Ranking Measures Research Articles

Answering Approximate Queries Over XML Data

With the increasing popularity of XML for data representations, there is a lot of interest in searching XML data. Due to the structural heterogeneity and textual content's diversity of XML, it is daunting for users to formulate exact queries and search accurate answers. Therefore, approximate matching is introduced to deal with the difficulty in answering users’ queries, and this matching could be addressed by first relaxing the structure and content of a given query and, then, looking for answers that match the relaxed queries. Ranking and returning the most relevant results of a query have become the most popular paradigm in XML query processing. However, the existing proposals do not adequately take structures into account, and they, therefore, lack the strength to elegantly combine structures with contents to answer the relaxed queries. To address this problem, we first propose a sophisticated framework of query relaxations for supporting approximate queries over XML data. The answers underlying this framework are not compelled to strictly satisfy the given query formulation; instead, they can be founded on properties inferable from the original query. We, then, develop a novel top- k retrieval approach that can smartly generate the most promising answers in an order correlated with the ranking measure. We complement the work with a comprehensive set of experiments to show the effectiveness of our proposed approach in terms of precision and recall metrics.

Identification of terms for detecting early signals of emerging infectious disease outbreaks on the web

Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. The work in this paper is part of the methodology for monitoring the web that we currently develop for the French epidemic intelligence team in animal health. We focus on the new and exotic infectious animal diseases that occur worldwide and that are of potential threat to the animal health in France.In order to detect relevant information on the web, we present an innovative approach that retrieves documents using queries based on terms automatically extracted from a corpus of relevant documents and validated with a consensus of domain experts (Delphi method). As a decision support tool to domain experts we introduce a new measure for ranking of extracted terms in order to highlight the more relevant terms. To categorise documents retrieved from the web we use Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers.We evaluated our approach on documents on African swine fever (ASF) outbreaks for the period from 2011 to 2014, retrieved from the Google search engine and the PubMed database. From 2400 terms extracted from two corpora of relevant ASF documents, 135 terms were relevant to characterise ASF emergence. The domain experts identified as highly specific to characterise ASF emergence the terms which describe mortality, fever and haemorrhagic clinical signs in Suidae.The new ranking measure correctly ranked the ASF relevant terms until position 161 and fairly until position 227, with areas under ROC curves (AUCs) of 0.802 and 0.709 respectively.Both classifiers were accurate to classify a set of 545 ASF documents (NB of 0.747 and SVM of 0.725) into appropriate categories of relevant (disease outbreak) and irrelevant (economic and general) documents.Our results show that relevant documents can serve as a source of terms to detect infectious animal disease emergence on the web.Our method is generic and can be used both in animal and public health domain.

Global Social Tolerance Index and multi-method country rankings sensitivity

Social tolerance refers to the extent of recognition and acceptance of differences, willingness to grant equal rights, and refraining from openly intolerant attitudes. Utilizing World Value Survey (WVS) data (56 countries, 83,000 usable respondents), we develop a Global Social Tolerance Index (GSTI) that incorporates gender, minority, immigrant, and religion tolerance dimensions. We develop this index using a multi-step data-driven procedure involving five data standardizations, seven weighting approaches, and five aggregation methods. They generate 124 replications for each country’s index and rank, yielding a median overall position and several measures of rank sensitivity/robustness to different weighting, standardization, and aggregation approaches. It builds on prior social tolerance indexes in terms of scope and dimensionality, and avoids problems associated with equal or subjective weighting. The GSTI index/rankings provide a tool for IB scholars to examine nations’ overall tolerance or tolerance regarding each dimension relative to other external criteria. Our procedure can be used to develop other indexes and rankings of nations or organizations within a country or region. In practice, any such non-participatory method should always serve as a starting point to facilitate deliberations of experts and/or decision-makers for policy recommendations.

