The Experts below are selected from a list of 34737 Experts worldwide ranked by ideXlab platform

H Sakamoto - One of the best experts on this subject based on the ideXlab platform.

  • extracting research communities from bibliographic data
    International Conference on Intelligent Information Processing, 2012
    Co-Authors: Yushi Nakamura, Toshihiko Horiike, Tetsuji Kuboyama, H Sakamoto
    Abstract:

    We develop a research community extraction algorithm from large bibliographic data, which was preliminarily reported in Horiike et al. [10] and Nakamura et al. [18]. A research community in bibliographic data is considered to be a set of the linked texts holding a common topic, in other words, it is a dense subgraph embedded in the directed graph. Our method is based on the maximum flow algorithm for finding web communities by Flake et al. [5]. We propose improvements of the algorithm to select community nodes and Initial Seeds taking account of the restriction that any directed graph is acyclic. We examine the improved algorithm for the list of keywords frequently appearing in the bibliographic data. In addition we propose a simple method to extract characteristic keywords for deciding Initial Seed nodes. This method is also evaluated by experiments.

  • an improved algorithm for extracting research communities from bibliographic data
    Database Systems for Advanced Applications, 2010
    Co-Authors: Yushi Nakamura, Toshihiko Horiike, Yoshimasa Taira, H Sakamoto
    Abstract:

    In this paper we improve the performance of the community extraction algorithm in [1] from bibliographic data, which was originally proposed for web community discovery by [2]. A web community is considered to be a set of web pages holding a common topic, in other words, it is a dense subgraph induced in web graph. Such subgraphs obtained by the max-flow algorithm are called max-flow communities, and this algorithm was improved to obtain research communities from bibliographic data by the strategy for selection of community nodes in [1]. We propose an improvement of this algorithm by carefully selecting Initial Seed node, and show the performance of this algorithm by experiments for the list of many keywords frequently appearing in data.

Nathan Ross - One of the best experts on this subject based on the ideXlab platform.

  • joint degree distributions of preferential attachment random graphs
    Advances in Applied Probability, 2017
    Co-Authors: Erol A Pekoz, Adrian Rollin, Nathan Ross
    Abstract:

    We find a simple representation for the limit distribution of the joint degree counts in proportional attachment random graphs and provide optimal rates of convergence to these limits. The results hold for models with any general Initial Seed graph and any fixed number of outgoing edges. Optimal rates of convergence to the maximum of the degrees are also derived.

  • joint degree distributions of preferential attachment random graphs
    arXiv: Probability, 2014
    Co-Authors: Erol A Pekoz, Adrian Rollin, Nathan Ross
    Abstract:

    We study the joint degree counts in proportional attachment random graphs and find a simple representation for the limit distribution in infinite sequence space. We show weak convergence with respect to the p-norm topology for appropriate p and also provide optimal rates of convergence of the finite dimensional distributions. The results hold for models with any general Initial Seed graph and any fixed number of Initial outgoing edges per vertex; we generate non-tree graphs using both a lumping and a sequential rule. Convergence of the order statistics and optimal rates of convergence to the maximum of the degrees is also established.

Giovanni Vigna - One of the best experts on this subject based on the ideXlab platform.

  • EvilSeed: A Guided Approach to Finding Malicious Web Pages
    2012
    Co-Authors: Luca Invernizzi, Stefano Benvenuti, Marco Cova, Paolo Milani Comparetti, Christopher Kruegel, Giovanni Vigna
    Abstract:

