17062756
Toward alternative measures for ranking venues: a case of database research community
Ranking of publication venues is often closely related with important issues such as evaluating the contributions of individual scholars/research groups, or subscription decision making. The development of large-scale digital libraries and the availability of various meta data provide the possibility of building new measures more efficiently and accurately. In this work, we propose two novel measures for ranking the impacts of academic venues an easy-to-implement seed-based measure that does not use citation analysis, and a realistic browsing-based measure that takes an article reader's behavior into account. Both measures are computationally efficient yet mimic the results of the widely accepted Impact Factor. In particular, our proposal exploits the fact that: (1)in most disciplines, there are "top" venues that most people agree on; and (2) articles that appeared in good venues are more likely to be viewed by readers. Our proposed measures are extensively evaluated on a test case of the Database research community using two real bibliography data sets - ACM and DBLP. Finally, ranks of venues by our proposed measures are compared against the Impact Factor using the Spearman's rank correlation coefficient, and their positive rank order relationship is proved with a statistical significance test.
6286269
Improving Grouped-Entity Resolution Using Quasi-Cliques
The entity resolution (ER) problem, which identifies duplicate entities that refer to the same real world entity, is essential in many applications. In this paper, in particular, we focus on resolving entities that contain a group of related elements in them (e.g., an author entity with a list of citations, a singer entity with song list, or an intermediate result by GROUP BY SQL query). Such entities, named as grouped-entities, frequently occur in many applications. The previous approaches toward grouped-entity resolution often rely on textual similarity, and produce a large number of false positives. As a complementing technique, in this paper, we present our experience of applying a recently proposed graph mining technique, Quasi-Clique, atop conventional ER solutions. Our approach exploits contextual information mined from the group of elements per entity in addition to syntactic similarity. Extensive experiments verify that our proposal improves precision and recall up to 83% when used together with a variety of existing ER solutions, but never worsens them.
811544
Measuring conference quality by mining program committee characteristics
Bibliometrics are important measures for venue quality in digital libraries. Impacts of venues are usually the major consideration for subscription decision-making, and for ranking and recommending high-quality venues and documents. For digital libraries in the Computer Science literature domain, conferences play a major role as an important publication and dissemination outlet. However, with a recent profusion of conferences and rapidly expanding fields, it is increasingly challenging for researchers and librarians to assess the quality of conferences. We propose a set of novel heuristics to automatically discover prestigious (and low-quality) conferences by mining the characteristics of Program Committee members. We examine the proposed cues both in isolation and combination under a classification scheme. Evaluation on a collection of 2,979 conferences and 16,147 PC members shows that our heuristics, when combined, correctly classify about 92% of the conferences, with a low false positive rate of 0.035 and a recall of more than 73% for identifying reputable conferences. Furthermore, we demonstrate empirically that our heuristics can also effectively detect a set of low-quality conferences, with a false positive rate of merely 0.002. We also report our experience of detecting two previously unknown low-quality conferences. Finally, we apply the proposed techniques to the entire quality spectrum by ranking conferences in the collection.
15086505
A fast content-based indexing and retrieval technique by the shape information in large image database
Abstract In this paper, we present an efficient content-based image retrieval (CBIR) system which employs the shape information of images to facilitate the retrieval process. For efficient feature extraction, we extract the shape feature of images automatically using edge detection and wavelet transform which is widely used in digital signal processing and image compression. To facilitate speedy retrieval, we also propose the Spherical Pyramid-Technique (SPY-TEC), a new indexing method for similarity search in high-dimensional data space. The SPY-TEC is based on a special space partitioning strategy, which is to divide the d-dimensional data space first into 2d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into 1-dimensional space. Thus, we are able to use a B+-tree to manage the transformed 1-dimensional data. We show that our image indexing method supports faster retrieval than other multi-dimensional indexing methods such as the R*-tree through various experiments. Finally, we show the retrieval effectiveness of our CBIR system using the measure proposed by the QBIC (C. Faloutsos et al., 1994) system.
2008990
Dual-Tree Fast Gauss Transforms
In previous work we presented an efficient approach to computing kernel summations which arise in many machine learning methods such as kernel density estimation. This approach, dual-tree recursion with finite-difference approximation, generalized existing methods for similar problems arising in computational physics in two ways appropriate for statistical problems: toward distribution sensitivity and general dimension, partly by avoiding series expansions. While this proved to be the fastest practical method for multivariate kernel density estimation at the optimal bandwidth, it is much less efficient at larger-than-optimal bandwidths. In this work, we explore the extent to which the dual-tree approach can be integrated with multipole-like Hermite expansions in order to achieve reasonable efficiency across all bandwidth scales, though only for low dimensionalities. In the process, we derive and demonstrate the first truly hierarchical fast Gauss transforms, effectively combining the best tools from discrete algorithms and continuous approximation theory.
36612071
SPY-TEC: An efficient indexing method for similarity search in high-dimensional data spaces
Most of all index structures based on the R-tree have failed to support eAcient indexing mechanisms for similarity search in high-dimensional data spaces. This is due to the fact that most of the index structures commonly use balanced split strategy in order to guarantee storage utilization and the shape of queries for similarity search is a hypersphere in high-dimensional spaces. In this paper, we propose the Spherical Pyramid-Technique (SPY-TEC), an eAcient indexing method for similarity search in high-dimensional data space. The SPY-TEC is based on a special partitioning strategy, which is to divide the d-dimensional data space first into 2d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into one-dimensional space as the Pyramid-Technique [14] does. Thus, we are able to use a B a -tree to manage the transformed one-dimensional data. We also propose the algorithms to process hyperspherical range queries on the data space partitioned by this partitioning strategy. Finally, we show that the SPY-TEC clearly outperforms other related techniques including the Pyramid-Technique in processing hyperspherical range queries through various experiments using synthetic and real data. ” 2000 Elsevier Science B.V. All rights reserved.
27807377
Protocol Feature Interactions
Feature interaction is one of the most intriguing problems in research and development of telecommunications systems. Features may interact and result in undesirable system behavior. Today the analysis of interactions is conducted in an ad hoc manner by subject matter experts. This leads to time-consuming feature design and testing without any interaction-free guarantee.
9367228
Verifying Web applications using bounded model checking
The authors describe the use of bounded model checking (BMC) for verifying Web application code. Vulnerable sections of code are patched automatically with runtime guards, allowing both verification and assurance to occur without user intervention. Model checking techniques are relatively complex compared to the typestate-based polynomial-time algorithm (TS) we adopted in an earlier paper, but they offer three benefits - they provide counterexamples, more precise models, and sound and complete verification. Compared to conventional model checking techniques, BMC offers a more practical approach to verifying programs containing large numbers of variables, but requires fixed program diameters to be complete. Formalizing Web application vulnerabilities as a secure information flow problem with fixed diameter allows for BMC application without drawback. Using BMC-produced counterexamples, errors that result from propagations of the same initial error can be reported as a single group rather than individually. This offers two distinct benefits. First, together with the counterexamples themselves, they allow for more descriptive and precise error reports. Second, it allows for automated patching at locations where errors are initially introduced rather than at locations where the propagated errors cause problems. Results from a TS-BMC comparison test using 230 open-source Web applications showed a 41.0% decrease in runtime instrumentations when BMC was used. In the 38 vulnerable projects identified by TS, BMC classified the TS-reported 980 individual errors into 578 groups, with each group requiring a minimal set of patches for repair.