13418116
An information-pattern-based approach to novelty detection
In this paper, a new novelty detection approach based on the identification of sentence level information patterns is proposed. First, ''novelty'' is redefined based on the proposed information patterns, and several different types of information patterns are given corresponding to different types of users' information needs. Second, a thorough analysis of sentence level information patterns is elaborated using data from the TREC novelty tracks, including sentence lengths, named entities (NEs), and sentence level opinion patterns. Finally, a unified information-pattern-based approach to novelty detection (ip-BAND) is presented for both specific NE topics and more general topics. Experiments on novelty detection on data from the TREC 2002, 2003 and 2004 novelty tracks show that the proposed approach significantly improves the performance of novelty detection in terms of precision at top ranks. Future research directions are suggested.
16393547
A new robust relevance model in the language model framework
In this paper, a new robust relevance model is proposed that can be applied to both pseudo and true relevance feedback in the language-modeling framework for document retrieval. There are at least three main differences between our new relevance model and other relevance models. The proposed model brings back the original query into the relevance model by treating it as a short, special document, in addition to a number of top-ranked documents returned from the first round retrieval for pseudo feedback, or a number of relevant documents for true relevance feedback. Second, instead of using a uniform prior as in the original relevance model proposed by Lavrenko and Croft, documents are assigned with different priors according to their lengths (in terms) and ranks in the first round retrieval. Third, the probability of a term in the relevance model is further adjusted by its probability in a background language model. In both pseudo and true relevance cases, we have compared the performance of our model to that of the two baselines: the original relevance model and a linear combination model. Our experimental results show that the proposed new model outperforms both of the two baselines in terms of mean average precision.
2304647
Evaluating Question-Answering Techniques in Chinese
An important first step in developing a cross-lingual question answering system is to understand whether techniques developed with English text will also work with other languages, such as Chinese. The Marsha Chinese question answering system described in this paper uses techniques similar to those used in the English systems developed for TREC. Marsha consists of three main components: the query processing module, the Hanquery search engine, and the answer extraction module. It also contains some specific techniques dealing with Chinese language characteristics, such as word segmentation and ordinals processing. Evaluation of the system is done using a method based on the TREC question-answering track. The results of the evaluation show that the performance of Marsha is comparable to some English question answering systems in TREC 8 track. An English language version of Marsha further indicates that the heuristics used are applicable to the English question answering task.
29798457
Adaptive Image Segmentation Using Modified Pulse Coupled Neural Network
This paper proposes a novel image segmentation algorithm based on Pulse Coupled Neural Network (PCNN).Unlike the traditional PCNN image segmentation methods, the presented algorithm can achieve the optimum parameters automatically. Experimental results show its good performance and robustness. The research fruits have great importance both on the theory research and practical application of PCNN.
16418153
The limits of localization using RSS
We characterize the fundamental limits of localization using signal strength in indoor environments. Signal strength approaches are attractive because they are widely applicable to wireless sensor networks and do not require additional localization hardware. We show that although a broad spectrum of algorithms can trade accuracy for precision, none has a significant advantage in localization performance. We found that using commodity 802.11 technology over a range of algorithms, approaches and environments, one can expect a median localization error of 10ft and 97th percentile of 30ft. We present strong evidence that these limitations are fundamental and that they are unlikely to be transcended without fundamentally more complex environmental models or additional localization infrastructure.
7724537
Design and Implementation of GeoBrain Online Analysis System (GeOnAS)
GeOnAS is an extensible, scalable and powerful online geospatial analysis system based on Service Oriented Architecture (SOA), and is designed and implemented with the complementary technologies, Asynchronous JavaScript and XML (Ajax) and Web services, which greatly increase the interactive capabilities of graphical user interfaces and improve the user experience. It provides a highly interoperable way of accessing Open Geospatial Consortium (OGC) compliant web services for geospatial data discovery, retrieval, visualization and analysis, and leverages web service standards to enable service discovery, selection, negotiation and invocation for making more informed decisions.
15133148
Modeling Image Data for Effective Indexing and Retrieval in Large General Image Databases
In this paper, we propose an image semantic model based on the knowledge and criteria in the field of linguistics and taxonomy. Our work bridges the "semantic gap" by seamlessly exploiting the synergy of both visual feature processing and semantic relevance computation in a new way, and provides improved query efficiency and effectiveness for large general image databases. Our main contributions are as follows: we design novel data structures, namely, a lexical hierarchy, an image-semantic hierarchy, and a number of atomic semantic domains, to capture the semantics and the features of the database, and to provide the indexing scheme. We present a novel image query algorithm based on the proposed structures. In addition, we propose a novel term expansion mechanism to improve the lexical processing. Our extensive experiments indicate that our proposed techniques are effective in achieving high runtime performance with improved retrieval accuracy. The experiments also show that the proposed method has good scalability.
13231021
Optimized Grey RBF Prediction Model Based on Genetic Algorithm
When combining grey system with RBF neural network, local optimization and convergence problems are still existed, so genetic algorithm is introduced to assist the modeling of grey neural network in this paper. Firstly, genetic algorithm is employed to solve the parameters of improved GM(1,1) with Lagrange's value theorem, and then RBF neural network is parallel connected to compensate errors. A new dynamic prediction model integrating genetic algorithm and grey RBF, for short GA-GRBF is proposed. This new model with preferable structure and parameters is applied to simulation and analysis of time-displacement data of wind response. The comparative experiment results show that this model is capable of predicting a small sample of data accurately, easily and conveniently.