Tagging amongst friends: an exploration of social media exchange on mobile devices

Mobile social software tools have great potential in transforming the way users communicate on the move, by augmenting their everyday environment with pertinent information from their online social networks. A fundamental aspect to the success of these tools is in developing an understanding of their emergent real-world use and also the aspirations of users; this thesis focuses on investigating one facet of this: the exchange of social media. To facilitate this investigation, three mobile social tools have been developed for use on locationaware smartphone handsets. The first is an exploratory social game, 'Gophers' that utilises task oriented gameplay, social agents and GSM cell positioning to create an engaging ecosystem in which users create and exchange geotagged social media. Supplementing this is a pair of social awareness and tagging services that integrate with a user's existing online social network; the 'ItchyFeet' service uses GPS positioning to allow the user and their social network peers to collaboratively build a landscape of socially important geotagged locations, which are used as indicators of a user's context on their Facebook profile; likewise 'MobiClouds' revisits this concept by exploring the novel concept of Bluetooth 'people tagging' to facilitate the creation of tags that are more indicative of users' social surroundings. The thesis reports on findings from formal trials of these technologies, using groups of volunteer social network users based around the city of Lincoln, UK, where the incorporation of daily diaries, interviews and automated logging precisely monitored application use. Through analysis of trial data, a guide for designers of future mobile social tools has been devised and the factors that typically influence users when creating tags are identified. The thesis makes a number of further contributions to the area. Firstly, it identifies the natural desire of users to update their status whilst mobile; a practice recently popularised by commercial 'check in' services. It also explores the overarching narratives that developed over time, which formed an integral part of the tagging process and augmented social media with a higher level meaning. Finally, it reveals how social media is affected by the tag positioning method selected and also by personal circumstances, such as the proximity of social peers

Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach

Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the differences in activity preference among the individual, adherence problems, and uncertainty of future health outcomes may reduce the effect of the mHealth information service. The current health service system usually provides recommendations based on fixed exercise plans that do not satisfy the user specific needs. This paper seeks an efficient way to make physical activity recommendation decisions on physical activity promotion in personalised mHealth information service by establishing data-driven model. In this study, we propose a real-time interaction model to select the optimal exercise plan for the individual considering the time-varying characteristics in maximising the long-term health utility of the user. We construct a framework for mHealth information service system comprising a personalised AI module, which is based on the scientific knowledge about physical activity to evaluate the individual exercise performance, which may increase the awareness of the mHealth artificial intelligence system. The proposed deep reinforcement learning (DRL) methodology combining two classes of approaches to improve the learning capability for the mHealth information service system. A deep learning method is introduced to construct the hybrid neural network combing long-short term memory (LSTM) network and deep neural network (DNN) techniques to infer the individual exercise behavior from the time series data. A reinforcement learning method is applied based on the asynchronous advantage actor-critic algorithm to find the optimal policy through exploration and exploitation

Acceptance and commitment therapy delivered in a dyad after a severe traumatic brain injury: a feasibility study

Objective: There is a high prevalence of complex psychological distress after a traumatic brain injury but limited evidence of effective interventions. We examined the feasibility of Acceptance and Commitment Therapy after a severe traumatic brain injury using the criteria, investigating a therapeutic effect, and reviewing the acceptability of measures, treatment protocol, and delivery method (in a dyad of two clients and a therapist). Method: Two male outpatients with severe traumatic brain injury and associated psychological distress jointly engaged in a seven session treatment program based on Acceptance and Commitment Therapy principles. Pre- and post-treatment measures of mood, psychological flexibility, and participation were taken in addition to weekly measures. Results: The intervention showed a therapeutic effect with one participant, and appeared to be acceptable for both participants with regard to program content, measures, and delivery mode by in a dyad. One participant showed both significant clinical and reliable change across several outcome measures including measures of mood and psychological flexibility. The second participant did not show a reduction in psychological inflexibility, but did show a significant drop in negative affect. Significant changes pre- to post-treatment for measures of participation were not indicated. Qualitatively, both participants engaged in committed action set in accordance with their values. Conclusions: This study suggests that Acceptance and Commitment Therapy may be feasible to be delivered in a dyad with individuals who have a severe traumatic brain injury. A further test of its potential efficacy in a phase II clinical trial is recommended

Learning preferences for personalisation in a pervasive environment

With ever increasing accessibility to technological devices, services and applications there is also an increasing burden on the end user to manage and configure such resources. This burden will continue to increase as the vision of pervasive environments, with ubiquitous access to a plethora of resources, continues to become a reality. It is key that appropriate mechanisms to relieve the user of such burdens are developed and provided. These mechanisms include personalisation systems that can adapt resources on behalf of the user in an appropriate way based on the user's current context and goals. The key knowledge base of many personalisation systems is the set of user preferences that indicate what adaptations should be performed under which contextual situations. This thesis investigates the challenges of developing a system that can learn such preferences by monitoring user behaviour within a pervasive environment. Based on the findings of related works and experience from EU project research, several key design requirements for such a system are identified. These requirements are used to drive the design of a system that can learn accurate and up to date preferences for personalisation in a pervasive environment. A standalone prototype of the preference learning system has been developed. In addition the preference learning system has been integrated into a pervasive platform developed through an EU research project. The preference learning system is fully evaluated in terms of its machine learning performance and also its utility in a pervasive environment with real end users

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques

Cloud computing as a fairly new commercial paradigm, widely investigated by different researchers, already has a great range of challenges. Pricing is a major problem in Cloud computing marketplace; as providers are competing to attract more customers without knowing the pricing policies of each other. To overcome this lack of knowledge, we model their competition by an incomplete-information game. Considering the issue, this work proposes a pricing policy related to the regret minimization algorithm and applies it to the considered incomplete-information game. Based on the competition based marketplace of the Cloud, providers update the distribution of their strategies using the experienced regret. The idea of iteratively applying the algorithm for updating probabilities of strategies causes the regret get minimized faster. The experimental results show much more increase in profits of the providers in comparison with other pricing policies. Besides, the efficiency of a variety of regret minimization techniques in a simulated marketplace of Cloud are discussed which have not been observed in the studied literature. Moreover, return on investment of providers in considered organizations is studied and promising results appeared

Human-computer interaction in e-business

E-business has brought much change to our daily life and will become a necessary part of business, economy, and society. At least for the foreseeable future, e-business will keep growing. Each study of this dissertation was devoted to human-computer interaction (HCI) in e-business to improve website usability. First, data input tools were compared and optimal design characteristics were suggested for usable web based interaction. When proper input tools are employed, higher usability can be achieved. Second, a practical design process and the use of web elements were studied through the simulation of an e-bookstore. Web design influences e-business traffic and sales. Third, a grid menu was designed and examined for situations in which a menu contains a larger number of options. The grid menu was observed to be both robust and efficient. Fourth, an interaction model for the pull-down menu, including perceptive, cognitive, and motor behavior processes, was studied. The resulting model fit the experimental data well. Fifth, problems with iconic interfaces on e-business websites were reported and a methodology suggested to improve user interface design

Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専