    Abstract—Malicious web pages that use drive-by download attacks or social engineering techniques to install unwanted software on a user’s computer have become the main avenue for the propagation of malicious code. To search for malicious web pages, the first step is typically to use a crawler to collect URLs that are live on the Internet. Then, fast prefiltering techniques are employed to reduce the amount of pages that need to be examined by more precise, but slower, analysis tools (such as honeyclients). While effective, these techniques require a substantial amount of resources. A key reason is that the crawler encounters many pages on the web that are benign, that is, the “toxicity ” of the stream of URLs being analyzed is low. In this paper, we present EVILSeed, an approach to search the web more efficiently for pages that are likely malicious. EVILSeed starts from an Initial Seed of known, malicious web pages. Using this Seed, our system automatically generates search engines queries to identify other malicious pages that are similar or related to the ones in the Initial Seed. By doing so, EVILSeed leverages the crawling infrastructure of search engines to retrieve URLs that are much more likely to be malicious than a random page on the web. In other words EVILSeed increases the “toxicity ” of the input URL stream. Also, we envision that the features that EVILSeed presents could be directly applied by search engines in their prefilters. We have implemented our approach, and we evaluated it on a large-scale dataset. The results show that EVILSeed is able to identify malicious web pages more efficiently when compared to crawler-based approaches

  • EvilSeed: A Guided Approach to Finding Malicious Web Pages
    2012
    Co-Authors: Luca Invernizzi, Stefano Benvenuti, Marco Cova, Paolo Milani Comparetti, Christopher Kruegel, Giovanni Vigna
    Abstract:

    Abstract—Malicious web pages that use drive-by download attacks or social engineering techniques to install unwanted software on a user’s computer have become the main avenue for the propagation of malicious code. To search for malicious web pages, the first step is typically to use a crawler to collect URLs that are live on the Internet. Then, fast prefiltering techniques are employed to reduce the amount of pages that need to be examined by more precise, but slower, analysis tools (such as honeyclients). While effective, these techniques require a substantial amount of resources. A key reason is that the crawler encounters many pages on the web that are benign, that is, the “toxicity ” of the stream of URLs being analyzed is low. In this paper, we present EVILSeed, an approach to search the web more efficiently for pages that are likely malicious. EVILSeed starts from an Initial Seed of known, malicious web pages. Using this Seed, our system automatically generates search engines queries to identify other malicious pages that are similar or related to the ones in the Initial Seed. By doing so, EVILSeed leverages the crawling infrastructure of search engines to retrieve URLs that are much more likely to be malicious than a random page on the web. In other words EVILSeed increases the “toxicity ” of the input URL stream. Also, we envision that the features that EVILSeed presents could be directly applied by search engines in their prefilters. We have implemented our approach, and we evaluated it on a large-scale dataset. The results show that EVILSeed is able to identify malicious web pages more efficiently when compared to crawler-based approaches. Keywords-Web Security, Drive-By Downloads, Guided Crawling I

Lawrence R Oliver - One of the best experts on this subject based on the ideXlab platform.

  • effect of tillage and interference on common cocklebur xanthium strumarium and sicklepod senna obtusifolia population Seed production and Seedbank
    Weed Science, 1998
    Co-Authors: Mohammad T Bararpour, Lawrence R Oliver
    Abstract:

    Common cocklebur and sicklepod are troublesome weeds in soybean in the southern United States. A field experiment was conducted from 1991 through 1995 to determine (1) the influence of tillage (no-till and tilled after Initial Seed deposition) and intraspecific and interspecific interference on Seed production potential, emergence pattern, and soil Seedbank of common cocklebur and sicklepod, and (2) the dominant species after introduction into a weed-free field. Under intraspecific interference, 1,430 and 1,392 common cocklebur achenes m−2 and 1,827 and 5,435 sicklepod Seed m−2 were deposited to the Seedbank after 1 and 2 yr of Seed production, respectively. For both species, approximately 11% of the Initial Seedbank emerged under tilled conditions the first year after deposition. Under no-till conditions, only 0.7% of common cocklebur and 1.6% of sicklepod emerged. The second year after deposition, common cocklebur emergence in no-till decreased to 0.25% of the Initial Seedbank, while sicklepod increased to 8% of the Initial Seedbank and remained higher than in tilled plots. Under tilled conditions, common cocklebur became the dominant species, and sicklepod became dominant under no-till conditions. Seedbank depletion was greater for both species under tillage. Three years after Initial Seed deposition, sicklepod Seed was 100% viable but common cocklebur achenes were not viable. Under no-till conditions, common cocklebur was depleted in the Seedbank but sicklepod was not. Thus, sicklepod poses a greater long-term weed problem than common cocklebur, especially under no-till conditions.

