5,102 research outputs found

    SHELDON Smart habitat for the elderly.

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    An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

    Context aware advertising

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    IP Television (IPTV) has created a new arena for digital advertising that has not been explored to its full potential yet. IPTV allows users to retrieve on demand content and recommended content; however, very limited research has been applied in the domain of advertising in IPTV systems. The diversity of the field led to a lot of mature efforts in the fields of content recommendation and mobile advertising. The introduction of IPTV and smart devices led to the ability to gather more context information that was not subject of study before. This research attempts at studying the different contextual parameters, how to enrich the advertising context to tailor better ads for users, devising a recommendation engine that utilizes the new context, building a prototype to prove the viability of the system and evaluating it on different quality of service and quality of experience measures. To tackle this problem, a review of the state of the art in the field of context-aware advertising as well as the related field of context-aware multimedia have been studied. The intent was to come up with the most relevant contextual parameters that can possibly yield a higher percentage precision for recommending advertisements to users. Subsequently, a prototype application was also developed to validate the feasibility and viability of the approach. The prototype gathers contextual information related to the number of viewers, their age, genders, viewing angles as well as their emotions. The gathered context is then dispatched to a web service which generates advertisement recommendations and sends them back to the user. A scheduler was also implemented to identify the most suitable time to push advertisements to users based on their attention span. To achieve our contributions, a corpus of 421 ads was gathered and processed for streaming. The advertisements were displayed in reality during the holy month of Ramadan, 2016. A data gathering application was developed where sample users were presented with 10 random ads and asked to rate and evaluate the advertisements according to a predetermined criteria. The gathered data was used for training the recommendation engine and computing the latent context-item preferences. This also served to identify the performance of a system that randomly sends advertisements to users. The resulting performance is used as a benchmark to compare our results against. When it comes to the recommendation engine itself, several implementation options were considered that pertain to the methodology to create a vector representation of an advertisement as well as the metric to use to measure the similarity between two advertisement vectors. The goal is to find a representation of advertisements that circumvents the cold start problem and the best similarity measure to use with the different vectorization techniques. A set of experiments have been designed and executed to identify the right vectorization methodology and similarity measure to apply in this problem domain. To evaluate the overall performance of the system, several experiments were designed and executed that cover different quality aspects of the system such as quality of service, quality of experience and quality of context. All three aspects have been measured and our results show that our recommendation engine exhibits a significant improvement over other mechanisms of pushing ads to users that are employed in currently existing systems. The other mechanisms placed in comparison are the random ad generation and targeted ad generation. Targeted ads mechanism relies on demographic information of the viewer with disregard to his/her historical consumption. Our system showed a precision percentage of 69.70% which means that roughly 7 out of 10 recommended ads are actually liked and viewed to the end by the viewer. The practice of randomly generating ads yields a result of 41.11% precision which means that only 4 out of 10 recommended ads are actually liked by viewers. The targeted ads system resulted in 51.39% precision. Our results show that a significant improvement can be introduced when employing context within a recommendation engine. When introducing emotion context, our results show a significant improvement in case the userñ€ℱs emotion is happiness; however, it showed a degradation of performance when the userñ€ℱs emotion is sadness. When considering all emotions, the overall results did not show a significant improvement. It is worth noting though that ads recommended based on detected emotions using our systems proved to always be relevant to the user\u27s current mood

