Reviewerly/exHarmony huggingface dataset:
Development of a course for training in modern cloud technologies

Purpose of the study. The aim of the study is to create a course on modern cloud technologies based on the openstack cloud virtualization platform. The popularity of the openstack platform makes it the main platform for building cloud technologies in many large Russian and international companies in various business areas. In the process of developing the course, the goal was to adhere to the principles of open education. Development of the course was motivated by the growing popularity of cloud technologies in most IT companies and demand for qualified specialists in this field. Materials and methods. Analytical reports in the field of modern trends in the development of cloud technologies, materials on current trends in hiring employees by industry, as well as official documentation on projects, used in the course are selected as the main materials for the study. During the development and implementation of the project, the following pedagogical techniques were used: acquisition of new knowledge based on pre-existing one; teamwork and demonstration of communication skills; making educational hypotheses to resolve problematic situations; planning to achieve the goal (getting results); private methods of students’ learning activities (detailing the tasks, initial conditions and the course of the study). Results. As a result of the study, the necessity of creating an educational course for students was substantiated, and a special course was developed for teaching the openstack cloud virtualization platform, which includes the necessary materials (presentations, lectures, seminars, as well as a platform and laboratory stands for students with access via the Internet). A special course web page was created where potential students have the opportunity to familiarize themselves with the description and basic components of the course, send a request for participation, and also get access to the materials (after confirmation of the participant by the administrator). Every year, the developed materials, the platform and laboratory stands are updated due to the release of new versions of openstack, adding new functionality and updating business needs. Conclusion. In conclusion, a decision was formulated on the advisability of creating a special course for students and specialists. The developed course was tested in leading higher educational institutions of the country, and is a convenient tool for both students and companies, interested in qualified personnel in the field of cloud technologies. The special course was tested on students of the Faculties of Computational Mathematics and Cybernetics, the Faculty of Mechanics and Mathematics of Lomonosov Moscow State University, Financial University under the Government of the Russian Federation, Plekhanov Russian University of Economics, Innopolis University and Moscow Technical University of Communications and Informatics. According to the results of the special course, students were selected for internships and practical training, which confirmed the effectiveness of the developed course.
Deterministic Identity-Based Encryption from Lattice-Based Programmable Hash Functions with High Min-Entropy

There only exists one deterministic identity-based encryption (DIBE) scheme which is adaptively secure in the auxiliary-input setting, under the learning with errors (LWE) assumption. However, the master public key consists of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi mathvariant="script">O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>λ</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> basic matrices. In this paper, we consider to construct adaptively secure DIBE schemes with more compact public parameters from the LWE problem. (i) On the one hand, we gave a generic DIBE construction from lattice-based programmable hash functions with high min-entropy. (ii) On the other hand, when instantiating our generic DIBE construction with four LPHFs with high min-entropy, we can get four adaptively secure DIBE schemes with more compact public parameters. In one of our DIBE schemes, the master public key only consists of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>ω</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mrow><mml:mi mathvariant="normal">log</mml:mi></mml:mrow><mml:mo>⁡</mml:mo><mml:mrow><mml:mi>λ</mml:mi></mml:mrow></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:math> basic matrices.
An improved fitness function for automated cryptanalysis using genetic algorithm

Genetic Algorithm (GA) is a popular desire for the researchers for creating an automated cryptanalysis system. GA strategy is useful for many problems. Genetic Algorithms try to solve problems by using genetic processes. Different techniques for deciding on fitness function relying on the ciphers have proposed by different researchers. The most necessary component is to set such a fitness function that can evaluate different types of ciphers on the identical scale. In this paper, we have proposed a combined fitness function that is valid for great sorts of ciphers. We use GA to select the fitness function. We have bought the higher result after imposing our proposed method.
Active Semi-supervised K-Means Clustering Based on Silhouette Coefficient

To improve the effectiveness of semi-supervised clustering algorithm that may be influenced by the quality of labeled samples, researchers integrates active learning and semi-supervised clustering to guide the model learning. This paper presents an active semi-supervised k-means clustering model based on silhouette coefficient (SCKmeans). SCKmeans utilizes a pairwise constraint clustering method (PCKmeans) and actively selects valuable samples to establish constraints (query to oracle) based on silhouette coefficient. We iterate the model learning until the number of queries reaches a threshold or the clustering algorithm achieves an acceptable performance. SCKmeans optimizes the semi-supervised k-means by using Local Sample Density (LDS) sampling strategy in order to ensure the stability of the algorithm. In addition, a distance-based sampling method, which can reduce the queries quantity as well as increase the number of constraint samples, is introduced to optimize the process of establishing pairwise constraints. These two methods can promote the effectiveness of clustering algorithm significantly. We conduct considerable amount of experiments over various datasets and baselines, the experimental results indicate that our model has better performance with 5% and 6% boost in MI and ARI respectively.
Performance of Fuzzy Filter and Mean Filter for Removing Gaussian Noise

