Reviewerly/exHarmony huggingface dataset:
Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO 2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.
Context-Aware Intelligent Mixing Systems

Intelligent Mixing Systems (IMS) are rapidly becoming integrated into music mixing and production workflows.The intelligences of a human mixer and IMS can be distinguished by their abilities to comprehend, assess, and appreciate context.Humans will factor context into decisions, particularly concerning the use and application of technologies.The utility of an IMS depends on both its affordances and the situation in which it is to be used.The appropriate use for conventional purposes, or its utility for misappropriation, is determined by the context.This study considers how context impacts mixing decisions and the use of technology, focusing on how the mixer's understanding of context can inform the use of IMS, and how the use of IMS can aid in informing a mixer of different contexts.
The Use of Modified K-Means Algorithm to Enhance the Performance of Support Vector Machine in Classifying Breast Cancer

Breast cancer has been recently considered as one of the broadly spread diseases that causes death among women.Early disease diagnosis is a critical aim in building the treatment policies and is extremely related to safety of patient.Therefore, there is a necessity for computer aided detection (CAD) in order to provide accurate and rapid diagnosis for breast cancer.Recently, many classification models utilizing machine learning approaches have been adopted and modified to diagnose breast cancer disease.Moreover, the performance of each model depends on different compositions such as the number and type of data features and the parameters of model.In order to enhance the performance of classification model, this research proposes a model using modified K-means algorithm to create a new training dataset of breast cancer which can highly improve the performance of support vector machine model.A modified K-means algorithm is also proposed to build a high quality training dataset that contributes significantly to reduce the training time of classifiers, and improve the performance of classifier.The proposed model handles the noise and irregularity in data and produce high quality dataset which represents all the cases of disease.The two recognized datasets Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) have been used to examine and appraise the performance of the proposed model.The experimental results show that the proposed model has a significant performance compared to other previous works and with accuracy level of 98.067%, sensitivity of 100%, specificity of 94.811%, precision of 97.011% and finally with area under the curve related to the receiver operating characteristic of 97.406%.
Ensemble-Based Out-of-Distribution Detection

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.
HISTORICAL AND PEDAGOGICAL DIALECTICS OF THE DEVELOPMENT OF ERSON LIFE-CREATION’S THEORY AND PRACTICE IN NATIONAL EDUCATION IN THE 16TH AND EARLY 20TH CENTURY

The article analyzes socio-political, cultural and educational prerequisites for the development of life-creation pedagogy at the Cossack times and during the times when Ukrainian lands were under the Russian and Austrian empires. It was found out in the study that the development of life-creation pedagogy at the Cossack times was facilitated by the following factors: personal liberty and dignity as social practice, emergence of a new educational phenomenon of Cossack pedagogy, which envisaged the development of a person as a creator of own life. During the period, when Ukrainian lands were ruled by the Russian and Austrian empires the development of life-creation pedagogy was complicated by enslavement and constant pressure on national culture for the purpose of its destruction. The study defined the contribution of such Ukrainian teachers as Hryhoriy Skovoroda, Pamfil Yurkevych, Mykola Korf, Ivan Franko, Borys Grinchenko to the development of conceptual framework of life-creation pedagogy. The scientific novelty of the obtained results is the following: for the first time a holistic historical and pedagogical analysis of the Ukrainian life-creation pedagogy theory and practice was conducted; the socio-political, cultural and educational prerequisites for the development of Ukrainian life-creation pedagogy from the Cossack times were comprehensively reviewed. The research specifies the following: the content of the life-creation concept, taking into account the procedural and practical, creative and acmeological, life-creation phenomenon evaluative and semantic aspects; the scientific argumentation concerning the theoretical structuring of life-creation pedagogy as an educational concept; the methodological approaches and principles for the correct interpretation of life-creation pedagogy conceptual ideas in historical and pedagogical context; the social, methodological and pedagogical criteria for the Ukrainian life-creation pedagogy theory and practice development periodization.
PROBLEMATIKA PEMBELAJARAN DARING SISWA KELAS VII SMPN 1 ENDE SELATAN, KABUPATEN ENDE

The pandemic of covid 19 had big effects in educational field. The transition of learning process from face to face into online had many obstacles. Teachers, students and parents had their own problems to face it. Parents had to provide facilities to their child during online learning process but their economic factors were inadequate, such as buying android phone or data package. There were parents and teachers were not able to use the technology effectively and efficiently. It can be effect to the students. Students will left behind the materials and the assignments had given by their teachers. Door to door was an approach offered to avoid or to minimize that problems. The aim of that approach was teachers could share the materials, the assignment and guiding their students directly. Learning guidance could be the materials which were not understood and teaching them how to use android phone to access the materials shared through online learning. In order that, door to door approach which had implemented besides helped students to understand more the materials but also the students got learning guidance effectively about the way to utilize the learning technology. It was about the materials were shared online. In this case, teacher should actively guiding their students to use the technology in better way. Furthermore, the teacher will adding new knowledge, so they do not seems out of touch of information and blinded technology.
Elliptic Curve Cryptography based Secure Image Transmission in Clustered Wireless Sensor Networks

