The recent emergence of social systems has transformed the Web from an information pool to a platform for communication and social interaction. As such, the issue of managing privacy of various types of user-created content in these open environments has become more of a concern. Existing social systems often define privacy either as a private/public dichotomy or in terms of a "network of friends relationship, in which all friends" are created equal and all relationships are reciprocal. We explore instead the idea of tagging people to create ego-centric groups of dynamic, non-reciprocal relationships to improve privacy management in this domain. In this paper, we introduce the principles and motivations behind people-tagging, discuss constraints that make people-tagging safe, trustable, and spam-free, describe a research implementation we have created to experiment with the concept, and provide the results of a preliminary empirical evaluation which shows the strength of the idea and indicates areas for future enhancements.

We report preliminary results of our ongoing field study of IT professionals who are involved in security management. We interviewed a dozen practitioners from five organizations to understand their workplace and tools. We analyzed the interviews using a variation of Grounded Theory and predesigned themes. Our results suggest that the job of IT security management is distributed across multiple employees, often affiliated with different organizational units or groups within a unit and responsible for different aspects of it. The workplace of our participants can be characterized by their responsibilities, goals, tasks, and skills. Three skills stand out as significant in the IT security management workplace: inferential analysis, pattern recognition, and bricolage.

This paper presents a grounded theory of information sharing behavior of the users of a personal learning space. A personal learning space is an environment consisted of weblog, ePortfolio, and social networking functionality. It is primarily used within education as a tool to enhance learning, but is also used as a knowledge management tool and to develop communities of practice. Our results identify privacy as a main concern for users of a personal learning space and illustrate challenges users face in ensuring privacy of their information and strategies they employ to achieve the desired level of privacy. We then identify factors that affect users' decisions regarding disclosure of their personal artifacts to various people and groups in a personal learning space. The three main themes as emerged in our study include current stage in the information life cycle, the nature of trust between the owner and the receiver of information, and the dynamics of the group or community within which the information is being shared. Together, these themes portrayed a clearer picture of users' perspective on the privacy of their information in a personal learning space. The findings offer some ideas about how to create privacy management mechanisms for personal learning spaces that are based on users' mental model of information privacy. Practical implications of the results are also discussed.

Email use in the context of everyday work practices, or email flow, has not been heavily studied. We present the results of a pair of studies examining how users interlace email with their day-to-day, ongoing work processes. We demonstrate that our subjects use email as a tool for managing moment-to-moment attention and task focus. We also provide a model of this workflow that builds upon an existing model by Venolia et al. Finally, we provide specific design recommendations to enhance the usability of email clients in support of these modes of interaction.

The usability of access control mechanisms in modern distributed systems has been widely criticized but little studied. In this paper, we carefully examine one such widely deployed access control mechanism, the one embedded in the WebDAV standard, from the point-of-view of an end-user trying to decide how to grant or deny access to some resource to a third party. This analysis points to problems with the conceptual usability of the system. Significant effort is required on the part of the user to determine how to implement the desired access rules; the user, however, has low interest and expertise in this task, given that such access management actions are almost always secondary to the collaborative task at hand. The analysis does however indicate a possible solution: to recast the access control puzzle as a decision support problem in which user intentions (i.e. the descriptions of desired system outputs) are interpreted by an access mediator that either automatically or semi-automatically decides how to achieve the designated goals and provides enough feedback to the user. We call such systems intentional access management (IAM) systems and describe them in both specific and general terms. To demonstrate the feasibility and usability of the proposed IAM models, we develop an intentional access management prototype for WebDAV. The results of a user study conducted on the system show its superior usability compared to traditional access management tools like the access control list editor.

We deomonstrate the potential for fruitful interaction between those studying artificial vision systems and those studying biological vision systems with the task of inferring global curves with corners and bifurcations from images. The talk outlines one slice through a large project on the computational neurobiology of vision. The Dilemma of Curve Detection from previous ones in that no attempt is made to match curves directly against the image, or even against measurements (i.e., "curve operators") evaluated over the image. Instead we stress the organizational point that a stable, reliable, but coarse description should be computed as an intermediate between the image (measurements) and the global curves. A particular intermediate representation—the discrete tangent field—is proposed.

Consider two wire gratings, superimposed and moving across each other. Under certain conditions the two gratings will cohere into a single, compound pattern, which will appear to be moving in another direction. Such coherent motion patterns have been studied for sinusoidal component gratings, and give rise to percepts of rigid, planar motions. In this paper we show how to construct coherent motion displays that give rise to nonuniform, nonrigid, and nonplanar percepts. Most significantly, they also can define percepts with corners. Since these patterns are more consistent with the structure of natural scenes than rigid sinusoidal gratings, they stand as interesting stimuli for both computational and physiological studies. To illustrate, our display with sharp corners (tangent discontinuities or singularities) separating regions of coherent motion suggests that smoothing does not cross tangent discontinuities, a point that argues against existing (regularization) algorithms for computing motion. This leads us to consider how singularities can be confronted directly within optical flow computations, and we conclude with two hypotheses: (1) that singularities are represented within the motion system as multiple directions at the same retinotopic location; and (2) for component gratings to cohere, they must be at the same depth from the viewer. Both hypotheses have implications for the neural computation of coherent motion.

The problem of detecting curves in visual images arises in both computer vision and biological visual systems. Our approach integrates constraints from these two sources and suggests that there are two different stages to curve detection, the first resulting in a local description, and the second in a global one. Each stage involves a different style of computation: in the first stage, hypotheses are represented explicitly and coarsely in a fixed, preconfigured architecture; in the second stage, hypotheses are represented implicitly and more finely in a dynamically constructed architecture. We also show how these stages could be related to physiology, specifying the earlier parts in a relatively fine-grained fashion and the later ones more coarsely.

We propose a new paradigm for curve detection in which an autonomous and atomic description is computed between measurements on the image and global curves. The description takes the form of a discrete tangent field, i.e.. an estimated representation of quantized tangents and curvatures at each possible trace point. We then present a new algorithm for inferring global curves through this tangent field. by inferring a covering of the global curve. The elements of the covering are short splines, each of which moves according to a potential distribution computed from the tangent field. Both stages of the algorithm are parallel and both bear some analogy to biological mechanisms for curve detection