The goal of image filtering is to remove the noise from the image in such a way that the "original" image is visible.Image filtering is a method by which we can enhance images .Image filtering methods are applied on images to remove the different types of noise that are either present in the image during capturing or injected into the image during transmission.Fuzzy Filter method for image de-noising based on Fuzzy set theory.This filter employs Fuzzy rules for deciding the gray level of a pixel within a window in the image.The Mean Filter is a linear filter which uses a mask over each pixel in the signal.Each of the components of the pixels which fall under the mask are averaged together to form a single pixel.This filter is also called as average filter.In these work Gaussian noise used and image filtering performed by fuzzy filter and mean filter.Further results have been compared for filters using Standard Deviation and Peak Signal to Noise Ratio.
Machine learning leads mathematicians to unsolvable problem

Simple artificial-intelligence problem puts researchers up against a logical paradox discovered by famed mathematician Kurt Gödel. Simple artificial-intelligence problem puts researchers up against a logical paradox discovered by famed mathematician Kurt Gödel.
Author Correction: Elastic pseudospin transport for integratable topological phononic circuits

The original version of this Article contained errors in the second sentence in the legend of Fig. 1, which incorrectly read 'These two elastic insulators are identical in lattice constant a (3a0), plate thickness (0.4a0), and radius of perforated holes r (0.18a0) but different hole-center distance characterized by b.' The correct version states 'plate thickness (√3 × 0.4a0)' in place of 'plate thickness (0.4a0)' and 'radius of perforated holes r (√3 × 0.18a0)' rather than 'radius of perforated holes r (0.18a0)'.The first sentence of the 'Sample preparation' section of the Methods originally incorrectly read 'Our samples are prepared exclusively on polished stainless-steel plates (Type 201, mass density 7803 kg m-3) with a fixed plate thickness 7.82 mm.' In the corrected version, 'mass density 7903 kg m-3' replaces 'mass density 7803 kg m-3'.The second sentence in the legend of Supplementary Fig. 3, originally incorrectly read 'The symmetry of the phononic crystal remains unchanged as C6ν, and thickness of the substrates H (equals to 0.4a0), lattice constant a (equals to 3a0) and radius of perforated holes r (equals to 0.18 a0) maintain constant.' The correct version states '√3 × 0.4a0' in place of '0.4a0' and '√3 × 0.18a0' rather than '0.18a0'.This has been corrected in both the PDF and HTML versions of the Article.
A Novel Semi-Supervised Learning Method Based on Fast Search and Density Peaks

Radar image recognition is a hotspot in the field of remote sensing. Under the condition of sufficiently labeled samples, recognition algorithms can achieve good classification results. However, labeled samples are scarce and costly to obtain. Our major interest in this paper is how to use these unlabeled samples to improve the performance of a recognition algorithm in the case of limited labeled samples. This is a semi-supervised learning problem. However, unlike the existing semi-supervised learning methods, we do not use unlabeled samples directly and, instead, look for safe and reliable unlabeled samples before using them. In this paper, two new semi-supervised learning methods are proposed: a semi-supervised learning method based on fast search and density peaks (S 2 DP) and an iterative S 2 DP method (IS 2 DP). When the labeled samples satisfy a certain requirement, S 2 DP uses fast search and a density peak clustering method to detect reliable unlabeled samples based on the weighted kernel Fisher discriminant analysis (WKFDA). Then, a labeling method based on clustering information (LCI) is designed to label the unlabeled samples. When the labeled samples are insufficient, IS 2 DP is used to iteratively search for reliable unlabeled samples for semi-supervision. Then, these samples are added to the labeled samples to improve the recognition performance of S 2 DP. In the experiments, real radar images are used to verify the performance of our proposed algorithm in dealing with the scarcity of the labeled samples. In addition, our algorithm is compared against several semi-supervised deep learning methods with similar structures. Experimental results demonstrate that the proposed algorithm has better stability than these methods.
Organizational Commitment and the Readiness towards Accrual Accounting: The Moderating Role of Job Satisfaction

