Reviewerly/exHarmony huggingface dataset:
Synthesis of Datasets with Specific Characteristics for the Clustering Problem

In this paper, we propose a method for synthesis of datasets with specific characteristics for the clustering task. Namely, we propose an algorithm, which can generate a clustering dataset given its meta-feature description. The method we propose is based on an evolutionary algorithm with crossover and mutation operators that are capable to improve candidate datasets in a natural way. We experimentally compared this method with two other approaches for dataset synthesis. We used meta-feature vectors of 247 real-world datasets as inputs. The proposed method outperformed existing ones with respect to Mahalanobis distance between target meta-feature vectors and characteristics of generated datasets.
A Performance Comparison of Segmentation Techniques for the Urdu Text

Segmentation is a procedure of splitting the image contents into its subparts (i.e., line and words). For all the common language-handling applications, for example document structure extraction, content rebuilding, optical character recognition, falsifications, security, graphology, and so forth, segmentation is an indispensable and primary step. This paper presents the quantitative performance of three different text line segmentation techniques: projection method, smearing method, and edge information-based method, for the Urdu Nastaleeq type-written text. The evaluation is performed over the gathered standard data samples taken from different magazines, poetry books, and newspapers, using precision and recall metrics. In the course of evaluation, the potency and debility of algorithms are analyzed and it is spotted that smearing segmentation method checkmates the other two methods.
Diagnóstico en torno al proceso de evaluación en la gestión del director escolar desde la socioformación

The article has the purpose of evaluating the management performance of the principal of the private elementary school based on the guidelines of the Ministry of Public Education of Mexico from the socioformation. This approach is shown in a field study conducted to the preschool's principal through which the importance of the directive management for school performance is explained in the rubric application result. For this, a documentary analysis based on the organization of the documentary information in articles of indexed journals and published books was carried out. The result was the evaluation of the abilities of the school principal understood as the global actions for institutional development. These skills for management, allow the school principal with his leadership to promote the training of students, teachers and administrative staff, to link the school with the community.
Deep Learning for Selecting Precoder Ranks

We study distributed rank selection in a multi-cell wideband cellular network with multiple antennas per node. The rank for a particular mobile is the number of independent data streams across all sub-carriers and antennas. Rank selection then involves both spatial multiplexing and sub-carrier allocations. We propose assigning frequency-space signatures across the data streams to unify the rank assignment over those two dimensions. A sum rate maximization problem is formulated for allocating the signatures and is solved using distributed bi-directional training. Starting with full-rank transmission, the precoders are iteratively updated, and the optimized rank for a mobile is the number of signatures assigned significant power after convergence. For systems with a large number of antennas and sub-carriers, it may take many iterations to converge. To reduce the training period, we propose using deep learning for estimating the ranks across mobiles before the precoder optimization phase. With a trained Deep Neural Network (DNN), each mobile estimates its own transmission rank directly from its local channel information and locations of neighboring mobiles. Simulation results show that the DNNs can significantly reduce the training period for precoder adaptation while achieving performance close to that with jointly optimized precoders and ranks. This is illustrated with different system parameters and mobile distributions.
Frequent Pattern Mining Algorithm based on Multi Minimum Support

An improved multi minimum support frequent pattern mining algorithm IMISFP-growth is proposed. Firstly, preprocessing the items in the transaction database before constructing the tree, deleting those items whose support is less than the minimum item support, and constructing multiple support trees using the remaining frequent items. Then a new method of constructing multiple item tree based on intersection rules is proposed. This method no longer uses a specific standard arrangement item to generate tree, but constructs a tree by the principle of intersection every time a new transaction item set is input. Finally, the IMISFP-growth algorithm is compared with the CFP-growth++ algorithm on five different databases. The experimental results show that the improved algorithm is superior to the CFP-growth++ algorithm in terms of running time, memory consumption and scalability.
Impact of I/O workloads on Ram Performance for Virtual Systems