  • effect of tillage and interference on common cocklebur xanthium strumarium and sicklepod senna obtusifolia population Seed production and Seedba nk
    Weed Science, 1998
    Co-Authors: Mohammad T Bararpour, Lawrence R Oliver
    Abstract:

    Lawrence R. Oliver Corresponding author. Department of Agronomy, University of Arkansas, Fayetteville, AR 72701 Common cocklebur and sicklepod are troublesome weeds in soybean in the southern United States. A field experiment was conducted from 1991 through 1995 to determine (1) the influence of tillage (no-till and tilled after Initial Seed deposition) and intraspecific and interspecific interference on Seed production potential, emergence pattern, and soil Seedbank of common cocklebur and sicklepod, and (2) the dominant species after introduction into a weed-free field. Under intraspecific interference, 1,430 and 1,392 common cocklebur achenes m-2 and 1,827 and 5,435 sicklepod Seed m-2 were deposited to the Seedbank after 1 and 2 yr of Seed production, respectively. For both species, approximately 11% of the Initial Seedbank emerged under tilled conditions the first year after deposition. Under no-till conditions, only 0.7% of common cocklebur and 1.6% of sicklepod emerged. The second year after deposition, common cocklebur emergence in no-till decreased to 0.25% of the Initial Seedbank, while sicklepod increased to 8% of the Initial Seedbank and remained higher than in tilled plots. Under tilled conditions, common cocklebur became the dominant species, and sicklepod became dominant under no-till conditions. Seedbank depletion was greater for both species under tillage. Three years after Initial Seed deposition, sicklepod Seed was 100% viable but common cocklebur achenes were not viable. Under no-till conditions, common cocklebur was depleted in the Seedbank but sicklepod was not. Thus, sicklepod poses a greater long-term weed problem than common cocklebur, especially under no-till conditions.

Collier Nigel - One of the best experts on this subject based on the ideXlab platform.

  • Visual Pivoting for (Unsupervised) Entity Alignment
    2020
    Co-Authors: Liu Fangyu, Chen Muhao, Roth Dan, Collier Nigel
    Abstract:

    This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs). Images are natural components of many existing KGs. By combining visual knowledge with other auxiliary information, we show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment. Besides, previous entity alignment methods require human labelled Seed alignment, restricting availability. EVA provides a completely unsupervised solution by leveraging the visual similarity of entities to create an Initial Seed dictionary (visual pivots). Experiments on benchmark data sets DBP15k and DWY15k show that EVA offers state-of-the-art performance on both monolingual and cross-lingual entity alignment tasks. Furthermore, we discover that images are particularly useful to align long-tail KG entities, which inherently lack the structural contexts necessary for capturing the correspondences.Comment: Preprint. 11 page

  • Visual Pivoting for (Unsupervised) Entity Alignment
    2020
    Co-Authors: Liu Fangyu, Chen Muhao, Roth Dan, Collier Nigel
    Abstract:

    This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs). Images are natural components of many existing KGs. By combining visual knowledge with other auxiliary information, we show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment. Besides, previous entity alignment methods require human labelled Seed alignment, restricting availability. EVA provides a completely unsupervised solution by leveraging the visual similarity of entities to create an Initial Seed dictionary (visual pivots). Experiments on benchmark data sets DBP15k and DWY15k show that EVA offers state-of-the-art performance on both monolingual and cross-lingual entity alignment tasks. Furthermore, we discover that images are particularly useful to align long-tail KG entities, which inherently lack the structural contexts necessary for capturing the correspondences.Comment: To appear at AAAI-202