Reviewerly/exHarmony huggingface dataset:
Software and Services Citation Guidelines and Examples

This document presents guidelines on how to cite software and services for authors of journal articles. Journal publishers can also provide this as a reference to their authors on how to cite software and services. These guidelines were developed by the Earth Science Information Partners (ESIP) Software and Services Citation Cluster and endorsed by the ESIP Assembly in January 2019.
ACADEMIC ARTICLES CLASSIFICATION USING NAIVE BAYES CLASSIFIER (NBC) METHOD

Sebelas Maret University has been publishing many academic articles. Classifying many articles at a time is not a simple task. The more articles need to be classified, the more energy and time needed. Naive Bayes Classifier method can be used to classify academic articles in short time. Naive Bayes Classifier classifies each article based on the field of study by analyzing its title and abstract. One of feature selection method Document Frequency Improved (DFM) is implemented for improving the classification performance. This study used of 292 articles as training data and 100 articles as testing data.  It tested by applying 5 threshold value from 1 to 2,5 with each threshold executed 5 times. The best results showed at 2 threshold level with the average value of accuracy, precision, recall, and f-measure respectively are 87,8%, 76,6%, 76,2%, and 76,0%.
Problem Of Updating Universal Methodology For Integration Of Teaching Units (Itu)

The article presents the research on the problem of effective application of the methodology of integrated teaching units (ITU) of P.M. Erdniev, academician of Russian Academy of Science in vocational education.The methods and techniques of ITU were initially focused on the teaching of mathematics in primary and secondary schools. In this article they are considered in the aspect of training future specialists in the system of secondary vocational and higher education. The authors show how ideas of the integration of teaching units (ITU), which became widespread in secondary schools, are actively used in the teaching of special disciplines in vocational education.The article provides examples of large-block presentation of educational material, both when studying humanitarian disciplines, and when studying general professional and special disciplines in higher education. The authors emphasize that the integration of teaching units in vocational education acquires a special meaning, first of all, connected with the organization of training, when “a large portion of educational material is supplied to a unit of time”, and by enlarging actions, when concepts and subject tasks are generalized, related blocks of educational material are studied at the same time, interrelated actions, operations, and functions are given to students. As a result it is concluded that the ITU technique, being universal, is currently actively used in professional education and allows creating conditions for the effective training of future specialists. The application of ITU allows conducting training in a shorter period, which is dictated by modern conditions of education system development.
Automated Optimum Route Generator and Data Analyzer

As a traveler, it has become a necessity to first select the most optimum analyze route that is the shortest path to travel the cities and then travel through the path.This paper is aimed to provide its users the most optimized path to travel, that will consume the minimum amount of time and distance.To achieve this, Travelling Salesman Problem algorithm through genetic algorithm has been implemented.The paper also provides with the feature of a data analyzer.This aims to analyze the data provided by the users for a particular city and generate the ratings based on the data.To provide the feature of data analyzer, Knuth-Morris-Pratt algorithm for pattern matching and TRIE data structure for efficient retrieval of data has been used.As every traveler always ensure the reviews and rating of the place before visiting it, getting a brief review and knowing the rating of the city will help them in selecting the city to visit.
Identification of plasmodium falciparum and plasmodium vivax on digital image of thin blood films gf

&lt;span&gt;Observing presence of &lt;em&gt;Plasmodium&lt;/em&gt; parasite of stained thick or thin blood films through microscopic examination is a gold standard for malaria diagnosis. Although the microscopic examination has been extensively used, misidentification might occur caused by human factors. In order to overcome misidentification problem, several studies have been conducted to develop a computer-aided malaria diagnosis (CADx) to assist paramedics in decision-making. This study proposes an approach to identify species and stage of &lt;em&gt;Plasmodium falciparum&lt;/em&gt; and &lt;em&gt;Plasmodium vivax&lt;/em&gt; on thin blood films collected from the Laboratory of Parasitology, Faculty of Medicine, Universitas Gadjah Mada. Adaptive k-means clustering is applied to segment &lt;em&gt;Plasmodium &lt;/em&gt;parasites. A total of 39 features consisting of shape and texture features are extracted and then selected by using wrapper-based forward and backward directions. Classification is evaluated in two schemes. The first scheme is to classify the species of parasite into two classes. The second scheme is to classify the species and stage of parasite into six classes. Three classifiers applied are k-nearest neighbour (KNN), support vector machine (SVM) and multi-layer perceptron (MLP). Furthermore, to facilitate the multiclass classification, one-versus-one (OVO) and one-versus-all (OVA) methods are implemented. The first scheme achieves the accuracy of 88.70% based on MLP classifier using three selected features. While the accuracy gained by the second scheme is 95.16% based on OVO and MLP classifier using 29 selected features. These results indicate that the proposed approach successfully identifies the species and stage of parasite on thin blood films and has potential to be implemented in the CADx system for assisting paramedics in diagnosing malaria.&lt;/span&gt;
Determinants of English as language choice among Unaccompanied Migrant Children (UMC) in Makassar, Indonesia