The government of Jordan has recently begun preparing to transition towards accrual accounting. However, this process is accompanied by many obstacles that might prevent its proper adoption. Therefore, there is a need to create readiness among the government servants to ensure successful adoption of the new accounting system. As such, this study intends to examine the readiness of Jordanian government servants in the process of transition towards accrual accounting. Besides that, this study intends to examine two possible factors that may affect the readiness of Jordanian government servants, which are: (i) the effect of organizational commitment and (ii) the moderating role of job satisfaction. Questionnaires were distributed to 331 Jordanian government servants in the Ministry of Finance (MOF) and the departments belonging to it. The results indicated that the government servants are ready to transition towards accrual accounting and inclined to accept the change. In addition, organizational commitment positively affects the readiness of government servants, and job satisfaction positively moderates the relationship. Finally, some limitations were considered in this study.
Near infrared star centroid detection by area analysis of multi-scale super pixel saliency fusion map

The centroid location of a near infrared star always deviates from the real center due to the effects of surrounding radiation. To determine a more accurate center of a near infrared star, this paper proposes a method to detect the star's saliency area and calculate the star's centroid via the pixels only in this area, which can greatly decrease the effect of the radiation. During saliency area detection, we calculated the boundary connectivity and gray similarity of every pixel to estimate how likely it was to be a background pixel. Aiming to simplify and speed up the calculation process, we divided the near infrared starry sky image into super pixel maps at multi-scale by Simple Linear Iterative Clustering (SLIC). Second, we detected the saliency map for every super pixel map of the image. Finally, we fused the saliency maps according to a weighted coefficient that is determined by the least square method. For the images used in our experiment, we set the multi-scale super pixel numbers to 100, 150, and 200. The results show that our method can obtain an offset variance of less than 0.27 for the center coordinates compared to the labelled centers.
Applying FireFly Algorithm to Solve the Problem of Balancing Curricula

The problem of assigning a balanced academic curriculum to academic periods of a curriculum, that is, the balancing curricula, represents a traditional challenge for every educational institution which look for a match among students and professors.This article proposes a solution for the balancing curricula problem using an optimization technique based on the attraction of fireflies (FA) meta-heuristic.We perform a set of test and real instances to measure the performance of our solution proposal just looking to deliver a system that will simplify the process of designing a curricular network in higher education institutions.The obtained results show that our solution achieves a fairly fast convergence and finds the optimum known in most of the tests carried out.
PSO Optimized Interval Type-2 Fuzzy Design for Elections Results Prediction

Interval type-2 fuzzy logic systems (IT2FLSs), have recently shown great potential in various applications with dynamic uncertainties.It is believed that additional degree of uncertainty provided by IT2FL allows for better representation of the uncertainty and vagueness present in prediction models.However, determining the parameters of the membership functions of IT2FL is important for providing optimum performance of the system.Particle Swarm Optimization (PSO) has attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving real-world optimization problems.In this paper, a novel optimal IT2FLS is designed, applied for predicting winning chances in elections.PSO is used as an optimized algorithm to tune the parameter of the primary membership function of the IT2FL to improve the performance and increase the accuracy of the IT2F set.Simulation results show the superiority of the PSO-IT2FL to the similar non-optimal IT2FL system with an increase in the prediction.
StegoGIS: A new steganography method using the geospatial domain

Geographic data are used on a variety of computing devices for many different applications including navigation, tracking, location planning, and marketing. The prevalence of geographic data makes it possible to envision new useful applications. In this paper, we propose using geographic data as a medium for secret communication, or steganography. We develop a method called StegoGIS for hiding messages in geographic coordinates in the well-known binary of fast-moving objects and transmitting them secretly. We show that discovering this secret communication is practically impossible for third parties. We also show that a large amount of secret data can be transmitted this way, and that the receiver can easily extract the hidden message. We argue that StegoGIS offers a novel and practical secret communication method with minimal overhead.
The Evaluation of Discovery: Models, Simulation and Search through “Big Data”

Abstract A central theme in western philosophy was to find formal methods that can reliably discover empirical relationships and their explanations from data assembled from experience. As a philosophical project, that ambition was abandoned in the 20th century and generally dismissed as impossible. It was replaced in philosophy by neo-Kantian efforts at reconstruction and justification, and in professional statistics by the more limited ambition to estimate a small number of parameters in pre-specified hypotheses. The influx of “big data” from climate science, neuropsychology, biology, astronomy and elsewhere implicitly called for a revival of the grander philosophical ambition. Search algorithms are meeting that call, but they pose a problem: how are their accuracies to be assessed in domains where experimentation is limited or impossible? Increasingly, the answer is through simulation of data from models of the kind of process in the domain. In some cases, these innovations require rethinking how the accuracy and informativeness of inference methods can be assessed. Focusing on causal inference, we give an example from neuroscience, but to show that the model/simulation strategy is not confined to causal inference, we also consider two classification problems from astrophysics: identifying exoplanets and identifying dark matter concentrations.
Improving Deep Image Clustering with Spatial Transformer Layers

