One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.

Due to the rapid increase of social network resources and services, Internet users are now overwhelmed by the vast quantity of social media available. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is still a need to reinforce the recommendation process in a systematic way, with context-adaptive information. This thesis proposes a recommendation model, called HPEM, that establishes a bridge between the multimedia resources, user collaborative preferences, and the detected contextual information, including physiological parameters. The collection of contextual information and the delivery of the resulted recommendation is made possible by adapting the user’s environment using Ambient Intelligent (AmI) interfaces. Additionally, this thesis presents the potential of including a user’s biological signal and leveraging it within an adapted collaborative filtering algorithm in the recommendation process. First, the different versions of the proposed HPEM model utilize existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. By leveraging the social tagging, our proposed model computes the hidden preferences of users in certain contexts from other similar contexts, as well as the hidden assignment of contexts for items from other similar items. Second, we demonstrate the use of an optimization function to maximize the Mean Average Prevision (MAP) measure of the resulted recommendations. We demonstrate the feasibility of HPEM with two prototype applications that use contextual information for recommendations. Offline and online experiments have been conducted to measure the accuracy of delivering personalized recommendations, based on the user’s context; two real-world and one collected semi-synthetic datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to state-of-the-art recommendation algorithms that consider contextual information. We also compare the proposed method to other algorithms, where user’s context is not used to personalize the recommendation results. Additionally, the results obtained demonstrate certain improvements on cold start situations, where relatively little information is known about a user or an item.

Network coding is an emerging technology that intelligently exploits the store/forward nature of routers to increase the efficiency of the network. Though the concept works in theory, the segregation of coding and routing decisions makes them inapplicable in almost any practical environment. Coding-aware routing takes the network coding a step further to lessen its disadvantages by allowing interlayer communication while making routing decisions. However, most of the existing work exploits coding benefits only for fixed wireless networks, making them dependent on the types of network medium, topology and mobility and thus inapplicable for wired and mobile Ad Hoc networks. The aim of this thesis is to present a generalized algorithm that can detect any possible coding opportunity in a network of any medium, topology and mobility while making routing decisions. We have tested and evaluated our algorithm in six different network topology settings i.e. small wired, big wired, small Ad Hoc network with regular trajectories, big Ad Hoc network with regular trajectories, small Ad Hoc with random trajectories and big Ad Hoc with random trajectories. Improved performance in network throughput, mean queue size and mean end-to-end delay confirms the validity of our algorithm.

In general, recommender systems attempt to estimate user preference based on historical data. A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts has been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems. Several methods have been used to incorporate contextual information into traditional recommendation algorithms. These methods focus on incorporating contextual information to improve general recommendations for users rather than identifying the different context applicable to the user and providing recommendations geared towards those specific contexts. In this thesis, we explore different context-aware recommendation techniques and present our context-aware coupled matrix factorization methods that use matrix factorization for estimating user preference and features in a specific contextual condition. We develop two methods: the first method attaches user preference across multiple contextual conditions, making the assumption that user preference remains the same, but the suitability of items differs across different contextual conditions; i.e., an item might not be suitable for certain conditions. The second method assumes that item suitability remains the same across different contextual conditions but user preference changes. We perform a number of experiments on the last.fm dataset to evaluate our methods. We also compared our work to other context-aware recommendation approaches. Our results show that grouping ratings by context and jointly factorizing with common factors improves prediction accuracy.

Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, and external influence. Experiments on the NYC Citi Bike system show the effectiveness and efficiency of our model when compared with the state-of-the-art methods.

Modern computer architectures expose an increasing number of parallel features supported by complex memory access and communication structures. Currently used task scheduling techniques perform poorly since they focus solely on balancing computation load across parallel features and remain oblivious to locality properties of support structures. We contribute with locality-aware task scheduling mechanisms which improve execution time performance on average by 44\% and 11\% respectively on two locality-sensitive architectures - the Tilera TILEPro64 manycore processor and an AMD Opteron 6172 processor based four socket SMP machine. Programmers need task performance metrics such as amount of task parallelism and task memory hierarchy utilization to analyze performance of task-based programs. However, existing tools indicate performance mainly using thread-centric metrics. Programmers therefore resort to using low-level and tedious thread-centric analysis methods to infer task performance. We contribute with tools and methods to characterize task-based OpenMP programs at the level of tasks using which programmers can quickly understand important properties of the task graph such as critical path and parallelism as well as properties of individual tasks such as instruction count and memory behavior. / <p>QC 20140212</p>