Identifying Influence of Nodes in Complex Networks with Coreness Centrality: Decreasing the Impact of Densely Local Connection

Ranking nodes by their spreading ability has been a fundamental issue related to many real applications, such as information and disease control. In the well-known coreness centrality measure, nodes' influence is ranked only by summing all neighbors' k-shell values while the effect of the topological connections among them on the nodes' spreading ability are ignored. In this work, we propose an improved coreness measure by decreasing the impact of densely local connections. Comparing the results from a series of susceptible-infected-recovered simulations on real networks, we show that our improved method can rank the nodes' spreading ability more accurately than other ranking measures such as k-shell, distance based method, mixed degree decomposition and coreness centrality method.

Learning to rank code examples for code search engines

Source code examples are used by developers to implement unfamiliar tasks by learning from existing solutions. To better support developers in finding existing solutions, code search engines are designed to locate and rank code examples relevant to user's queries. Essentially, a code search engine provides a ranking schema, which combines a set of ranking features to calculate the relevance between a query and candidate code examples. Consequently, the ranking schema places relevant code examples at the top of the result list. However, it is difficult to determine the configurations of the ranking schemas subjectively. In this paper, we propose a code example search approach that applies a machine learning technique to automatically train a ranking schema. We use the trained ranking schema to rank candidate code examples for new queries at run-time. We evaluate the ranking performance of our approach using a corpus of over 360,000 code snippets crawled from 586 open-source Android projects. The performance evaluation study shows that the learning-to-rank approach can effectively rank code examples, and outperform the existing ranking schemas by about 35.65 % and 48.42 % in terms of normalized discounted cumulative gain (NDCG) and expected reciprocal rank (ERR) measures respectively.

Results of the first survey for the current status of inflammatory bowel disease management in Asian countries.

Results of the first survey for the current status of inflammatory bowel disease management in Asian countries.

Measurement of Rank and Other Properties of Direct and Scattered Signals

We have designed an experiment for low-cost indoor measurements of rank and other properties of direct and scattered signals with radar interference suppression in mind. The signal rank is important also in many other applications, for example, DOA (Direction of Arrival) estimation, estimation of the number of and location of transmitters in electronic warfare, and increasing the capacity in wireless communications. In real radar applications, such measurements can be very expensive, for example, involving airborne radars with array antennas. We have performed the measurements in an anechoic chamber with several transmitters, a receiving array antenna, and a moving reflector. Our experiment takes several aspects into account: transmitted signals with different correlation, decorrelation of the signals during the acquisition interval, covariance matrix estimation, noise eigenvalue spread, calibration, near-field compensation, scattering in a rough surface, and good control of the influencing factors. With our measurements we have observed rank, DOA spectrum, and eigenpatterns of direct and scattered signals. The agreement of our measured properties with theoretic and simulated results in the literature shows that our experiment is realistic and sound. The detailed description of our experiment could serve as help for conducting other well-controlled experiments.

Ontology Knowledge Mining Based Association Rules Ranking

Medical association rules induction is used to discover useful correlations between pertinent concepts from large medical databases. Nevertheless, ARs algorithms produce huge amount of delivered rules and do not guarantee the usefulness and interestingness of the generated knowledge. To overcome this drawback, we propose an ontology based interestingness measure for ARs ranking. According to domain expert, the goal of the use of ARs is to discover implicit relationships between items of different categories such as ‘clinical features and disorders’,‘clinical features and radiological observations’, etc. That's to say, the itemsets which are composed of “similar” items are uninteresting. Therefore, the dissimilarity between the rule's items can be used to judge the interestingness of association rules; the more different are the items, the more interesting the rule is. In this paper, we design a distinct approach for ranking semantically interesting association rules involving the use of an ontology knowledge mining approach. The basic idea is to organize the ontology's concepts into a hierarchical structure of conceptual clusters of targeted subjects, where each cluster encapsulates “similar” concepts suggesting a specific category of the domain knowledge. The interestingness of association rules is, then, defined as the dissimilarity between corresponding clusters. That's to say, the further are the clusters of the items in the AR, the more interesting the rule is. We apply the method in our domain of interest – mammographic domain-using an existing mammographic ontology called Mammo*, with the goal of deriving interesting rules from past experiences, to discover implicit relationships between concepts modeling the domain.*http://sourceforge.net/p/gimimammography/code/HEAD/tree/trunk/owl

