Ranking Measures Research Articles

Demystifying Gender Bias in Service Based Organizations

Women experience organizational barriers despite links between greater representation and increases in financial results. As organizations seek for a competitive means they should not neglect moving women to the top of their strategy. This research investigated gender bias by functional area and examined the linkages between gender, gender bias, leadership rank, and equal opportunity measures to support organizations in using gender as a competitive strategy. Findings demonstrated more women than men experienced bias while both genders reported equality in the workplace. Gender bias is explained through the lens of Pluralistic Ignorance to promote organizational awareness and direct their efforts.

A new local and multidimensional ranking measure to detect spreaders in social networks

Spreaders detection is a vital issue in complex networks because spreaders can spread information to a massive number of nodes in the network. There are many centrality measures to rank nodes based on their ability to spread information. Some local and global centrality measures including DIL, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, PageRank centrality and k-shell decomposition method are used to identify spreader nodes. However, they may have some problems such as finding inappropriate spreaders, unreliable spreader detection, higher time complexity or incompatibility with some networks. In this paper, a new local ranking measure is proposed to identify the influence of a node. The proposed method measures the spreading ability of nodes based on their important location parameters such as node degree, the degree of its neighbors, common links between a node and its neighbors and inverse cluster coefficient. The main advantage of the proposed method is to clear important hubs and low-degree bridges in an efficient manner. To test the efficiency of the proposed method, experiments are conducted on eight real and four synthetic networks. Comparisons based on Susceptible Infected Recovered and Susceptible Infected models reveal that the proposed method outperforms the compared well-known centralities.

ERR.Rank: An algorithm based on learning to rank for direct optimization of Expected Reciprocal Rank

Learning to rank (LTR) is a machine learning-based ranking technique that constructs a ranking model to sort objects in response to a query, and is used in many applications especially in information retrieval. LTR ranking models are generally evaluated using information retrieval measures. Listwise approaches are among the most important learning to rank algorithms. A subset of listwise approaches try to optimize the evaluation measures. These evaluation measures are dependent only on the document positions in the ranking and are discontinuous and non-convex with respect to the scores of the ranking function. The majority of evaluation measures used by current listwise techniques ignore the relationship between a document at a position and the documents at higher positions. To overcome this problem, we propose a new listwise algorithm, which aims to directly optimize the Expected Reciprocal Rank (ERR) measure. ERR considers the importance of a document at a position to be dependent on the documents ranked higher than this document. Our algorithm uses a probabilistic framework to optimize the expected value of ERR. We use a boosting approach using a gradient descent in order to find the optimal ranking function. The presented algorithm is compared with state of the art algorithms. The results obtained on the ’LETOR 3.0’ standard dataset indicate that the proposed method outperforms the baselines.

Influence of selected factors of Polish coking coals on the Hardgrove Grindability Index (HGI)

ABSTRACT This paper presents the results of a study on the influence of selected parameters of coal on the Hardgrove Grindability Index (HGI). The samples used in the investigation came from the same Polish coking-coal mine. The samples varied in terms of their quality parameters, i.e., volatile matter, ash content, chemical composition, maceral composition, and vitrinite reflectance). The influence of the aforementioned parameters on HGI was assessed. It was found that, among the investigated coals, the mean random vitrinite reflectance value, a measure of the coal rank, was the parameter that had the most significant effect on coal grindability (positive correlation). Such a correlation was also found for the carbon and vitrinite content. However, for volatile matter, liptinite, and total sulfur content, a negative correlation was found. The content and chemical composition of ash (as a reflection of mineral matter) has no significant effect on HGI. There were some weak negative correlations observed between HGI and the SO3 and the TiO2 content of ash. Of the tested parameters, vitrinite reflectance has the greatest effect on HGI.

Enhanced Web Page Ranking Method Using Laplacian Centrality

In today's era of computer technology where users want not only the most relevant data but they also want the data as quickly as possible. Hence, ranking web pages becomes a crucial task. The purpose of this research is to find a centrality measure that can be used in place of original page rank. In this article concept of Laplacian centrality measure for directed web graph has been introduced to identify the web page ranks. Comparison between the original page rank and Laplacian centrality based Page rank has been made. Kendall's correlation co-efficient has been used as a measure to find the correlation between the original page rank and Laplacian centrality measure based page rank.

Short-term impact of rapid maxillary expansion on ectopically and normally erupting canines.

