Reviewerly/exHarmony huggingface dataset:
БАРЬЕРЫ ВНЕДРЕНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ОБРАЗОВАНИЕ: МИФЫ И РЕАЛЬНОСТЬ

Introduction. At present, the questions about what requirements society and the environment will impose on a person in the future are particularly acute. In a rapidly changing context, it is essential to prepare students for their future professional activity. Implementation of the latest scientific achievements in the educational process fosters the fulfillment of these requirements. Methods and literature review. The paper discusses the possibilities of adopting artificial intelligence in education. The barriers to introducing artificial intelligence in education are identified. These are the lack of a formulated social requirement for the level of education of the future specialist; the problems of organizing the interaction between a teacher and artificial intelligence; the lack of a methodology developed to adopt artificial intelligence in education, the lack of appropriate scientific research, and the problem of knowledge representation for information systems. Conclusions. The findings suggest that the identified barriers are not myths. These obstacles hinder the adoption of artificial intelligence technologies in education. Neglect of these is- sues slows down innovation processes in education. The study of the objectivity of these barriers helps outline the ways to overcome them. The developed automated information systems are considered to be prerequisites for the adoption of artificial intelligence in the education system of the Irkutsk region.
Evaluating the priority of email using machine learning

E-mail is an application that is widely used worldwide to help shorten the time, distance, and cost of the mailing process.In this paper, we propose a method of evaluating the role and importance of emails based on the process of analyzing and evaluating the content of emails.Our approach is based on Natural Language Processing and machine learning algorithms to classify emails into important or unimportant.Accordingly, our method is based on 2 main techniques: i) Processing and clustering of keywords in email.For this process, we will use data clustering algorithms.ii) Evaluating email content.For this process, we will use a supervised machine learning algorithm.The research results shown in the paper will provide email systems with a way to identify and classify important emails in order to assist users in sending and receiving emails.
A hybrid Genetic Programming approach to feature detection and image classification

© 2015 IEEE. Image classification is a crucial task in Computer Vision. Feature detection represents a key component of the image classification process, which aims at detecting a set of important features that have the potential to facilitate the classification task. In this paper, we propose a Genetic Programming (GP) approach to image feature detection. The proposed method uses the Speeded Up Robust Features (SURF) method to extract features from regions automatically selected by GP, and adopts a wrapper approach combined with a voting scheme to perform image classification. The proposed approach is evaluated using three datasets of increasing difficulty, and is compared to five popularly used machine learning methods: Support Vector Machines, Random Forest, Naive Bayes, Decision Trees, and Adaptive Boosting. The experimental results show the proposed approach has achieved comparable or better performance than the five existing methods on all three datasets, and reveal its capability to automatically detect good regions from a large image from which good features are automatically constructed.
Supplemental Material: Geologic map of central Panamint Range, California

&lt;div&gt;Digital map data files in ArcGIS shapefile format for data collection points, geologic contacts, and geologic unit polygons.&lt;br&gt;&lt;/div&gt;
Fragile Water-Marking Based Image Authentication Scheme using LSBs

This paper proposed a Quick response code (QR code) based strategy to provide authentication to our digital images. Quick response code is used to provide protection to digital images because of its important characteristics like detection from direction and large data encoding capacity. First of all, Least Significant Bit (LSB) approach is applied on the original image to select LSBs from each block of the image. Next, LSB image is partitioned into sized blocks and mean is calculated for each block of the cell. Then Singular Value Decomposition function is performed on this cell to get singular values which are used as authentication data. After that QR Code generator is used to generate QR code matrix from these singular values. And finally this code is inserted into MSBs to get an authenticated image. Experimental result shows that proposed method produces images with good quality.
Multilevel Image Thresholding for Image Segmentation using Hybrid Algorithm

Image thresholding is an extraction method of objects from a background scene, which is used most of the time to evaluate and interpret images because of their advanced simplicity, robustness, time reduced, and precision. The main objective is to distinguish the subject from the background of the image segmentation. As the ordinary image segmentation threshold approach is computerized costly while the necessity for optimization techniques are highly recommended for multi-tier image thresholds. Level object segmentation threshold by using Shannon entropy and Fuzzy entropy maximized with hGSA-PS. An entropy maximization of hGSA-PS dependent multilevel image thresholds is developed, where the results are best demonstrated in PSNR, misclassification, structural similarity index and segmented image quality compared to the Firefly algorithm, adaptive cuckoo search algorithm and the search algorithm gravitational.
Adaptive beam forming of MIMO system using optimal steering vector with hybrid bacterial foraging optimisation algorithm for channel selection