Revisiting the Anti-Hazing Law in Selected Higher Education Institutions in Nueva Ecija, Philippines: A Policy Review

Hazing is a serious social problem. It wasted not only the lives of future and promising leaders of society but also their aspirations for their families. The study is an evaluation of the implementation of the Anti-Hazing Law. 80 Student Organization Members and Officers (SOMO) and 20 Student Affairs Professionals (SAP) from four (4) universities in Nueva Ecija, Philippines are interviewed. From the point of view of the respondents, five (5) variables related to the implementation of the anti-hazing law are measured using measure of central tendency and percentile ranking. They are: a) monitoring of organizational meeting; b) monitoring of student organization activities; c) accreditation and recognition of student organizations; d) university initiative on resolving organizational conflicts and e) involvement of alumni and parents in student organizations program for development. The findings showed that SOMO and SAP rated sometimes in the first variable and often on the second and third variables; sometimes on the last two variables. Both the SOMO and SAP exhibited “awareness” on the anti-hazing law. In view of the findings of the study, a clear and comprehensive anti-hazing prevention policy and education campaign among SOMO is recommended.

Controlling Selmer groups in the higher core rank case

We define Kolyvagin systems and Stark systems attached to $p$-adic representations in the case of arbitrary `core rank' (the core rank is a measure of the generic Selmer rank in a family of Selmer groups). Previous work dealt only with the case of core rank one, where the Kolyvagin and Stark systems are collections of cohomology classes. For general core rank, they are collections of elements of exterior powers of cohomology groups. We show under mild hypotheses that for general core rank these systems still control the size and structure of Selmer groups, and that the module of all Kolyvagin (or Stark) systems is free of rank one.

Extractive Text Summarization System using Fuzzy Clustering Algorithm

The digital world is full of tons and tons of electronic documents. This kind of large amount of information is cumbersome to read at a glance and also it takes more time to download the large content. In this paper, we propose sentence ranking measure to process the sentences and rank it with appropriate sentence rank score. Similar sentences are grouped into clusters. Based on the score, the sentences with highest score are considered as important sentences. The sentences with top scores from each cluster are retrieved for summary report. This summary report eases the user to quickly analyse the information. The experimental result proves the effectiveness of the system.

Binarised regression tasks: methods and evaluation metrics

Some supervised tasks are presented with a numerical output but decisions have to be made in a discrete, binarised, way, according to a particular cutoff. This binarised regression task is a very common situation that requires its own analysis, different from regression and classification--and ordinal regression. We first investigate the application cases in terms of the information about the distribution and range of the cutoffs and distinguish six possible scenarios, some of which are more common than others. Next, we study two basic approaches: the retraining approach, which discretises the training set whenever the cutoff is available and learns a new classifier from it, and the reframing approach, which learns a regression model and sets the cutoff when this is available during deployment. In order to assess the binarised regression task, we introduce context plots featuring error against cutoff. Two special cases are of interest, the $$ UCE $$UCE and $$ OCE $$OCE curves, showing that the area under the former is the mean absolute error and the latter is a new metric that is in between a ranking measure and a residual-based measure. A comprehensive evaluation of the retraining and reframing approaches is performed using a repository of binarised regression problems created on purpose, concluding that no method is clearly better than the other, except when the size of the training data is small.

