For a group of friends going to a concert or a festival, finding concerts that everyone is happy with can be challenging as everyone have their own preferences and wishes when it comes to music.In this thesis, a prototype of a group recommendation system for concerts is presented to solve this issue. The prototype is context sensitive; it takes a user's location and time into account when giving recommendations. The prototype implements three algorithms to recommend concerts by taking advantage of what users have listened to before: a collaborative filtering algorithm (k-Nearest Neighbor), a Matrix Factorization algorithm, and a Hybrid approach of these two.The thesis was written following the Design Science Research paradigm. The thesis covers the design and implementation of the prototype in addition to a brief review of the state of the art of the recommendation systems literature. The usability of the prototype was evaluated using the System Usability Scale, and a user centered evaluation was performed to evaluate the quality of recommendations. The results from the usability evaluation shows that users generally were satisfied with the usability of the prototype. The results from the Quality Evaluation shows that the k-Nearest Neighbor and Hybrid approach produces satisfactory results whereas the Matrix Factorization implementation is lagging a bit behind. The users testing the prototype were generally satisfied with the quality of recommendations, however further evaluation is needed to draw any final conclusions.

SINTEF Energy (hereafter called SINTEF) develops complex compu- tational models for energy production in Norway. These models are controlled by command line interfaces when used for testing, in meetings, or in other contexts. They may, to some extent, be visualized with Python or MATLAB scripts, but lacks a good graphical user interface, as well as including the users in an interactive manner. SINTEF desires to look at the capabilities of moving some of today’s CLI-based workflow to a mobile environment, with an interactive user interface. They want to expand their view, exploring new ways of working. They also want to know if their current information models are able to handle these new environments.We will develop a front-end application for an Android tablet. With an agile prototyping methodology, we identify requirements to make such an application both user-friendly and useful to the end users. The application is primarily a proof-of-concept to illustrate the potential of making a new interface to SINTEFs existing computational models. The development process is also used to see if SINTEFs existing information models are extensive enough to match with the new mobile environments. The application will first and foremost be evaluated by its potential to present a prototype GUI, but also by a set of design principles regarding mobile design, and its strengths and weaknesses.Our concept illustrates the potential of visualizing parts of SINTEFs existing applications to a mobile device in an interactive manner. The users saw a potential in saving time by using such an application. The application is also used to suggest an extension to SINTEFs existing information model. This proposed CIM extension has been implemented in our application, and is generally viable for other software applications utilizing information model.

This project has been performed as a master thesis and contributes to the UbiCollab project, which is a service-oriented platform for ubiquitous collaboration where social interactions may occur both naturally and unconstrained of situation and location.Social medias have had a rapid growth throughout the last decade.People tend to share everything they do, and where they do it. With the simultaneously growth of mobile applications, application developers integrate social networking services into their application to reach a greater audience. It is easy to integrate one network into their application, but when multiple networks are integrated, the code tends to become more complex. Developers usually choose only one service to keep it simple. This has the disadvantage of making users of different social medias unable to share their geographical location and communicate with each other.During our work with this master, we have created a middleware that is able to share and manage geographical places across social networks, in order to make it easier for developers to make location-based applications for multiple social services.With this implementation we support core features for sharing geographical places in social networks. However, the system needs to support additional features to be treated as a suitable alternative to existing tools.

Logging security incidents is a required security measure in every moderately complex computer system. But while most systems produce large quantities of textual logs, these logs are often neglected or infrequently monitored by untrained personnel. One of the reasons for this neglect is the poor usability offered by distributed repositories of plain text log data, using different log formats and contradictory terminology. The use of security visualization has established itself as a promising research area, aiming to improve the usability of security logs by utilizing the visual perception system's abilities to absorb large data quantities. This thesis examines the state of the art in security log usability, and proposes two ideas to the areas of security log usability and security visualization: First, we introduce LogWheels, an interactive dashboard offering remote monitoring of security incident logs, through a user friendly visualization interface. By offering three levels of granularity, LogWheels provides both an overview of the entire system, and the opportunity to request details on demand. Second, we introduce the incident wheel, the core visualization component of LogWheels. The incident wheel presents three key dimensions of security incidents -- 'what', 'when', and 'where' -- all within a single screen. In addition to a specification of LogWheels architecture and visualization scheme, the thesis is accompanied by a functional proof-of-concept, which allows demonstrations of the system on real or simulated security data.

