Action: A New Metric for Evaluating the Energy Efficiency on High Performance Computing Platforms (ranked on Green500 List)

The need for new and more reliable metrics is always in demand. In this paper, a new metric is proposed for the evaluation of high performance computing platforms in conjunction with their energy consumption. The aim of the new metric is to reliably compare different HPC systems concerning their energy efficiency. The metric provides a mean to rank supercomputers of similar capabilities, avoiding the misleading results of metrics like performance-per-watt, currently used for ranking systems, as in the Green500 list, where systems with totally different sizes and capabilities are ranked consecutively. An example of this misuse for two adjacent systems in the Green500 list, is discussed. A comparative study for the energy efficiency of three high performance computing platforms, with different architectures, using the proposed metric is presented.

Cloud Provider Selection Based on Accountability and Security Using Interval Valued Fuzzy TOPSIS

Cloud computing enables on-demand access to a public resource pool. Many businesses are migrating to the cloud due to its popularity and financial benefits. As a result, finding a suitable and best Cloud Service Provider is a difficult task for all cloud users. Many ranking systems, such as ANP, AHP and TOPSIS, have been proposed in the literature .However, many of the studies concentrated on quantitative data. But qualitative attributes are equally significant in many applications where the user is more concerned with the qualitative features.The implementation of MCDM approach for the ranking and the selection of the best player in the market as per the qualitative need of the cloud users like business organization or cloud brokers is the aim of this article. An ISO approved standard SMI framework is available for the evaluation of the CSPs.The authors have considered SMI attributes like accountability and security as the criteria for evaluation of the CSPs. The MCDM approach called IVF-TOPSIS that can handle the inherent vagueness in the cloud dataset is implemented in this work

The Development and Transformation of the Bank Card Market as an Imperative for Digitalization: the Case of Central and Eastern European Countries

The purpose of this study is to determine the development trends of the major determinants of the bank card market in eight countries of Central and Eastern Europe in the period from 2010 to 2019. Continuing a study carried out in 2018, further comparative analysis of the “Bank Cards Market Index” proposed earlier and based on a system of interrelated indicators of bank payment cards, ATMs and POS‑terminals, was carried out. We provide an overview of the rankings of Ukraine, Belarus, Moldova, Russia, Romania, Poland, Hungary, Slovakia using international ranking systems such as “The Legatum Prosperity Index,” “Doing Business,” “The Index of Economic Freedom,” and the “Вank Cards Market Index.” Further studies of three international ranking systems, as well as the “Bank Cards Market Index,” again confirmed the similarity of the development models of the bank card market in Poland and Ukraine. To study the impact of the digitalization of economics and Covid–19 on the bank card market, a deeper analysis of two cases (Poland and Ukraine, as two similar bank card markets) was carried out using the “Digital Evolution Index.” In the course of the research, it was concluded that the “Вank Cards Market Index” can be successfully used for further research of the banking sector of different countries. Also, the growth trend of cashless payments in the bank card market and the possible transformation of the market under the influence of Covid–19, and the global digitalization of economics were noticed. Taking into account the above trend, further studies of the system of interrelated indicators of bank payment cards, ATMs, and POS terminals should be carried out using the “Digital Evolution Index” or other international indexes that characterize the level of digitalization of the economy in the researched countries.