    Semantically-enhanced recommendations in cultural heritage

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    In the Web 2.0 environment, institutes and organizations are starting to open up their previously isolated and heterogeneous collections in order to provide visitors with maximal access. Semantic Web technologies act as instrumental in integrating these rich collections of metadata by defining ontologies which accommodate different representation schemata and inconsistent naming conventions over the various vocabularies. Facing the large amount of metadata with complex semantic structures, it is becoming more and more important to support visitors with a proper selection and presentation of information. In this context, the Dutch Science Foundation (NWO) funded the Cultural Heritage Information Personalization (CHIP) project in early 2005, as part of the Continuous Access to Cultural Heritage (CATCH) program in the Netherlands. It is a collaborative project between the Rijksmuseum Amsterdam, the Eindhoven University of Technology and the Telematica Instituut. The problem statement that guides the research of this thesis is as follows: Can we support visitors with personalized access to semantically-enriched collections? To study this question, we chose cultural heritage (museums) as an application domain, and the semantically rich background knowledge about the museum collection provides a basis to our research. On top of it, we deployed user modeling and recommendation technologies in order to provide personalized services for museum visitors. Our main contributions are: (i) we developed an interactive rating dialog of artworks and art concepts for a quick instantiation of the CHIP user model, which is built as a specialization of FOAF and mapped to an existing event model ontology SEM; (ii) we proposed a hybrid recommendation algorithm, combining both explicit and implicit relations from the semantic structure of the collection. On the presentation level, we developed three tools for end-users: Art Recommender, Tour Wizard and Mobile Tour Guide. Following a user-centered design cycle, we performed a series of evaluations with museum visitors to test the effectiveness of recommendations using the rating dialog, different ways to build an optimal user model and the prediction accuracy of the hybrid algorithm. Chapter 1 introduces the research questions, our approaches and the outline of this thesis. Chapter 2 gives an overview of our work at the first stage. It includes (i) the semantic enrichment of the Rijksmuseum collection, which is mapped to three Getty vocabularies (ULAN, AAT, TGN) and the Iconclass thesaurus; (ii) the minimal user model ontology defined as a specialization of FOAF, which only stores user ratings at that time, (iii) the first implementation of the content-based recommendation algorithm in our first tool, the CHIP Art Recommender. Chapter 3 presents two other tools: Tour Wizard and Mobile Tour Guide. Based on the user's ratings, the Web-based Tour Wizard recommends museum tours consisting of recommended artworks that are currently available for museum exhibitions. The Mobile Tour Guide converts recommended tours to mobile devices (e.g. PDA) that can be used in the physical museum space. To connect users' various interactions with these tools, we made a conversion of the online user model stored in RDF into XML format which the mobile guide can parse, and in this way we keep the online and on-site user models dynamically synchronized. Chapter 4 presents the second generation of the Mobile Tour Guide with a real time routing system on different mobile devices (e.g. iPod). Compared with the first generation, it can adapt museum tours based on the user's ratings artworks and concepts, her/his current location in the physical museum and the coordinates of the artworks and rooms in the museum. In addition, we mapped the CHIP user model to an existing event model ontology SEM. Besides ratings, it can store additional user activities, such as following a tour and viewing artworks. Chapter 5 identifies a number of semantic relations within one vocabulary (e.g. a concept has a broader/narrower concept) and across multiple vocabularies (e.g. an artist is associated to an art style). We applied all these relations as well as the basic artwork features in content-based recommendations and compared all of them in terms of usefulness. This investigation also enables us to look at the combined use of artwork features and semantic relations in sequence and derive user navigation patterns. Chapter 6 defines the task of personalized recommendations and decomposes the task into a number of inference steps for ontology-based recommender systems, from a perspective of knowledge engineering. We proposed a hybrid approach combining both explicit and implicit recommendations. The explicit relations include artworks features and semantic relations with preliminary weights which are derived from the evaluation in Chapter 5. The implicit relations are built between art concepts based on instance-based ontology matching. Chapter 7 gives an example of reusing user interaction data generated by one application into another one for providing cross-application recommendations. In this example, user tagging about cultural events, gathered by iCITY, is used to enrich the user model for generating content-based recommendations in the CHIP Art Recommender. To realize full tagging interoperability, we investigated the problems that arise in mapping user tags to domain ontologies, and proposed additional mechanisms, such as the use of SKOS matching operators to deal with the possible mis-alignment of tags and domain-specific ontologies. We summarized to what extent the problem statement and each of the research questions are answered in Chapter 8. We also discussed a number of limitations in our research and looked ahead at what may follow as future work

    An architecture for evolving the electronic programme guide for online viewing

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    Watching television and video content is changing towards online viewing due to the proliferation of content providers and the prevalence of high speed broadband. This trend is coupled to an acceleration in the move to watching content using non-traditional viewing devices such as laptops, tablets and smart phones. This, in turn, poses a problem for the viewer in that it is becoming increasingly difficult to locate those programmes of interest across such a broad range of providers. In this thesis, an architecture of a generic cloud-based Electronic Programme Guide (EPG) system has been developed to meet this challenge. The key feature of this architecture is the way in which it can access content from all of the available online content providers and be personalized depending on the viewer’s preferences and interests, viewing device, internet connection speed and their social network interactions. Fundamental to its operation is the translation of programme metadata adopted by each provider into a unified format that is used within the core system. This approach ensures that the architecture is extensible, being able to accommodate any new online content provider through the addition of a small tailored search agent module. The EPG system takes the programme as its core focus and provides a single list of recommendations to each user regardless of their origins. A prototype has been developed in order to validate the proposed system and evaluate its operation. Results have been obtained through a series of user trials to assess the system’s effectiveness in being able to extract content from several sources and to produce a list of recommendations which match the user’s preferences and context. Results show that the EPG is able to offer users a single interface to online television and video content providers and that its integration with social networks ensures that the recommendation process is able to match or exceed the published results from comparable, but more constrained, systems

