Titlebook: Location- and Context-Awareness; Second International Mike Hazas,John Krumm,Thomas Strang Conference proceedings 2006 Springer-Verlag Berli

湿润

Towards Personalized Mobile Interruptibility Estimationnt people differ in the way they rate their interruptibility. In this paper we investigate three options how to adapt an interruptibility estimation system to a particular user: by finding prototypical users, using experience sampling, or using knowledge of prototypical situations. We have experimen

Anticoagulant

Unsupervised Discovery of Structure in Activity Data Using Multiple Eigenspacess data in terms of multiple low-dimensional eigenspaces. We describe the algorithm and propose an extension that allows to handle multiple time scales. The validity of the approach is demonstrated on several data sets and using two types of acceleration features. Finally, we report on experiments th

agglomerate

Toward Scalable Activity Recognition for Sensor Networksciency, safety, and security. To be practical, such a network must be economical to manufacture, install and maintain. Similarly, the methodology must be efficient and must scale well to very large spaces. Finally, be be widely acceptable, it must be inherently privacy-sensitive. We propose to addre

Bmd955

Nomatic: Location By, For, and Of Crowdsagenda is to leverage new computing opportunities that arise when . people are simultaneously localizing themselves. By aggregating this and other types of context information we intend to develop a statistically powerful data set that can be used by urban planners, users and their software. This pa

Gorilla

An Unsupervised Learning Paradigm for Peer-to-Peer Labeling and Naming of Locations and Contextsess used a consistent approach to naming contexts is required. A novel paradigm for labeling contexts is described based on close range wireless connections between devices and a very simple, unsupervised learning algorithm. It is shown by simulation analysis that it is possible to achieve a labelin

不自然

赤字

Evaluating Performance in Continuous Context Recognition Using Event-Driven Error Characterisationdealing with this, and in general methods and measures are adapted from related fields such as speech and vision. Much of the problem stems from the often imprecise and ambiguous nature of the real-world events that an activity recognition system has to deal with. A recognised event might have varia

abracadabra

Melodrama

Chauvinistic

8楼