A novel risk attitudinal ranking method for intuitionistic fuzzy values and application to MADM

The ranking of intuitionistic fuzzy sets (IFSs) is very important for the intuitionistic fuzzy decision making. The aim of this paper is to propose a new risk attitudinal ranking method of IFSs and apply to multi-attribute decision making (MADM) with incomplete weight information. Motivated by technique for order preference by similarity to ideal solution (TOPSIS), we utilize the closeness degree to characterize the amount of information according to the geometrical representation of an IFS. The area of triangle is calculated to measure the reliability of information. It is proved that the closeness degree and the triangle area just form an interval. Thereby, a new lexicographical method is proposed based on the intervals for ranking the intuitionistic fuzzy values (IFVs). Furthermore, considered the risk attitude of decision maker sufficiently, a novel risk attitudinal ranking measure is developed to rank the IFVs on the basis of the continuous ordered weighted average (C-OWA) operator and this interval. Through maximizing the closeness degrees of alternatives, we construct a multi-objective fractional programming model which is transformed into a linear program. Thus, the attribute weights are derived objectively by solving this linear program. Then, a new method is put forward for MADM with IFVs and incomplete weight information. Finally, an example analysis of a teacher selection is given to verify the effectiveness and practicability of the proposed method.

A comprehensive test of the Trivers–Willard hypothesis in pronghorn ( Antilocapra americana )

Abstract The Trivers–Willard hypothesis (TWH) predicts that in a polygynous mating system, when fitness of male offspring is more variable than fitness of female offspring, mothers should invest more heavily in the sex with the highest marginal fitness returns. Females in good condition or high social rank should benefit by investing in sons, and females in poor condition or low social rank should benefit by investing in daughters. Many empirical studies have tested different aspects of the TWH, but no study has tested the assumptions and predictions in a single polygynous species using measures of maternal condition and maternal social rank, while accounting for random effects that can also influence offspring growth and survival. Here, we followed individuals in an isolated population of pronghorn on the National Bison Range, Montana, over multiple generations and tested the assumptions and predictions of the TWH. Pronghorn females who were in good condition or were socially dominant weaned larger fawns that were in better condition, but this advantage did not increase male fawn survival or reproductive success. We detected a slight bias in birth sex ratios according to maternal social rank, but overall we did not detect any adaptive benefit to mothers adopting a sex-biased investment strategy.

Automatic parsing of lane and road boundaries in challenging traffic scenes

Automatic detection of road boundaries in traffic surveillance imagery can greatly aid subsequent traffic analysis tasks, such as vehicle flow, erratic driving, and stranded vehicles. This paper develops an online technique for identifying the dominant road boundary in video sequences captured by traffic cameras under challenging environmental and lighting conditions, e.g., unlit highways captured at night. The proposed method works in real time of up to 20 frames/s and generates a ranked list of road regions that identify road and lane boundaries. Our method begins by segmenting each frame into a set of superpixels. An adaptive sampling step approximates superpixel contours to a collection of edge segments. Next, we show how online hierarchical clustering can be efficiently used to organize edges into clusters of colinearly similar sets. Promising clusters are paired with each other to form cluster pairs. Then we present and prove a statistical ranking measure that is used along with road-activity and perspective cues to find the dominant road boundaries. We evaluate the proposed approach on two real-world datasets to test our method under camera viewpoint changes and extreme environmental and lighting conditions. Results show that our method outperforms two state-of-the-art techniques in precision, recall, and runtime.

Evidence for stratigraphically controlled paleogeotherms in the Illinois Basin based on vitrinite-reflectance analysis: Implications for interpreting coal-rank anomalies