The purpose of this study was to evaluate the short-term impact of rapid maxillary expansion (RME) on the eruption paths of ectopically and normally erupting maxillary canines in the mixed dentition. Thirty-two patients with 49 ectopically erupting canines (EEC group; age, 9.53±1.10years) and 18 patients with 27 normally erupting canines (NEC group; age, 9.25±1.06years) underwent RME. Thirty-six subjects with 54 normally erupting canines composed the untreated control group (UC group; age, 9.03±0.72years). Horizontal, vertical, and angular positions of canines and adjacent teeth were evaluated in the expanded (EEC and NEC groups) and unexpanded (UC group) patients using panoramic radiographs taken at 2 times with a 1-year interval. The radiographic evaluation methods included score ranking and proportional measurements to minimize panoramic radiograph limitations. Statistical comparisons were performed among the groups (P<0.05). Before expansion, the EEC group's canines were significantly closer to the midline, more distant from the occlusal plane, and more mesially angulated than those in the UC group. After expansion, the canine positions in the EEC and UC groups were similar, whereas the NEC group had a more favorable canine position for eruption. The EEC and NEC groups showed similar canine positional changes, whereas the UC group had the smallest changes. The positions of teeth adjacent to the canine were also significantly affected by RME, and these changes may be associated with improvement of the ectopic canine position. The changes produced by RME reduced the percentage of ectopic eruption paths and maintained the nonectopic eruption percentage.

Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative.

The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi‐ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre‐specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.

Exceptional intelligence and easygoingness may hurt your prospects: Threshold effects for rated mate characteristics.

Prospective mate characteristics such as kindness, intelligence, easygoingness, and physical attraction are ranked consistently highly by both men and women. However, rank measurement does not allow for determinations of what level of a mate characteristic is rated most desirable. Based on a more informative percentile scale measurement approach, it was reported recently that mean desirability ratings of IQ in a prospective partner peaked at the 90th percentile, with a statistically significant reduction from the 90th to the 99th percentiles. The purpose of this investigation was to replicate the recently reported non-linear desirability effect associated with IQ, in addition to the evaluation of three other valued mate characteristics: easygoing, kindness, and physical attraction. Based on a sample of 214 young adults, it was found that all four mate characteristics peaked at the 90th percentile. However, the IQ and easygoing mean desirability ratings evidenced statistically significant mean reductions across the 90th to the 99th percentiles, whereas kindness and physical attraction did not. Finally, the objectively and subjectively assessed intelligence of the participants was not found to be associated with the participants' desirability ratings of IQ. We interpreted the results to be consistent with a broadly conceptualized threshold hypothesis, which states that the perceived benefits of valued mate characteristics may not extend beyond a certain point. However, mate characteristics such as intelligence and easygoing become somewhat less attractive at very elevated levels, at least based on preference ratings, for reasons that may be biological and/or psycho-social in nature.

In or out? Exploring effects of being ranked for high reputation

As third-party evaluations like the Fortune 500 or USNWR rankings have become widespread, they have been shown to affect a variety of firm outcomes. As a consequence, a consensus has emerged in the literature that appearing in a ranking measure provides a signal of quality to stakeholders that will eventually benefit the receiving firm. Using signaling theory, we challenge this view, arguing that for high reputation firms, not only the direction, but also the consistency of the signal that comes with appearing in a ranking measure determine organizational outcomes like stakeholder interest. We propose that the inconsistent signals that come with entering and leaving a ranking measure make stakeholders question the quality of high reputation firms, such that not appearing in a ranking measure can provide a more consistent signal of quality, leading to increased stakeholder interest. We test our hypotheses in context of the global field of business schools using a novel data set from the Financial Times Global MBA rankings. Our findings provide broad support for our hypotheses. We conclude that appearing in a ranking measure can have unintended negative consequences for firms, and discuss the implications of our findings for the literature on rankings and reputation.

Is Gender Associated With Success in Academic Oral and Maxillofacial Surgery?

