Formula For Rank Research Articles

Decision support system for determining the activities of the study program using the Preference Selection Index

Each year the study program carries out activities that have been planned through departmental meetings. However, study program activities that are not on target will result in less accreditation scores. Accreditation is a government effort to guarantee the quality of educational institutions. The value of accreditation determines the main assets in the field of education, the quality of study programs and the feasibility of graduates. Therefore, this study tries to provide a ranking formula for study program activities based on the assessment of study program accreditation forms. The system is built using one of type multi-criteria decision making (MCDM), namely the Preference Selection Index (PSI). PSI method using statistical concepts until preference index as a reference for alternative ranking. This developed system can help the department to recommend the main activities that can be done to support the efforts of the study program in achieving optimal accreditation values. Finally, it was concluded that the PSI method is the right method for problems that require decision support with a large number of attributes, such as the criteria used in recommendations for study program activities that have been adapted to the study program accreditation form assessment matrix.

Evaluation of model refinement in CASP13.

Performance in the model refinement category of the 13th round of Critical Assessment of Structure Prediction (CASP13) is assessed, showing that some groups consistently improve most starting models whereas the majority of participants continue to degrade the starting model on average. Using the ranking formula developed for CASP12, it is shown that only 7 of 32 groups perform better than a “naïve predictor” who just submits the starting model. Common features in their approaches include a dependence on physics‐based force fields to judge alternative conformations and the use of molecular dynamics to relax models to local minima, usually with some restraints to prevent excessively large movements. In addition to the traditional CASP metrics that focus largely on the quality of the overall fold, alternative metrics are evaluated, including comparisons of the main‐chain and side‐chain torsion angles, and the utility of the models for solving crystal structures by the molecular replacement method. It is proposed that the introduction of these metrics, as well as consideration of the accuracy of coordinate error estimates, would improve the discrimination between good and very good models.

Comparative Study of Ranking Methods for Fuzzy Transportation

There are several methods that are used to solve the traditional transportation problems whose units of supply, demand quantities, and cost transportation are known exactly. These methods obtain basic solution, and develop it to the best solution through a series of consecutive calculations to obtain the optimal solution.The steps are more complex with fuzzy variables, so this paper presents the disadvantages of solutions of the traditional ways with existence of variables in the fuzzy form.This paper also presents a comparison between the results that emerged after using different conversion ranking formulas to convert from fuzzy form to crisp form on the same numerical example with a full fuzzy form. The problem has been then converted into a linear programming model, and the BIG-M method to be later used to find the optimal solution that represents the number of units transferred from processing or supply centers to a number of demand centers based on the known cost of transportation.Achieving the goal of the problem is by finding the lowest total transportation cost,while the comparison is based on that value. The results are presented in acomprehensive table that organizes data and results in a way that facilitates quickand accurate comparison. An amendment to one of the order formats was suggested,because it has different results compared to other formulas. One of the rankingequations is modified, because it has different results compared to other methods..

Kategorisasi Tingkat Kemandirian Belajar Siswa Madrasah Aliyah

The purpose of this study was to determine the level of student learning independence, based on high, low and high categorization. Data is obtained based on the results of filling in the independence questionnaire. Research uses a quantitative approach with survey methods. The data measurement tool uses a scale questionnaire. Based on aspects of independence. Participants in this study were 18th grade students of the Putra Rajak MA MA totaling 18 students. Based on the results of the analysis obtained a score of 25 which if included in the rank formula, then vulnerable 11.1> 25.7 is in the "low" category. Based on the analysis, it can be seen that the level of student learning independence is in the low level category, that is, from 18 participants who were sampled in the study, 85% (12 participants) were included in the low category. The high category is 15% (6 participants), while the low result is 0%.

An Advanced Mining Services in Predicting and Ranking User Vitality across Dynamic and High Dimensional Data Sets

