Design and evaluation of user-generated media production and sharing in Human-Computer Interaction (HCI) often focus on formal and informal media sharing, such as communication within social networks, automatic notifications of activities, and the exchange of digital artifacts. However, conceptual tools for understanding how people relate to the audiences they reach through these systems are limited. The increasing interest in user-generated content in HCI demands the infusion of new methods and theories that explicitly engage the construction and use of media within and among large groups of individuals and systems. In this paper, we suggest that the notion of “publics,” drawn from media theory, provides useful insights into user-driven, social, and cultural forms of technology use and digital content creation. We illustrate this by employing the notion of publics to the findings from a two-month deployment of a mobile photo sharing platform in a youth housing community. The results of this empirical work coupled with a theoretical examination of publics stimulate reflection on prevailing interpretations of user-designer-reader roles. The paper provides an outlook for potentially new and productive ways of understanding interdependencies within those activities. Implications that can be drawn from this work concern the role of digital media creation and sharing for the formation of collectives and how people position themselves collectively in relation to larger social groups and societal norms. The analysis suggests fruitful crossovers among HCI, Media Theory and New Media Research by approaching the user as both consumer and producer of digital content.

In this paper, we present a method for representing and discovering skills by a robot based on observed data. First, the capabilities of a robot to transform a situation from one to another based on available control actions are extracted from the data, and represented in the situation-action space as a feasible situation transition manifold (FSTM). The multi-resolution globally competitive and locally cooperative algorithm is formulated to self-organize the FSTM in terms of an union of hyper-ellipsoidal subregions in various sizes and shapes. An optimal sequence of transitions from the initial to the goal situations is searched for as a skill, under the constraint imposed by the FSTM. The search of an optimal sequence is based on the novel bidirectional dynamic path planning algorithm formulated based on the potential field method. The proposed methodology is applied for the discovery of a nonholonomic motion planning skill of a car-like robot and for a telemanipulation skill.

This paper presents a new approach for representing and registering skills based on state transition graphs. First, we generate clusters of feasible state transitions from the experimental data such that the union of these clusters represents the feasible state transition region of the system. Globally competitive and locally cooperative (GCLC) algorithm is formulated for self-organizing clusters and state transitions accurately through interpolation. A state transition graph is then constructed by representing possible transitions between clusters. Skills are considered embedded in this state transition graph and can be extracted by searching for an optimal path from the current to the goal states. The reactive nature of skills can be implemented by forming priority indices for sibling branches for local decision with possible merging and splitting of clusters to ensure consistency in prioritization. Experimental results are shown.>

This paper presents a method of automatically constructing a model or a set of constraints from domain principles for solving the dynamic behavior of a mechanical system through qualitative simulation. It emphasizes the following issues: the extraction of a set of necessary and sufficient constraints which provides the minimum uncertainty associated with qualitative simulation, from the fundamental principles and laws of physics based solely on the physical description of a given mechanical system; and the modification of constraints through time by detecting and identifying “system discontinuities” due to collisions, separations, and other critical states associated with each of the object primitives. The first is accomplished by describing a mechanical system by a collection of object and inter-connection primitives, which allows direct instantiating of all the relevant physics laws from the knowledge base. Then, the final set of constraints having the minimum complexity and uncertainty is extracted from the relevant physics laws, based on an A* algorithm with heuristics providing the problem solving expertise. The second is accomplished by monitoring whether the states of any individual sub-systems evolve to system discontinuities represented by intra/inter subsystem critical states.

A framework for applying cognitive abstraction in the domain of qualitative physics to suppress irrelevant distinctions between the possible behaviors is presented. Cognitive abstraction is especially useful for inferring the behavior of a physical system based on incremental qualitative simulation. The framework of a prototype of cognitive abstraction, based on QSIM, is presented. The limitations of the approach are discussed.>

A method is presented for automatically generating a set of qualitative constraints as the model of a physical system. It emphasizes (1) the generation of qualitative constraints which provide minimum uncertainty for qualitative simulation, and (2) the generation of mathematical constraints for reducing the uncertainty during qualitative simulation. The first is accomplished by searching for a set of redundant constraints based on fundamental principles and the laws of physics. The second is accomplished by applying mathematical knowledge to generate mathematical constraints which are used as global filters during qualitative simulation.>