Virtualization is the main technology that powers today's cloud computing systems. Virtualization is abstracting computer resources; it is able to run many operating systems simultaneously on a single physical machine. It is crucial for both cloud consumers and cloud providers to understand the various factors that may significantly affect the performance of applications running in a virtualized cloud[1]. In this paper we evaluate ram workloads and its impact on the performance of a virtual system. The experimental environment was conducted on server Cisco UCS B200 M4 which was the host machine and the virtual environment that is encapsulated within the physical layer which hosts the guest virtual machines consists of virtual hardware, Guest OSs, and third-party applications. The host server runs on Vmware 5.1 and Proxmox 5.3 hypervisors which consists of virtual machines with one operating system, CentOS 7 64 bit. For performance evaluation purposes, the guest operating system was configured and allocated the same amount of virtual system resources on initial stage. Testing the memory performance with ramspeed, VMware performs 16 to 26% better than Proxmox. In the case of writing, VMware observed 31 to 51% better than Proxmox. This will help to configure and allocate efficient resources to virtual machines that will provide optimal performance. The use of stress tools helps to find out how the system behaves in different situations. Vmware handles more ram workloads load under stress over a duration of time while proxmox performs well when block sizes are small.
Building professional competence of physical culture educators in higher education institutions

Aim of the research: to explore the possibility of building professional competence of physical culture educa- tors in higher education institutions. Methods and organization of the research: theoretical modeling, interpretation, survey and diagnostic meth- ods, expert assessment, observation, pedagogical experiment, mathematical processing of research outcomes. The study involved 30 teachers of the Department of physical education and sports and coaching staff in Ka- zan State University of architecture and civil engineering. At first, we selected the tools and methodology of the study. Secondly, we designed a competency development model for physical culture educators in higher education institutions. The third step included experimental research on building professional competence of educators with consideration of the developed model. Research results: At the beginning of the experiment, we identified the professional competence baseline for the educators in the control and experimental groups. Building competence of teachers in the control group was carried out by means of advanced training courses on the «standard» program. The program included lec- tures and seminars on various program modules. The course was terminated with a final certification project on the selected topic. Teachers in the experimental group participated in the professional development pro- gram based on the competency development model. At the end of training, we re-identified previously studied indicators and compared the data. The results of the study revealed that the level of professional competence of teachers in the experimental group had considerably increased relative to the control group. Conclusion. The results of the study demonstrated that «standard» professional development programs avail- able in higher education institutions are not enough for effective teaching competence enhancement. It is essential to have a special training technology, which is implemented in the developed educational model of teaching competence enhancement.
Research on similarity measurement method of statistical sorting region based on differentiation and location information

In the process of content-based image retrieval, making reasonable matching strategy and similarity measurement is the key technology to ensure the accuracy, quality and matching speed of image matching and retrieval. In this chapter, we focus on the problem of image feature selection and similarity measurement in content-based image retrieval technology, and propose a regional similarity measurement method based on differentiation and location information by using large image plane position constraint. This method mainly includes the following two aspects. One is the feature selection method based on global feature differentiation; the other is the similarity measurement method based on location information. Finally, we use the idea of statistical sorting to synthesize the image matching strategy and similarity measurement criteria through the set of feature points in the hot area. Thus, the problem of image similarity measurement from single feature point matching to target region can be solved.
Multivariate Curve Resolution—Error in Curve Resolution

This article summarizes different methods for uncertainties evaluation in model-free or soft-modeling multivariate curve resolution (MCR) results. We consider two different error sources: unresolved ambiguities and propagation of experimental errors. On the one hand, it is well known that solutions obtained by curve resolution methods present rotational and scale ambiguities, meaning that these solutions are not unique and that a range of feasible solutions fitting equally well experimental data and fulfilling the constraints of the system are possible. On the other, experimental errors and uncertainties are propagated into MCR solutions and it is also important to account for them. Both types of errors, those caused by curve resolution ambiguities and those caused by experimental error propagation, are in fact intermixed in the final curve resolution solutions, and it is often difficult to discern among them. In this article, some proposals to cope with these problems are described and some examples of applications are given.
Modeling of Complex Information Processes in the Oversaturated Media Environment (to the article by A.P. Sukhodolov, I.V. Anokhov, V.A. Marenko Informational Impulse-Wave Interaction between the Media and Society)

