16343248
iRANK: an interactive ranking framework and its application in query-focused summarization
We address the problem of unsupervised ensemble ranking in this paper. Traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. Beyond the aforementioned combine-then-rank and rank-then-combine approaches, we propose a novel rank-learn-combine ranking framework, called Interactive Ranking (iRANK), which allows two base rankers to "teach" each other before combination during the ranking process by providing their own ranking results as feedback to the others so as to boost the ranking performance. This mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. We apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the DUC 2005 data set. The results are encouraging with consistent and promising improvements.
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.
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.
2440328
Image transmissions with security enhancement based on region and path diversity in wireless sensor networks
Transmissions of large sized images can be a bottleneck for a wireless sensor network (WSN) due to its limited resources. Security can be another concern. This paper proposes a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide the transmission and security sharing patterns based on the path diversities. Our proposed approach for secret image sharing on multiple node-disjoint paths for image delivery is to achieve high security without any key distribution and management, and thus the key management related problems do not exist. The energy efficiency is another major contribution made in this paper. This scheme does not only allow each image sensor to transmit optimal fractions of overlapped images through appropriate transmission paths in an energy-efficient way, but also provides unequal protection to overlapped image regions by path selections and adaptive bit error rate (BER) requirement. The simulation results show that the proposed scheme can achieve considerable gains in terms of network lifetime extension, image transmission security enhancement, image quality improvement, and energy efficiency for wireless sensor networks.
14948142
ADenTS: An Adaptive Density-Based Tree Structure for Approximating Aggregate Queries over Real Attributes
In many fields and applications, it is critical for users to make decisions through OLAP queries. How to promote accuracy and efficiency while answering multiple aggregate queries, e.g. COUNT, SUM, AVG, MAX, MIN and MEDIAN? It has been the urgent problem in the fields of OLAP and data summarization recently. There have been a few solutions such as MRA-Tree and GENHIST for it. However, they could only answer a certain aggregate query which was defined in a particular data cube with some limited applications. In this paper, we develop a novel framework ADenTS, i.e. Adaptive Density-based Tree Structure, to answer various types of aggregate queries within a single data cube. We represent the whole cube by building a coherent tree structure. Several techniques for approximation are also proposed. The experimental results show that our method outperforms others in effectiveness.
1166455
Comparing Graph Representations of Protein Structure for Mining Family-Specific Residue-Based Packing Motifs
We find recurring amino-acid residue packing patterns, or spatial motifs, that are characteristic of protein structural families, by applying a novel frequent subgraph mining algorithm to graph representations of protein three-dimensional structure. Graph nodes represent amino acids, and edges are chosen in one of three ways: first, using a threshold for contact distance between residues; second, using Delaunay tessellation; and third, using the recently developed almost-Delaunay edges. For a set of graphs representing a protein family from the Structural Classification of Proteins (SCOP) database, subgraph mining typically identifies several hundred common subgraphs corresponding to spatial motifs that are frequently found in proteins in the family but rarely found outside of it. We find that some of the large motifs map onto known functional regions in two protein families explored in this study, i.e., serine proteases and kinases. We find that graphs based on almost-Delaunay edges significantly reduce the number of edges in the graph representation and hence present computational advantage, yet the patterns extracted from such graphs have a biological interpretation approximately equivalent to that of those extracted from distance based graphs.
7588231
Efficient Algorithms for Mining Maximal Frequent Concatenate Sequences in Biological Datasets
The growth of bioinformatics has resulted in datasets with new characteristics. The DNA sequences typically contain a large number of items. From them biologists assemble a whole genome of species based on frequent concatenate sequences, which ordinarily have hundreds of items. Such datasets pose a great challenge for existing frequent pattern discovery algorithms. Almost all of them are Apriori-like and so have an exponential dependence on the average sequence length. PrefixSpan is the most efficient algorithm, which presented the projection-based sequential pattern-growth approach. However it grows sequential patterns by exploring length-1 frequent patterns and so is not suitable for biological dataset with long frequent concatenate sequences. In this paper, we propose two novel algorithms, called MacosFSpan and MacosVSpan, to mine maximal frequent concatenate sequences. They are specially designed to handle datasets having long frequent concatenate sequences. Our performance study shows that MacosFSpan outperforms the traditional methods with length-1 sequences exploration and MacosVSpan is more efficient than Macos VSpan
2256492
Autonomic Intrusion Detection System
We propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection in unlabeled audit data streams. The framework owns ability of self-managing: self-labeling, self-updating and self-adapting. Affinity Propagation (AP) uses the framework to learn a subject's behavior through dynamical clustering of the streaming data. The testing results with a large real HTTP log stream demonstrate the effectiveness and efficiency of the method.