Context information is information that describes the user's context. The goal of the Adaptive Context Aware Services (ACAS) project is to enable applications to use context information in order to adapt their behaviour to the user and his environment without requiring the user to manually change/manage parameters. While the concept of linking context aware entities together to form a logical "context network" was introduced earlier in the project, some questions regarding context information discovery and the discovery of context aware entities were previously unanswered. The goal of this thesis was to design and evaluate such a context network allowing entities todiscover each other and exchange information regarding their services and context information. For this purpose, a "Context Entity Registrar" has been developed allowing entities to register, thus they can easily be found by other entities who can query this registrar. During the design of this proposed solution, a special focus has been given to the performance of the registrar, especially how it scales when answering a large number of requests, in order to validate the design's potential as a solution to context aware entity discovery. Measurements have shown that this proposed solution scales well, making it a key element of a context network. Discovery of other entities and of context information play an important role to determine the performances of a context aware implementation. This masters thesis addresses first the issue of the architecture of the context network and then some tests to measure the performances of the proposed solution. / Context information är information som beskriver användarens omgivning. Adaptive Context Aware Services (ACAS) projektet har som mål att möjliggöra applikationer att använda kontext information för att anpassa sitt beteende till användaren och dess miljö, utan att kräva att användaren ska sätta eller hantera alla parametrar manuellt. ACAS projektet har tidigare infört konceptet "context network" som förbinder context aware enheter. Det finns dock kvar några obesvarade frågor angående upptäckt av context information och av context aware enheter.Trots att sättet att länka ihop kontextmedvetna enheter för att forma ett logiskt 'kontext nätverk' introducerades tidigare i projektet, finns det kvar några obesvarade frågor angående upptäckt av kontext information och upptäckt av kontextmedvetna enheter. Examensarbetets mål är att utforma och utvärdera ett sådant kontextnätverk som ger enheterna möjlighet att upptäcka varandra och utbyta information om tjänster och context information. Därför utvecklades "Context Entity Registrar" så att enheterna kan registrera sig för att kunna bli upptäckta av andra enheter som kan göra förfrågningar till detta register. Under designen av denna föreslagna lösning har särskild fokus lagts på registrens prestanda, speciellt avseende skalbarhet med avseende på antalet förfrågningar for att validera designens potential som lösning för kontextmedveten upptäckt av enheter. Mätningar har visat att lösningen skalar bra vilket gör kontext registret till ett nyckelelement i ett kontextnätverk. Upptäkten av andra enheter och av kontextinformation har en viktig roll i att bestämma en kontextmedveten implementations prestanda. Detta examensarbete kommer först att behandla kontextnätverkets arkitektur och därefter några testerna för att mäta prestanda i den föreslagna lösningen.

Today customization of services and applications is one of the major challenges in facilitating ease of use. More and more people are interested in context aware services. In this work, I will study context awareness: What contributions can it make? What technical issues are raised? I will concentrate on the semantic problem and show how new technologies such as a web ontology language can facilitate creating context aware services. An application was implemented using these principles as a proof of concept and to enable some evaluation of this approach. This application has shown that combining semantic processing with SIP call processing is feasible and measurements have demonstrated a highly scalable context aware application for at least simple CPL scripts. / I dagens läge är en av de stora utmaningarna att skräddarsy applikationer och tjänster efter kundens begär för att höja användarvänligheten. Fler och fler kunder intresserar sig för tjänster som är kontextuppmärksamma. Jag kommer i mitt arbete att studera detta, mina frågeställningar är huvudsakligen, hur är detta användbart? Vad slags tekniska problem uppstår kring applikationen av detta koncept? Jag kommer att koncentrera mig på semantiska problem och visa hur nya teknologier så som webbontologispråk kan underlätta då man skapar tjänster som är kontextuppmärksamma. Jag kommer även att skapa en applikation där jag använder dessa principer för att visa konceptet och för att underlätta för en utvärdering av detta tillvägagångssätt. Denna applikation samt mätdata visar att det är möjligt att kombinera semantisk bearbetning och SIP anropsbearbetning för att skapa oerhört robusta applikationer, åtminstonde för enkla CPL script, som är hållbara i ett stort omfång av systemstorlekar.