Wireless Sensor Networks (WSN) is arising as a potential computing platform in diverse zones such as weather forecasting, modern robotization, medical health care, and military systems, etc.Since the sensors are constantly gathering information from the actual world and communicate with one another through remote connections, keeping up the security and protection of WSN communication is a prerequisite.In this paper, safe confirmation and key organization scheme dependent on Elliptic Curve Cryptography (ECC) has been suggested to make sure about information/picture transmission in WSNs.The scheme proposed in this paper is protected, competent, and appropriate for providing sensor technology based IoT services and applications.The protocol provides all the security features such as mutual authentication, confidentiality, data integrity, perfect forward secrecy, fair key agreement, etc. and is secure against hello flood attack, DoS attack, man-in-middle attack, etc. Simulation software AVISPA has confirmed the safety of the protocol for the known assaults.The performance analysis ensures the superiority of the projected proposal over the existing schemes.
Generalized and transferable patient language representation for phenotyping with limited data

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 respiratory diseases, and 17 genitourinary diseases. We find multi-task pre-training increases learning efficiency and achieves consistently high performance across the majority of phenotypes. Most important, the multi-task pre-training is almost always either the best-performing model or performs tolerably close to the best-performing model, a property we refer to as robust. All these results lead us to conclude that this multi-task transfer learning architecture is a robust approach for developing generalized and transferable patient language representations for numerous phenotypes.
Bengali News Headline Categorization Using Optimized Machine Learning Pipeline

Bengali text based news portal is now very common and increasing day by day.With easy access of internet technology, reading news through online is now a regular task.Different types of news are represented in the news portal.The system presented in this paper categorizes the news headline of news portal or sites.Prediction is made by machine learning algorithm.Large number of collected data are trained and tested.As pre-processing tasks such as tokenization, digit removal, removing punctuation marks, symbols, and deletion of stop words are processed.A set of stop words is also created manually.Strong stop words leads to better performance.Stop words deletion plays a lead role in feature selection.For optimization, genetic algorithm is used which results in reduced feature size.A comparison is also explored without optimization process.Dataset is established by collecting news headline from various Bengali news portal and sites.Resultant output shows well performance in categorization.
A Survey of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> Nearest Neighbor Algorithms for Solving the Class Imbalanced Problem

<math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> nearest neighbor ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> NN) is a simple and widely used classifier; it can achieve comparable performance with more complex classifiers including decision tree and artificial neural network. Therefore, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>k</mi> </math> NN has been listed as one of the top 10 algorithms in machine learning and data mining. On the other hand, in many classification problems, such as medical diagnosis and intrusion detection, the collected training sets are usually class imbalanced. In class imbalanced data, although positive examples are heavily outnumbered by negative ones, positive examples usually carry more meaningful information and are more important than negative examples. Similar to other classical classifiers, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>k</mi> </math> NN is also proposed under the assumption that the training set has approximately balanced class distribution, leading to its unsatisfactory performance on imbalanced data. In addition, under a class imbalanced scenario, the global resampling strategies that are suitable to decision tree and artificial neural network often do not work well for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>k</mi> </math> NN, which is a local information-oriented classifier. To solve this problem, researchers have conducted many works for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>k</mi> </math> NN over the past decade. This paper presents a comprehensive survey of these works according to their different perspectives and analyzes and compares their characteristics. At last, several future directions are pointed out.
Gaussian process approximations for multicolor Pólya urn models

Abstract Motivated by mathematical tissue growth modelling, we consider the problem of approximating the dynamics of multicolor Pólya urn processes that start with large numbers of balls of different colors and run for a long time. Using strong approximation theorems for empirical and quantile processes, we establish Gaussian process approximations for the Pólya urn processes. The approximating processes are sums of a multivariate Brownian motion process and an independent linear drift with a random Gaussian coefficient. The dominating term between the two depends on the ratio of the number of time steps n to the initial number of balls N in the urn. We also establish an upper bound of the form $c(n^{-1/2}+N^{-1/2})$ for the maximum deviation over the class of convex Borel sets of the step- n urn composition distribution from the approximating normal law.
THE APPLICATION OF MOBILE ASSISTED LANGUAGE LEARNING (MALL) IN TEACHING PRONUNCIATION