The modern communication systems face several issues that are quite challenging, more so in those involving smart antennas such as the antenna array beam. In this work, an optimised bacterial foraging optimisation algorithm (BFOA) along with a greedy algorithm and a Tabu search (TS) algorithm for the channel selection having an adaptive beam which is formed by using a steering vector is proposed. In the proposed hybrid, adaptive seed dispersion will make the BFOA-TS be able to converge faster when compared to a BFOA-Greedy. This type of behaviour has been duly verified by means of an application of the BFOA-TS and the BFOA-Greedy on the test functions which are well-known. The results of this experiment proved that the method proposed was able to achieve a better performance compared to that of the others.
SELF-MARKETING IN THE PROFESSIONAL ACTIVITY OF A UNIVERSITY TEACHER

The article reveals modern approaches to the definition of selfmarketing. It focuses specifics in implementing the self-marketing strategies in the professional activities of university teachers. Self-marketing is a process of continuous professional development and self-improvement of teachers, formation and promotion of their personal brand. For university teachers the purpose of active self-marketing is an increase in the competitiveness and market value of oneself and ensuring the stability of their positions in the conditions of instability of the professional intra-university and inter-university labor market. Self-marketing is closely related to the selfdevelopment of the teacher and is realized in the objective need to constantly improve knowledge and perfect professional competencies in accordance with the current requirements of educational programs. The article reveals the factors of the internal and external environment that determine the main directions of self-marketing as a competitive strategy of behavior for a university teacher. The practice of using marketing tools in professional promotion and self-development of a teacher is analyzed in three positions: "teacher as a researcher", "teacher as a teacher", "teacher as an expert". The main functions of self-marketing of a university teacher are highlighted: evaluative, information-analytical, corrective, stimulating, image-building. The development and implementation of marketing technologies in professional activities makes it possible to improve the quality of the educational process and ensure more stable positions of teachers in the professional labor market.
Synote Usage on E learning Search

E-Learning plays a necessary role in the way of searching a secured educational video resources for the students. In this paper, e-learning domain introduces a concept of searching a video resources in annotated way with linked data cloud. The purpose for the student to choose e-learning is that the learner can continue learning anywhere and at any time .This paper goes with unique approach of synote tool where synote tool allows the e-learners to search the syntactic web information accurately and with different online video resources. Linked data cloud concept is applied in this concept to search a secured search. The result is proved for secured search of educational video resources and shows the reusability of resources for e-learners.
Review on P-model v1.0

I have a mixed feeling for this review.On the good side, this is incredibly well written.All figures and analysis are highly professional.On the other side, this manuscript degraded the elegance of optimality hypothesis.I fully understand the original paper on optimality hypothesis (Han et al 2017) was not perfect.It had much room for improvements.But the way to improve in this manuscript is not attractive in my view with following reasons.1) AET/PET was used for aridity index to consider drought effects.The authors used SPLASH model.If AET can be modeled so well, then GPP must be modeled well too as they are both tightly correlated via stomata conductance.
Cybersecurity Best Practices and Cultural Change in Global Business

This chapter discusses how the adoption of best practices to increase cybersecurity in business affects the cultural value of the company. Primarily focused on the European Union, the chapter defines and analyzes both the impact of cyber security on the global business conducted in the European Union, and its relation to cloud computing. A brief analysis of the ISO27000 family of standards is made, to finally analyze the relationship between cybersecurity, cultural change and best practices in IT-based business. Businesses are facing now the Third Industrial Revolution characterized by real-time communications through Information and Communication Technologies, where fighting against cybercrime is essential to have safer and more reliable IT-based systems, given the negative effects emanated from cyber insecurity.
Accumulative energy-based seam carving for image resizing

