Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend

FamTV : an architecture for presence-aware personalized television

Since the advent of the digital era, the traditional TV scenario has rapidly evolved towards an ecosystem comprised of a myriad of services, applications, channels, and contents. As a direct consequence, the amount of available information and configuration options targeted at today's end consumers have become unmanageable. Thus, personalization and usability emerge as indispensable elements to improve our content-overloaded digital homes. With these requirements in mind, we present a way to combine content adaptation paradigms together with presence detection in order to allow a seamless and personalized entertainment experience when watching TV.This work has been partially supported by the Community of Madrid (CAM), Spain under the contract number S2009/TIC-1650.Publicad

MarathOn Multiscreen: group television watching and interaction in a viewing ecology

This paper reports and discusses the findings of an exploratory study into collaborative user practice with a multiscreen television application. MarathOn Multiscreen allows users to view, share and curate amateur and professional video footage of a community marathon event. Our investigations focused on collaborative sharing practices across different viewing activities and devices, the roles taken by different devices in a viewing ecology, and observations on how users consume professional and amateur content. Our Work uncovers significant differences in user behaviour and collaboration when engaged in more participatory viewing activities, such as sorting and ranking footage, which has implications for awareness of other users’ interactions while viewing together and alone. In addition, user appreciation and use of amateur video content is dependent not only on quality and activity but their personal involvement in the contents

Context aware advertising

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 user’s emotion is happiness; however, it showed a degradation of performance when the user’s 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

MarathOn multiscreen: group television watching and interaction in a viewing ecology

This paper reports and discusses the findings of an exploratory study into collaborative user practice with a multiscreen television application. MarathOn Multiscreen allows users to view, share and curate amateur and professional video footage of a community marathon event. Our investigations focused on collaborative sharing practices across different viewing activities and devices, the roles taken by different devices in a viewing ecology, and observations on how users consume professional and amateur content. Our Work uncovers significant differences in user behaviour and collaboration when engaged in more participatory viewing activities, such as sorting and ranking footage, which has implications for awareness of other users’ interactions while viewing together and alone. In addition, user appreciation and use of amateur video content is dependent not only on quality and activity but their personal involvement in the contents

Many-screen viewing: collaborative consumption of television media across multiple devices

The landscape of television is changing. Modern Internet enabled sets are now capable computing devices offering new forms of connectivity and interaction to viewers. One development enabled by this transition is the distribution of auxiliary content to a portable computing device, such as a mobile phone or tablet, working in concert with the television. These configurations are enabled by second screen applications that provide relevant content in synchronisation with the programme on a nearby television set. This thesis extends the notion of second screen to arrangements that incorporate multiple mobile devices working with the television, utilised by collocated groups of participants. Herein these arrangements are referred to as ‘many-screen’ television. Two many-screen applications were developed for the augmentation of sports programming in preparation of this thesis; the Olympic Companion and MarathOn Multiscreen Applications. Both of these applications were informed by background literature on second screen television and wider issues in HCI multiscreen research. In addition, the design of both applications was inspired by the needs of traditional and online broadcasters, through an internship with BBC Research and Development and involvement in a YouTube sponsored project. Both the applications were evaluated by collocated groups of users in formative user studies. These studies centred on how users share and organise what to watch, incorporate activity within the traditionally passive television viewing experience and the integration of user-generated video content in a many-screen system. The primary contribution of this thesis is a series of industry validated guidelines for the design of many-screen applications. The guidelines highlight issues around user awareness devices, content and other user’s actions, the balance between communal and private viewing and the appropriation of user-generated content in many-screen watching