6338266
HyperSum: hypergraph based semi-supervised sentence ranking for query-oriented summarization
Graph based sentence ranking algorithms such as PageRank and HITS have been successfully used in query-oriented summarization. With these algorithms, the documents to be summarized are often modeled as a text graph where nodes represent sentences and edges represent pairwise similarity relationships between two sentences. A deficiency of conventional graph modeling is its incapability of naturally and effectively representing complex group relationships shared among multiple objects. Simply squeezing complex relationships into pairwise ones will inevitably lead to loss of information which can be useful for ranking and learning. In this paper, we propose to take advantage of hypergraph, i.e. a generalization of graph, to remedy this defect. In a text hypergraph, nodes still represent sentences, yet hyperedges are allowed to connect more than two sentences. With a text hypergraph, we are thus able to integrate both group relationships formulated among multiple sentences and pairwise relationships formulated between two sentences in a unified framework. As essential work, it is first addressed in the paper that how a text hypergraph can be built for summarization by applying clustering techniques. Then, a hypergraph based semi-supervised sentence ranking algorithm is developed for query-oriented extractive summarization, where the influence of query is propagated to sentences through the structure of the constructed text hypergraph. When evaluated on DUC data sets, performance of the proposed approach is remarkable.
6483143
Using Proximity in Query Focused Multi-document Extractive Summarization
The query focused multi-document summarization tasks usually tend to answer the queries in the summary. In this paper, we suggest introducing an effective feature which can represent the relation of key terms in the query. Here, we adopt the feature of term proximity commonly used in the field of information retrieval, which has improved the retrieval performance according to the relative position of terms. To resolve the problem of data sparseness and to represent the proximity in the semantic level, concept expansion is conducted based on WordNet. By leveraging the term importance, the proximity feature is further improved and weighted according to the inverse term frequency of terms. The experimental results show that our proposed feature can contribute to improving the summarization performance.
45342895
Experiment Research on Feature Selection and Learning Method in Keyphrase Extraction
Keyphrases can provide a brief summary of documents. Keyphrase extraction, defined as automatic selection of important phrases within the body of a document, is important in some fields. Generally the keyphrase extraction process is seen as a classification task, where feature selection and learning model are the key problems. In this paper, different shallow features are surveyed and the commonly used learning methods are compared. The experimental results demonstrate that the detailed survey of shallow features plus a simpler method can more enhance the extraction performance.
14320982
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, ApproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.
3251955
Containment join size estimation: models and methods
Recent years witnessed an increasing interest in researches in XML, partly due to the fact that XML has now become the de facto standard for data interchange over the internet. A large amount of work has been reported on XML storage models and query processing techniques. However, few works have addressed issues of XML query optimization. In this paper, we report our study on one of the challenges in XML query optimization: containment join size estimation. Containment join is well accepted as an important operation in XML query processing. Estimating the size of its results is no doubt essential to generate efficient XML query processing plans. We propose two models, the interval model and the position model, and a set of estimation methods based on these two models. Comprehensive performance studies were conducted. The results not only demonstrate the advantages of our new algorithms over existing algorithms, but also provide valuable insights into the tradeoff among various parameters.
29222863
An adaptive approach for image encryption and secure transmission over multirate wireless sensor networks
In this paper, we propose an innovative adaptive secure image delivery approach, to achieve effective and efficient image encryption based on an optimal transmission rate in terms of energy efficiency in wireless sensor networks (WSN). Our contribution is three folds—First, the proposed component based selective encryption scheme achieves high image security, while incurring little encryption overhead; furthermore, the proposed adaptive encryption scheme achieve the best security effort within delay bound; finally, the proposed transmission rate optimization improves energy efficiency, and feeds back effective path capacity parameters for adaptive encryption. Simulation results show a significant gain in both energy efficiency and security based on the proposed approach for image secure transmissions in WSN. Copyright # 2007 John Wiley & Sons, Ltd.
27183356
A Novel Ray Tracing Based Multipath Modeling Approach for Site-Specific WLAN Simulations
Current propagation models used in network simulations neglect obstacles of a propagation environment and employ over-simplified assumptions that the received signal strength is a simple function of distance and the transmission area of a node is circular. These assumptions are in contradiction with the actual tests and the measurement results. To run reasonable simulations for evaluating the performance of a specific outdoor wireless testbed, a novel multipath propagation framework is proposed in this paper, which is based on the ray tracing technique and uses site-specific geographic data of the analyzed area. It provides accurate propagation predictions, and it does not significantly prolong the simulation duration.
7337824
Meta-patterns: revealing hidden periodic patterns
Discovery of periodic patterns in time series data has become an active research area with many applications. These patterns can be hierarchical in nature, where a higher level pattern may consist of repetitions of lower level patterns. Unfortunately, the presence of noise may prevent these higher level patterns from being recognized in the sense that two portions (of a data sequence) that support the same (high level) pattern may have different layouts of occurrences of basic symbols. There may not exist any common representation in terms of raw symbol combinations; and hence such (high level) patterns may not be expressed by any previous model (defined on raw symbols or symbol combinations) and would not be properly recognized by any existing method. In this paper, we propose a novel model, namely meta-pattern, to capture these high level patterns. As a more flexible model, the number of potential meta-patterns could be very large. A substantial difficulty is how to identify the proper pattern candidates. However the well-known a priori properly is not able to provide sufficient pruning power. A new property, namely component location, is identified and used to conduct candidate generation so that an efficient computation-based mining algorithm can be developed. We apply our algorithm to real and synthetic sequences and interesting patterns are discovered.