Coal rank reflects the temperature of coalification, with higher rank coal forming at higher temperatures. If the temperature of coalification depended only on the Earth’s geothermal gradient, then the maximum rank of coal in a sedimentary basin should be directly proportional to the thickness of strata above the coal. This association does not occur in the Illinois Basin, a continental-interior basin in the midwestern United States, where the overall coal rank observed is higher than can be explained by past burial depth alone. Recognition of this anomaly has led many authors to suggest that hot groundwater flowing from south to north, during a Paleozoic basin-scale groundwater-migration event, increased coal rank. We analyzed vitrinite reflectance ![Formula][1] , a measure of rank, as a function of depth in wells across the basin to determine how paleogeotherms, representing variation in temperature with depth, change with location. Our results show that the basin can be divided into three zones: (1) in the southern zone, the paleogeotherm varies irregularly with depth in strata above the sub-Absaroka unconformity (the contact separating Pennsylvanian and Mississippian strata); (2) in the central zone, the paleogeotherm displays a distinct inflection at the unconformity, for the rate of increase in ![Formula][2] with depth is greater above the unconformity than below the unconformity; and (3) the observed inflection in the paleogeotherm dies out northward, until, in the northern zone, the paleogeotherm has the same slope both above and below the unconformity. We propose that the inflection in the paleogeotherm of the central zone indicates that the hot groundwater responsible for causing an increase in coal rank flowed through a high-permeability zone just below the sub-Absaroka unconformity. This flow, which advected heat into Pennsylvanian strata above, cooled as it moved northward, so it did not influence the paleogeotherm in the northern zone. Preliminary studies of vein paragenesis, stable-isotope composition, and fluid-inclusions, as well as computer modeling of flow-related heat advection, support this proposal. [1]: /embed/mml-math-1.gif [2]: /embed/mml-math-2.gif

Are Quantitative Measures of Academic Productivity Correlated with Academic Rank in Plastic Surgery? A National Study.

The purpose of this study was to investigate the correlation between quantitative measures of academic productivity and academic rank among full-time academic plastic surgeons. Bibliometric indices were computed for all full-time academic plastic surgeons in the United States. The primary study variable was academic rank. Bibliometric predictors included the Hirsch index, I-10 index, number of publications, number of citations, and highest number of citations for a single publication. Descriptive, bivariate, and correlation analyses were computed. Multiple comparisons testing was used to calculate adjusted associations for subgroups. For all analyses, a value of p < 0.05 was considered significant. The cohort consisted of 607 plastic surgeons across 91 Accreditation Council for Graduate Medical Education-approved programs. Of them, 4.1 percent were instructors/lecturers, 43.7 percent were assistant professors, 22.1 percent were associate professors, 25.7 percent were professors, and 4.4 percent were endowed professors. Mean values were as follows: Hirsch index, 10.2 ± 9.0; I-10 index, 17.2 ± 10.2; total number of publications, 45.5 ± 69.4; total number of citations, 725.0 ± 1448.8; and highest number of citations for a single work, 117.8 ± 262.4. Correlation analyses revealed strong associations of the Hirsch index, I-10 index, number of publications, and number of citations with academic rank (rs = 0.62 to 0.64; p < 0.001). Academic rank in plastic surgery is strongly correlated with several quantitative metrics of research productivity. Although academic promotion is the result of success in multiple different areas, bibliometric measures may be useful adjuncts for assessment of research productivity.

Do Quantitative Measures of Research Productivity Correlate with Academic Rank in Oral and Maxillofacial Surgery?

Academic promotion is linked to research productivity. The purpose of this study was to assess the correlation between quantitative measures of academic productivity and academic rank among academic oral and maxillofacial surgeons. This was a cross-sectional study of full-time academic oral and maxillofacial surgeons in the United States. The predictor variables were categorized as demographic (gender, medical degree, research doctorate, other advanced degree) and quantitative measures of academic productivity (total number of publications, total number of citations, maximum number of citations for a single article, I-10 index [number of publications with ≥ 10 citations], and h-index [number of publications h with ≥ h citations each]). The outcome variable was current academic rank (instructor, assistant professor, associate professor, professor, or endowed professor). Descriptive, bivariate, and multiple regression statistics were computed to evaluate associations between the predictors and academic rank. Receiver-operator characteristic curves were computed to identify thresholds for academic promotion. The sample consisted of 324 academic oral and maxillofacial surgeons, of whom 11.7% were female, 40% had medical degrees, and 8% had research doctorates. The h-index was the most strongly correlated with academic rank (ρ = 0.62, p < 0.001). H-indexes of ≥ 4, ≥ 8, and ≥ 13 were identified as thresholds for promotion to associate professor, professor, and endowed professor, respectively (p < 0.001). This study found that the h-index was strongly correlated with academic rank among oral and maxillofacial surgery faculty members and thus suggests that promotions committees should consider using the h-index as an additional method to assess research activity.