The essence of language is human activity. Communication with language is carried out through two basic human activities; speaking and listening during the interaction in a group of people. Immigrants in Makassar city communicate with immigrant communities and Makassar people. They used English and Indonesia to communicate with others. The aims of this article were to find out determinant factors of English as language choice among Unaccompanied Migrant Children (UMC) in Makassar and why they used English as their language choice to communicate with other people out of them. The data were taken from UMC in the shelter under the auspices of Makassar’s Social Office and in the public area of Makassar. This research was a qualitative approach; it was from a sociolinguistic perspective and focuses its analysis with the language choice among UMC. This research showed that most immigrants chose English as their language choice since they were in Makassar because they have acquired better than other international language and it has been mastered naturally by doing social interaction among themselves and people outside their community. UMC had more difficulties to socialize with Indonesian than the adult of Immigrants. Other than their lack of language mastery, they also have the anxiety to adapt to other immigrants and Makassar people. English was used by UMC to show their status as a foreigner who lived in a multicultural situation. Language becomes a power for a human being and it becomes a social identity for language user in one community. During the interaction of UMC in Makassar city, the role of English as an International language is shown. &#x0D;
Improving the Efficiency and Performance of Remote Application Monitoring System by PROXMOX Virtual Environment

Virtualization is used to set of connections and configure various operating systems on a single host at the same time. Now-a-days many technologies prefer Open source platform for virtualized infrastructure because it provides an enormous space to the user to implement their own module. In this paper, we used piece based virtual machine (KVM) is the main open source finish virtualization arrangement on x86 equipment and it underpins all major working framework including Linux and windows.
Dual Security Using Image Steganography Based Matrix Partition

Recently, the mode of living became more complicated without computer systems.The techniques of camouflage information have acquired a vital role with the requirement of intensifying trade of multimedia content.Steganography is the technique that utilizes disguise in a way that prohibits unauthorized access from suspicion of the existence of confidential information exchanged during communication channels between the connected parties.In this paper, an integrated image steganographic system is designed to conceal images, messages or together where the mainly deliberate the improvement of embedding capacity through embedding text with image simultaneously.For that purpose, used matrix partition to partition the secret image then embedded each partition separately after scrambling each pixel by replacing msb instead of lsb to provide the second level of security furthermore to steganography.The simulation results clarify the better performance of the proposed algorithms.
Kernel canonical correlation analysis approximates operators for the detection of coherent structures in dynamical data

We illustrate relationships between classical kernel-based dimensionality reduction techniques and eigendecompositions of empirical estimates of reproducing kernel Hilbert space (RKHS) operators associated with dynamical systems. In particular, we show that kernel canonical correlation analysis (CCA) can be interpreted in terms of kernel transfer operators and that it can be obtained by optimizing the variational approach for Markov processes (VAMP) score. As a result, we show that coherent sets of particle trajectories can be computed by kernel CCA. We demonstrate the efficiency of this approach with several examples, namely the well-known Bickley jet, ocean drifter data, and a molecular dynamics problem with a time-dependent potential. Finally, we propose a straightforward generalization of dynamic mode decomposition (DMD) called coherent mode decomposition (CMD). Our results provide a generic machine learning approach to the computation of coherent sets with an objective score that can be used for cross-validation and the comparison of different methods.
Research on Virtualization Security Technology in Cloud Computing Environment

As a fusion of multiple technologies, cloud computing virtualization technology brought new security challenges to cloud resource management and data privacy protection.This paper studied existing cloud computing the attack types from five aspects of cloud virtualization security technology, and made a cloud platform security analysis on the aspects of ensuring the availability of cloud computing services, confidentiality and reliability of data, and privacy protection.The attack instances, attack principles, attack effects, representative defense schemes, and limitations of existing research were also analyzed and compared to provide security for the healthy and sustainable development of cloud computing.
Arduino Applicability Model for the Construction of Flight Controller for Drones