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With the constant increase in smartphone sales, integrated sensors have becomeavailable to the average user. This allows for mobile applications to utilise theuser’s context to provide more accurate information. The popularity of smartphones also attract developers to create audio functionalities that have earlier been restricted to calling interfaces. There is an increasing interest for Automatic Speech Recognition (ASR) services aimed at everyday tasks, where Apple’s release of SIRI is a good example of a system that has contributed to the gained popularity. This report describes TaleTUC, a proof of concept system for the domain of bus route information. TaleTUC uses ASR combined with context-awareness through Case-based Reasoning (CBR), to recognise spoken bus stop names. It is built on a client-server architecture, where theTABuss (Marcussen and Eliassen, 2011) Android application has been extendedto operate as a client. As a TaleTUC client, TABuss uses speech as input to itsmain query functionality, which provides bus route suggestions through BusTUC and AtB’s real-time system. Three modules have been developed server-side, where one is used for ASR, and the two others are used for context-awareness. Testing of the three modulescombined showed improved results compared to the ASR module alone, which indicates that context-awareness is a suitable technology to combine with ASR.

In this report we introduce a classification system named Grouping of Features (GoF), together with a theoretical exploration of some of the important concepts in the Instant Based Learning(IBL)-field that are related to this system.A dataset's original features are by the GoF-system grouped together into abstract features. Each of these groups may capture inherent structures in one of the classes in the data. A genetic algorithm is used to extract a tree of such groups that can be used for measuring similarity between samples. As each class may have different inherent structures, different trees of groups are found for the different classes. To adjust the importance of one group in regards to the classifier, the concept of power average is used. A group's power-average may let either the smallest or the largest value of its group dominate, or take any value in-between. Tests show that the GoF-system outperforms kNN at many classification tasks.The system started as a research project by Verdande Technology, and a set of algorithms had been fully or partially implemented before the start of this thesis project. There existed no documentation however, so we have built an understanding of the fields on which the system relies, analyzed their properties, documented this understanding in explicit method descriptions, and tested, modified and extended the original system.During this project we found that scaling or weighting features as a data pre-processing step or during classification often is crucial for the performance of the classification-algorithm. Our hypothesis then was that by letting the weights vary between features and between groups of features, more complex structures could be captured. This would also make the classifier less dependent on how the features are originally scaled. We therefore implemented the Weighted Grouping of Features, an extension of the GoF-system.Notable results in this thesis include a 95.48 percent and 100.00 percent correctly classified non-scaled UCI Wine dataset using the GoF- and WGoF-system, respectively.

AbstractThe master thesis focuses on ensemble approaches applied to intrusion detection systems (IDSs). The ensemble approach is a relatively new trend in artificial intelligence in which several machine learning algorithms are combined. The main idea is to exploit the strengths of each algorithm of the ensemble to obtain a robust classifier. Moreover, ensembles are particularly useful when a problem can be segmented into subproblems. In this case, each module of the ensemble, which can include one or more algorithms, is assigned to one particular subproblem. Network attacks can be divided into four classes: denial of service, user to root, remote to local and probe. One module of the ensemble designed in this work is itself an ensemble of decision trees and is specialized on the detection of one class of attacks. The inner structure of each module uses bagging techniques to increase the accuracy of the IDS. Experiments showed that IDSs obtain better results when each class of attacks is treated as a separate problem and handled by specialized algorithms. This work have also concluded that these algorithms need to be trained with specific subsets of fea- tures selected according to their relevance to the class of attack being detected. The efficiency of ensemble approaches is also highlighted. In all experiments, the ensemble was able to bring down the number of false positives and false negatives. However, we also observed the limitations of the KDD99 dataset. In particular, the distribution of examples of remote to local attacks between the training set and test set made difficult the evaluation of the ensemble for this class of attack.

Careful architectural decisions are required in order to create a highly available and scalable search system. This requires an in-depth analysis and understanding of the architecture and context of each deployment. Different requirements placed upon the system by different deployments mean different solutions provide the best case by case result, thus benchmarks provide an invaluable source of information.This thesis provides an overview of common components and important aspects of a distributed search system. It then gives an overview of different partitioning techniques before going into the details of repartitioning and rebalancing in a document-partitioned full-text search system.A processing framework that draws inspiration from flow-based programming literature is introduced, which is shown a valuable tool in creating custom tailored search solutions. The implementation is used to benchmark different repartitioning and rebalancing strategies.In conclusion, the techniques mentioned in the thesis show great promise in creating custom, maintainable and flexible partitions. The processing framework enables each specific deployment to easily compare different partitioning schemes and associated manageability and maintenance costs to determine the best fit for any given situation.

Texas Hold'em poker provides an interesting test-bed for AI research with characteristics such as uncertainty and imperfect information, which can also be found in domains like medical decision making. Poker introduces these characteristics through its stochastic nature and limited information about other players strategy and hidden cards. This thesis presents the development of a Bayesian Case-based Reasoner for Poker (BayCaRP). BayCaRP uses a Bayesian network to model opponent behaviour and infer information about their most likely cards. The case-based reasoner uses this information to make an informed betting decision. Our results suggests that the two reasoning methodologies combined achieve a better performance than either could on its own.