Social media network solutions have actually prevailed at numerous online neighborhoods such as Twitter.com as well as Weibo.com, where countless individuals maintain engaging with each various other each day. One intriguing as well as crucial trouble in the social networking solutions is to rate customers based upon their vigor in a prompt style. A precise ranking listing of customer vigor can profit several celebrations in social media solutions such as the advertisements suppliers as well as website drivers. Although it is extremely appealing to get a vitality-based ranking listing of customers, there are numerous technological difficulties as a result of the huge range and also characteristics of social networking information. In this paper, we suggest a special viewpoint to accomplish this objective, which is measuring individual vigor by evaluating the vibrant communications amongst customers on social media networks. Instances of social media consist of yet are not restricted to socials media in microblog websites and also academicals partnership networks. Without effort, if a customer has numerous communications with his good friends within an amount of time as well as the majority of his buddies do not have several communications with their pals all at once; it is highly likely that this customer has high vigor. Based upon this suggestion, we establish measurable dimensions for individual vigor as well as recommend our initial formula for ranking customers based vigor. Likewise we better think about the common impact in between customers while calculating the vigor dimensions and also recommend the 2nd ranking formula, which calculates individual vigor in a repetitive means. Apart from individual vigor position, we additionally present a vigor forecast trouble, which is likewise of fantastic value for numerous applications in social networking solutions. Along this line, we create a personalized forecast design to address the vigor forecast trouble. To examine the efficiency of our formulas, we accumulate 2 vibrant social.

Supplier selection using a flexible interval-valued fuzzy VIKOR

One of the major issues in a supply chain (SC) is the selection of the appropriate supplier. Supplier selection (SS) plays a vital role in achieving an effective and successful SC, hence gaining a competitive advantage. This article proposes a novel flexible multi-attribute group decision-making method for SS based on VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) using interval-valued fuzzy sets (IVFSs). The method avoids defuzzification, minimizes the computations, and considers the decision-makers’ optimism level. Avoiding defuzzification keeps the characteristics of (IVFSs) and prevents the loss of information. To minimize the computations, two main modifications are done. While all VIKOR-based techniques use both the best and the worst solutions; the proposed VIKOR uses the best solution only, and the division operations by the difference between two fuzzy sets to compute the separation measures and the index Q are eliminated. Ranking plays a crucial role in VIKOR, since three ranking lists are required. The signed distance is modified and used for ranking due to its simple, few computations. None of the previously VIKOR-based techniques accounted for the decision-makers’ optimism level. Therefore, the signed distance is used in its basic form to keep the α-level explicitly in the ranking formula. Thus, the proposed technique preserves fuzziness, reduces the computations substantially, and allows the participation of the decision-makers through the optimism level. Two examples are solved: one for illustration and the other to compare the results with previously used methods.

The non-congruent numbers via Monsky’s formula

This paper gives some new families of non-congruent numbers with arbitrarily many prime divisors. The main idea is based on Monsky’s formula for the 2-Selmer rank of congruent elliptic curves.

Rank and inertia formulas for covariance matrices of BLUPs in general linear mixed models

We consider an extended general linear model containing new observations without making any restrictions on correlation of random vectors and any rank assumptions. We give variety of equalities and inequalities in the comparison of covariance matrix of BLUP of new observations with any other unbiased predictors’ covariance matrices by using rank and inertia formulas. We next consider the general linear mixed model under the assumption that the random effects and the random errors can be correlated. Using connection between the general linear mixed model and the extended general linear model, we give some equalities and inequalities for comparing the covariance matrices of BLUP of linear function of fixed and random effects with covariance matrices of any other type of unbiased predictors. We also give results for special cases and for linear function of partial fixed and random effects.

You Are Not Acting Like Yourself: A Study on Soft Biometric Classification, Person Identification, and Mobile Device Use

In this paper, we explore the soft biometric classification of 13 demographic and behavioral attributes using phone data collected from 46 subjects. We utilize the results of this analysis to further evaluate reduced search spaces for the primary identification task by implementing a ranking formula which quantifies the performance of each attribute considering the predictability of some attributes compared to others. Results show that soft biometric classification is feasible with up to 90% accuracy; however, because people exhibit high intra-class variance, templates and queries are significantly affected in terms of how well they match, even with reduced search spaces. We analyze these findings using a combination of approaches, including an evaluation of the biometric menagerie, visualizing the distribution of the data, and observing how subjects vary in their soft biometric class across different times of the day.

