This thesis is the continuation of our specialization project, Lecture Quiz 2.5.This platform is a game-like system where lecturers can hold quizzes in lectures to increase student participation and interactivity.The current version is a finished lecture quiz system that can be used in lecture environments.Lecture Quiz 3.0 has moved away from earlier implementations, by centralizing and minimizing the effort to start and run quizzes.One focus was multi-platform and we developed the system to support Microsoft Windows, Mac OS X and Linux.This system can be used in lecture environments to promote more student participation, and enable variation in teaching methods.To run quiz games, the lecturer can use a PC, connect it to the projector, and run the Presentation Client.Students access the Player Client via a mobile device such as a smart phone or notebook, the address to the Player Client web page is presented on the Presentation Client.Once connected, they choose a username, and answer multiple choice questions, which are presented on the projector screen.To keep things interesting for the students, we focused on the visual expression of the Presentation Client and Player Client.This is to give the players the experience of playing a game, rather than answering a questionnaire.We developed the system with usability in mind.This is to ensure that the system feel easy to use, for both students and lecturers.One of the main goals is to make lecturers see the system as an alternative to a regular presentation, and not as extra work.A lecturer might be interested in collecting statistics about the students' overall progress in the course.This way they might be able to give a larger focus to the parts of the syllabus where the students lack performance.Another factor is that creating quizzes is time consuming, and needs to be done in advance of a lecture.We developed a separate quiz manager and statistics tool that can be used by lecturers, named Quiz Server.It is a Web based application, utilizing Java EE to enable multi-platform support.We performed an experiment in a lecture to get feedback from students on how they perceived the Lecture Quiz game.This experiment was performed by running a quiz in the lecture hall and then the students were asked to fill in an evaluation form.The students who participated thought that Lecture Quiz had a positive effect on the lecture.

Målet med denne oppgaven var å demonstrere et konsept om innendørs navigering med 3D kart-visning på android-platformen. Basert på tilgjengelig dokumentasjon, hjelp av noen utvalgte kode og applikasjonseksempler, samt utforsking av en android-enhet ble det gjort observasjoner som gjorde det mulig å selv utvikle en applikasjon for denne platformen.Prosjektet har gitt en god innsikt i android-platformen og programmering med OpenGL ES, som har gitt meg mye ny kunnskap.

This dissertation investigates the classification capabilities of artificial neural networks (ANNs). The goal is to generalize over the features of a writing system, and thus classify the writing system of a previously unseen glyph. The complexity of the problem necessitates a large network, which hampers the tuning of the weights. ANNs were created using three different hybrids of back-propagation (BP) learning and evolution, and a pure BP algorithm for comparison. The purpose was to find the method best suited for this kind of generalization and classification networks. The results suggest that ANNs are able to generalize enough to solve the classification task, but it is depending on the weight tuning algorithm. A pure BP algorithm is preferable to any of the hybrid algorithms, due to the size of the ANN. This algorithm had both the best classification results and the fastest runtime, in addition to the least complex implementation.

This report presents the work done to develop an autonomous driverfor the Simulated Car Racing Championship (SCRC), a competition incomputational intelligence based on The Open Racing Car Simulator(TORCS). Autonomous race driving based only on local sensory datais a complex problem, and previous SCRC entries' work show a widevariety of approaches taken to address it. We describe CRABCAR,a controller that combines oine learning prior to the competitionwith online learning during the competition to optimize its performance in the SCRC context. The presented approach extends and builds on track modelling and racing line optimization techniques introduced previously, addressing known problems said techniques have with noisy sensory input and non-perfect track information. CRABCAR's performance is compared to previous entries from the SCRC, with results showing CRABCAR at a performance level similar to the others. We conclude that a system for online adaption is essential when pre-learned strategies are applied to discretely segmented and non-perfect track models in the SCRC context.