Strömmande musiktjänster erbjuder ett stort utbud av musik. Förutom att användare kan skapa egna listor från detta utbud, erbjuder tjänsterna ofta personliga rekommendationer. Även om rekommendationerna passar användaren väl, passar de inte alltid för situationen de befinner sig i. I denna studie presenteras en artefakt i form av en kontextmedveten musikapplikation som använder Spotify för rekommendation och uppspelning av musik. En kontextmedveten musikapplikation är en applikation som tar användarens kontext i beaktning vid musikrekommendation. I denna studie refererar kontext till den situation som en potentiell användare befinner sig i, exempelvis "ute och springer i parken klockan tre". Vi presenterar en enkätundersökning om vilka kontextuella faktorer användare tycker är viktiga, och frågor kring lyssnarbeteende. Artefakten testas i en användarstudie och resultaten analyseras och diskuteras i relation till tidigare forskning. Vi ser att användare har en positiv inställning till att kontextuella faktorer påverkar vilken musik de lyssnar på, och att det finns en positiv inställning till kontextmedvetna musikapplikationer. Vidare ser vi att aktivitet är den mest relevanta kontextuella faktorn för användare. / Music streaming services offer a large quantity of music. Apart from users being able to create their own playlists, these services also offer personal music recommendations. Even though these recommendations meet the users preferences, they don't always fit the users current situation. In this study, we present an artifact in the shape of a context-aware music recommender application, that uses Spotify services to recommend and handle playback of music. A context-aware music application is an application that takes a users current context in consideration when recommending music. In this study, context-aware refers to the situation a given user might find themselves in, e.g. "jogging in the park at 3pm". We present a questionnaire about which contextual factors users think are important, and questions about listening preferences. The artifact is tested in a user study, and the results are analysed and discussed in relation to previous studies. We found that users have a positive attitude towards contextual factors influencing which music they listen to, and that there is a positive attitude towards contextual music recommenders. Furthermore we found that activity is the most relevant contextuall factor to users.

Bakgrund: Psykisk ohälsa ökar bland befolkningen och är en betydande riskfaktor för fullbordad suicid. Genom att arbeta förebyggande med suicidpreventiva program kan resultera i att minska suicid i framtiden. Därför är det viktigt att implementera och vidmakthålla beprövade insatser som ger önskad effekt. Youth Aware of Mental Health [YAM] är ett suicidpreventivt medvetarprogram som utförs bland ungdomar och påvisas ge effekt att minska försök till suicid i framtiden. Syfte: Syftet med studien är att undersöka instruktörernas erfarenheter vid genomförandet av det skolbaserade programmet Youth Aware of Mental Health [YAM] samt vidmakthållande av programmet i Sörmland. Metod: För att studera genomförandet samt vidmakthållande av det skolbaserade programmet YAM, valdes en kvalitativ intervjumetod för att undersöka instruktörernas upplevelser och erfarenheter av programmet. Urvalet i studien är utbildade YAM-instruktörer som deltagit under ett eller flera programtillfällen med högstadieelever i Sörmland. Insamlade intervjudata analyserades genom innehållsanalys och resulterade i tre huvudkategorier för att besvara studiens syfte. Huvudkategorierna i studien är; programmets utformning, genomförandet av programmet samt förbättringspotential och vidmakthållande av programmet. Resultat: Resultatet visar att instruktörerna upplevde en bestämd struktur med hjälp av programmets manual om hur programmet skall genomföras bland högstadieeleverna. Dock upplevde instruktörerna att det bör finnas tydligare riktlinjer om hur programmet skall förverkligas för att kunna nå en förbättrad effekt. Förbättringspotentialen som instruktörerna upplever är att förbättra samverkan mellan instruktörerna, kommunen och Region Sörmland för att vidmakthålla programmet i Sörmland. Slutsatser: För att kunna lyckas med programmet är det betydelsefullt att ge instruktörerna förutsättningar att kunna genomföra programmet samt skapa en god samverkan mellan instruktörerna, kommunen och Region Sörmland. Om detta görs kan det leda till förbättrat genomförande samt vidmakthållande av programmet bland samtliga högstadieskolor i Sörmland.