Closed-form formulas for calculating the max–min ranks of a triple matrix product composed by generalized inverses

In matrix theory and its applications, people often meet with various matrix expressions or matrix equalities that involve inverses of nonsingular matrices or generalized inverses of singular matrices. One of the fundamental research problems about these matrix expressions is to determine their singularity and nonsingularity, or alternatively, to determine upper and lower bounds of the ranks of the matrix expressions. Matrix rank formulas were introduced in the development of generalized inverse theory in 1970s, and many fundamental closed-form formulas for calculating ranks of matrices and their generalized inverses were established. In this paper, we consider the problem of determining the maximum and minimum ranks of a triple matrix product $$P^{(i,\ldots ,j)}NQ^{(i,\ldots ,j)}$$ , where P, Q, and N are given matrices, $$P^{(i,\ldots ,j)}$$ and $$Q^{(i,\ldots ,j)}$$ are $$\{i,\ldots , j\}$$ -generalized inverses of P and Q, respectively. We first rewrite the product as certain linear or nonlinear matrix-valued function with one or more variable matrices. We then establish a family of closed-form formulas for calculating the maximum and minimum ranks of $$P^{(i,\ldots ,j)}NQ^{(i,\ldots ,j)}$$ with respect to $$\{i,\ldots , j\}$$ -inverses of P and Q, respectively. Some applications of these max–min rank formulas are also discussed.

A Group Decision Making Model for Integrating Heterogeneous Information

This paper proposes a group decision-making (GDM) method for integrating heterogeneous information. To avoid information loss, instead of transforming heterogeneous information into a single form, the proposed method integrates heterogeneous information using a weighted-power average operator. The consensus degree between the individual-decision matrix and the group-decision matrix is then calculated based on the deviation degree. In addition, the feedback mechanism with the iterative algorithm is used to adjust the individual decision matrix, which does not reach the consensus. Furthermore, a ranking formula with heterogeneous technique for order preference by similarity to an ideal solution is adopted to select the best alternative. A numerical example of supplier selection is introduced to validate the proposed model and compare it with other similar GDM models. The results illustrate that the proposed method cannot only avoid information loss, but also effectively integrate heterogeneous information in heterogeneous GDM environment.

New document scoring model based on interval tree

Classical BM25 scoring is designed for unstructured documents. In the past years, people try to adapt the BM25 ranking formula to deal with structured documents. Most works on structured document retrieval treat the combination of field scores, but it is hard to determine the field weights before the formation of document score. We aim to establish a new method to sort the field weights. The motivation comes from two aspects. On the one hand, the construction of interval tree reflects retrieval results with higher-order proximity for a text field. According to writing style, the important sentence or phrase for representing main idea frequently appear in the front or the rear part of a text-field. Therefore, the proximity scoring for different part in a text-field should be different. We thus take higher factor for calculating proximity scoring in the front and the rear parts than in the middle part. On the other hand, the more the interval length includes inquiring terms, the less the proximity scoring is, thereby the higher tf value for term appearing in an interval should affect the computation of proximity scoring. Therefore, we develop a new method for calculating the field weights based on the ranking score. The ranking score for each field can be calculated by interval tree based on terms relevance. Interval tree can be viewed as a tool of higher terms proximity in text visualization. This new field weights reflect the terms proximity and can be used to calculate document scoring for terms retrieval. Experimental results show that the new document scoring model well reflects the terms proximity, and the new document scoring scheme ScoreComp, combined with interval scoring, is more sensitive than scheme FreqComp combined with interval scoring.

Supermodularity in Unweighted Graph Optimization I: Branchings and Matchings

The main result of this paper is motivated by the following two apparently unrelated graph optimization problems: (A) As an extension of Edmonds’ disjoint branchings theorem, characterize digraphs comprising k disjoint branchings Bi each having a specified number μi of arcs. (B) As an extension of Ryser’s maximum term rank formula, determine the largest possible matching number of simple bipartite graphs complying with degree-constraints. The solutions to these problems and to their generalizations will be obtained from a new min-max theorem on covering a supermodular function by a simple degree-constrained bipartite graph. A specific feature of the result is that its minimum cost extension is already NP-hard. Therefore classic polyhedral tools themselves definitely cannot be sufficient for solving the problem, even though they make some good service in our approach.

Supermodularity in Unweighted Graph Optimization II: Matroidal Term Rank Augmentation

Ryser’s max term rank formula with graph theoretic terminology is equivalent to a characterization of degree sequences of simple bipartite graphs with a specific matching number. In a previous paper by the authors, a generalization was developed for the case when the degrees are constrained by upper and lower bounds. Here, two other extensions of Ryser’s theorem are discussed. The first one is a matroidal model, while the second one settles the augmentation version. In fact, the two directions shall be integrated into one single framework.

