In Web-based CSCW (Computer-Supported Cooperative Work) often including cooperative learning, remote members communicate their intentions in cyberspace, using textual sentences, pictures and voice. However, often, communication between members cannot be correctly done and interface errors occur. Different from face-to-face communication, partners' situations including their interest, concentration, boredom, and tiredness cannot be easily transmitted. Oversight and mishearing of remote partners is often overlooked. Besides, it is further difficult to understand their real intentions sufficiently. To overcome these problems, “Augmented Cyberspace” for dependable Web-based CSCW Systems, is proposed, which is also applicable to system such as e-learning, e-commerce, etc. This assesses situations of remote users through timely fusing information of multiple biological sensors and the related contexts. By exploiting the timely assessment, the system augments the cyberspace through emphasizing the situation of remote users or providing warnings in conventional media such as text, image, and voice. Experimental results showed the necessity and feasibility of such assessment by information fusion of multiple sensors.

Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy. In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.

As robots become more common, it becomes increasingly useful for them to communicate and effectively share knowledge that they have learned through their individual experiences.  Learning from experiences, however, is often-times embodiment-specific; that is, the knowledge learned is grounded in the robot’s unique sensors and actuators.  This type of learning raises questions as to how communication and knowledge exchange via social interaction can occur, as properties of the world can be grounded differently in different robots.  This is especially true when the robots are heterogeneous, with different sensors and perceptual features used to define the properties.  In this paper, we present methods and representations that allow heterogeneous robots to learn grounded property representations, such as that of color categories, and then build models of their similarities and differences in order to map their respective representations.  We use a conceptual space representation, where object properties are learned and represented as regions in a metric space, implemented via supervised learning of Gaussian Mixture Models.  We then propose to use confusion matrices that are built using instances from each robot, obtained in a shared context, in order to learn mappings between the properties of each robot.  Results are demonstrated using two perceptually heterogeneous Pioneer robots, one with a web camera and another with a camcorder.

We compare two approaches to Bayesian network inference, called variable elimination (VE) and arc reversal (AR). It is established that VE never requires more space than AR, and never requires more computation (multiplications and additions) than AR.

The paper concentrates on obtaining hidden relationships among individual clauses of complex sentences from the Prague Dependency Treebank. The treebank contains only an information about mutual relationships among individual tokens (words, punctuation marks), not about more complex units (clauses). For the experiments with clauses and their parts (segments) it was therefore necessary to develop an automatic method transforming the original annotation into a scheme describing the syntactic relationships between clauses. The task was complicated by a certain degree of inconsistency in original annotation with regard to clauses and their structure. The paper describes the algorithm of deriving clause-related information from the existing annotation and its evaluation.

Emotions are a ubiquitous component of motivation and learning. We have developed an affective behavior model for intelligent tutoring systems that considers both the affective and knowledge state of the student to generate tutorial actions. The affective behavior model (ABM) was designed based on teachers' expertise obtained through interviews. It relies on a dynamic decision network with a utility measure on both student learning and affect to generate tutorial actions aimed at balancing the two. We have integrated and evaluated the ABM in an educational game to learn number factorization. We carried out a controlled user study to evaluate the impact of the affective model on learning. The results show that for the younger students there is a significant improvement on learning when the affective behavior model is incorporated.

Conference and special track committees responsible for the 2009 FLAIRS conference.

Manual knowledge acquisition is usually a costly and time-consuming process. Automatic knowledge acquisition methods can then significantly support the knowledge engineer. In this paper, we propose an approach for rapid knowledge capture. The methodology is based on textual subgroup mining in order to discover dependencies for rule prototyping.

Commonsense reasoning, in particular qualitative spatial and temporal reasoning (QSTR), provides flexible and intuitive methods for reasoning about vague and uncertain information including spatial orientation, topology and proximity.  Despite a number of theoretical advances in QSTR, there are relatively few applications that employ these methods.  The central problem is a significant lack of application level standards and validation methods for supporting developers in adapting and integrating QSTR with their domain specific qualitative spatial and temporal models.  To address this we present a significantly novel methodology for QSTR application validation, inspired by research in software engineering.  In this paper we focus on unit testing, and adapt the software engineering strategy of defining boundary cases.  We present two critical boundary concepts, a methodology for isolating the units under testing from other parts of the model, and methods to assist the designer in integrating our critical boundary unit testing approach with a broader validation plan.

LEGO Mindstorms robots are very popular with colleges and universities for teaching computer concepts and programming.  These robots elicit excitement in students and provide a nontrivial, real-world platform for exploring algorithmic concepts.  We created a simple algorithmic language, called Robolang, and wrote a translator that turns it into Lejos code, a variant of Java that can be run on the RCX version of the LEGO Mindstorms robots.  Seeing that students were eager to explore programming with the RCX robots at home, we wrote a graphical simulator to visualize actions of our penbot, a configuration of the RCX robot that we used in most assignments.  Using an emulator approach, we intercept the ROM calls to the RCX's hardware made by the TinyVM, the stripped-down Java Virtual Machine that runs compiled Java bytecodes.  Our system then forwards these calls to a software model that represents the actual robot hardware.  The software model creates the graphics to mimic the penbot using Java2D. This approach greatly simplified coding by capitalizing on existing software, namely the Java compiler and the JVM.  Students can program either in Robolang or in actual Lejos and use the simulator to visualize the actions of the robot acting as a sort of visual debugger.