Privacy policies are commonly used by service providers to notify users what information is collected, how it will be used and with whom it will be shared. These policies are however known to be notoriously long and hard to understand, and several studies have shown that very few users actually read them. Alternative solutions that accurately communicates the most important parts of the policy in a way that is more enjoyable to read, is therefore needed to aid the users in making informed decisions on whether or not to share information with a provider.By following a design science strategy we first explore current solutions, and based on an initial evaluation we find the Nutrition Label to be the current approach best suited to base further work on. Through an assess and refine cycle we first evaluate the Nutrition Label based on usability literature, and propose a set of design criteria which is used as a basis for developing an alternative solution, entitled the Privacy Table. By following an iterative design process, we evaluate the Privacy Table in terms of accuracy, time-to-response and likeability through a pre-test, a laboratory experiment with 15 participants, and finally through an Internet experiment with 24 participants, where each iteration results in a re-designed version of the Privacy Table. While we don't find clear evidence for any difference between the formats, we find indications for that they perform similarly in terms of accuracy and enjoyability. We discover several issues regarding the Nutrition Label where some are related to the terminology used, which could indicate that it would need modifications in order to be usable among non-native English speakers. We also suggest that future research on the Nutrition Label should focus on its usability rather than further expansion, and that it should be considered to base it on a more simplified underlying technology than the P3P language. Finally we find that a merged version of the Privacy Table and the Nutrition Label could be advantageous to use in relation with current and future privacy enhancing technologies, as a top layer to communicate the most important privacy practices.

In this report, we have explored swarm intelligence through a box-pushing taskwith physical robots called e-pucks. Research on social insects has been presentedtogether with dierent ways of controlling autonomous robots, where combiningthis knowledge has been essential in our quest to make a biological plausible antretrieving system.Inspired by ants and behavior-based robotics, we have created the system CRABS.It is based on Brooks' subsumption architecture to control six dierent behaviors,from a xed input-output scheme. The system is designed to easily handle addingor removal of behavior layers. Behavior modules can also be used separately andported to other software or hardware platforms.During this project we came across several hardware and software challenges in-vestigating cooperative behavior. With the use of the simulation tool Webots, wewere able to determine e-pucks' capabilities, and through this knowledge able todesign and construct an articial food source. This operated as the box-item in thebox-pushing task.Based on two types of sensors and two actuators (wheels), we had a strategy toaccomplish the box-pushing task following the biological principles of social insects.The guidelines of the ant retrieving model made CRABS a self-organized systemthat given three or more e-pucks, will always succeed in retrieving the box back tothe wall. The most remarkable view on this accomplishment is that is done throughthe use of only stigmergy and positive/negative feedback.One of the things we've experienced throughout this thesis is that hardware is a morework demanding and inconsistent platform than your usual software simulation.Everything is not given, and although Webots provided helpful shortcuts, a lot oftime and hard work was put down in order to get the system up and running. Withthat being said, we are pleased that we took the hardware rout and were able totest and validate our system on physical robots.

This thesis has evaluated the use of the computationally expensivemaximum-likelihood (ML) method coupled with an evolutionaryalgorithm (EA) for the problem of inferring evolutionaryrelationships among species (phylogenies) from molecular data. MLmethods allow using all the information from molecular data, suchas DNA sequences, and have several beneficial properties compared toother methods. Evolutionary algorithms is a class of optimizationalgorithms that often perform well in complex fitness landscapes.EAs are also proclaimed to be easy to parallelize, an aspect thatis increasingly more important.A parallel EA system has been implemented and tested on a clusterfor the task of phylogeny inference. The system shows promisingresults and is able to utilize processors of a massively parallelsystem in a transparent manner.