The article discusses promising areas of modeling informational interaction between the media and the society from the standpoint of the Paradigm of Complexity. Increasing complexity of the society’s media system, turbulence in the media environment, transformation of the functions of traditional media, changing methods of information production and consumption, and development of Web 2.0 technologies are critical challenges for media theorists. Basing on the theory of behavioral economics, the authors prove that in the context of a significant growth of information volume, most people demonstrate a shift of thinking from System 1 to System 2. System 1 is a sub-system of the brain responsible for making rational, well-considered and balanced decisions, whereas System 2 is the area of automatic quick, spontaneous decision-making. This shift results in the dominance of populism, post-truth and fake news in the media space. The authors show that Web 2.0 technologies, which help people, regardless their occupation, to generate info-content, extends the boundaries of journalism and media. This, particularly, leads to development of citizen journalism and the increasing role of social communities (networks) as isolated media-systems that fragment the media space. The article substantiates that traditional media’s abilities to manipulate mass consciousness in the current supersofisticated media environment are extremely limited. The authors put forward a hypothesis about a radical change in the media’s key role, namely, a transition from content production to creating a semantic context. The methodological reflections presented in the article may serve as new impulses to building up synergetic, nonequilibrium, evolutionary and other models of complex adaptive media systems.
Continuous Conflict Prediction during Collaborative Software Development

Concurrent activities of collaborative developers over shared project repositories might lead to direct and indirect conflicts. Software Configuration Management systems are designed to capture direct or merge conflicts which arise due to concurrent editing of same shared artifact. However, inconsistencies caused owing to indirect conflicts which arise because of concurrent editing of related artifacts might enter the codebase, since SCM systems have limited capabilities to capture these. Although, Continuous Integration process which is deployed to build the entire codebase with every commit, is quite effective in capturing several type of inconsistencies. However, still few categories of behavioral semantic inconsistencies might evade the build process and penetrate into codebase. In this paper, we propose the Continuous Conflict Prediction Framework which describes a cyclic, real-time, continuous process for conflict prediction which is executed during the process of code creation by collaborative developers. This framework entails a critical conflict-prediction and awareness-generation process which helps in capturing conflicts during development process itself and hence minimizes the number of conflicts entering the project codebase. The proposed framework is realized through implementation of the tool named Collaboration Over GitHub.
The Role of Reviews in Decision-Making

With the rise of social media such as blogs and social networks, these interpersonal communication expressed by online reviews has become more and more important as an influential source of information both for the managers and for the consumers. In-depth purchasing-related information is made available to markers. Now we can utilize this new source of information to understand how consumers evaluate products and make decision in relation with it. Since reviews are text data, new ways to analyze the data is needed and text-mining plays the role here together with the help of traditional statistical methods. With these methods, we can examine the contents of reviews and identify the key areas that impact consumers’ decision-making.
The Analysis of Economic Indicators Based on the Information Model of N. Wiener

Abstract Mathematical models for determining the amount of entropy for analysis of future economic indicators are considered. Possibilities of application of mathematical models for the purposes of accounting in need of reflection of estimated values of future indicators in public reports of the companies are determined. The models are based on the information model of N. Wiener. The role of information uncertainty in the analysis problem is proved and the applicability of some approaches to uncertainty assessment of indicators is considered. The probabilistic-statistical approach, which most fully corresponds to the possibilities of using the Wiener’s information model, is highlighted among the approaches. On its basis the description model and mathematical expressions of determination of size of private entropy of an economic system object are constructed. The mathematical model of description the entropy of indicators of the economic system is constructed and its component is a model of private entropy. The created mathematical model allows to determine the amount of entropy for one and two variables, taking into account the correlation between them. The model is useful for assessing the state of the economic system at selected time intervals.
Information space data modeling with context-awareness and complex semantic association

Data model in information space is the basis of effectively managing heterogeneous and interrelated data sources. However, traditional data modeling approaches fall short when representing context-dependent heterogeneous information and complex semantic associations, and when supporting semantic association reasoning. To overcome these drawbacks, in this paper, we propose a semi-structured graph model named context-aware and complex semantic association network model. In particular, we introduce the notion of context-aware interpreted object which encapsulates heterogeneous information about the underlying data and context information. We descript model complex semantic associations in terms of a set of constraint components such as order constraints, aggregation constraints, and attribute constraints. Formally, we define the semantic association rule in favor of semantic association reasoning. Also, we propose a snapshot generation algorithm for our model and further illustrate the snapshot of the data graph according to our model.
Secure Queryable Dynamic Graphs using Blockchain

