Ranked Output Research Articles

Hierarchical partitioning of the output space in multi-label data

Hierarchy Of Multi-label classifiERs (HOMER) is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarchy of labels from a given label set and secondly employing a given base multi-label classifier (MLC) to the resulting sub-problems. The primary goal is to effectively address class imbalance and scalability issues that often arise in real-world multi-label classification problems. In this work, we present the general setup for a HOMER model and a simple extension of the algorithm that is suited for MLCs that output rankings. Furthermore, we provide a detailed analysis of the properties of the algorithm, both from an aspect of effectiveness and computational complexity. A secondary contribution involves the presentation of a balanced variant of the k means algorithm, which serves in the first step of the label hierarchy construction. We conduct extensive experiments on six real-world data sets, studying empirically HOMER's parameters and providing examples of instantiations of the algorithm with different clustering approaches and MLCs, The empirical results demonstrate a significant improvement over the given base MLC.

Ranking Wily People Who Rank Each Other

We study rank aggregation algorithms that take as input the opinions of players over their peers, represented as rankings, and output a social ordering of the players (which reflects, e.g., relative contribution to a project or fit for a job). To prevent strategic behavior, these algorithms must be impartial, i.e., players should not be able to influence their own position in the output ranking. We design several randomized algorithms that are impartial and closely emulate given (non-impartial) rank aggregation rules in a rigorous sense. Experimental results further support the efficacy and practicability of our algorithms.

Reference gene selection for molecular studies of dormancy in wild oat (Avena fatua L.) caryopses by RT-qPCR method.

Molecular studies of primary and secondary dormancy in Avena fatua L., a serious weed of cereal and other crops, are intended to reveal the species-specific details of underlying molecular mechanisms which in turn may be useable in weed management. Among others, quantitative real-time PCR (RT-qPCR) data of comparative gene expression analysis may give some insight into the involvement of particular wild oat genes in dormancy release, maintenance or induction by unfavorable conditions. To assure obtaining biologically significant results using this method, the expression stability of selected candidate reference genes in different data subsets was evaluated using four statistical algorithms i.e. geNorm, NormFinder, Best Keeper and ΔCt method. Although some discrepancies in their ranking outputs were noticed, evidently two ubiquitin-conjugating enzyme homologs, AfUBC1 and AfUBC2, as well as one homolog of glyceraldehyde 3-phosphate dehydrogenase AfGAPDH1 and TATA-binding protein AfTBP2 appeared as more stably expressed than AfEF1a (translation elongation factor 1α), AfGAPDH2 or the least stable α-tubulin homolog AfTUA1 in caryopses and seedlings of A. fatua. Gene expression analysis of a dormancy-related wild oat transcription factor VIVIPAROUS1 (AfVP1) allowed for a validation of candidate reference genes performance. Based on the obtained results it can be recommended that the normalization factor calculated as a geometric mean of Cq values of AfUBC1, AfUBC2 and AfGAPDH1 would be optimal for RT-qPCR results normalization in the experiments comprising A. fatua caryopses of different dormancy status.

Super-Efficiency of Education Institutions: An Application to Economics Departments

This paper investigates the efficiency of 188 economics departments around the world using data from RePEc. We go beyond the heavily used data envelopment analysis and utilize partial frontier analysis - specifically order-α and order-m - which addresses some of the drawbacks of the standard efficiency frontier analysis and allows for so-called super-efficient departments. We examine the particularities of these approaches and find that the super-efficient departments are not only the “usual suspects”. Furthermore, standard output rankings are not well correlated with our estimated efficiency rankings, which themselves are rather similar.

Economic, environmental, and social impacts of different sugarcane production systems

AbstractMechanization in the sugarcane agriculture has increased over the last few years, especially in harvesting and planting operations, in the Brazilian Center‐South region. The consequences of such a technological shift, however, are not fully comprehended when multiple perspectives are considered such as economic aspects, environmental regulations, and social context. The main goal of this study is to generate comprehensive information to subsidize decision‐making processes not only in Brazil but also in other countries where sugarcane production is still under development. Manual and mechanical technologies for planting and harvesting were evaluated (with and without pre‐harvest burning), as well as straw recovery, seeking to identify their advantages and disadvantages, considering economic, environmental, and social aspects. Considering vertically integrated production systems (agricultural and industrial phases), sugarcane production scenarios were compared under the metrics from engineering economics, life cycle assessment (LCA), and social LCA. Manual technologies were related to the highest job creation levels; however, lower internal rates of return and higher ethanol production costs were also observed. In general, mechanized scenarios were associated with lower ethanol production costs and higher internal rates of return due to lower biomass production cost, higher ethanol yield, and higher electricity surplus. Considering the restrictions for sugarcane burning and practical difficulties of manual harvesting of green cane, environmental analysis showed that mechanical harvesting of green cane with straw recovery presents, in general, the best comparative balance of environmental impacts. A multi‐criteria decision analysis was performed to generate an output rank, confirming that mechanized scenarios presented the best sustainability performances. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd

Advanced searching framework for open online educational video lectures

The appearance of massive open online courses has caused an increase in the volume of open online educational videos on the web. Therefore, there is a vast amount of information to be managed by the Internet users. The present work aims to optimize the educational video lecture searching in social networks. The research presents a novel ranking procedure for the educational video lectures that takes into account their popularity with content-based social media communities. The popularity formula combines quantitative and qualitative characteristics, taking into account not only the positive and the negative elements of the web page containing the video, but also the opinion of the users based on their comments. Thus, a novel social parameter is proposed which is embedded in the content-based ranking process. Furthermore, a user evaluation procedure is carried out, and initial results indicate that this integration produces a ranking output that better matches the user’s preferences.

Validation of reference genes for the normalization of the RT-qPCR gene expression of virulence genes of Erwinia amylovora in apple shoots

To study the expression of pathogenicity-related genes in Erwinia amylovora, seven candidate reference genes (ffh, glyA, gyrA, proC, pykA, recA, rpoB) were selected and validated with the following five different mathematic algorithms: geNorm, NormFinder, BestKeeper, the delta CT method and the RefFinder web-based tool. An overall comprehensive ranking output from each of the selected software programs revealed that proC and recA, followed by ffh and pykA, were the most stably expressed genes and can be recommended for the normalization of RT-qPCR data. A combination of the three reference genes, proC, recA and ffh, allowed for the accurate expression analysis of amsB and hrpN genes and the calculation of their fold change in E. amylovora after its infection of susceptible and resistant apple cultivars. To the best of our knowledge, this is the first study presenting a list of the most suitable reference genes for use in the relative quantification of target gene expression in E. amylovora in planta, selected on the basis of a multi-algorithm analysis.

Learning Ranking Functions For Information Retrieval Using Layered Multi-Population Genetic Programming

Ranking plays a key role in many applications, such as document retrieval, recommendation, question answering, and machine translation. In practice, a ranking function (or model) is exploited to determine the rank-order relations between objects, with respect to a particular criterion. In this paper, a layered multipopulation genetic programming based method, known as RankMGP, is proposed to learn ranking functions for document retrieval by incorporating various types of retrieval models into a singular one with high effectiveness. RankMGP represents a potential solution (i.e., a ranking function) as an individual in a population of genetic programming and aims to directly optimize information retrieval evaluation measures in the evolution process. Overall, RankMGP consists of a set of layers and a sequential workflow running through the layers. In one layer, multiple populations evolve independently to generate a set of the best individuals. When the evolution process is completed, a new training dataset is created using the best individuals and the input training set of the layer. Then, the populations in the next layer evolve with the new training dataset. In the final layer, the best individual is obtained as the output ranking function. The proposed method is evaluated using the LETOR datasets and is found to be superior to classical information retrieval models, such as Okapi BM25. It is also statistically competitive with the state-of-the-art methods, including Ranking SVM, ListNet, AdaRank and RankBoost.

PERSEPSI ANALIS KARTU KREDIT TENTANG RESIKO KARTU KREDIT PADA BANK KONVENSIONAL DAN BANK SYARIAH

This study aimed to compare the perception of risk analysts credit card at conventional banks and Islamic banks , case studies on the BNI and BNI Syariah in Semarang which includes the process of risk management application, credit card risk identification and appearing stagnant credit card. This study was measured using a questionnaire method is to assess the perception of risk analysts credit card at conventional banks and Islamic banks . Then to compare the perception of risk analysts credit card at conventional banks and Islamic banks performed different tests using the Mann - Whitney test because the data presented is not normal , by ten respondents . The results of the research analyst ‘s perception of the risk of credit card shows that on each item tested on conventional banks aresuperior to Islamic banks when viewed from the mean rank in Mann Whitney rank output , but this difference was not significant , so that could be said about the credit card analyst perception risk credit card at conventional banks and Islamic banks are the same .

PS 08-36 ANTIHYPERTENSIVE DRUG RECOMMENDATIONS FOR CKD PATIENTS

Objective: Chronic kidney disease (CKD) is a key challenge for public health worldwide, and hypertension is one of the most prevalent cardiovascular risk factors for CKD. Traditionally, drug recommendations have been provided using methods of evidence-based medicine. The aim of our study is to recommend influential antihypertensive drugs for patients with CKD by mining the MEDLINE literature database. Design and Method: A graph-based ranking algorithm was used to calculate the most influential antihypertensive drugs for CKD. Results: Over an exhaustive study on all research literatures published in MEDLINE, Irbesartan is ranked as the most influential antihypertensive drug for patients with CKD in our study. The status of loop diuretics has lessened over time, and the importance of angiotensin receptor blockers has increased rapidly. The recommended antihypertensive drug categories determined by MedRank appeared prior to the release of hypertension guidelines. The concordance between physician rankings collected via questionnaires and our system output rankings was not high (average overlap, 0.53 ± 0.20); however, the concordance increased with increasing position of physicians (P < 0.05). Conclusions: In conclusion, among patients with CKD, irbesartan was considered as the most influential antihypertensive drug by MedRank, which is a crucial method for determining antihypertensive drug recommendations for patients with CKD. This approach is a potential method for medical literature discovery, decision support, and guideline development.