The aim of this study was to develop a flight developer for an unmanned aeronautical vehicle (UAV) with copyright technology.The project takes into account several aspects of Mechanical Engineering, as well as knowledge of aircraft dynamics, remote control, sensin g and electronics, composing a multidisciplinary work of Electronics, Mechatronics and Aeronautical Engineering.In this project, the UAV is divided into its fundamental components: structure/base , engines, propellers, flight controllers, batteries, sensors and radio, each one being studied and specified for the integration of a full system.Simulation tools and calculation softwares are used to estimate the main features of the final product.We presume that this article serves as a reference for a drone project in general and a guide to the physical production of such a drone, from the initial design with specifications to the selection and integration of components for future work and research projects, becoming a valuable tool of great added value.
Real Estate using AR, VR, DR, Geo-referencing and other mobile applications

Purpose - The use of Mobile Applications, Geo-referencing, Augmented Reality (AR) or Virtual Reality (VR) brings knowledge and understanding of how we possible deal with Real Estate (RE) nowadays. Clients want fast access to information, real details, and sometimes a sample of a tour. Design/methodology/approach – In this paper, first it is analysed in some interviews with owners of small and medium enterprises in RA. Then it is presented some pieces of evidence on the use of those technologies and applications. Finally, we point out some conclusions, some limitations and recommendations for future studies. Findings – The AR and VR technologies and new applications for personal computers (PCs) or mobile engines are rising sharply in the area of medicine. We also see some advances in architecture and the exhibition of properties, in particular in views of interiors, guide visits to apartments, or houses. These issues can easily be into the RE. Practical implications – Including Mobile Applications, Geo-referencing, AR or VR in RE business we can expect a better understanding of what consumers want, accurate details or proximity with a real word in a near future. Originality/value – We have few investigations both in RE and very new Technologies and Mobile Applications, mainly looking towards clients. Few articles are referring to those applications in RE and considering Portugal studies are rare. This kind of research should be more frequent and compared, as possible, with other countries and other situations.
Patch similarity in ultrasound images with hypothesis testing and stochastic distances

Patch-based techniques have been largely applied to process ultrasound (US) images, with applications in various fields as denoising, segmentation, and registration. An important aspect of the performance of these techniques is how to measure the similarity between patches. While it is usual to base the similarity on the Euclidean distance when processing images corrupted by additive Gaussian noise, finding measures suitable for the multiplicative nature of the speckle in US images is still an open research. In this work, we propose new stochastic distances based on the statistical characteristics of speckle in US. Additionally, we derive statistical measures to compose hypothesis tests that allow a quantitative decision on the patch similarity of US images. Good results with experiments in denoising, segmentation and selecting similar patches confirm the potential of the proposed measures.
PEDAGOGICAL EVALUATION OF DIGITAL TOOLS’ SAFETY IN EDUCATION

Federal State Autonomous Educational Institution of Higher Education “South Ural State University" (RUSSIAN FEDERATION)
Bayesian Neural Networks for Sparse Coding

Deep learning is actively used in the area of sparse coding. In current deep sparse coding methods uncertainty of predictions is rarely estimated, thus providing the results that lack the quantitative justification. Bayesian learning provides the way to estimate the uncertainty of predictions in neural networks (NNs) by imposing the prior distributions on weights, propagating the resulting uncertainty through the layers and computing the posterior distributions of predictions. We propose a novel method of propagating the uncertainty through the sparsity-promoiting layers of NNs for the first time. We design a Bayesian Learned Iterative Shrinkage-Thresholding network (BayesLIsTA). An efficient posterior inference algorithm based on probabilistic backpropagation is developed. Experiments on sparse coding show that the proposed framework provides both accurate predictions and sensible estimates of uncertainty in these predictions.
Integrated Security System for Residential Home

In today's growing concern for home security, we have developed an advanced security system using integrated digital signature and DNA cryptography. The digital signature is formed using multifeature biometric traits which include both fingerprints as well as iris image. We further increase the security by using DNA cryptography which is embedded on a smart card. In order to prevent unauthorized access manually or digitally, we use geo-detection which compares the unregistered devices location with the user's location using any of their personal devices such as smart phone or tab.
Towards Proof-of-Work Cryptocurrency Valuation: Mining Games, Network Effects and the Social Value of Blockchain