Several studies of surgical specialties have shown disparities in measures of research productivity and academic rank between female and male surgeons. The purpose of this work was to measure the role of surgeon gender in academic success in oral and maxillofacial surgery. We performed a cross-sectional study of full-time academic oral and maxillofacial surgeons (OMSs) in the United States as of June 2017. The primary study variable was surgeon gender (male or female). The primary outcome variable was research productivity assessed using 2 different parameters: 1) h index (number of publications h with at least h citations each) and 2) academic rank. The other study variables were demographic characteristics potentially related to the outcome measures. Descriptive, bivariate, and regression statistics were computed. The study sample comprised 306 full-time academic OMSs, 53 (17.3%) of whom were women. On average, female OMSs had shorter academic careers (mean time since completion of training, 11.0±8.2years for female OMSs vs 22.0±14.1years for male OMSs; P<.001). There were no other significant differences between male and female OMSs regarding the secondary measures (P≥.23). Male OMSs had a higher mean h index than female OMSs (7.1±8.6 vs 5.1±7.9, P=.01). Academic rank was statistically significantly different between female and male OMSs, with a greater proportion of higher ranks seen in male OMSs (P=.001). After adjustment for career length and other confounders or effect modifiers, gender was not an independent predictor of the h index or academic rank (P≥.22). Although female surgeons represent a minority of full-time academic OMSs, academic success measured using research productivity and academic rank was not associated with gender.

A Ranking Distance Analysis for Performance Assessment of UCS Versus SPT-N Correlations

It is important to know the reliability of the correlations unconfined compressive strength (UCS) versus blow count of standard penetration test (SPT-N) available in the literature for foundation design of constructions. However, selection of best performed equation verified with local data always becomes a primary issue for the site of interest foundation constructed. Thus, this paper investigates the performances of some UCS versus SPT-N correlations for the applicability to fine-grained soils in an interested area. A novel ranking measure called ranking distance (RD) that accounts equal weighting to the accuracy and precision of estimation has been used for the performance assessment of the equations, in comparison with alternative measures. The RD index is a comprehensive ranking measure for performing the dependability of an empirical equation with regard to the accuracy and precision, using the ratios of estimated and measured UCS. Dependent upon the analysis results in the study, the performances of equations have been ranked high, moderate, low and unacceptable (the worst equations). The equations have also been categorized whether they influence more weighting to precise or accuracy, or equal accuracy and precise during the estimations of UCS. From the results, it is concluded that the best performed estimation based on the RD is proposed by the equation of low plastic clay (Sivrikaya and Togrol, in: 5th international congress on advances in civil engineering, Istanbul, Turkey, pp 943–952, 2002; Eng Geol 86:52–69, 2006). Consequently, the study has successfully demonstrated the use of RD for assessing the best equation by confirming its results with the alternative methodologies compared. This could be beneficial for the experimenters in practice to attempt the validations of different empirical equations with different parameter through a good ranking procedure.

Assessing Euro 2020 Strategy Using Multi-criteria Decision Making Methods: VIKOR and TOPSIS

The European Union (EU) 2020 Strategy aims at forming the conditions for smart, sustainable and inclusive growth targets. Assessment of the EU countries’ situation is of vital importance in attaining the EU 2020 Strategy. This paper presents an impartial evaluation of the performance of 27 EU member countries in terms of each EU 2020 Strategy. For the basis of the evaluation, we propose an effective and easily practicable measure for ranking and monitoring the countries according to their performance by using the VIKOR and the TOPSIS methods, multi-criteria decision making (MCDM) methods, which allows for the integration of the 22 indicators, and be capable of considering such a broad spectrum of criteria including various economic, financial, demographic, educational and innovational. Our study provides a comparative analysis of the above-two methods. The contribution of the study to the literature is that these methods can be applied for assessing countries in terms of the EU 2020 Strategy which have the multi–dimensionality targets. The results point out new EU member countries such as Slovenia and Romania have attained higher scores than many of the 15 EU countries.

ROAR: Robust Label Ranking for Social Emotion Mining

Understanding and predicting latent emotions of users toward online contents, known as social emotion mining, has become increasingly important to both social platforms and businesses alike. Despite recent developments, however, very little attention has been made to the issues of nuance, subjectivity, and bias of social emotions. In this paper, we fill this gap by formulating social emotion mining as a robust label ranking problem, and propose: (1) a robust measure, named as G-mean-rank (GMR), which sets a formal criterion consistent with practical intuition; and (2) a simple yet effective label ranking model, named as ROAR, that is more robust toward unbalanced datasets (which are common). Through comprehensive empirical validation using 4 real datasets and 16 benchmark semi-synthetic label ranking datasets, and a case study, we demonstrate the superiorities of our proposals over 2 popular label ranking measures and 6 competing label ranking algorithms. The datasets and implementations used in the empirical validation are available for access.

