15174288
Create a large-scale digital library for geo-referenced information
One of the most challenging research issues for geospatial collections is to develop techniques to support fuzzy, conceptbased, geographic information retrieval (GIR). Based on an artificial intelligence approach, this project presents a Geospatial Knowledge Representation System (GKRS) prototype system that integrates multiple multimedia (textual and image) knowledge sources to support concept-based GIR. Based on semantic network and neural network representations, GKRS loosely couples different knowledge sources and adopts spreading activation algorithms for concept-based knowledge inferencing. Both textual analysis and image processing techniques have been employed to create textual and visual geographical knowledge structures. This project suggests a framework for developing a complete GKRS-based system and describes in detail the prototype.
9274167
Communication-Garden System: Visualizing a computer-mediated communication process
Archives of computer-mediated communication (CMC) could be valuable organizational resources. Most CMC archive systems focus on presenting one of the three aspects of a CMC community: discussion content, participants' behavior, or social networks among participants. Very few CMC archive systems support the easy integration of these three aspects. This paper thus describes two-phase research to propose an automatic approach that facilitates users' integrated understanding of discussion content and behavior of CMC participants. We validated the approach through the development and evaluation of a prototype system, the Communication-Garden system.
16961887
Newsmap: a knowledge map for online news
Information technology has made possible the capture and accessing of a large number of data and knowledge bases, which in turn has brought about the problem of information overload. Text mining to turn textual information into knowledge has become a very active research area, but much of the research remains restricted to the English language. Due to the differences in linguistic characteristics and methods of natural language processing, many existing text analysis approaches have yet to be shown to be useful for the Chinese language. This research focuses on the automatic generation of a hierarchical knowledge map NewsMap, based on online Chinese news, particularly the finance and health sections. Whether in print or online, news still represents one important knowledge source that people produce and consume on a daily basis. The hierarchical knowledge map can be used as a tool for browsing business intelligence and medical knowledge hidden in news articles. In order to assess the quality of the map, an empirical study was conducted which shows that the categories of the hierarchical knowledge map generated by NewsMap are better than those generated by regular news readers, both in terms of recall and precision, on the sub-level categories but not on the top-level categories. NewsMap employs an improved interface combining a ID alphabetical hierarchical list and a 2D Self-Organizing Map (SOM) island display. Another empirical study compared the two visualization displays and found that users' performances can be improved by taking advantage of the visual cues of the 2D SOM display.
30023273
MedTextus: an intelligent web-based medical meta-search system
We propose to demonstrate a web-based prototype system that integrates the meta-search approach with existing information analysis and visualization technologies to facilitate concept-based searching behavior over the medical domain. The system distinguishes itself from other meta-search engines through two features. It incorporates the co-occurrence analysis and existing ontology to understand user's query. It also utilizes the self-organizing map (SOM) to categorize and visualize search results.
770502
Visualizing the archive of a computer mediated communication process
The archive of computer-mediated communication (CMC) process contains knowledge shared and information about participants' behavior patterns. However, most CMC systems focus only on organizing the content of discussions. We propose to demo a prototype system that integrates a social visualization technique with existing information analysis technologies to graphically summarize both the content and behavior of a CMC process.
12630328
Creating a large-scale content-based airphoto image digital library
This paper describes a content-based image retrieval digital library that supports geographical image retrieval over a testbed of 800 aerial photographs, each 25 megabytes in size. In addition, this paper also introduces a methodology to evaluate the performance of the algorithms in the prototype system. There are two major contributions: we suggest an approach that incorporates various image processing techniques including Gabor filters, image enhancement and image compression, as well as information analysis techniques such as the self-organizing map (SOM) into an effective large-scale geographical image retrieval system. We present two experiments that evaluate the performance of the Gabor-filter-extracted features along with the corresponding similarity measure against that of human perception, addressing the lack of studies in assessing the consistency between an image representation algorithm or an image categorization method and human mental model.
19870915
Research on Network-Based Collaborative Commodity Development
In this paper, a detailed method is proposed to make the transformation of a product model to a commodity model. A collaborative commodity development theory based on the three properties of physical, energy and information is proposed. An integrated model composed of four sub-models is analyzed: designing sub-model, supplying sub-model, managing sub-model, and profit sub-model. The key techniques in the system development are discussed, including the network-based parts application technology and the commercial model optimization and evaluation technology. For the former, a search engine using the functional agent technology for network-based parts searching is described. A prototype system has been implemented to validate the proposed approach.
38129543
Enhanced Verification-Based Decoding for Packet-Based LDPC Codes over Wireless Channels
In this paper, we apply the enhanced verification-based decoding algorithm (EVA) for packet-based LDPC codes over wireless channels. Compared with the verification algorithm (VA) in the literature, the EVA algorithm enhances the verification condition thereby reducing the likelihood of false verification. The wireless channels in simulations are modeled by binary symmetric channel (BSC) and Gilbert-Elliott (GE) channel. The numerical results indicate the proposed algorithm gives a superior performance but with only moderate computation increase compared with VA. For example, when the bit error probability is less than 10-3 in bad state of GE channel, the EVA reduces the frame error rate (FER) by one order with less than 35% complexity increase compared with VA.
13626017
Automatic Numeric Control Programming System for Locators
3094023
Fuzzy Classifier with Probabilistic IF-THEN Rules
The typical fuzzy classifier consists of rules each one describing one of the classes. This paper presents a new fuzzy classifier with probabilistic IF-THEN rules. A learning algorithm based on the gradient descent method is proposed to identify the probabilistic IF-THEN rules from the training data set. This new fuzzy classifier is finally applied to the well-known Wisconsin breast cancer classification problem, and a compact, interpretable and accurate probabilistic IF-THEN rule base is achieved.
4515160
Alert Correlation for Extracting Attack Strategies
Alert correlation is an important technique for managing large the volume of intrusion alerts that are raised by heterogenous Intrusion Detection Systems (IDSs). The recent trend of research in this area is towards extracting attack strategies from raw intrusion alerts. It is generally believed that pure intrusion detection no longer can satisfy the security needs of organizations. Intrusion response and prevention are now becoming crucially important for protecting the network and minimizing damage. Knowing the real security situation of a network and the strategies used by the attackers enables network administrators to launches appropriate response to stop attacks and prevent them from escalating. This is also the primary goal of using alert correlation technique. However, most of the current alert correlation techniques only focus on clustering inter-connected alerts into different groups without further analyzing the strategies of the attackers. Some techniques for extracting attack strategies have been proposed in recent years, but they normally require defining a larger number of rules. This paper focuses on developing a new alert correlation technique that can help to automatically extract attack strategies from a large volume of intrusion alerts, without specific prior knowledge about these alerts. The proposed approach is based on two different neural network approaches, namely, Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The probabilistic output of these two methods is used to determine with which previous alerts this current alert should be correlated. This suggests the causal relationship of two alerts, which is helpful for constructing attack scenarios. One of the distinguishing feature of the proposed technique is that an Alert Correlation Matrix (ACM) is used to store correlation strengthes of any two types of alerts. ACM is updated in the training process, and the information (correlation strength) is then used for extracting high level attack strategies.