Improving writing processes using lean and Kanban

Key points Concepts from lean manufacturing and Kanban production can usefully be applied to writing for academic publication. Value and pull focus the author's attention on the needs of reviewers, editors, and readers. Value stream and flow emphasize an end‐to‐end process of prioritization, writing, editing, revision, resubmission, and publication Perfection places emphasis on publication quality. A Kanban board is advocated to plan and monitor the writing and publication lifecycle. The author's experience shows a steady improvement in output rankings and researcher reputation metrics over a four‐year period.

Online unconstrained handwritten Tibetan character recognition using statistical recognition method

This paper describes a recognition system for online handwritten Tibetan characters using advanced techniques in character recognition. To eliminate noise points of handwriting trajectories, we introduce a de-noising approach by using dilation, erosion, thinning operators of mathematical morphology. Selecting appropriate structuring elements, we can clear up large amounts of noises in the glyphs of the character. To enhance the recognition performance, we adopt a three-stage classification strategy, where the top rank output classes by the baseline classifier are re-classified by similarcharacter discrimination classifier. Experiments have been carried out on two databases MRG-OHTC and IIP-OHTC. Test results show the used recognition algorithm is effective and can be applied to pen-based mobile devices.

Optimal Aggregation of Uncertain Preferences

A paradigmatic problem in social choice theory deals with the aggregation of subjective preferences of individuals --- represented as rankings of alternatives --- into a social ranking. We are interested in settings where individuals are uncertain about their own preferences, and represent their uncertainty as distributions over rankings. Under the classic objective of minimizing the (expected) sum of Kendall tau distances between the input rankings and the output ranking, we establish that preference elicitation is surprisingly straightforward and near-optimal solutions can be obtained in polynomial time. We show, both in theory and using real data, that ignoring uncertainty altogether can lead to suboptimal outcomes.

Voting rules as error-correcting codes

We present the first model of optimal voting under adversarial noise. From this viewpoint, voting rules are seen as error-correcting codes: their goal is to correct errors in the input rankings and recover a ranking that is close to the ground truth. We derive worst-case bounds on the relation between the average accuracy of the input votes, and the accuracy of the output ranking. Empirical results from real data show that our approach produces significantly more accurate rankings than alternative approaches.

Intuitionistic fuzzy variables: Concepts and applications in decision making

The present paper aims at advancing the well-recognized uncertainty theory built upon the credibility measure. In face of the increasing level of uncertainty embedded within the real world problem solving, it is discovered that the fuzzy variables defined in the credibility space are far from adequate to reflect the hesitance incurred during decision making. Although more complex fuzzy quantities are introduced, such as the fuzzy possibility space based type-2 fuzzy variables, the area of intuitionistic fuzzy variables is still a largely unexplored territory. Therefore, a novel concept of intuitionistic fuzzy variable is introduced hereby as an attempt to extend the uncertainty theory, which possesses a decent self-dual property compared with the fuzzy set theory. Unlike the intuitionistic fuzzy set theory, which has been widely implemented in various application fields, the intuitionistic fuzzy variables have hardly been employed in practices, especially the decision making sector. To bridge the gap, three outranking methods are developed to help evaluate intuitionistic fuzzy variables. The sufficient conditions of the three methods in distinguishing different fuzzy variables are also proved in the present work. The benefits of ranking with the three methods include the minimum amount of defuzzification, which prevents the information loss to a significant extent. Furthermore, in order to validate the proposed methods, a numerical study is performed. And the consistency of the ranking outputs from the three proposed methods is analyzed afterward. Besides, several managerial insights are presented regarding the sensitivity analysis of each ranking method.

Voting Rules As Error-Correcting Codes

We present the first model of optimal voting under adversarial noise. From this viewpoint, voting rules are seen as error-correcting codes: their goal is to correct errors in the input rankings and recover a ranking that is close to the ground truth. We derive worst-case bounds on the relation between the average accuracy of the input votes, and the accuracy of the output ranking. Empirical results from real data show that our approach produces significantly more accurate rankings than alternative approaches.