Given a spatial location and a set of keywords, a spatial keyword query locates spatio-textual objects based on both the location of the objects, and the textual relevance of the query keywords to the description of the objects. Spatial keyword queries can be used to answer challenging questions such as finding the nearest spatio-textual object relevant for the query keywords "restaurant sushi".Our focus in this project is on a new type of spatial keyword query that takes a road network into account during query processing. These queries are based on the fact that the distance between two objects in the real-world are constrained by the pre-defined paths that comprise the road network. Different from traditional spatial keyword queries that employ the Euclidean distance, the spatial keyword query on road networks assumes the shortest path between the query location and the objects. Unfortunately, no approach currently exists that supports processing of spatial keyword queries on road networks.In this thesis we address the challenging problem of locating spatio-textual objects in a road network given a spatial location and a set of keywords. We first propose a baseline framework that combines existing state-of-the-art approaches to support processing of keyword-based spatial queries such as range and k-nearest neighbour on road networks. Then, we present a novel framework termed Road Network Indexing (RNI) that permits efficient processing of such queries by indexing the spatio-textual objects in each road segment using inverted files.Moreover, we present algorithms to evaluate keyword k-nearest neighbour and keyword range queries on both the baseline and the RNI framework.Finally, we show through an experimental evaluation using real-world datasets, that our RNI framework performs nearest neighbour queries on road networks in around one order of magnitude faster than the baseline approach in terms of response time and I/O.

This thesis presents a case study of Game Mechanic based E-Learning. This is put forward as a new approach to E-Learning that tries to mimic games to harness some of their motivational properties. A prototype system was developed as a web application, using an Agile and Lean development approach.The system was evaluated with a class at the Norwegian University of Science and Technology.This was done to give an indication of the system's ability to make work with exercises more engaging and fun. To give context in this thesis, the growing trend of Gamification is unveiled and explained in detail.The major technological delivery posited by this thesis was the prototype, implementedas a web application (dynamic webpage). The major research acheivement was evaluatingrespondents perception of the system. It was discovered that the chosen Game Mechanicwas indeed considered to make work with exercises more engaging, although this effect wasmarginal. The evaluation was also used to arrive at a general definition for games.This definition can be used to distinguish Game Mechanics basedsystems from games. It also serves as a much needed guide to designing games andnon-game systems that tries to acheive similar motivational benefits as games.

In this project I have designed a model of features, human behavior and intentions. The model suggests a set of features that can be used to describe the interaction between a human being and an automated sliding door. The model also defines symbols representing value sets for the features. The symbols are then combined in order to describe different events, mapping features to intentions. This model provides a framework guiding the capturing process as well as the reasoning process.Further, I have designed a mechanism for capturing human movement and extracting the features as suggested by the model of features, human behavior and intentions. The solution components are based on research done within computer vision, where different tools and algorithms were reviewed and evaluated. Parts of the suggested solution are provided as software libraries, while others had to be implemented. The solution includes using an Xbox Kinect as a sensor device, and the OpenNI framework together with the middleware NITE for Human body tracking and skeletal joint extraction.A reasoning mechanism was designed, that utilizes the designed model in order to reach a conclusion about the intention of a human interacting with the door. Different reasoning techniques were reviewed in context of the sliding doors problem. Based on the review I suggest using rule-based reasoning. By using the events described in the model and by giving values to the different symbols I was able to form the rules for the reasoning process.The designed mechanisms were put together in an implementation in C/C++ comprising depth and RGB image capture, body tracking, user handling and feature extraction, rule-based reasoning and door control.A motorized sliding door was built, together with a door controller allowing a computer to interface with the door, giving open and close commands.Finally, the door was tested both through a live demo and a laboratory style, structured observation. The door proved a superior performance to the traditional sliding doors when it came to identifying negative intentions, thus reducing the number of false positives drastically. However, both false positives and false negatives occurred, leaving room for improved accuracy.With my solution I have managed to interpret the intention of a user interacting with an automated sliding door. I have lifted the reasoning process to a symbolic level, dealing with symbols and events easy to understand. Although the model is limited to a very specific domain, and the solution has got some limitations and weaknesses, this is a good starting point for further work.