37056316
Learning about the Ethical Treatment of Human Subjects in Experiments on a Web Facility with a Conversational Agent and ITS Components
The Human Use Regulatory Affairs Advisor (HURAA) is a web-based help facility that provides training on the ethical use of human subjects in research, and that serves as an information retrieval system on ethical policies. The content for HURAA is derived from United States Federal agency documents and regulations. HURAA has a number of components that go beyond conventional page-turning or hypertext systems, including (1) an animated conversational agent that serves as a navigational guide for the web facility, (2) an enhanced multimedia introduction, (3) lessons with case-based and explanation-based reasoning, and (4) document retrieval through natural language queries or a Point & Query facility. The effectiveness of HURAA was tested on a small sample of participants (N = 18) who were assigned to either the full HURAA version or a conventional computer-based training version.
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.
28021583
The Human Use Regulatory Affairs Advisor: A Web Based Information Retrieval System with a User-Friendly Interface Design
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.
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.
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].
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.
2425436
Cluster Ensemble and Its Applications in Gene Expression Analysis
Huge amount of gene expression data have been generated as a result of the human genomic project. Clustering has been used extensively in mining these gene expression data to find important genetic and biological information. Obtaining high quality clustering results is very challenging because of the inconsistency of the results of different clustering algorithms and noise in the gene expression data. Many clustering algorithms are available and different clustering algorithms may generate different clustering results due to their bias and assumptions. It is a challenging and daunting task for the genomic researchers to choose the best clustering algorithm and generate the best clustering results for their data sets. In this paper, we present a cluster ensemble framework for gene expression analysis to generate high quality and robust clustering results. In our framework, the clustering results of individual clustering algorithm are converted into a distance matrix, these distance matrices are combined and a weighted graph is constructed according to the combined matrix. Then a graph partitioning approach is used to cluster the graph to generate the final clusters. The experiment results indicate that cluster ensemble approach yields better clustering results than the single best clustering algorithm on both synthetic data set and yeast gene expression data set.
2863452
Optimal Polygon Cover Problems and Applications
Polygon cover problems are important in several applied areas, such as material layout, layered manufacturing, radiation therapy and radiosurgery, etc. In this paper, we study three optimal polygon cover problems: monotone polygon cover among obstacles, star-shaped polygon cover among obstacles, and strip cover for trapezoidalized polygons. Based on some interesting geometric observations, we develop efficient algorithms for solving these problems. The complexity bounds of our monotone cover and star-shaped cover algorithms are comparable to those of the previously best known algorithms for simpler cases of the problems without considering obstacles. Our strip cover algorithm improves the quality of the previously best known solutions.
18070841
Ontology-Based Scalable and Portable Information Extraction System to Extract Biological Knowledge from Huge Collection of Biomedical Web Documents
Automated discovery and extraction of biological knowledge from biomedical web documents has become essential because of the enormous amount of biomedical literature published each year. In this paper we present an ontology-based scalable and portable information extraction system to automatically extract biological knowledge from huge collection of online biomedical web documents. Our method integrates ontology-based semantic tagging, information extraction and data mining together, automatically learns the patterns based on a few user seed tuples, and then extract new tuples from the biomedical web documents based on the discovered patterns. A novel system SPIE (Scalable and Portable Information Extraction) is implemented and tested on the PuBMed to find the chromatin protein-protein interaction and the experimental results indicate our approach is very effective in extracting biological knowledge from huge collection of biomedical web documents.
6446139
A semi-supervised efficient learning approach to extract biological relationships from web-based biomedical digital library
Many biological results are published only in plain --text documents and these documents or their abstracts are collected in web-based digital libraries such as PubMed and BioMed Central. To expedite the progress of functional bioinformatics, it is important to efficiently process large amounts of these documents, to extract these results into a structured format, and to store them in a database so that these results can be retrieved and analyzed by biologists and medical researchers. Automated discovery and extraction of the biological knowledge from biomedical web documents has become essential because of the enormous amount of biomedical literature published each year. In this paper we present a semi-supervised efficient learning approach to automatically extract biological knowledge from the web-based digital libraries. Our method integrates ontology-based semantic tagging as well as information extraction and data mining together. Our method automatically learns the patterns based on a few user seed tuples and then extracts new tuples from the biomedical web documents based on the discovered patterns. A novel system, SPIE Scalable and Portable Information Extraction, is implemented and tested on the PuBMed to find the chromatin protein --protein interaction. The experimental results indicate our approach is very effective in extracting biological knowledge from a huge collection of biomedical web documents.