Comparative Analysis of Multiple Criteria Evaluations of Suppliers in Different Industries

The paper presents the comparative analysis of suppliers’ selection problems in different industries, formulated as multiple criteria ranking problems. The evaluation of logistics service providers (LSPs) in a household chemistry industry and meat suppliers in a food industry are carried out. The author compares both the alternative formulations of the decision problems (input) and generated results – output rankings of suppliers. On the input side he confronts: alternative definitions of variants, construction of different families of evaluation criteria, various ways of modelling of the DM's preferences. On the output side the results of computational experiments performed with the application of ELECTRE III/IV and AHP methods are demonstrated. Different aspects of the decision processes concerning the selection of suppliers are thoroughly discussed.

Adventitious shoot regeneration from leaves of apple rootstock ‘Pingyitiancha’ (Malus hupehensisvar.pinyiensis) and genetic fidelity of regenerated plantlets using SSR markers

Jin, W., Wang, Y. and Wang, H. 2014. Adventitious shoot regeneration from leaves of apple rootstock ‘Pingyitiancha’ (Malus hupehensis var. pinyiensis) and genetic fidelity of regenerated plantlets using SSR markers. Can. J. Plant Sci. 94: 1345–1354. Apple is one of the major fruit tree species in China, its cultivation area and total output rank first in the world. ‘Pingyitiancha’ (Malus hupehensis var. pinyiensis) is a widely used rootstock for apple cultivation in China. Several factors affecting leaf regeneration were investigated. In this study, a successful adventitious shoot regeneration protocol for this cultivar was established. ‘Pingyitiancha’ leaves were a suitable source of explants for regeneration of adventitious shoots. The optimal adventitious shoot regeneration protocol involved subculturing seedling leaves for 30–35 d. The optimum medium was Murashige and Skoog (MS) medium containing 2.0 mg L−1thidiazuron and 0.2 mg L−1indole-3-butyric acid. Explants with the abaxial surface in contact with the medium kept for 14 d in the dark showed the highest regeneration percentage of adventitious shoots of explants (100%), and produced an average of 3.6 shoots per regenerating explant. Shoots regenerated from leaves were rooted on half-strength MS medium containing 0.4 mg L−11-naphthalene acetic acid. The rooting percentage was 94.4%. Using SSR markers, all banding profiles from regenerated plantlets were monomorphic and same to those of the mother plant. It showed that the uniformity of the in vitro regenerated plantlets was maintained.

Adventitious shoot regeneration from leaves of apple rootstock ‘Pingyitiancha’ (Malus hupehensis var. pinyiensis) and genetic fidelity of regenerated plantlets using SSR markers

Jin, W., Wang, Y. and Wang, H. 2014. Adventitious shoot regeneration from leaves of apple rootstock ‘Pingyitiancha’ (Malus hupehensis var. pinyiensis) and genetic fidelity of regenerated plantlets using SSR markers. Can. J. Plant Sci. 94: 1345-1354. Apple is one of the major fruit tree species in China, its cultivation area and total output rank first in the world. ‘Pingyitiancha’ (Malus hupehensis var. pinyiensis) is a widely used rootstock for apple cultivation in China. Several factors affecting leaf regeneration were investigated. In this study, a successful adventitious shoot regeneration protocol for this cultivar was established. ‘Pingyitiancha’ leaves were a suitable source of explants for regeneration of adventitious shoots. The optimal adventitious shoot regeneration protocol involved subculturing seedling leaves for 30-35 d. The optimum medium was Murashige and Skoog (MS) medium containing 2.0 mg L-1 thidiazuron and 0.2 mg L-1 indole-3-butyric acid. Explants with the abaxial surface in contact with the medium kept for 14 d in the dark showed the highest regeneration percentage of adventitious shoots of explants (100%), and produced an average of 3.6 shoots per regenerating explant. Shoots regenerated from leaves were rooted on half-strength MS medium containing 0.4 mg L-1 1-naphthalene acetic acid. The rooting percentage was 94.4%. Using SSR markers, all banding profiles from regenerated plantlets were monomorphic and same to those of the mother plant. It showed that the uniformity of the in vitro regenerated plantlets was maintained.

Counting and comparing publication output with and without equalizing and inflationary bias

Abstract This paper examines the effects of inflationary and equalizing bias on publication output rankings. Any identifiable amount of bias in authorship accreditation was detrimental to accuracy when ranking a select group of leading Canadian aquaculture researchers. Bias arose when publication scores were calculated without taking into account information about multiple authorship and differential coauthor contributions. The ensuing biased equal credit scores, whether fractional or inflated, produced rankings that were fundamentally different from the ranking of harmonic estimates of actual credit calculated by using all relevant byline information in the source data. In conclusion, the results indicate that both fractional and inflated rankings are misleading, and suggest that accurate accreditation of coauthors is the key to reliable publication performance rankings.