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.
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].
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.
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.
14227751
Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning
Two complementary and non-interactive literature sets of articles, when they are considered together, can reveal useful information of scientific interest not apparent in either of the two document sets. Swanson called the existence of such knowledge, undiscovered public knowledge (UDPK). This paper proposes a semantic-based mining model for UDPK. Our method replaces manual ad-hoc pruning with using semantic knowledge from the biomedical ontologies. Using the semantic types and semantic relationships of the biomedical concepts, our prototype system can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. The system successfully replicates Swanson's two famous discoveries: Raynaud disease/fish oils and migraine/magnesium. Compared with previous approaches, our methods generate much fewer but more relevant novel hypotheses, and require much less human intervention in the discovery procedure.
27491397
Hot spot detection based on feature space representation of visual search
This paper presents a new framework for capturing intrinsic visual search behavior of different observers in image understanding by analysing saccadic eye movements in feature space. The method is based on the information theory for identifying salient image features based on which visual search is performed. We demonstrate how to obtain feature space fixation density functions that are normalized to the image content along the scan paths. This allows a reliable identification of salient image features that can be mapped back to spatial space for highlighting regions of interest and attention selection. A two-color conjunction search experiment has been implemented to illustrate the theoretical framework of the proposed method including feature selection, hot spot detection, and back-projection. The practical value of the method is demonstrated with computed tomography image of centrilobular emphysema, and we discuss how the proposed framework can be used as a basis for decision support in medical image understanding.
14024342
A Semantic Approach for Mining Hidden Links from Complementary and Non-interactive Biomedical Literature
Two complementary and non-interactive literature sets of articles, when they are considered together, can reveal useful information of scientific interest not apparent in either of the two sets alone. Swanson called the existence of such hidden links as undiscovered public knowledge (UPK). The novel connection between Raynaud disease and fish oils was uncovered from complementary and non-interactive biomedical literature by Swanson in 1986. Since then, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections. This paper proposes a semantic-based mining model for undiscovered public knowledge using the biomedical literature. Our method replaces manual ad-hoc pruning by using semantic knowledge from the biomedical ontologies. Using the semantic types and semantic relationships of the biomedical concepts, our prototype system can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. The system successfully replicates Swanson’s two famous discoveries: Raynaud disease/fish oils and migraine/magnesium. Compared with previous approaches such as LSI-based and traditional association rule-based methods, our method generates much fewer but more relevant novel hypotheses, and requires much less human intervention in the discovery procedure
10466292
Via-aware global routing for good VLSI manufacturability and high yield
CAD tools have become more and more important for integrated circuit (IC) design since a complicated system can be designed into a single chip, called system-on-a-chip (SOC), in which physical design tool is an essential and critical part. We try to consider the via minimization problem as early as possible in physical design. We propose a routing method focusing on minimizing vias while considering mutability and wire-length constraint. That is, in the global routing phase, we minimize the number of bends, which is closely related to the number of vias. Previous work only dealt with very small nets, but our algorithm is general for the nets with any size. Experimental results show that our algorithm can greatly reduce the count of bends for various sizes of nets while meeting the constraints of congestion and wire-length.