Reviewerly/exHarmony huggingface dataset:
DESIGNING PROFESSIONAL AND PEDAGOGICAL PROFILE OF A TEACHER OF AN INSTITUTION OF PROFESSIONAL (VOCATIONAL) EDUCATION

The article discusses the problem of importance of professional-pedagogical profile of a teacher working at an institution of professional (vocational) education as the necessary condition for his/her professional development. The methodological approaches to the design and substantiation of the professional-pedagogical profile have been defined. This paper presents author’s definition of the concept of “professional and-pedagogical profile of a professional (vocational) institution teacher”. Module and competence approach was chosen while developing teacher’s professional-pedagogical profile. It has been taken into account that the functional activity of a professional (vocational) institution teacher is inherent in the dual nature, since it relates both to the pedagogical and the industrial spheres of activity in a certain specialty. The author defines such modules of competences of a professional (vocational) institution teacher as a normative and legal, psychological, pedagogical, methodological, information and communication, experimental, cultural and educational, professional, represented in the functional structure of his/her vocational and pedagogical profile. It is emphasized that the functional structure of the professional-pedagogical profile of a professional (vocational) institution teacher consists of two integrated components, which according to the innovative processes in the industrial and pedagogical spheres are in constant dynamic development. It is noted that the professional-pedagogical profile of the teacher performs various functions: educational, diagnostic, reflective, motivational, planning, predictive, and evaluative. The article presents developed professional and pedagogical profile of a teacher of a professional (vocational) institution. The given profile includes allocated relevant vocational and pedagogical competences, which can be used as a system factor for professional development of a teacher, as well as means for identifying educational needs, the level of professionalism, certification, and serve as a benchmark for qualification growth and building teacher’s professional career. The suggested methodological approaches to the design of professional-pedagogical profile of a professional (vocational) institution teacher, and the given competencies can be taken into account while developing the higher education standard for training a teacher of an institution of professional (vocational) education.
THE IMPORTANCE OF TEACHING ALGORITHMS AND PROGRAMMING LANGUAGES IN THE CREATION OF ELECTRONIC EDUCATION RESOURCES

Modern, advanced educational technologies and non-traditional methods of teaching are being introduced into the educational process.In this article, we will focus on some aspects of using the methods developed in the teaching of the science of algorithms.It is a well-known fact that university graduates work in various fields, work extensively and achieve good results. The subjects the student is studying and the time allotted for teaching them, the science program is the main focus. By teaching the science of algorithms in close connection with other disciplines, students will be able to form secular knowledge and be able to work effectively in any field in the future.The spiral form of education focuses on the complex teaching of previously taught topics and the study of deeper algorithms and programming. If we pay attention to the fact that the topics in the secondary school course are given in the form of superstructures, and the topics in academic lyceums and colleges are repeated, we can see that the previous topics are the basis of this superstructure.They carry out the highest and most up-to-date educational process, as their graduates work in various fields and continue their education in the next types of education.The use of e-learning resources in the teaching of mathematics in secondary schools serves to improve the quality and effectiveness of education. The science of algorithms and programming languages and their teaching have great importance in the creation of e-learning resources.
Cartoon Face Recognition: A Benchmark Dataset

Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets proposed. In this work, we first present a new challenging benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters annotated with identity, bounding box, pose, and other auxiliary attributes. The dataset, named iCartoonFace, is currently the largest-scale, high-quality, richannotated, and spanning multiple occurrences in the field of image recognition, including near-duplications, occlusions, and appearance changes. In addition, we provide two types of annotations for cartoon media, i.e., face recognition, and face detection, with the help of a semi-automatic labeling algorithm. To further investigate this challenging dataset, we propose a multi-task domain adaptation approach that jointly utilizes the human and cartoon domain knowledge with three discriminative regularizations. We hence perform a benchmark analysis of the proposed dataset and verify the superiority of the proposed approach in the cartoon face recognition task. We believe this public availability will attract more research attention in broad practical application scenarios.
Games of Miners

