With the increased availability of rich behavioral data sets, we present a novel combination of tools to analyze to analyze this information.  Using criminal offense records as an example, we employ cross-correlation measures, eigenvalue spectrum analysis, and results from random matrix theory to identify spatiotemporal patterns.  Finally, with multivariate autoregressive models, we demonstrate a possible source of structure within the data.

Online social lending is the Web 2.0's response to classical bank loans. Borrowers publish credit applications on websites which match them with private investors. We point to a conflict between economic interests and privacy goals in online social lending, empirically analyze the effect of data disclosure on credit conditions, and outline directions towards efficient yet privacy-friendly alternative credit markets.

Cellular phones can be used as mobile sensors, continuously logging users’ behavior including movement, communication and proximity to others. While it is well understood that data generated from mobile phones includes a record of phone calls, there are also more sophisticated data types, such as Bluetooth or cell tower proximity logging, which reveal movement patterns and day-to-day human interactions. We explore the possibility of using mobile phone data to compare movement and communication patterns across cultures. The goal of this proof-of-concept study is to quantify behavior in order to compare different populations. We compare our ability to predict future calling behavior and movement patterns from the cellular phone data of subjects in two distinct groups: a set of university students at MIT in the United States and the University of Nairobi in Kenya. In addition, we show how Bluetooth data may be used to estimate the diffusion of an airborne pathogen outbreak in the different populations.

Evaluating new educational programs and tools, especially those targeted at difficult-to-assess learning goals can be quite challenging due to the small number of participants typically engaged with pilot programs. The focus of the evaluation, then, should be on collecting rich data from each participant about their experience in the workshop and their progress towards meeting the workshop’s learning goals. We present a novel evaluation technique, the debugging task, that seeks to assess at post-workshop a participant’s independent ability to use the tools, skills, and materials of the workshop. The technique is presented in the context of Robot Diaries, a program to develop a robotics design activity centered on crafts materials and expressiveness, and targeted to middle school girls. The paper discusses the rationale for the debugging task, its implementation, and the results and analyses of girls completing the task.

A Digital Product Memory provides a digital diary of the complete product life cycle that is embedded in the product itself using smart wireless sensor technology. The data is hereby gathered by recording relevant ambient parameters in digital form. In this paper, we present the architecture and cost-efficient implementation of an autonomous digital product memory that generates and interprets its diary using rule-based reasoning methods. As we assume an open, heterogeneous sensor infrastructure, we rely on standard syntax and semantics provided by the Web Ontology Language OWL. The digital product memory collects and provides data using the OWL fragment OWL2 RL which can be processed with standard rule engines. As rule engine we use CLIPS on embedded hardware and exemplify the application of the digital product memory e.g. for predictive maintenance.

The Web of Data (WoD) is growing at an amazing rate and it will no longer be feasible to deal with it in a global way, by centralising the data or reasoning processes making use of that data. We believe that Computational Intelligence techniques provides the adaptiveness, robustness and scalability that will be required to exploit the full value of ever growing amounts of dynamic Semantic Web data.

Time perception and inferences there from are of critical importance to many autonomous agents. But time is not perceived directly by any sensory organ. We argue that time is constructed by cognitive processes. Here we present a model for time perception that concentrates on succession and duration, and that generates these concepts and others, such as continuity, immediate present duration, and lengths of time. These concepts are grounded through the perceptual process itself. The LIDA cognitive model is used to illustrate these ideas.

On interactive surfaces, an accurate system calibration is crucial for a precise user interaction. Today, geometric distortions are eliminated by a static calibration. However, this calibration is specific to a user’s posture, and parallax distortions occur if this changes (i.e. if the user moves or if multiple users take turns). Within this paper, we describe an approach to model automatic online re-calibration to cope with changing viewpoints by using Partially Observable Markov Decision Processes (POMDP). Hereby, the viewpoint is stochastically deducted from the precision of user interactions on the surface. To enable the implementation on embedded systems, a small model is defined using states and observations, which are formulated relative to the current assumed viewpoint. We show the structure of a family of models, that can be generated automatically based on the user’s position probability and pointing accuracy.

Navigation and localization are still one of the most fundamental tasks to be accomplished by mobile autonomous robots. One of the main purposes of the navigation and localization process is to build a precise, usually allocentric spatial static representation (e.g. [S. Thrun and Schulz, 2000] ). Although robots are able to carry more and more powerful sensors, the question is, which informations are needed for localization and navigation. One way to do these tasks with only a minimal amount of resources is via landmarks. Furthermore it is an easy and failsafe way to do so. Localization can be done with only a single 180 degree camera, and a navigation by the change of the landmark ordering is very robust against misinterpretations and errors. This technique uses the fact that, seen from the agent, landmarks are switching locations only in a certain way( [Wagner, Visser, and Herzog, 2004] ). With an additional timer the robustness of this technique can be further increased. But with timing and the use of angles between the landmarks, it is also possible to measure the distances between the landmarks and the agent. Furthermore this technique can be extended to detect moving objects and to compute the speed and direction of them.

Integrating robotics activities in science curriculum provides rich opportunities to engage students in real world science and help them to develop conceptual understanding of physics principles through the process of investigation, data analysis, engineering design, and construction. In addition, students become more confident learners and develop better problem-solving and teamwork skills. In this paper we describe a successful use of LEGO® MINDSTORMS® in designing robotics-based activities for teaching high school physics classes. Students design and perform novel science investigations with a toolset that helps them achieve a high reproducibility in their experimental designs. Several example projects that utilize LEGO MINDSTORMS are presented.