Sentence ranking is one of the most important research issues in text analysis. It can be used in extracted textual summarization and information retrieval. Graph-based methods are a common way of ranking and extracti...
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Sentence ranking is one of the most important research issues in text analysis. It can be used in extracted textual summarization and information retrieval. Graph-based methods are a common way of ranking and extracting sentences. In graph based methods, sentences are nodes of graph and edges are built based on the sentence similarities or on sentence co-occurrence relationship. PageRank style algorithms can be applied to get sentence ranks. In this paper, we focus on how to rank sentences in a single scientific paper. A scientific literature has more structural information than general texts and this structural information has not been fully explored yet in graph based ranking models. We investigated several different methods that used structural information such as paragraph and section information to construct a heterogeneous graph for ranking sentences. We conducted experiments on these methods to compare the results on sentence ranking. It shows that structural information can help identify more representative sentences.
At present, the software's type forms present diversity, and how to automatically analyze the software's risk behaviors become an urgent problem. This paper used some software behavior crawlers and dynamic ana...
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Bat algorithm (BA) is a novel intelligent optimization algorithm. In order to improve the poor local search capability of the algorithm, we present a new variant of bat optimization algorithm based on local centroid s...
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Bat algorithm (BA) is a novel intelligent optimization algorithm. In order to improve the poor local search capability of the algorithm, we present a new variant of bat optimization algorithm based on local centroid strategy (LCBA). Meanwhile, LEACH is a low energy adaptive clustering hierarchy algorithm for wireless sensor networks (WSN). And it has many disadvantages such as random selection of cluster head, taking no account of the remaining energy and position of nodes. To solve these problems, this paper proposes an improved LEACH protocol based on local centroid bat algorithm. The improved protocol divides the cluster head selection process into optimization of temporary cluster head and formal cluster head selection. First, we generate temporary cluster heads by traditional LEACH protocol, then optimize these cluster heads based on LCBA and select formal cluster heads according to the remaining energy of nodes. The result of experiment shows that, comparing with LEACH, LEACH-LCBA can balance the network load efficiently, improve the energy utilization, prolong the network lifetime.
The aim of this paper is to develop an improved AP clustering algorithm based on the quotient space granularity selection. Firstly, we give the characteristics of the quotient space granularity and Affinity Propagatio...
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Deaf person has a large social community around the world. The smooth communication is very difficult for these hard of hearings. Automatic Sign Language Recognition (SLR) can build the bridge between the deaf and the...
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ISBN:
(纸本)9781479999545
Deaf person has a large social community around the world. The smooth communication is very difficult for these hard of hearings. Automatic Sign Language Recognition (SLR) can build the bridge between the deaf and the hearings and turn the seamless interaction into reality. This paper presents a visualized communication tool for the hard of hearings, i.e. a large vocabulary sign language recognition system based on the RGB-D data input. A novel Grassmann Covariance Matrix (GCM) representation is used to encode a long-term dynamics of a sign sequence and the discriminative kernel SVM is adopted for the sign classification. For continuous sign language recognition, a probability inference method is used to determine the spotting from the labels of sequential frames. Some basic evaluation and comparison of our recognition algorithms are conducted in our collected datasets. This demo will show the recognition of both isolated sign words and the continuous sign language sentences.
Estimation of distribution algorithms (EDAs) is a new kind of evolution algorithm. In EDAs, through the statistics of the information of selected individuals in current group, the probability of the individual distrib...
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pLink is a search engine for high-throughput identification of cross-linked peptides from their tandem mass spectra, which is the data-analysis step in chemical cross-linking of proteins coupled with mass spectrometry...
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This paper presents a saliency based bag-of-phrases (Saliency-BoP for short) method for image retrieval. It combines saliency detection with visual phrase construction to extract bag-of-phrase features. To achieve thi...
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In this paper, we propose an augmented dependency-to-string model to combine the merits of both the head-dependents relations at handling long distance reordering and the fixed and floating structures at handling loca...
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ISBN:
(纸本)9781941643266
In this paper, we propose an augmented dependency-to-string model to combine the merits of both the head-dependents relations at handling long distance reordering and the fixed and floating structures at handling local reordering. For this purpose, we first compactly represent both the head-dependent relation and the fixed and floating structures into translation rules;second, in decoding we build "on-the-fly" new translation rules from the compact translation rules that can incorporate non-syntactic phrases into translations, thus alleviate the non-syntactic phrase coverage problem of dependency-to-string translation (Xie et al., 2011). Large-scale experiments on Chinese-to-English translation show that our augmented dependency-to-string model gains significant improvement of averaged +0.85 BLEU scores on three test sets over the dependencyto- string model.
Artificial plant optimization algorithm is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. In this paper, a new variant, which is called APOA-DC is proposed by incor...
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Artificial plant optimization algorithm is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. In this paper, a new variant, which is called APOA-DC is proposed by incorporating with dynamic population size and cluster methods, furthermore, to investigate the performance, APOA-DC is applied to optimize the DV-Hop localization algorithm, simulation results show it achieves better performance than the DV-Hop algorithm.
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