Activity recognition is a key function for many context-aware applications in a smart environment. However, data collection and annotation for activity recognition is both time-consuming and costly. This paper proposes the hierarchical activity representation to enhance data reusability and introduces Personal Activity Logger (PAL), a computer aided tool with it, to reduce annotation efforts. We experimented with PAL in annotating activities within a personal space from power meters and a webcam in the office. Preliminary results show that PAL is effective in reducing the annotation efforts with only a slight loss in quality. In addition, we indicate the potential possibility to identify users from the distribution of events in their activities through the data analysis.

We describe work on representing and using a rich notion ofcontext that goes beyond current networking applications focusingmostly on location. Our context model includes locationand surroundings, the presence of people and devices,inferred activities and the roles people fill in them. A keyelement of our work is the use of collaborative informationsharing where devices share and integrate knowledge abouttheir context. This introduces a requirement that users canset appropriate levels of privacy to protect the personal informationbeing collected and the inferences that can be drawnfrom it. We use Semantic Web technologies to model contextand to specify high-level, declarative policies specifying informationsharing constraints. The policies involve attributesof the subject (i.e., information recipient), target (i.e., the information)and their dynamic context (e.g., are the parties copresent).We discuss our ongoing work on context representationand inference and present a model for protecting andcontrolling the sharing of private data in context-aware mobileapplications.

In this paper, we present some of our work in mobile user modeling following the three steps in a general user modeling process. First, we outline a framework for mobile user activity logging. The framework integrates various hardware and software sensors on smartphones. Second, we have worked on learning relevant user locations for personal information management and recognizing user activities from sensor data to analyze the collected data. Third, the user model can be used to adapt mobile information access, for example in mobile recommender systems. The paper also outlines some requirements for an Activity Context Representation and Exchange Language from the perspective of mobile user modeling.

Mobile applications can be greatly enhanced if they have information about the situation of the user. Situations may be inferred by analyzing several types of contextual information drawn from device sensors, such as location, motion, ambiance and proximity. To capture a richer understanding of users’ situations, we introduce an ontology describing the relations between background knowledge about the user and contexts inferred from sensor data. With the right combination of machine learning and semantic modeling, it is possible to create high-level interpretations of user behaviors and situations. However, the potential of understanding and interpreting behavior with such detailed granularity poses significant threats to personal privacy. We propose a framework to mitigate privacy risks by filtering sensitive data in a context-aware way, and maintain provenance of inferred situations as well as relations between existing contexts when sharing information with other parties.

Activity recognition is a widely researched area with applications in health care, security and other domains. With each recognition system considering its own set of activities and sensors, it is difficult to compare the performance of these different systems and more importantly it makes the task of selecting an appropriate set of technologies and tools for recognizing an activity challenging. In this work-in-progress paper we attempt to characterize activities in terms of a complexity measure. We define activity complexity along three dimensions – sensing, computation and performance and illustrate different parameters that parameterize these dimensions. We look at grammars for representing activities and use grammar complexity as a measurement for activity complexity. Then we describe how these measurements can help evaluate the complexity of activities of daily living that are commonly considered by various researchers.

Lack of context in information is a serious problem for knowledge-workers. Effective utilization of computational aids for supporting knowledge-workers require a rich understanding of the nature of context of information and related knowledge-works. It also needs specifications about how such understanding can be leveraged in computer-based systems. In this paper we propose a holistic model of context of knowledge-works and information created in course of their performances. We also demonstrate with an example how such a model can be used as basis for developing a formal, machine-deployable specification of activity context.

The goal of representing context in a mixed initiative sys-tem is to model the information at a level of abstraction that is actionable for both the human and automated system. A potential solution to this problem is the Context for Human and Automation Teams (CHAT). This paper introduces the CHAT model and provides example implementations from several different applications such as task scheduling tech-niques, multi-agent systems, and human-robot interaction.

Representing context information associated with people and digital devices performing activities is presented using a formal systems model based on a legal but simplified version of set theory. A five set Venn diagram, the PentaVenn diagram, allows analysts to work using a graphical logic rather than with equations. Model symmetry is shown to facilitate identifying different types of context, tangible and intangible.

This position paper describes the Activity-Based Computing (ABC) project which has been ongoing in Denmark since 2003. Originally, the project took its outset in the design of a pervasive computing platform suited for the mobile, collaborative, and time-critical work of clinicians in a hospital setting. Out of this grew a conceptual framework, a set of six ABC principles, and a programming and runtime framework for the development of activity-based computing infrastructures and applications. Lately, these principles and technologies have been successfully moved to other application areas, and is now used to design and implement activity-based computing support for work in a biology laboratory and for global software development.

We study the theoretical advantages of active learning over passive learning. Specifically, we prove that, in noise-free classifier learning for VC classes, any passive learning algorithm can be transformed into an active learning algorithm with asymptotically strictly superior label complexity for all nontrivial target functions and distributions. We further provide a general characterization of the magnitudes of these improvements in terms of a novel generalization of the disagreement coefficient. We also extend these results to active learning in the presence of label noise, and find that even under broad classes of noise distributions, we can typically guarantee strict improvements over the known results for passive learning.