Investigation of laser rangefinders with sensor network interface

The object of research is a laser rangefinder with a wireless sensor network interface protocol ZigBee, which in this study is proposed for the function of measuring the distance between objects. The main spheres of application are spheres where reliable information about the location of objects relative to each other is required. To increase the reliability, it is proposed to combine the possibility of measuring the distance between objects of sensor networks and data obtained from a laser rangefinder. These two methods are combined in one device. One of the most problematic places is the error of the measuring equipment. Also, for the objects described above, various obstacles are typical, like mechanical (trees, hills, lowlands), and radio interference. In the course of the research, the method of increasing the accuracy of measuring the distance of wireless sensor networks was used. To do this, they are combined with laser rangefinders, which are part of computerized distance measuring systems built on the basis of a combination of measurement methods of a different nature. Site localization studies were carried out for the average deviation value when ranking in 1 m (20 measurements), as well as on the progressive function of the required number of transactions. To calibrate the measured measurements, a minimum period of two-sided pass was calculated for the average value, when the receivers are in close proximity to one another (0.01 m). This average value on was calculated from each ranking measurements before converting to an immediate distance value. The results confirm an improvement in the efficiency of the ranking by averaging a plurality of samples to an error value of 6.0 m. The accuracy of the method was constant throughout the test propagation range (within a radius of 250 m).

Improving Image Search through MKFCM Clustering Strategy-Based Re-ranking Measure

Abstract The main intention of this research is to develop a novel ranking measure for content-based image retrieval system. Owing to the achievement of data retrieval, most commercial search engines still utilize a text-based search approach for image search by utilizing encompassing textual information. As the text information is, in some cases, noisy and even inaccessible, the drawback of such a recovery strategy is to the extent that it cannot depict the contents of images precisely, subsequently hampering the execution of image search. In order to improve the performance of image search, we propose in this work a novel algorithm for improving image search through a multi-kernel fuzzy c-means (MKFCM) algorithm. In the initial step of our method, images are retrieved using four-level discrete wavelet transform-based features and the MKFCM clustering algorithm. Next, the retrieved images are analyzed using fuzzy c-means clustering methods, and the rank of the results is adjusted according to the distance of a cluster from a query. To improve the ranking performance, we combine the retrieved result and ranking result. At last, we obtain the ranked retrieved images. In addition, we analyze the effects of different clustering methods. The effectiveness of the proposed methodology is analyzed with the help of precision, recall, and F-measures.

Effect of density and relative aggressiveness on agonistic and affiliative interactions in a newly formed group of horses

Abstract Group housing provides horses with social contact, a lack of which is associated with health and behavior problems. Despite the benefits of group housing for horses, horse owners are concerned about aggression and resulting injuries. This study focused on agonistic and affiliative interactions in a focal group of (originally) 12 horses with short-term and longer term changes in composition, variation in available area, and presence of periparturient mares. Age and density had no significant effect on agonistic or affiliative behavior. However, when agonistic behavior was considered within 3 subcategories, density did have a significant positive effect on contact and threat aggression but not on passive aggression (avoid and displace). When analyzing only the days without the most aggressive horse, age and density had a positive effect on agonistic interactions, and density had a negative effect on affiliative interactions. Days with and without the most aggressive horse showed no significant overall differences in either category of social interactions. Agonistic and affiliative interactions were not significantly correlated, but agonistic interactions had a significant linear and quadratic effect on affiliative interactions. Relative level of aggressiveness was used instead of a formal measure of dominance rank as aggression level is the main issue for horse owners. Comparing interactions with more and less aggressive horses, there was no overall effect for affiliative interactions. However, overall, horses in the focal group were on average 3.7 times more aggressive toward less aggressive horses, compared to more aggressive horses. Relationships between agonistic and affiliative interactions, density, age, and the role of relative aggressiveness are complicated, and more research is needed to clarify the most relevant factors under particular sets of circumstances and their contribution to intraspecific aggression in various contexts.

Assessing Potentially Preventable Hospitalizations at the County Level: A Comparison of Measures Using Medicare Data and State Hospital Discharge Data.