Simultaneous Localization of Software Faults Based on Complex Network Theory

Effective debugging is necessary for producing high quality and reliable software. Fault localization plays a vital role in the debugging process. However, fault localization is the most tedious and expensive activity in program debugging, as such, effective fault localization techniques that can identify the exact location of faults is eminent. Despite various fault localization techniques proposed, their application in multiple-fault programs is limited. The presence of multiple faults in a program reduces the efficacy of the existing fault localization techniques to locate faults effectively due to fault interference. Moreover, most of these techniques are unable to localize faults simultaneously. This has led researchers to adopt alternative approaches, such as one-fault-at-a-time debugging and parallel debugging. In this paper, we propose a novel fault localization technique based on complex network theory (FLCN) to improve localization effectiveness in programs with single-fault and multiple-fault and to aid developers to localize multiple faults simultaneously in a single diagnosis rank list. The proposed technique ranks faulty statements based on their behavioral abnormalities and distance between faulty statements in both passed and failed test executions. Two graph-based centrality measures are adopted for fault diagnosis, namely, degree centrality and closeness centrality and a new ranking formula is proposed. FLCN is evaluated across 14 subjects with both single-fault and multiple-fault programs. Our experimental results show that FLCN is more effective at locating faults when compared with existing state-of-the-art fault localization techniques with improvement of fault localization effectiveness in both single-fault and multiple-fault programs.

Predictions and estimations under a group of linear models with random coefficients

ABSTRACTThis article investigates the problem of establishing best linear unbiased predictors and best linear unbiased estimators of all unknown parameters in a group of linear models with random coefficients and correlated covariance matrix. We shall derive a variety of fundamental statistical properties of the predictors and estimators by using some matrix analysis tools. In particular, we shall establish necessary and sufficient conditions for the predictors and estimators to be equivalent under single and combined equations in the group of models by using the method of matrix equations, matrix rank formulas, and partitioned matrix calculations.

Homology groups for particles on one-connected graphs

We present a mathematical framework for describing the topology of configuration spaces for particles on one-connected graphs. In particular, we compute the homology groups over integers for different classes of one-connected graphs. Our approach is based on some fundamental combinatorial properties of the configuration spaces, Mayer-Vietoris sequences for different parts of configuration spaces, and some limited use of discrete Morse theory. As one of the results, we derive the closed-form formulae for ranks of the homology groups for indistinguishable particles on tree graphs. We also give a detailed discussion of the second homology group of the configuration space of both distinguishable and indistinguishable particles. Our motivation is the search for new kinds of quantum statistics.

The composite voxel technique for inelastic problems

The composite voxel technique was developed in the framework of linear elasticity and hyperelasticity for regular voxel grid discretizations which cannot resolve material interfaces exactly in general. In this work, we study the inelastic behavior of two-phase laminates. In particular, we derive an analytical nonlinear formula for the unknown rank one jump of the strain across the interface. This enables us to extend the composite voxel technique to account for inelastic material behavior at small strains.We demonstrate by numerical experiments on continuously and discontinuously reinforced thermoplastics with elastoplastic matrix behavior that FFT-based computational homogenization on downsampled microstructures equipped with composite voxels produces stress–strain curves mimicking those obtained for the full resolution. For industrial sized microstructures it turns out that the computations can be accelerated by a factor of up to 40 compared to a direct parallelization of the fully resolved problem.

Upper and lower bounds for the rank of the matrix-valued function when X has a fixed rank

Given a matrix-valued function , where , , and are known matrices, we first review two known fundamental formulas for calculating the maximum and minimum ranks of the matrix-valued function when X runs over . We then establish some new formulas for calculating the maximum and minimum ranks of when X runs over with a restriction by using a simultaneous decomposition of A, B and C, and some tricky matrix operations. Some applications of the rank formulas in completing partially-specified matrices are also given.

Mining collective knowledge: inferring functional labels from online review for business

With the increasing popularity of online e-commerce services, a large volume of online reviews have been constantly generated by users. In this paper, we propose to study the problem of inferring functional labels using online review text. Functional labels summarize and highlight the main characteristics of a business, which can serve as bridges between the consumption needs and the service functions. We consider two kinds of semantic similarities: lexical similarity and embedding similarity, which characterize the relatedness in two different perspectives. To measure the lexical similarity, we use the classic probabilistic ranking formula, i.e., BM25; to measure the embedding similarity, we propose an extended embedding model which can incorporate weak supervised information derived from review text. These two kinds of similarities compensate each other and capture the semantic relatedness in a more comprehensive way. We construct a test collection consisting of four different domains based on a Yelp dataset and consider multiple baseline methods for comparison. Extensive experiments have shown that the proposed methods are very effective.