Conventional wisdom believes cryptocurrency miners should always work on particular token at their full power. In this paper, however, we show that miners' equilibrium strategy deviates from it, which affects the system security and energy-efficiency. Specifically, we model mining as a game where each miner has limited mining power and compete for multiple tokens. We analyze both pure Nash-equilibrium and Stackelberg-equilibrium of this game, deriving their closed-forms. It is suggested that miners might not exert full power, which, compared with fully-powered mining, provides less mining power for a token and thus makes it more vulnerable to attacks, while it helps to reduce energy consumption. Simulation results show that with more disparate capacity, this effect is more significant. Our results also show that miners should disperse power among all compatible tokens instead of only one, which matches realistic statistics well.
A NEW IMPERCEPTIBLE STEGANOGRAPHY METHOD FOR GRAYSCALE IMAGES

A standard Least Significant Bit (LSB) algorithm changes average 50% of the least significant bits of an image. This causes approximately a 0.2% distortion on a grayscale image. Therefore, the application of LSB on non-sharp grayscale images causes too much noise that can be easily detected by the human visual system. In addition; when the image is scanned pixel by pixel, hidden message can be easily read by unauthorized persons. A robust and imperceptible block-based LSB steganography method is developed in this study. First, cover image is divided into blocks, then median geometric mean and arithmetic mean of each block is calculated to select blocks those contain less detail of the image. Method has an advantage of calculating data load capacity of image before embedding process. Proposed method also makes it difficult for unauthorized people to detect hidden messages. Performance of the method is measured by mathematical methods such as MSE, PSNR and SSIM. The method achieved structured similarity score over 0.9999% on four test images, indicating a 20-fold better result than the standard LSB algorithm.
Am I Building a White Box Agent or Interpreting a Black Box Agent?

The rule extraction literature contains the notion of a fidelity-accuracy dilemma: when building an interpretable model of a black box function, optimising for fidelity is likely to reduce performance on the underlying task, and vice versa. I reassert the relevance of this dilemma for the modern field of explainable artificial intelligence, and highlight how it is compounded when the black box is an agent interacting with a dynamic environment. I then discuss two independent research directions - building white box agents and interpreting black box agents - which are both coherent and worthy of attention, but must not be conflated by researchers embarking on projects in the domain of agent interpretability.
Validation of Models for the Calculation of Sun Positions and mapped Radiation on inclined Surfaces

In order to validate building simulation programs and to identify sources of errors in implemented models, it is necessary to evaluate individual physical effects step by step. For building simulations, the radiation load that hits buildings is a significant boundary condition. In order to reproduce these loads as accurately as possible, sun position models and weather data adapted to them are necessary. With the help of sun position models, the radiation data (usually available in horizontal or normal radiation) are mapped onto inclined and oriented surfaces. This paper examines a possible validation. For the analysis, different building simulation tools (NANDRAD, Radiance, IDA ICE, Modelica, TRNSYS, ETU Simulation) are compared with different time steps for specific dates using the result data azimuth and altitude. The locations are distributed over the northern and southern hemispheres, inside and outside the tropics and polar circles, and with large deviations from the standard meridian, thus allowing errors to be found in the implemented sun position models. In the following step, these building simulation platforms are compared with regard to their minutely radiation loads on building façades. Errors in the radiation mapping on inclined surfaces are hereby determined.
Identification of Kernel Memory Corruption Using Kernel Memory Secret Observation Mechanism

Countermeasures against attacks targeting an operating system are highly effective in preventing security compromises caused by kernel vulnerability. An adversary uses such attacks to overwrite credential information, thereby overcoming security features through arbitrary program execution. CPU features such as Supervisor Mode Access Prevention, Supervisor Mode Execution Prevention and the No eXecute bit facilitate access permission control and data execution in virtual memory. Additionally, Linux reduces actual attacks through kernel vulnerability affects via several protection methods including Kernel Address Space Layout Randomization, Control Flow Integrity, and Kernel Page Table Isolation. Although the combination of these methods can mitigate attacks as kernel vulnerability relies on the interaction between the user and the kernel modes, kernel virtual memory corruption can still occur (e.g., the eBPF vulnerability allows malicious memory overwriting only in the kernel mode). We present the Kernel Memory Observer (KMO), which has a secret observation mechanism to monitor kernel virtual memory. KMO is an alternative design for virtual memory can detect illegal data manipulation/writing in the kernel virtual memory. KMO determines kernel virtual memory corruption, inspects system call arguments, and forcibly unmaps the direct mapping area. An evaluation of KMO reveals that it can detect kernel virtual memory corruption that contains the defeating security feature through actual kernel vulnerabilities. In addition, the results indicate that the system call overhead latency ranges from 0.002 µs to 8.246 µs, and the web application benchmark ranges from 39.70 µs to 390.52 µs for each HTTP access, whereas KMO reduces these overheads by using tag-based Translation Lookaside Buffers.
Covid-19: Four in 10 cases in Italian town that locked down early were asymptomatic

