29,234 research outputs found

    What Would You Ask to Your Home if It Were Intelligent? Exploring User Expectations about Next-Generation Homes

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    Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications; however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deploymen

    A model for mobile content filtering on non-interactive recommendation systems

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    To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems

    Mobile content personalisation using intelligent user profile approach

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    As there are several limitations using mobile internet, mobile content personalisation seems to be an alternative to enhance the experience of using mobile internet. In this paper, we propose the mobile content personalisation framework to facilitate collaboration between the client and the server. This paper investigates clustering and classification techniques using K-means and Artificial Neural Networks (ANN) to predict user's desired content and WAP pages based on device's listed-oriented menu approach. We make use of the user profile and user's information ranking matrix to make prediction of the desired information for the user. Experimental results show that it can generate promising prediction. The results show that it works best when used for predicting 1 matched menu item on the screen

    ENHANCING USERS’ EXPERIENCE WITH SMART MOBILE TECHNOLOGY

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    The aim of this thesis is to investigate mobile guides for use with smartphones. Mobile guides have been successfully used to provide information, personalisation and navigation for the user. The researcher also wanted to ascertain how and in what ways mobile guides can enhance users' experience. This research involved designing and developing web based applications to run on smartphones. Four studies were conducted, two of which involved testing of the particular application. The applications tested were a museum mobile guide application and a university mobile guide mapping application. Initial testing examined the prototype work for the ‘Chronology of His Majesty Sultan Haji Hassanal Bolkiah’ application. The results were used to assess the potential of using similar mobile guides in Brunei Darussalam’s museums. The second study involved testing of the ‘Kent LiveMap’ application for use at the University of Kent. Students at the university tested this mapping application, which uses crowdsourcing of information to provide live data. The results were promising and indicate that users' experience was enhanced when using the application. Overall results from testing and using the two applications that were developed as part of this thesis show that mobile guides have the potential to be implemented in Brunei Darussalam’s museums and on campus at the University of Kent. However, modifications to both applications are required to fulfil their potential and take them beyond the prototype stage in order to be fully functioning and commercially viable

    Bluetooth friendly names: bringing classic HCI questions into the mobile space

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    We explore the use of Bluetooth friendly names within the mobile space. Each Bluetooth-enabled device possesses a short string known as a 'friendly name' used to help identify a device to human users. In our analysis, we collected friendly names in use on 9,854 Bluetooth-enabled devices over a 7-month period. These names were then classified and the results analysed. We discovered that a broad range of HCI themes are applicable to the domain of Bluetooth friendly names, including previous work on personalisation, naming strategies and anonymity in computer mediated communication. We also found that Bluetooth is already being used as a platform for social interaction and communication amongst collocated groups and has moved beyond its original intention of file exchange

    Natural Notation for the Domestic Internet of Things

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    This study explores the use of natural language to give instructions that might be interpreted by Internet of Things (IoT) devices in a domestic `smart home' environment. We start from the proposition that reminders can be considered as a type of end-user programming, in which the executed actions might be performed either by an automated agent or by the author of the reminder. We conducted an experiment in which people wrote sticky notes specifying future actions in their home. In different conditions, these notes were addressed to themselves, to others, or to a computer agent.We analyse the linguistic features and strategies that are used to achieve these tasks, including the use of graphical resources as an informal visual language. The findings provide a basis for design guidance related to end-user development for the Internet of Things.Comment: Proceedings of the 5th International symposium on End-User Development (IS-EUD), Madrid, Spain, May, 201
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