With the diversified development of the digital devices, such as computer, mobile phone and television, how to resize an image or video to adapt to different display screens has been a heated topic. Seam carving does well in image resizing at most times, however, it sometimes produces discontinuity image content or impaired salient objects. Therefore, we propose an accumulative energy-based seam carving method for image resizing. We distribute the energy of each pixel on the seam to its adjacent 8-connected pixels to avoid the extreme concentration of seams. In addition, we add the image saliency and the edge information into the energy function to reduce the distortion. To compute more efficiently, we use parallel computing environment as well. Experimental results show that compared with the existing methods, our method can both avoid the discontinuity of image content and distortions as well as better maintain the shape of the salient objects.
Pairing-free certificateless signature with revocation

How to revoke a user is an important problem in public key cryptosystems. Free of key escrow and costly certificate management, the certificateless public key cryptosystem (CLPKC) is advantageous over both the identity-based public key system and the traditional public key system . However, there are few solutions to the revocation problem in CLPKC. In this paper, we present an efficient revocable certificateless signature scheme. This new scheme can revoke a user with high efficiency. We also give a method to improve the scheme to be signing-key-exposure-resilient. Based on the discrete logarithm problem, our scheme is provably secure.
Enhancement of Levelwise Cluster Head Selection with the Concept of New Nodes As Cluster Head Nodes

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Coexisting bournonite-seligmanite and tennantite-tetrahedrite solid solutions in Darasun gold deposit (Eastern Transbaikalia, Russia): Estimation of the mineral formation temperature

Mineral associations, intergrowth features, chemical composition and Sb and As distribution in coexisting fahlore and bournonite-seligmanite solid solutions in the Darasun gold deposit were studied. For the first time the almost complete solid solution was found between bournonite and seligmanite with a continuous isomorphism between Sb and As in the range of Sb/(Sb + As) ratios from 0.21 to 1.00 in the Darasun deposit by EMPA. The composition of the fahlore coexisting with bournonite varies widely: Sb/(Sb + As) ratios 0.03-0.96 and Fe/(Fe + Zn) ratios 0.36-0.87. The coupled compositional evolution from early antimony to late arsenic in coexisting solid solutions has been established. Based on the investigation of the Sb and As distribution between coexisting fahlore and bournonite-seligmanite, the temperatures of their joint deposition and, as a result, of the productive stage, in which they are closely associated with native gold and tellurides (90–335 °С), are estimated.
Outlier Detection in High Dimensional Data Based on Elastic Net Regression

Outlier detection in large datasets is the dynamic research area in computer science such as data mining, database systems, and distributed systems. Outlier detection faces many challenges due to the absence of data samples from the outlier class. Massive algorithms have been projected to conquer the challenges in this field to improve the efficiency of regression approach for large datasets. Currently, no particular efficient regression technique is designed for outlier detection. In this research, we proposed an ElasticNet regression model for detecting the outliers in high dimensional data. To validate the efficiency and competence of our projected algorithm, it is implemented in the open source software called Weka Explorer. The parameters such as Mean absolute error 0.0022, RMSE 0.0387, Relative absolute error (RAE) 0.4562 and Root relative squared error (RSE) 7.8722 are calculated using annthyroid dataset. ElasticNet model consumes less computational time, generates fast convergence results, provides high accuracy and correctly classified accuracy is 98.25%.
Design of information technology classification based on medical data

Decision-making processes associated with assigning a person to a risk group for diseases are accompanied by the need to analyze large volumes of medical and social data. At the same time, a qualified doctor must operate both the patient's personal data and the relevant treatment protocols and instructions. Early prediction of the risks of disease occurrence allows medical workers to plan, develop a system of preventive measures, and the like. Therefore, the object of research is to support and provide information and analytical support for decision-making processes for early predicting the risks of diseases in individuals based on medical data. Such support is necessary to analyze the experience of a medical worker, which is recorded in the form of statistical data. One of the most problematic areas at the design and implementation stage of relevant information technology is the collection and analysis of statistical data on the problem under study.The study was carried out in accordance with the systematic approach methodology. All stages of the design of information technology for forecasting based on medical data correspond to the stages of a systematic approach: systematization, formalization, goal orientation. The developed technology is based on the classification method based on Wald's sequential analysis.The research resulted in:- built a mathematical model of the problem of predicting the risks of disease occurrence as a classification problem;- a functional diagram of an information and analytical system has been developed to solve the classification problem based on medical data. The analytical core of the information and analytical system is formed by algorithms for statistical data processing, as well as a classification method based on Wald's sequential analysis;- experimental verification of the developed technology for the task of predicting the occurrence of dentoalveolar anomalies in children has been performed. Based on the available statistical data, a differential prognostic table was constructed. All calculations are performed. The examples demonstrate the effectiveness of the developed technology.The developed information technology can be used by medical workers in the process of early forecasting of the risks of disease.
Exploring Cloud-Based Distributed Disaster Management With Dynamic Multi-Agents Workflow System