This study examined the representativeness of the County Health Rankings and Roadmaps (CHR) measure of potentially preventable hospitalizations, which is derived from Medicare inpatient claims data, as an indicator of potentially preventable hospitalizations for adults aged ≥18 years. Potentially preventable hospitalizations were evaluated using rates of ambulatory care sensitive conditions (ACSCs). CHR rates of hospitalization for ACSCs based on Medicare data for 2010, Agency for Healthcare Research and Quality Prevention Quality Indicator #90 Overall Composite (PQI #90 Composite) rates of ACSCs based on hospital inpatient data for adults aged ≥18 years for 2011, and 2011 total mortality rates for adults aged ≥18 years for 212 counties in 3 US states were evaluated. Pearson correlation analyses were used to assess the linear association between the PQI #90 Composite and CHR rates of hospitalization for ACSCs as well as associations of these measures with total mortality. Steiger's Z-test was conducted to examine whether the PQI #90 Composite and CHR measures of health care quality were similarly correlated with total mortality. The age- and sex-adjusted PQI #90 Composite for adults ≥18 years was moderately correlated with the CHR rate of hospitalization for ACSCs. The PQI #90 Composite and CHR measures of hospitalization for ACSCs were similarly correlated with mortality. These findings suggest that in the absence of easily accessible, high-quality data for adults aged ≥18 years, the CHR measure of potentially preventable hospitalizations provides a modest but acceptable approximation of county-level disparities in potentially preventable hospitalizations for the US adult population.

Temporal feature vector for video analysis and retrieval in high efficiency video coding compressed domain

A new feature vector based on motion structure of video sequences is proposed which can be used independently or along with motion vectors in video analysis and retrieval applications. The proposed feature vector employs size of prediction units (PUs) in P and B frames of high efficiency video coding (HEVC) coded videos. The feature vector can be extracted by partial decoding of compressed video in the same decoding stage as motion vectors. Video retrieval application is selected to evaluate the performance of the new feature vector. In experimental evaluations, the proposed temporal feature vector independently shows on average 12% improvement in performance against motion vectors and by using it along with motion vectors, the performance of motion vector improves on average by 32% in average normalised modified retrieval rank (ANMRR) measure.

Computational Epitope Prediction and Screening Precision Antibody Therapeutics for Alzheimer's Disease

Using Molecular Dynamic simulations, we predicted the epitopes on Aβ oligomers, which would be distinct from those in either Aβ monomer or Aβ fibril. Starting from the predicted epitopes, 66 antibodies are raised by passive immunization in mice, and their binding to Aβ monomer, oligomer and plaque, along with preferential binding to Aβ protein in the cerebrospinal fluid and brain homogenate from deceased Alzheimer Disease (AD) patients vs healthy controls are measured experimentally. These experimental measurements provide up to eight different screening criteria to select lead candidates for clinical stages of drug development. The screening criteria do not perfectly correlate with each other and there is uncertainty in the criteria values as well as their importance/weights, making the selection of lead candidates a nontrivial maximum-likelihood ranking problem. In other fields, this problem is referred to generally as multiple criteria decision making or MCDM, and has been applied in various fields including finance and economics, energy policy, and environmental science, but not—at least to our knowledge—to the problem of screening candidate drug therapeutics. We employ a stochastic MCDM method named “SMAA-TOPSIS” to screen and rank the measured large cohort of the antibodies, in order to find lead candidate therapeutics. We obtain distributions of candidate rankings due to uncertainty in screening measurements, as well as the user-defined weight of importance attributed to each screening criterion. In choosing lead candidates, we propose a quantity “topness” as the most robust measure of ranking. Our hope is that this method may enable more systematic screening of candidate therapeutics when it becomes difficult intuitively to process multi-variate screening data that distinguishes candidates, so that additional candidates will be exposed as potential leads, increasing the likelihood of success in downstream clinical trials.

Approach to Mine the Modularity of Software Network Based on the Most Vital Nodes

Analysis on the modularity of software network plays a critical role in the detection of software vulnerabilities and in the improvement of software stability, reliability, and robustness. This paper intends to propose a method based on the most vital nodes to analyze the modularity of software of different versions. To this end, it first tracked software dynamic execution traces to build a dynamic software network model , and then it mapped these traces to a complex network of a dynamic invoke software network . Second, it identified the most vital nodes in two steps, namely the calInitialWeightForNode and calFinalWeightForNode in order to compute the initial weights and the final weights of nodes iteratively. Third, it mined top-k nodes as the original communities to create a framework for detecting new community, and expanded these nodes to the community by the algorithm expandTheCommunity and evaluated the results with criterion ${Q}$ . Finally, it calculated the modularity of software of different versions. Experimental results show that the most vital nodes are reasonable to be taken in comparison with other ranking measures, and that the analysis approach to the modularity of software network is effective in evaluating software community structure, and can help assist the developer to refactor the software and improve the software quality.