In this study, a mathematical model of proof-of-work cryptocurrency valuation is developed based on the concepts of simultaneous equilibria in two mining games, the purchasing power parity in the system of equations of exchange and the network effects of transaction cost optimisation in the economy where agents utilise both conventional and digital currencies to process payments. The model lays the foundation for rigorous long-term proof-of-work cryptocurrency valuation superior to existing approaches that are solely based on variations of Metcalfe’s law or costs of mining. Based on the model, the long-term Bitcoin equilibrium price conditional on current block size limit is $106, while if the block size limit is abandoned, the conditional equilibrium price is $6725. Network hashrate is consistent with the no-limit scenario, while the transaction fees are consistent with the status quo, revealing an important source of uncertainty on the market. The developed framework is also applied to the measure the net social value of proof-of-work cryptocurrencies, and Bitcoin long-term social value of equilibrium is shown to be positive at $126,200 per block. The comparison of equilibrium and optimal cases shows that external intervention is not necessary to guarantee socially beneficial outcomes.
Concurrent query processing in a GPU-based database system

The unrivaled computing capabilities of modern GPUs meet the demand of processing massive amounts of data seen in many application domains. While traditional HPC systems support applications as standalone entities that occupy entire GPUs, there are GPU-based DBMSs where multiple tasks are meant to be run at the same time in the same device. To that end, system-level resource management mechanisms are needed to fully unleash the computing power of GPUs in large data processing, and there were some researches focusing on it. In our previous work, we explored the single compute-bound kernel modeling on GPUs under NVidia's CUDA framework and provided an in-depth anatomy of the NVidia's concurrent kernel execution mechanism (CUDA stream). This paper focuses on resource allocation of multiple GPU applications towards optimization of system throughput in the context of systems. Comparing to earlier studies of enabling concurrent tasks support on GPU such as MultiQx-GPU, we use a different approach that is to control the launching parameters of multiple GPU kernels as provided by compile-time performance modeling as a kernel-level optimization and also a more general pre-processing model with batch-level control to enhance performance. Specifically, we construct a variation of multi-dimensional knapsack model to maximize concurrency in a multi-kernel environment. We present an in-depth analysis of our model and develop an algorithm based on dynamic programming technique to solve the model. We prove the algorithm can find optimal solutions (in terms of thread concurrency) to the problem and bears pseudopolynomial complexity on both time and space. Such results are verified by extensive experiments running on our microbenchmark that consists of real-world GPU queries. Furthermore, solutions identified by our method also significantly reduce the total running time of the workload, as compared to sequential and MultiQx-GPU executions.
A Study of Syntactic Complexity via Coh-Metrix: Similarities and Differences of Ph.D. Dissertations Written by Iranian University Students and English Native Speakers

The present study sought to identify the similarities and/or differences between texts written by Iranian university students of English teaching major and those written by English natives in terms of syntactic complexity. To this end, an automated computational web tool, namely Coh-Metrix was used to scrutinize a corpus containing 83 text excerpts extracted from 10 dissertations written by Iranian Ph.D. students as well as a comparison corpus including 94 text excerpts selected from 10 Ph.D. dissertations written by English native speakers in terms of four specific measures representing syntactic complexity. The results indicated that among the four measures, Mean Number of Modifiers and Sentence Syntax Similarity functioned as distinctive factors differentiating between the first language (L1) and second language (L2) texts, whereas Left Embeddedness and Minimal Edit Distance were found to be similar between the two corpora. The findings may have several implications for EFL practitioners.
Meta-heuristic Techniques for Placement of Virtual Machines in Distributed Cloud Centers

The Virtual Machine Placement techniques provide an attractive opportunity to achieve the goal of energy conservation in Cloud Centers.The Virtual Machines can be broadly classified into data intensive and CPU intensive based on their workload characteristics.Placing or migrating large number of data intensive Virtual Machines can lead to degradation of task execution efficiency in Cloud Centers.Hence, the Virtual Machine Placement techniques need to consider the Virtual Machine workload characteristics to prevent associated negative consequences.Even though workload characteristics aware Virtual Machine migration technique has been presented in the literature for single-location Cloud Centers, it does not cater to Distributed Cloud Centers, where the cloud resources are distributed in different geographical locations, and in such setting, new performance issues arise which have to be effectively addressed.In this work, the NP-Complete Bin Packing Problem framework is extended to model the Workload Characteristic aware Virtual Machine Placement problem in Distributed Cloud Centers.The proposed solution is based on two meta-heuristic techniques having polynomial complexity: Particle Swarm Optimization and Ant Colony Optimization technique.The proposed techniques are compared against the contemporary technique in simulated environment.In the simulated study of the proposed techniques, both these techniques discover approximate solutions to the Bin Packing Problem which are extremely close to the optimal solution, and thereby directly contribute in efficient load distribution in Distributed Cloud Centers.