    “WARES”, a Web Analytics Recommender System

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    Il est difficile d'imaginer des entreprises modernes sans analyse, c'est une tendance dans les entreprises modernes, mĂȘme les petites entreprises et les entrepreneurs individuels commencent Ă  utiliser des outils d'analyse d'une maniĂšre ou d'une autre pour leur entreprise. Pas Ă©tonnant qu'il existe un grand nombre d'outils diffĂ©rents pour les diffĂ©rents domaines, ils varient dans le but de simples statistiques d'amis et de visites pour votre page Facebook Ă  grands et sophistiquĂ©s dans le cas des systĂšmes conçus pour les grandes entreprises, ils pourraient ĂȘtre shareware ou payĂ©s. Parfois, vous devez passer une formation spĂ©ciale, ĂȘtre un spĂ©cialiste certifiĂ©s, ou mĂȘme avoir un diplĂŽme afin d'ĂȘtre en mesure d'utiliser l'outil d'analyse. D'autres outils offrent une interface d’utilisateur simple, avec des tableaux de bord, pour satisfaire leur comprĂ©hension d’information pour tous ceux qui les ont vus pour la premiĂšre fois. Ce travail sera consacrĂ© aux outils d'analyse Web. Quoi qu'il en soit pour tous ceux qui pensent Ă  utiliser l'analyse pour ses propres besoins se pose une question: "quel outil doit je utiliser, qui convient Ă  mes besoins, et comment payer moins et obtenir un gain maximum". Dans ce travail je vais essayer de donner une rĂ©ponse sur cette question en proposant le systĂšme de recommandation pour les outils analytiques web –WARES, qui aideront l'utilisateur avec cette tĂąche "simple". Le systĂšme WARES utilise l'approche hybride, mais surtout, utilise des techniques basĂ©es sur le contenu pour faire des suggestions. Le systĂšme utilise certains ratings initiaux faites par utilisateur, comme entrĂ©e, pour rĂ©soudre le problĂšme du “dĂ©marrage Ă  froid”, offrant la meilleure solution possible en fonction des besoins des utilisateurs. Le besoin de consultations coĂ»teuses avec des experts ou de passer beaucoup d'heures sur Internet, en essayant de trouver le bon outil. Le systĂšme lui–mĂȘme devrait effectuer une recherche en ligne en utilisant certaines donnĂ©es prĂ©alablement mises en cache dans la base de donnĂ©es hors ligne, reprĂ©sentĂ©e comme une ontologie d'outils analytiques web existants extraits lors de la recherche en ligne prĂ©cĂ©dente.It is hard to imagine modern business without analytics; it is a trend in modern business, even small companies and individual entrepreneurs start using analytics tools, in one way or another, for their business. Not surprising that there exist many different tools for different domains, they vary in purpose from simple friends and visits statistic for your Facebook page, to big and sophisticated systems designed for the big corporations, they could be free or paid. Sometimes you need to pass special training, be a certified specialist, or even have a degree to be able to use analytics tool, other tools offers simple user interface with dashboards for easy understanding and availability for everyone who saw them for the first time. Anyway, for everyone who is thinking about using analytics for his/her own needs stands a question: “what tool should I use, which one suits my needs and how to pay less and get maximum gain”. In this work, I will try to give an answer to this question by proposing a recommender tool, which will help the user with this “simple task”. This paper is devoted to the creation of WARES, as reduction from Web Analytics REcommender System. Proposed recommender system uses hybrid approach, but mostly, utilize content–based techniques for making suggestions, while using some user’s ratings as an input for “cold start” search. System produces recommendations depending on user’s needs, also allowing quick adjustments in selection without need of expensive consultations with experts or spending lots of hours for Internet search, trying to find out the right tool. The system itself should perform as an online search using some pre–cached data in offline database, represented as an ontology of existing web analytics tools, extracted during the previous online search

