11107722
Teaching with the help of talking heads
Talking heads were integrated with two learning systems. In AutoTutor, students learn about computer literacy by holding a conversation with a student. AutoTutor is an animated pedagogical agent that asks deep reasoning questions and engages in a mixed initiative dialog as answers emerge. Students type in information via keyboard whereas AutoTutor delivers discourse sensitive contributions with facial expressions, synthesized speech, and gestures. In the Human Use Regulator Affairs (HURA) Advisor, high ranking officers in the military learn about the ethical use of human subjects on a Web site with a conversational navigational agent.
11533272
The Impact of Conversational Navigational Guides on the Learning, Use, and Perceptions of Users of a Web Site
Knowledge management systems will presumably benefit from intelligent interfaces, including those with animated conversational agents. One of the functions of an animated conversational agent is to serve as a navigational guide that nudges the user how to use the interface in a productive way. This is a different function from delivering the content of the material. We conducted a study on college students who used a web facility in one of four navigational guide conditions: Full Guide (speech and face), Voice Guide, Print Guide, and No Guide. The web site was the Human Use Regulatory Affairs Advisor (HURAA), a web-based facility that provides help and training on research ethics, based on documents and regulations in United States Federal agencies. The college students used HURAA to complete a number of learning modules and document retrieval tasks. There was no significant facilitation of any of the guides on several measures of learning and performance, compared with the No Guide condition. This result suggests that the potential benefits of conversational guides are not ubiquitous, but they may save time and increase learning under specific conditions that are yet to be isolated.
206601195
The Role of Discourse Structure and Response Time in Multimodal Communication
In an ongoing project on multimodal communication in humans and agents [1], we investigate the interaction between two linguistic modalities (prosody and dialog structure) and two non-linguistic modalities (eye gaze and facial expressions). The goal is to gain a better understanding of the use of communicative channels in discourse and can subsequently aid the development of more effective animated conversational agents. We studied conversations between humans involved in the Map Task scenario whereby an Instruction Giver (IG) navigates an Instruction Follower (IF) from a starting point to an end point on a map [2].
12292456
The Embodiment of Amodal Symbolic Knowledge Representations
Latent semantic analysis (LSA) is a statistical, corpus-based technique of representing knowledge. It has been successfully used in a variety of applications including intelligent tutoring systems, essay grading and coherence metrics. From its very introduction LSA has been claimed to simulate aspects of human knowledge representation. This amodal symbolic view immediately resulted in a rebuttal from cognitive scientists who argued that LSA can never come close to human knowledge representation, because it lacks embodiment into perceptual experience or action as in human cognition. The ultimate test to determine the embodiment of LSA is to evaluate how well it operates on typical embodied dimensions like spatiality and temporality. The results of such a test would have an impact on theories of meaning and knowledge representation, and would help us in the development of natural language processing modules for applications that use these embodied dimensions. Six Multidimensional Scaling representations are derived from the LSA cosines between spatial and temporal words. Results show that by combining LSA with MDS previously hidden spatial and temporal relations can now be revealed.
28021583
The Human Use Regulatory Affairs Advisor: A Web Based Information Retrieval System with a User-Friendly Interface Design
9758881
Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature clustering
We introduce a novel document clustering approach that overcomes those problems by combining a semantic-based bipartite graph representation and a mutual refinement strategy. The primary contributions of this paper are the following. First, we introduce a new representation of documents using a bipartite graph between documents and co-occurrence concepts in the documents. Second, we show how to enhance clustering quality by applying the mutual refinement strategy to the initial clustering results. Third, through the experiments on MEDLINE documents, we show that our integrated method significantly enhances cluster quality and clustering reliability compared to existing clustering methods. Our approach improves on the average 29.5 cluster quality and 26.3 clustering reliability, in terms of misclassification index, over Bisecting K-means with the best parameters.
10956379
DraXRouter: global routing in X-architecture with dynamic resource assignment
In recent years, the X-architecture is introduced to obtain better performance for integrated circuit physical design. This paper reformulates the global routing problem in X-architecture under the liquid routing model. Then, a dynamic resource assignment (Dra) method is presented to reduce potential vias. At last, a global router called DraXRouter, is designed, in which we adopt a dynamic-tabulist-based tree construction algorithm and a stochastic optimization strategy to gain high quality routing solution. Tested on ISPD'98 benchmarks, DraXRouter achieves better routing performance compared with two recent global routers.
6833692
Clustering Large Collection of Biomedical Literature Based on Ontology-Enriched Bipartite Graph Representation and Mutual Refinement Strategy
In this paper we introduce a novel document clustering approach that solves some major problems of traditional document clustering approaches. Instead of depending on traditional vector space model, this approach represents a set of documents as bipartite graphs using domain knowledge in ontology. In this representation, the concepts of the documents are classified according to their relationships with documents that are reflected on the bipartite graph. Using the concept groups, documents are clustered based on the concepts' contribution to each document. Through the mutual-refinement relationship with concept groups and document groups, the two groups are recursively refined. Our experimental results on MEDLINE articles show that our approach outperforms two leading document clustering algorithms: BiSecting K-means and CLUTO. In addition to its decent performance, our approach provides a meaningful explanation for each document cluster by identifying its most contributing concepts, thus helps users to understand and interpret documents and clustering results.
12307619
The use of visual search for knowledge gathering in image decision support
This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.
269762
Formation constrained multi-agent control
We propose a model independent coordination strategy for multi-agent formation control. The main theorem states that under a bounded tracking error assumption our method stabilizes the formation error. We illustrate the usefulness of the method by applying it to rigid body constrained motions, as well as to mobile manipulation.