Global and national university rankings: A multivariate analysis

Introduction. In the context of globalisation and internationalisation of higher education, university rankings are becoming an important tool for assessing the quality of education received by students at various higher education institutions around the world. These processes actualise the issues of possibilities for practical use of methodologies for calculation of global and national university rankings.The aim of the study was to develop and apply a methodological approach to multivariate the analysis of Higher Education Institutions (HEIs) classification procedures, to construct and analyse aggregated indicators for global and national rating systems of higher education organisations, and to assess the relationship between them.Methodology and research methods. The current paper presents a system analysis of databases of rating systems and an aggregation of independent evaluations of global and national rankings of HEIs using the methodology of league table analysis based on mathematical apparatus of the voting theory. The dependence of global and national university rankings indicators was investigated using correlation, cluster, factor, regression (linear and polynomial) and dispersion methods of analysis.Results and scientific novelty. A comprehensive comparative analysis of ranking systems and their results was carried out. The authors solved the problem of aggregating multiple heterogeneous studies of global and national ranking systems with their qualitative and quantitative variety of criteria, indicators and methods of assessment. The correlation between the indicators of aggregated global and national rankings was revealed; the regression dependence of the integral national ranking with the results of the leading global rating systems was determined.Practical significance. The developed methodical approach is a convenient and effective mechanism for comprehensive monitoring of the members of educational process.

Determining the Standard of Quality: Examining the Turkish University Ranking Systems According to the Berlin Principles

The increasing competitive environment arising from the commercialization and internationalization of higher education (HE) and the increasing influence of the liberal character of educational policies have led to the emergence of HE ranking systems which have become an inevitable part of HE with the effect they have created. The lack of unambiguous methodological processes is one of the oft-cited critical problems in the HE ranking system literature. These systems vary due to the particular methods they adopt in creating their ranking results. This variation requires a set of principles on how best to perform these rankings. The principles established under Berlin Principles in 2006 aimed to address this need. This study aims to determine to what extent Turkish ranking systems comply with the basic tenets determined in the Berlin Principles. The study was designed as a methodological assessment study and the data were obtained from the websites and printed and electronic publications related to Turkish ranking systems. The data obtained were scored according to an evaluation form consisting of the tenets set out in the Berlin Principles. The findings show that the majority of the Turkish university ranking systems were not designed to be fully compliant with the Berlin Principles and instead they evaluate HE institutions by their research approach and quality definitions.

The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System

Rock tensile strength (TS) is an essential parameter for designing structures in rock-based projects such as tunnels, dams, and foundations. During the preliminary phase of geotechnical projects, rock TS can be determined through laboratory works, i.e., Brazilian tensile strength (BTS) test. However, this approach is often restricted by laborious and costly procedures. Hence, this study attempts to estimate the BTS values of rock by employing three non-destructive rock index tests. BTS predictive models were developed using 127 granitic rock samples. Since the simple regression analysis did not yield a meaningful result, the development of models that integrate multiple input parameters were considered to improve the prediction accuracy. The effects of non-destructive rock index tests were examined through the use of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) approaches. Different strategies and scenarios were implemented during modelling of MLR and ANFIS approaches, where the focus was to consider the most important parameters of these techniques. As a result, and according to background and behaviour of the ANFIS (or neuro-fuzzy) model, the predicted values obtained by this intelligent methodology are closer to the actual BTS compared to MLR which works based on linear statistical rules. For instance, in terms of system error and a-20 index, values of (0.84 and 1.20) and (0.96 and 0.80) were obtained for evaluation parts of ANFIS and MLR techniques, which revealed that the ANFIS model outperforms the MLR in forecasting BTS values. In addition, the same results were obtained through ranking systems by the authors. The neuro-fuzzy developed in this study is a strong technique in terms of prediction capacity and it can be used in the other rock-based projects for solving relevant problems.

Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions (Extended Abstract)

State-of-the-art Learning to Rank (LTR) methods for optimizing ranking systems based on user interactions are divided into online approaches – that learn by direct interaction – and counterfactual approaches – that learn from historical interactions. We propose a novel intervention-aware estimator to bridge this online/counterfactual division. The estimator corrects for the effect of position bias, trust bias, and item-selection bias by using corrections based on the behavior of the logging policy and on online interventions: changes to the logging policy made during the gathering of click data. Our experimental results show that, unlike existing counterfactual LTR methods, the intervention-aware estimator can greatly benefit from online interventions. To the best of our knowledge, this is the first method that is shown to be highly effective in both online and counterfactual scenarios.