Image clustering is an important but challenging task in machine learning. As in most image processing areas, the latest improvements came from models based on the deep learning approach. However, classical deep learning methods have problems to deal with spatial image transformations like scale and rotation. In this paper, we propose the use of visual attention techniques to reduce this problem in image clustering methods. We evaluate the combination of a deep image clustering model called Deep Adaptive Clustering (DAC) with the Spatial Transformer Networks (STN). The proposed model is evaluated in the datasets MNIST and FashionMNIST and outperformed the baseline model.
FMU-proxy: A Framework for Distributed Access to Functional Mock-up Units

The main goal of the Functional Mock-up Interface (FMI) standard is to allow simulation models to be shared across tools.To accomplish this, FMI relies on a combination of XML-files and compiled C-code packaged in a zip archive.This archive is called an Functional Mock-up Unit (FMU) and uses the extension .fmu.In theory, an FMU can support multiple platforms, however this is not always the case and depends on the type of binaries the exporting tool was able to provide.Furthermore, a library providing FMI support may not be available in a particular language, and/or it may not support the whole standard.Another issue is related to the protection of Intellectual Property (IP).While an FMU is free to only provide the C-code in binary form, other resources shipped with the FMU may be unprotected.In order to overcome these challenges, this paper presents FMU-proxy, an open-source framework for accessing FMUs across languages and platforms.This is done by wrapping one or more FMUs behind a server program supporting multiple language independent Remote Procedure Call (RPC) technologies over several network protocols.Currently, Apache Thrift (TCP/IP, HTTP), gRPC (HTTP/2) and JSON-RPC (HTTP, WebSockets, TPC/IP, ZeroMQ) are supported.Together, they allow FMUs to be invoked from virtually any language on any platform.As users don't have direct access to the FMU or the resources within it, IP is more effectively protected.
PEMBUATAN APLIKASI SISTEM UJIAN ONLINE PADA SMK GARUDA NUSANTARA BEKASI

Exam is one way to evaluate the learning process and is a medium to make measurements on the ability of students in a field of study. The school exams at SMK Garuda Nusantara Bekasi are still paper based, using paper for either the test booklet or the answer sheet. This paper based test for surely resulted in high monetary budget and inefficiency of correcting time. Due to this condition, the writer decided to create an online exam system application to make the implementation and test data processing easier either for the students or the teacher. The method of research used in this study was software development, especially the waterfall and data collection techniques by making direct observations about the process of running the school exams and interviewing the school community. The results of the study were in the form of analysis, design and implementation of online exam system applications using the PHP programming language with MySQL database processing. The application has made significant impact on the increasing of the teaching and learning process effectiveness. In addition, it also increases the management value it can be done online using internet technology.
Reducing Hash Function Complexity: MD5 and SHA-1 as Examples

Hash functions algorithms also called message digest algorithms, compress a message input of an arbitrary length, and produce an output with a fixed length that is distributed randomly.Several hash algorithms exist such as Md5 and SHA1.These algorithms verify data integrity and restrict unauthorized data modification.However, they experience some complexities, especially when implemented in bitcoin mining, and low computation devices, in particularly IoT devices.As a remedy, this paper suggests a new compression function that reduces the complexity of the hash function algorithms such as MD5 and SHA-1.Also, proves that we can obtain the same results which are achieved by the original compression function.
Learning in the presence of class imbalance and concept drift

As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been tested extensively with static data. However, real-world applications often involve dynamic data streams, which suffer from concept drift problems where the data distribution changes overtime. The performance of GBDT model is degraded when applied to predict data streams with concept drift. Although incremental learning can help to alleviate such degrading, finding a perfect learning rate (i.e., the iteration in GBDT) that suits all time periods with all their different drift severity levels can be difficult. In this paper, we convert the issue of determining an optimal learning rate into the issue of choosing the best adaptive iterations when tuning GBDT. We theoretically prove that drift severity is closely related to the convergence rate of model. Accordingly, we propose a novel drift adaptation method, called adaptive iterations (AdIter), that automatically chooses the number of iterations for different drift severities to improve the prediction accuracy for data streams under concept drift. In a series of comprehensive tests with seven state-of-the-art drift adaptation methods on both synthetic and real-world data, AdIter yielded superior accuracy levels.