What recommenders recommend: an analysis of recommendation biases and possible countermeasures

Most real-world recommender systems are deployed in a commercial context or designed to represent a value-adding service, e.g., on shopping or Social Web platforms, and typical success indicators for such systems include conversion rates, customer loyalty or sales numbers. In academic research, in contrast, the evaluation and comparison of different recommendation algorithms is mostly based on offline experimental designs and accuracy or rank measures which are used as proxies to assess an algorithm's recommendation quality. In this paper, we show that popular recommendation techniques--despite often being similar when compared with the help of accuracy measures--can be quite different with respect to which items they recommend. We report the results of an in-depth analysis in which we compare several recommendations strategies from different perspectives, including accuracy, catalog coverage and their bias to recommend popular items. Our analyses reveal that some recent techniques that perform well with respect to accuracy measures focus their recommendations on a tiny fraction of the item spectrum or recommend mostly top sellers. We analyze the reasons for some of these biases in terms of algorithmic design and parameterization and show how the characteristics of the recommendations can be altered by hyperparameter tuning. Finally, we propose two novel algorithmic schemes to counter these popularity biases.

Opening New Opportunities With Fast Reservoir-Performance Evaluation Under Uncertainty: Brugge Field Case Study

SummaryDecision making under uncertainty can be quite challenging, especially when complex numerical simulations are considered in the work flow and the decision has to be made relatively fast (e.g., in hours). This is the case when one needs to rank a given field portfolio within a limited budget and with acquisition constraints. If the ranking measure associated with each field is properly and rapidly evaluated, new prospect opportunities, which may lead to a favorable strategic position, can be readily identified.In this paper, we propose an efficient methodology for computing a “production-potential” measure that can be used to rank greenfield portfolios in the presence of geological uncertainty, quantifying both uncertainty and risk propagation. Next, we briefly describe the basics of the method proposed. First, uncertainty in sedimentary variability and flow behavior has to be characterized by a number of representative geological realizations. Sampling techniques are used to significantly reduce the number of realizations while preserving accuracy in the description and uncertainty propagation. Thereafter, multiple and varied field-development plans, based on primary/secondary-recovery mechanisms, are automatically generated while accounting for key parameters related to the number, drilling locations, and drilling sequence of wells. In these plans the reservoir is clustered by areas with similar production/injection potential, and the well locations and drilling schedules are obtained accordingly. The well controls are determined through estimations of the field-recovery factor. By means of experimental-design techniques a relatively small number of field-development plans are selected to capture the most significant production profiles. Each of these development plans is simulated for the realizations sampled previously, and the production-potential measure [e.g., average net present value (NPV) over all sampled realizations] is computed for all the plans. The highest of these measures (i.e., the best development plan) can be used for ranking the greenfield in the portfolio. Response-surface procedures are considered to perform additional analysis computations within iterative optimization procedures. It is important to note that other statistics related to the exploitation potential (e.g., standard deviation of the NPV) can also be used to complement the ranking, thereby mitigating the decision makers’ risk tolerance. The methodology has been tested on the Brugge Field benchmark, which presents 104 realizations of multiple geological parameters. The benchmark has been modified to simulate a greenfield scenario. The ranking measure is the (discounted) NPV averaged over the 104 realizations. The proposed work flow yields a ranking measure of USD 5.43 billion, and the computational cost is approximately 1,900 simulations (performed in a parallel-computing environment). This NPV is somewhat higher than those found for the Brugge benchmark (with similar modified settings) by other researchers. To validate the results, we performed more-exhaustive checking by use of approximately 17,000 simulations, and the ranking measure found was USD 5.51 billion.The new work flow presented allows one to efficiently and in a sufficiently accurate manner support decision making in greenfield-portfolio evaluation. Fast reservoir-performance-evaluation engines open new prospect opportunities that, with traditional decision-making techniques, may be frequently lost.