The privacy dangers of data proliferation on the Web are well-known. Information on the Web has facilitated the deanonymization of anonymous bloggers, the de-sanitization of government records and the identification of individuals based on search engine queries. What has received less attention is Web-mining in support of privacy. In this position paper we argue that the very ability ofWeb data to breach privacy demonstrates its value as a laboratory for the detection of privacy breaches before they happen. In addition, we argue that privacy-invasive services may become privacy-respecting by mining publicly available Web data, with little decrease in performance and efficiency.

Teams of University have mentored middle and high school students primary and secondary school students participate in one of from Durham Public Schools as part of the Duke three competitions: rescue, dance, or soccer. Rescue teams RoboCupJunior program, a project-oriented, team-based build and program a robot capable of navigating a course academic enrichment program. The objective of the while identifying and rescuing victims by following lines program is to foster interest and competence in computing, and responding to color cues. Dance teams choreograph a science, and mathematics, while simultaneously dance routine using robots they build and decorate, developing problem-solving skills, enabling creative costumes they prepare, and music they select. Soccer teams thinking and design, and providing a domain for build and program two robots, which then face off against application of scientific concepts. Robotics is a popular other teams' robots in a soccer match. Winners of regional domain for attracting students to computing and competitions are eligible to attend the annual RoboCup engineering (Sklar, Parsons 2002) and can be used in World Finals, held at various locations around the globe.

In this position paper, we argue that the Linked Open Data (LoD) Cloud, in its current form, is only of limited value for furthering the Semantic Web vision. Being merely a weakly linked triple collection, it will only be of very limited benefit for the AI or Semantic Web communities. We describe the corresponding problems with the LoD Cloud and give directions for research to remedy the situation.

As the pace of Linked data generation and usage increases, so does the interest in intelligent, usable, and scalable browsing tools. Faceted browsing has potential to provide a foundation for effective dataset navigation. In this paper, we will discuss some of the anticipated benefits along with some associated challenges in building the next-generation faceted browsing system for the Web of Linked Data. We also present our initial system design and implementation.

Internal conflict, in which a character is torn by opposing motivations, is central to drama. Actors portray such conflict in part by mimicking involuntary behaviors that occur as a result of such conflicts. In this paper, we examine the role of timing – pauses and hesitation, in particular – in internal conflict. We argue that virtual actors can be made more expressive if we can emulate the underlying structures of inhibition and conflict detection believed to operate in the human system. We discuss work in progress on this problem that uses the Twig procedural animation system.

Modern web sites provide easy access to large amounts of data via open application programming interfaces. Users interacting with these sites constantly change the underlying data sets, which can be represented in graph-structured form. Nodes in these dynamic graph structures exhibit dependencies over time. Analysing these dependencies is crucial for understanding and predicting the dynamics inherent to temporally changing graph structures on the web. When the graphs become large however, it is not feasible to take into account all properties of the graph and in general it is unclear how to choose the appropriate features. Moreover, comparing two nodes becomes difficult, if the nodes do not share exactly the same features. In this work we propose an algorithm that automatically learns the features that govern temporal dependencies between nodes in large dynamic graph structures. We present preliminary results of applying the algorithm to data collected from the web, discuss potential extensions of the framework and anticipate how a major problem in data mining, sparse data, could be tackled by leveraging Linked Data.

Anticipating the actions of others is key to coordinating joint activities. We propose the notion of anticipatory action and perception for for robots acting with humans. We describe four systems in which anticipation has been modeled for human-robot interaction; two in a teamwork setting, and two in a human-robot joint performance setting. In evaluating the effects of anticipatory agent activity, we find in one study that anticipation aids in team efficiency, as well as in the perceived commitment of the robot to the team and its contribution to the team's fluency and success. In another study we see anticipatory action and perception affect the human partner's sense of team fluency, the team's improvement over time, the robot’s contribution to the efficiency and fluency, the robot's intelligence, and the robot’s adaptation to the task. We also find that subjects working with the anticipatory robot attribute more human qualities to the robot, such as gender and intelligence.

This paper presents an approach for mining agricultural problems that have been accumulated in a textual database over a period of 5 years. The problems, which are accompanied by their solutions, offer a wealth of knowledge that can be used by decision makers, researchers, and farmers alike. However, this wealth of knowledge can not be unlocked without a) representing these problems in a structured format, and b) applying algorithms that can summarize and analyze this information. Towards the achievement of the first goal, a multi-faceted object extraction methodology is presented, and for the achievement of the second, association rules are employed. As a proof of concept, the tool was applied of a set of weed problems. The presented methodology can be modified to work with any help and support textual database where both problems and their solutions are present.

In this paper, we describe our initial experiences using a mobile robot as a teaching aid in a stage movement class, taught in the Performing Arts Department of Washington University in St. Louis. The robot participated in a number of exercises, intended to teach the fundamentals of movement, and interacted closely with human acting students. We describe these exercises, what they are designed to teach the students, and discuss how using a robot as a teaching aid can enhance the students' experience. We describe two classes in which a robot participated, one under tele-operation and one fully autonomously, and discuss both the students' reaction to the robots, and our subjective evaluations of the systems success.

Traffic monitoring systems usually make assumptions about the movement of vehicles, such as that they drive in dedicated lanes, and that those lanes rarely include non-vehicle clutter. Urban settings within developing countries often present extremely chaotic traffic scenarios which make these assumptions unrealistic. We show how a standard approach to traffic monitoring can be made more robust by using probabilistic inference, and in such a way that we bypass the need for vehicle segmentation. Instead of tracking individual vehicles but treat a lane of traffic as a fluid and estimate the rate of flow. Our modelling of uncertainty allows us to accurately monitor traffic flow even in the presence of substantial clutter.