Mobile learning is experiencing a drastic change. Whereas the initial period of mobile learning tend to propose carefully skilled activities by teachers and technology, students are gradually motivated by their personal learning needs, including those that arise from greater mobility and traveling activities. Through the process, the mobile learning use, particularly the Mobile Assisted Language Learning (MALL), have become essential tools in the teaching and learning process. This research investigates the use of Mobile Assisted Language Learning (MALL) in teaching pronunciation at a secondary school. Using experimental study, the research aims to know the effectiveness of MALL in teaching pronunciation in experimental group, compared to another group with non-MALL. Result shows that students in experimental group performed better in pronunciation skill compared to their peers. Thus the use of MALL in teaching pronunciation was also effective in improving students’ pronunciation skill with the t-count (5.97) which is higher than the t-table (2.00 and 2.67). &#x0D; Keywords: Mobile Assisted Language Learning (MALL), Pronunciation, Secondary school, Experimental study, Method
Review of Public Procurement Fraud Detection Techniques Powered by Emerging Technologies

Numerous studies and various methods have been used to detect and prevent corruption in public procurement.With the development of IT technology and thus the digitization of the Public Procurement Process (PPP), the amount of available data is increasing.Studies have shown progress in this area and have revealed many challenges and open issues geared to the various goals outlined in this paper.Different data mining and business intelligence techniques and methods are being used to develop models that will find any suspicious public procurement process, contracts, economic operators, or to classify observations as corrupt.In addition to using classification models, methods such as association rules and graph databases are used to find relationships between economic operators and contracting authorities, as well as to find daughter companies that participate in PPP collusion.Therefore, this paper addresses a comprehensive review of the emerging techniques and models used for the detection of suspicious or corrupted observations, their goals, open issues, challenges, methods and metrics used, tools, and relevant data sources.The findings show that models are mostly fitted on historical data and move in the direction of an early warning system.Moreover, the efficiency of fraud or anomaly detection depends on data set quality and detection of the most important red flags.The study is presenting a summary of identified fraud detection model objectives such as predicting fraud risk in contracts and contractors or finding split purchases, and detection of used data sources such as public procurement process or economic operator data.
Prediction for Breast Cancer in BI-RADS Category 4 Lesion Categorized by Age and Breast Composition of Women in Songklanagarind Hospital

Background: Older age and dense breast are the important risk factors for breast cancer. The ACR BI-RADS lexicon 5th edition does not mention how patient age and breast density may affect the category assessment. The aim of this study was to investigate whether patient age and breast density influence the positive predictive value (PPV) of mammographic and ultrasonographic findings categorized as BI-RADS category 4 and subcategories 4a, 4b, and 4c among female patients. Materials and Methods: A retrospective study was conducted in Songklanagarind Hospital between January 1, 2016 and December 31, 2017 in female patients older than 18 years who had breast lesions categorized as BI-RADS category 4 and subcategories 4a, 4b, 4c. A total of 961 breast lesions consisted of 772 (80.33%) benign lesions and 189 (19.67%) malignant lesions. Categorization was done in each lesion based on age ranges of ≤35 years, >35 to 60 years, and >60 years and breast density according to mammographic breast composition. The PPV for each BI-RADS category was calculated based on the pathological diagnoses and were compared using the chi-square test. Results: The overall PPV in each subcategory was in the reference range. The PPV increased with increasing age: 4% vs. 22.63% vs. 36.67% for category 4 (p-value=0.01); 0% vs. 5.81% vs. 6.88% for subcategory 4a (p-value=0.002); 6.67% vs. 26.62% vs. 51.35% for subcategory 4b (p-value=0.001); and 33.33% vs. 76.92% vs. 81.82% for subcategory 4c (p-value=0.02). An association was not found between PPV and breast density. Conclusion: A significantly positive association was found between PPV and age in patients in BI-RADS subcategories 4a, 4b, and 4c. This study could not determine that mammographic breast composition according to the ACR BI-RADS 5th edition was associated with PPV due to improper sample distribution.
Exploring the connection between Question Under Discussion and scalar diversity

Previous research has revealed that different scalar expressions give rise to scalar inferences (SIs) at different rates. This variation has been termed scalar diversity. In this study, we investigate the role of Questions Under Discussion (QUDs) in explaining this variation in SI rates. Investigating 43 different scalar expressions, we first show that explicit QUDs robustly affect calculation rates: questions based on the stronger of two scalar terms lead to higher SI rates than questions promoting the weaker one. Second, we explore whether the likelihood of asking the stronger question in general (Question Availability) can explain the scalar diversity effect. Our results suggest that Question Availability is indeed a predictor of scalar diversity, but only for scales where both terms denote intervals (unbounded scales), and not for scales where the stronger member has a fixed meaning (bounded scales).