Thank you for your interest in spreading the word about The BMJ.NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address. Username * Your Email * Send To * You are going to email the following Covid-19: Four in 10 cases in Italian town that locked down early were asymptomatic Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Mechanised Modal Model Theory

In this paper, we discuss the mechanisation of some fundamental propositional modal model theory. The focus on models is novel: previous work in mechanisations of modal logic have centered on proof systems and applications in model-checking. We have mechanised a number of fundamental results from the first two chapters of a standard textbook (Blackburn et al. [1]). Among others, one important result, the Van Benthem characterisation theorem, characterises the connection between modal logic and first order logic. This latter captures the desired saturation property of ultraproduct models on countably incomplete ultrafilters.
Make E Smart Again (Short Paper)

In this work in progress, we demonstrate a new use-case for the ENIGMA system. The ENIGMA system using the XGBoost implementation of gradient boosted decision trees has demonstrated high capability to learn to guide the E theorem prover’s inferences in real-time. Here, we strip E to the bare bones: we replace the KBO term ordering with an identity relation as the minimal possible ordering, disable literal selection, and replace evolved strategies with a simple combination of the clause weight and FIFO (first in first out) clause evaluation functions. We experimentally demonstrate that ENIGMA can learn to guide E as well as the smart, evolved strategies even without these standard automated theorem prover functionalities. To this end, we experiment with XGBoost’s meta-parameters over a dozen loops.
Study of The ID3 and C4.5 Learning Algorithms

Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms such as ID3, C4.5 and CART etc. The present study considers ID3 and C4.5 algorithms to build a decision tree by using the “entropy” and “information gain” measures that are the basics components behind the construction of a classifier model
An instructional design for vocabulary acquisition with a hidden disability of dyslexia

The focus of this study is on an undiagnosed language learner with Dyslexia, facing problems in vocabulary acquisition. The purpose of this study is to enable the learner to acquire a pre-defined number of vocabularies within a given time limit. The method of this study is a case study in a real-life context with quantitative evidence that relies on multiple data collection tools, such as checklist, interviews, questionnaire and report cards of the learner. The results of this study demonstrate that the capability of the dyslexic learner can be increased to acquire 55 vocabularies in a week, which is the same number expected from their peers. The performance increase of the learner can be attributed to a new method of learning English vocabulary through game-based learning supported with spaced repetition. The individualised instruction designed for self-learning can be transformed into a group-based instruction in a classroom setting for anyone experiencing difficulties in vocabulary acquisition.&#x0D; &#x0D; Keywords: Instructional design, performance increase, dyslexic learners, game-based learning, retention.&#x0D; &#x0D;
Research on the question of determination of the total number of fraud victims committed via Internet

The object of this research is the statistical aggregate of people who by objective (external) reasons are prone to become the victims of fraud within the Russian segment of the global computer network. The subject of this research is the quantitative aspect of the aforementioned phenomenon. Special attention is given to the search of effective methods for determination of quantitative aspects of victimization. The goal consists in most accurate assessment of the total number of the potential victims of fraud committed via Internet. The main results of this work contain the most accurate among previously existed in criminology numbers of: potential victims of fraud in the Internet (the author specifies the number of users of the Russian segment of Internet aged from 6 to 80), persons out of 24-hour Internet audience of criminogenic age; and a number of statistical indicators of victimization. All of the listed above can assist proper organization of research carried out by scholars dealing with the problems of cybercrimes.
4th Industrial Revolution(4IR)for Smart Learning

