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.

In this thesis we will construct a recommender system for course selection in higher education (more specifically, at The Norwegian University of Science and Technology). Some of what makes our approach novel compared with existing solutions is that we weight each user in the collaborative filtering process based on their chosen degree subject (major) and wether or not the two users being compared are friends. Also we utilize both collaborative filtering and content-based recommendations in a hybrid solution. Another novel aspect of our solution is that we construct our system on top of an existing website for rating courses. This gives us unique access to a dataset containing thousands of user-ratings of courses.

In the field of artificial intelligence, recommender systems are methods for predicting the relevance items to a users. The items can be just about anything, for example documents, articles, movies, music, events or other users. Recommender systems examine data such as ratings, query logs, user behavior and social connections to predict what each user will think of each item.Modern recommender systems combine multiple standard recommenders in order to leverage disjoint patterns in available data. By combining different methods, complex predictions that rely on much evidence can be made. These aggregations can for example be done by estimating weights that result in an optimal combination.However, we posit these systems have an important weakness. There exists an underlying, misplaced subjectivity to relevance prediction. Each chosen recommender system reflects one view of how users and items should be modeled. We believe the selection of recommender methods should be automatically chosen based on their predicted accuracy for each user and item. After all, a system that insists on being adaptive in one particular way is not really adaptive at all.This thesis presents a novel method for prediction aggregation that we call adaptive recommenders. Multiple recommender systems are combined on a per-user and per-item basis by estimating their individual accuracy in the current context. This is done by creating a secondary set of error estimating recommenders. The core insight is that standard recommenders can be used to estimate the accuracy of other recommenders. As far as we know, this type of adaptive prediction aggregation has not been done before.Prediction aggregation (combining scores) is tested in a recommendation scenario. Rank aggregation (sorting results lists) is tested in a personalized search scenario. Our initial results are promising and show that adaptive recommenders can outperform both standard recommenders and simple aggregation methods. We will also discuss the implications and limitations of our results.

Power consumption is a major concern for portable or battery-operated devices.Recently, new low power consumption techniques have been used to achieveacceptable autonomy battery-powered systems. FreeRTOS is a real-time kernel designedespecially for embedded low-power MCUs. Energy Micro develops and sellsenergy friendly microcontrollers based on the industry leading ARM Cortex-M332-bit architecture. The aim of this thesis is to propose a new FreeRTOS TicklessFramework solution that exploits the power modes provided by EFM32. Three differentsolutions have been proposed, such as FreeRTOS RTC, FreeRTOS Ticklesswith prescaling and FreeRTOS Tickless without prescaling. The simulations showedthat the Tickless Framework saves energy from 15x to 44x more than Original versionof FreeRTOS. Using a self-made benchmark the battery (1500 mAh) lifetimehas been increased from 11 days to 487 days.

Rigid body simulations are useful in many areas, most notably video games and computer animation.However, the requirements for accuracy and performance vary greatly between applications.In this project we combine methods and techniques from different sources to implement a rigid body simulation.The simulation uses a particle representation to approximate objects with the intent of reaching better performance at the cost of accuracy.We simulate cubes in order to showcase the behavior of our simulation, and also to highlight its flaws.We also write a graphical interface for our simulation using OpenGL which allows us to move and zoom around our simulation, and choose whether to render cube geometry or the particle representations.We show how our simulation behaves in a realistic way, and when running our simulation on a CPU we are able to simulate several hundred cubes in real-time.We use OpenCL to accelerate our simulation on a GPU, and take advantage of OpenCL/OpenGL interoperability to increase performance.Our OpenCL implementation achieves speedups up to 12 compared to the CPU version, and is able to simulate thousands of cubes in real-time.

The Lattice Boltzmann Method (LBM) is widely used to simulate different types of flow, such as water, oil and gas in porous reservoirs. In the oil industry it is commonly used to estimate petrophysical properties of porous rocks, such as the permeability. To achieve the required accuracy it is necessary to use big simulation models requiring large amounts of memory. The method is highly data intensive making it suitable for offloading to the GPU. However, the limited amount of memory available on modern GPUs severely limits the size of the dataset possible to simulate.In this thesis, we increase the size of the datasets possible to simulate using techniques to lower the memory requirement while retaining numerical precision. These techniques improve the size possible to simulate on a single GPU by about 20 times for datasets with 15% porosity.We then develop multi-GPU simulations for different hardware configurations using OpenCL and MPI to investigate how LBM scales when simulating large datasets.The performance of the implementations are measured using three porous rock datasets provided by Numerical Rocks AS. By connecting two Tesla S2070s to a single host we are able to achieve a speedup of 1.95, compared to using a single GPU. For large datasets we are able to completely hide the host to host communication in a cluster configuration, showing that LBM scales well and is suitable for simulation on a cluster with GPUs. The correctness of the implementations is confirmed against an analytically known flow, and three datasets with known permeability also provided by Numerical Rocks AS.

The goal of this thesis was to investigate the usage of mobile devices with multitouch capabilities in the learning of procedural knowledge. A system, consisting of three prototypes, was to be implemented as a way of examining our two hypotheses:H1: Through using a conceptual model close to how the human mind perceive objects, we can increase consistency both in the creation of new user manuals and in the learning process.H2: By taking advantage of multitouch technologies we can introduce a more natural way of interacting on virtual representations of real-life objects.A lot of research was conducted on the usage of a conceptual model containing information on the physical attributes and the procedural knowledge to back our applications, and how this best could be realized. Existing technologies for creating 3D models was investigated, but was quickly discarded due to the unique representation that was needed to successfully integrate the model with GOMS. The research process concluded that an application for describing new devices would have to be developed as well.Three applications was developed to investigate our hypotheses: an application for describing the aspects of a device, written for Mac OS, a server for communicating with prolog over TCP, written in Java, and an application for displaying the device and allowing for interaction, written for the iOS platform. The final versions of these three prototypes made it possible to create objects consisting of cubes, storing them on the server, and rendering them on the mobile application. The report concludes by discussing the utility of our prototype in regards to the hypotheses. Although not in its optimal state, the prototype demonstrates the utility of pure gestural interfaces, and how well established technologies such as prolog and GOMS can be used to empower them. Finally, interesting extensions and further work based on this thesis is proposed, demonstrating its versatility.