A graph is a non-linear data structure with nodes and edges for depicting relationships between nodes in a wide range of systems such as social networks, maps, dependency network, electronic circuits, etc. Graphs are often inputs to various computational processes such as those for finding shortest paths between nodes, minimum spanning tree, graph coloring, etc. Such algorithms are essential components of many large-scale information systems. Guaranteeing the integrity of responses to various graph queries (like shortest path, number of disjoint sets, etc.) becomes especially challenging for graphs with very large numbers of nodes and/or edges. The goal of the blockchain-based Secure Queryable Dynamic Graphs (SQDG) protocol is to assure the correctness of responses to various types of graph queries, irrespective of the scale of the graph, without the need to rely on trusted third parties.
Wrong Question Set Generation and Answer Statistics Based on Reviewed Markers Recognition

In present education informatization, there is a lack of convenient and efficient methods to collect daily homework information. This paper proposes a method of generating wrong question set and statistic answers based on the reviewing recognition. The system scans the students' quick response code (QR code) pasted on the first page of the test paper by the camera (or mobile phone camera) connected with personal computer(PC), and obtains the students' information; scans the inside page of the test paper regularly, corrects current image based on the identity document(ID) QR code of the test paper's inside page, and obtains the page number; according to the marking points of the test questions, segments the image region; based on red reviewing markers, combined with the marking points, valid test area and page number, the student number-title number-right and wrong information is saved into the answer database, and the student number-wrong title number-wrong title image is saved into the wrong question set. There is no interference in the reviewing until the process is completed, and the students' answer statistics can be made automatically. Wrong question set can be used by teachers and students to significantly improve the efficiency of teaching and learning. This method can be applied to the collection of wrong questions and the statistics of answers in the manual reviewing of paper materials which are widely used at present. Compared with existing technologies, the method has advantages of no interference to teachers' reviewing habits, high efficiency, easy operation, low hardware requirements, etc.
Search Engine Optimization Challenges and Solutions

Search engines use algorithms for filtering out the most useful websites out of millions of websites on the Internet in minimum time span to optimize user search/query. SEO is the technique used by search engine optimizers to make their websites appear in the top results of search engines. But sometimes some unethical developers use black hat SEO techniques to bring their websites amongst the top results which misleads both, the search engines as well as the users. In this paper, search engine algorithms such as PageRank, HillTop, black hat SEO techniques like content spamming and the concept of Elasticsearch are studied and analysed.
Data Collection for Natural Language Processing Systems

Any NLP system needs enough data for training and testing purposes. They can be split into two datasets: correct and incorrect (erroneous) data. Usually, it is not a problem to find and get a set of correct data because the correct texts are available from different sources, although they may also contain some mistakes. On the other hand, it is a hard task to get data containing errors like typos, mistakes and misspellings. This kind of data is usually obtained by a lengthy manual process and it requires annotation by human. One way to get the incorrect dataset faster is to generate it. However, this creates a problem how to generate incorrect texts so that they correspond to real human mistakes. In this paper, we focused on getting the incorrect dataset by help of humans. We created an automated web application (a game) that allows to collect incorrect texts and misspellings from players for texts written in the Slovak language. Based on the obtained data, we built a model of common errors that can be used to generate a large amount of authentic looking erroneous texts.
Data Mining Based on Condensed Hierarchical Clustering Algorithm

Clustering is an important part of data mining, which aims to divide unknown sample data into multiple classes according to similarity relationship. According to the characteristics of multi-subject high-dimension of training data, this paper preprocesses the training data by linear dimensionality reduction and analyzes the training data based on condensed hierarchical clustering algorithm. From the perspective of practical application, this clustering analysis method of training data effectively differentiates personnel reasonably and intuitively reflects the performance characteristics of various types of personnel, providing scientific guidance for the development and implementation of training plans.
A Medical Video Coding Scheme with Preserved Diagnostic Quality

Medical video is increasingly playing a vital role in medicine, with its transmission becoming more mainstream. To support this, better compression techniques which can maintain the diagnostic capabilities of the video are required, especially for transmission over bandwidth-limited channels. This work presents a novel medical video compression system that uses Super Resolution (SR) as a processing step and an Enhancement Layer (EL) to enhance the quality in Regions of Interest (ROIs). This system achieves an average gain of 28.25% in Compression Ratio (CR) over full resolution lossless compression, at a Peak Signal-to-Noise Ratio (PSNR) of 38.17dB, a Structure Similarity (SSIM) of 0.9843 and Multi-Scale Structure Similarity (MS-SSIM) of 0.9991. Moreover, subjective evaluations show that the diagnostic quality is retained. A reduction in encoding time is also registered due to the downscaling of the medical video.