    Encouraging Inactive Users towards Effective Recommendation

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    Disagreement amongst users in a social network might occur when some of them have different opinion or preferences towards certain items (e.g. topics). Some of the users in the social network might have dynamic preferences due to certain situations. With these differences in opinion amongst the users, some of the users might decide to become either less-active or inactive in providing their opinions on items for recommendation processes to be possible or effective. The current state of the users will lead to a cold-start problem where the recommender system will be unable to find accurate preference information of the users for a recommendation of new items to be provided to them. It will also be difficult to identify these inactive or less-active users within a group for the recommendation of items to be done effectively. Attempts have been made by several researchers to reduce the cold-start problem using singular value decomposition (SVD) algorithm, but the disagreement problem amongst users will still occur due to the dynamic preferences of the users towards items. It was hypothesized in this thesis that an influence based preference modelling could resolve the disagreement problem. It is possible to encourage less-active or inactive users to become active only if they have been identified with a group of their trustworthy neighbours. A suitable clustering technique that does not require pre-specified parameters (e.g. the number of clusters or the number of cluster members) was needed to accurately identify trustworthy users with groups (i.e. clusters) and also identify exemplars (i.e. Cluster representatives) from each group. Several existing clustering techniques such as Highly connected subgraphs (HCS), Markov clustering and Affinity Propagation (AP) clustering were explored in this thesis to check if they have the capabilities to achieve these required outputs. The suitable clustering technique amongst these techniques that is able to identify exemplars in each cluster could be validated using pattern information of past social activities, estimated trust values or familiarity values. The proposed method for estimating these values was based on psychological theories such as the theory of interpersonal behaviour (TIB) and rational choice theory as it was necessary to predict the trustworthiness behaviour of social users. It will also be revealed that users with high trust values (i.e. Trustworthy users) are not necessarily exemplars of various clusters, but they are more likely to encourage less active users in accepting recommended items preferred by the exemplar of their respective cluster

    Changing the focus: worker-centric optimization in human-in-the-loop computations

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    A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back to humans, and study different data analytics problems, by recognizing characteristics of the human workers, and how to incorporate those in a principled fashion inside the computation loop. The first contribution of this dissertation is to propose an optimization framework and a real world system to personalize worker’s behavior by developing a worker model and using that to better understand and estimate task completion time. The framework judiciously frames questions and solicits worker feedback on those to update the worker model. Next, improving workers skills through peer interaction during collaborative task completion is studied. A suite of optimization problems are identified in that context considering collaborativeness between the members as it plays a major role in peer learning. Finally, “diversified” sequence of work sessions for human workers is designed to improve worker satisfaction and engagement while completing tasks

    A user-centered approach to road design : blending distributed situation awareness with self-explaining roads

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    Driving is a complex dynamic task. As the car driver drives along a route they have to adjust their driving technique in accordance with the traffic level, infrastructure and environment around them. The amount of information in the environment would be overwhelming were it not for the presence of stored mental templates, accumulated through training and experience, which become active when certain features are encountered. Problems occur when the environment triggers the incorrect templates, or fails to trigger the correct templates. Problems like these can be overcome by adopting a “self-explaining” (SER) approach to road design. That is to say, purposefully designed roads which trigger correct behaviour. A concept which can help improve the theoretical robustness of the SER approach is Situation Awareness (SA). SA describes how the environment and mental templates work together to ensure drivers remain coupled to the dynamics of their situation. It is a widely researched concept in the field of Human Factors but not in the domain of Self-Explaining Roads (SER), despite the very obvious conceptual overlaps. This thesis, for the first time, blends the two approaches, SA and SER, together. From this the ability to extract cognitively salient features and ability to enhance driving behaviour and their effects on driving behaviour are sufficiently enhanced. After establishing SA as critical to driving through literature review the experiment phase started with determining the source of driver SA. Road environment was found to be of utmost importance for feeding into driver SA. This was also confirmed with the results of the on-road exploratory study. The success of the exploratory study led to large scale naturalistic study. It provided data on driver mental workload, subjective situation awareness, speed profile and endemic feature. Endemic features are unique characteristics of a road which make a road what it is. It was found that not all endemic features contribute to SA of a road system. Therefore through social network analysis list of cognitive salient features were derived. It is these cognitive salient features which hold compatible SA and facilitate SA transaction in a road system. These features were found to reduce speed variance among drivers on a road. The thesis ends by proposing a ‘road drivability tool’ which can predict potentially dangerous zones. Overall, the findings contribute to new imaginative ways road design in order to maximize safety and efficiency
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