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The role of computers in the modern office has divided our activities between virtual interactions in the realm of the computer and physical interactions with real objects within the traditional office infrastructure. This paper extends previous work that has attempted to bridge this gap, to connect physical objects with virtual representations or computational functionality, via various types of tags. We discuss a variety of scenarios we have implemented using a novel combination of inexpensive, unobtrusive and easy to use RFID tags, tag readers, portable computers and wireless networking. This novel combination demonstrates the utility of invisibly, seamlessly and portably linking physical objects to networked electronic services and actions that are naturally associated with their form.

This paper reports on the design and use of tactile user interfaces embedded within or wrapped around the devices that they control. We discuss three different interaction prototypes that we built These interfaces were embedded onto two handheld devices of dramatically different form factors. We describe the design and implementation challenges, and user feedback and reactions to these prototypes. Implications for future design in the area of manipulative or haptic user interfaces are highlighted.

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.

need to be qualified strongly by recognising that our cross sectional data preclude any analysis of changes in risk factors. Our conclusions are suggestive and need to be tested with longitudinal data; however, we are not aware of any longitudinal data sets that contain measures of the relevant risk factors at different stages of life, including childhood.The data of the west of Scotland collaborative study were collected in the early 1970s. It would be useful to establish whether risk factors for cardiovascular disease are still related in the same way to father's and own social class. Although coronary heart disease is less prevalent in women than in men, any study of the current situation should include women.

Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mobile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intelligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a prediction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the realtime spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered features and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use.

As statistical machine learning algorithms and techniques continue to mature, many researchers and developers see statistical machine learning not only as a topic of expert study, but also as a tool for software development. Extensive prior work has studied software development, but little prior work has studied software developers applying statistical machine learning. This paper presents interviews of eleven researchers experienced in applying statistical machine learning algorithms and techniques to human-computer interaction problems, as well as a study of ten participants working during a five-hour study to apply statistical machine learning algorithms and techniques to a realistic problem. We distill three related categories of difficulties that arise in applying statistical machine learning as a tool for software development: (1) difficulty pursuing statistical machine learning as an iterative and exploratory process, (2) difficulty understanding relationships between data and the behavior of statistical machine learning algorithms, and (3) difficulty evaluating the performance of statistical machine learning algorithms and techniques in the context of applications. This paper provides important new insight into these difficulties and the need for development tools that better support the application of statistical machine learning.

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