alarms (unnecessary driver interventions) and misses (collision or near collisions). Significant differences were also found in distance error, adequate deceleration rates, panic braking and reaction times on the peripheral detection task. These results were also largely supported by the subjective measures. The proposed concepts have shown methods of reducing the ADAS learning phase and accelerating drivers behaviour to a skill level. The theoretical and empirical work described in this thesis plays an important role in deriving recommendations for systems that reduce the amount of learning demand on the driver and eliminates learnability issues that can lead to safety-critical traffic situations.

Information visualization tools provide visual representations of data (commonly known as visualizations), textual representations of data and interactive operations on both these representations. It is possible, in theory, to make use of only the textual representations in order to detect trends and patterns in data. However, it would be extremely laborious and ineffective and it defeats the purpose of a visualization tool. Novices have had the problem of relying on text and failing to successfully detect trends in data because it was too laborious. We want users to effectively use visual representations to detect trends in data. Information visualization tools have been shown to be successful with experts. But can novice users using a visualization tool for the first-time, adopt visualization-based strategies to finding trends and patterns in data? This thesis derives a framework of learnable elements in an interactive coordinated-view visualization tool. This framework provides an outline of prerequisites to be learned in order to effectively use visualizations. That is, the new aspects of visualization tools must be mastered so that novices can use the tool effectively. Three interface design principles are derived to make these elements learnable to novice users: • The data-first approach - Provide a prominent overview of all available data, as opposed to showing only visualizations. This helps understand the data structure - this may be essential knowledge in being able to navigate to required data attributes. • The "less visualization, more explanation" approach - Show more explanations of visualizations at the expense of being able to pack in more visualizations. Explanations help identify how data is mapped onto visual marks, a crucial step in understanding visualizations. • The "predetermined task-based coordinations" approach - Provide separate predetermined sets of coordinated visualizations that help achieve different tasks. This is different from techniques that simply present all visualization types and expect users to choose coordinations according to tasks. Two versions of Datamaps visualization tool for Census data were tested. Both were equivalent in functionality and the kinds of visualizations offered. But the new version was specifically designed based on the three design principles. A usability study showed that the version that was implemented according to the three design principles successfully led novices to effectively use visualization-based strategies to detect trends and patterns in data. / Master of Science

Excel offers great flexibility and allows non-programmers to create complex functionality – but at the same time, it can become very nested with cells pointing to other cells, especially if there have been many changes over a more extended period. This has happened to ICS – a small company who has its focus on calibration, out of an array of different things relating to material testing. The system they have for field calibrations today have been overly complicated and hard to maintain and consists of multiple Excel spreadsheets. The conclusion has been that a new system needs to be developed – but question how, remains. By creating a prototype using modern web-technologies, this study has evaluated if a web application can meet the specific functional requirements ICS have and if it is a suitable solution for a new system. The prototype was put under manual user test, and the results find that the prototype meets all the requirements, meaning that a webapplication could work as a replacement. During the user tests, this study has also evaluated the differences in learnability and efficiency of users, between the static user interface of the current Excel-based system and the dynamic user interface of the web-based prototype. The users have performed a calibration with both systems, and parameters such as time to completion or number of errors made have been recorded. By comparing the test results from both systems, this study has concluded that a dynamic user interface is more likely to improve learnability for novice users, but have a low impact on efficiency for expert users.

Usability and User Experience are two important factors that play a big role in user interaction and design. A poorly designed website can drive users away in frustration, costing a company money. A well-designed website will create a positive user experience, which will help attract new customers. These users will remain the company's customers. The study is looking into whether there is a difference in usability and user experience between two types of webpages. One uses different design principles while the other does not apply to any design principles. This study uses six methods of data collection to investigate the usability and user experience of the astrastad.se website and even find out what actions should be taken to help the company with a usability deficiency. These methods have been implemented to address user problems with using the website. In the study, it was found that there are a great variety of results between the tests.

During these last recent years cloud computing and cloud services have started to gain traction, which has been most notable among companies. Amazon have proven to be one of the powerhouses on providing scalable and flexible cloud computing services. However, cloud computing is still a relatively new area. From an outsider’s point of view, the overwhelming information and available services might prove to be difficult to familiarize with. The aim of this thesis is to explore how Amazon Web Services can be applied during software development and observing how difficult it might be to use these services. Three test applications that utilized different Amazon Web Services were implemented to get an insight into how Amazon Web Services can be applied from a cloud computing beginner’s point of view. These applications were developed in an iterative manner, where a case study was performed on each application. At the start of each new iteration a literature study was conducted, where sources were reviewed to see if it provided essential information. In total, nine different Amazon Web Services were used to implement and test the three respective test applications. Results of the case study were interpreted and evaluated with regards to the learnability and appliance of Amazon Web Services. Issues that were identified during the development process showed that Amazon Web Services were not userfriendly for users that have little to no experience with cloud computing services. Further research on other Amazon Web Services, such as Elastic Cloud Computing, as well as other cloud computing platforms like Google or IBM, may provide a deeper and more accurate insight on the appliances of cloud computing. / Under dem senaste åren så har molntjänster blivit ett allt mer populärt område, speciellt inom företag. Ett av dem största utgivare inom molntjänst branschen är Amazon som erbjuder skalbara och flexibla molntjänster. Molntjänster är dock ett relativt nytt område, vilket innebär att någon som inte är insatt i ämnet kan finna all tillgänglig information överväldigande och svår att bekanta sig med. Målet med det här tesen är att utforska olika Amazon Web Service som kan användas inom mjukvaruutveckling och observera problem som kan uppstå med dessa tjänster. Tre testapplikationer som använde sig av Amazon Web Services var skapade för att få en fördjupad kunskap om hur dessa tjänster fungerar och vad för möjligheter de har. Dessa applikationer utvecklades iterativt och en fallstudie utfördes för varje applikation. I början av varje ny iteration genomfördes en litteraturstudie, där källorna var kritiskt granskade för att se ifall dem innehöll väsentlig information för tesen. Sammanlagt användes nio olika Amazon Web Services för att implementera och testa de tre olika testapplikationerna. Resultaten från fallstudien tolkades och utvärderades med avseende på Amazon Web Services lärbarhet och tillämpningsbarhet. Problem som samlades ihop under utvecklingsprocessen visade att Amazons Web Services inte var särskilt användarvänligt för utvecklare med liten eller ingen erfarenhet inom Amazon Web Services. Ytterligare forskning inom andra Amazon Web Services som Elastic Cloud Computing och forskning som undersöker andra molntjänst plattformar som Google Cloud, skulle kunna bidra med en djupare förståelse och mer exakt inblick kring tillämpning av molntjänster.