Natural disaster is one of the important topics in current researches. Disaster Management System (DMS) is a complex system and needs to perform a collection of tasks collaboratively along with the potentiality to change the configurations of the system dynamically. In the research era of workflow model, existing models mainly deal with temporal and static constrains. However they cannot be used to keep pace with an uncertainly dynamic system like disaster management. Considering all these significant DMS attributes we have designed a new dynamically configurable and changeable workflow model with the support of adaptive scheduling, for both successful and failed situations, and implemented in a distributed cloud system to maintain the rescue and reorganization activities of disaster situation. In order to simplify the system architecture, we have used Multi Agent System (MAS) for our design. The proposed system achieves a comparatively higher rate of successful job completion-higher rescheduling success rate and comparatively lower dropout rate.
Effective Avoidance of Queuing in Super Market using Li-Fi Technology

In extensive general stores, clients feel awkward to remain in long line for charging the obtained items. This will deteriorate at the season of celebration or regular deal. This paper gives an awesome answer for every one of these issues utilizing Li-Fi innovation. Li-Fi is another rising innovation in pattern which employments light waves to exchange information. In this paper, we propose a programmed charging framework which is not just time compelling additionally diminishes human exertion. This framework utilizes Li-Fi innovation to exchange information rapidly. The free accessible android application is conveyed in versatile utilizing which we get the item subtle elements and the installment is prepared in the versatile itself. For security, the items are confirmed in the door area by checking the items in the trolley. The fundamental target of this paper is to maintain a strategic distance from lines in grocery stores and shopping centers.
Application of mixed teaching based on Linux information processing technology platform : Taking the course of basic accounting as an example

With the development of educational technology, traditional classroom is gradually changing to mixed learning. Hybrid teaching mode is a new teaching mode that organically combines online teaching with traditional classroom teaching. Teachers change their role from teaching to guidance, students change from passive to active, and learning activities are more rich and flexible. The implementation of hybrid teaching needs to rely on the teaching platform with interactive function, the open teaching platform based on Embedded Linux information processing technology, take imx6 as the core processor, and operate independently in process mode according to the functional requirements of different information processing, so as to facilitate the upload and playback of video resources and effectively improve the teaching quality. As an introductory course of accounting major, Fundamentals of accounting adopts mixed teaching method, which has significantly improved the teaching effect and is deeply welcomed by students.
Scanning the Issue

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Estimating Query Timings in Elasticsearch

In a shared Elasticsearch environment it can be useful to know how long a particular query will take to execute. This information can be used to enforce rate limiting or distribute requests equitably among multiple clusters. Elasticsearch uses multiple Lucene instances on multiple hosts as an underlying search engine implementation, but this abstraction makes it difficult to predict execution with previously known predictors such as the number of postings. This research investigates the ability of different pre-retrieval statistics, available through Elasticsearch, to accurately predict the execution time of queries on a typical Elasticsearch cluster. The number of terms in a query and the Total Term Frequency (TTF) from Elasticsearch’s API are found to significantly predict execution time. Regression models are then built and compared to find the most accurate method for predicting query time.
Differentiation of one-dimensional functions

This textbook is a comprehensive introduction to the key disciplines of mathematics - linear algebra, calculus, and geometry - needed in the undergraduate physics curriculum. Its leitmotiv is that success in learning these subjects depends on a good balance between theory and practice. Reflecting this belief, mathematical foundations are explained in pedagogical depth, and computational methods are introduced from a physicist's perspective and in a timely manner. This original approach presents concepts and methods as inseparable entities, facilitating in-depth understanding and making even advanced mathematics tangible. The book guides the reader from high-school level to advanced subjects such as tensor algebra, complex functions, and differential geometry. It contains numerous worked examples, info sections providing context, biographical boxes, several detailed case studies, over 300 problems, and fully worked solutions for all odd-numbered problems. An online solutions manual for all even-numbered problems will be made available to instructors.