This paper provides an assessment of how 4IR   revolutions have impacted Smart education. It develops technical capacity in emerging technologies in active and project-based settings. The societal changes from the 4IR will require education to develop greater capacity for ethical and intercultural understanding, placing a premium on liberal arts-type education with modifications to adapt to the particular issues raised by 4IR technologies and their disruptions to society. It requires a rapid adjustment of curriculum by expanding its capacity to accommodate the acquisition of new knowledge by students, faculty and alumni, with new modalities of instruction that leverage the digital advances from the Third Industrial Revolution.
Deep Bilateral Retinex for Low-Light Image Enhancement

Low-light images, i.e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise. Low-light image enhancement is about improving the visibility of low-light images. As the measurement noise in low-light images is usually significant yet complex with spatially-varying characteristic, how to handle the noise effectively is an important yet challenging problem in low-light image enhancement. Based on the Retinex decomposition of natural images, this paper proposes a deep learning method for low-light image enhancement with a particular focus on handling the measurement noise. The basic idea is to train a neural network to generate a set of pixel-wise operators for simultaneously predicting the noise and the illumination layer, where the operators are defined in the bilateral space. Such an integrated approach allows us to have an accurate prediction of the reflectance layer in the presence of significant spatially-varying measurement noise. Extensive experiments on several benchmark datasets have shown that the proposed method is very competitive to the state-of-the-art methods, and has significant advantage over others when processing images captured in extremely low lighting conditions.
Sparsity Reduction Technique Using Grouping Method for Matrix Factorization in Differentially Private Recommendation Systems

Web service users are overwhelmed by the amount of information presented to them and have difficulties in finding the information that they need. Therefore, a recommendation system that predicts users' taste is an essential factor for the success of businesses. However, recommendation systems require users' personal information and can thus lead to serious privacy violations. To solve this problem, many research has been conducted about protecting personal information in recommendation systems and implementing differential privacy, a privacy protection technique that inserts noise into the original data. However, previous studies did not examine the following factors in applying differential privacy to recommendation systems. First, they did not consider the sparsity of user rating information. The total number of items is much more than the number of user-rated items. Therefore, a rating matrix created for users and items will be very sparse. This characteristic renders the identification of user patterns in rating matrixes difficult. Therefore, the sparsity issue should be considered in the application of differential privacy to recommendation systems. Second, previous studies focused on protecting user rating information but did not aim to protect the lists of user-rated items. Recommendation systems should protect these item lists because they also disclose user preferences. In this study, we propose a differentially private recommendation scheme that bases on a grouping method to solve the sparsity issue and to protect user-rated item lists and user rating information. The proposed technique shows better performance and privacy protection on actual movie rating data in comparison with an existing technique.
THE POTENTIAL OF INTERDICIPLINARY RESEARCHES: RUSSIAN BIOMEDICAL PUBLICATIONS IN COLLABORATION WITH LIBRARY SPECIALISTS

The article reviews interdisciplinary problems as a versatile phenomenon. Interdisciplinary is more often understood as interaction between different scientific disciplines, which can range from simple exchange of ideas to organization of joint research activities. Taking into account this understanding of the interdisciplinary phenomenon, the prerequisites for joint scientific activity between library specialists and the medical (biologists) community are considered. An attempt is made to analyze the possibilities of the library scientific community to participate in such studies. In the practical part, the Russian biomedical publications with the library employees’ participation, presented on the e Library platform, are analyzed: an array of interdisciplinary articles on this subject is revealed, the analysis of the sample by year, participation of authors, libraries, disciplines, scientometric indicators is carried out. The results made it possible to understand the features of the tasks and the specifics of research strategies. Suggestions on possible trends of interdisciplinary researches development in the libraries of Russia are formulated. Conditions, contributing to increasing the chances of library specialists to be included in interdisciplinary researches are stated based on the Reference and Bibliographic Department of SB RAS experience. Foreign interdisciplinary publications are considered by referring to reviews on this issue. The studied articles from authoritative journals on informatics and librarianship testifies to the number growth publications in biomedical disciplines prepared with participation of library specialists. A review publication in the Journal of the Medical Library Association covering interdisciplinary publications with participation of library professionals for 2008 – 2017 served as a model for the analysis. The authors concluded that the main topics of cooperation between librarians and tutors were related to the assessment of medical and social aspects of patients’ health, the search for medical information and clinical decisionmaking.