Reviewerly/exHarmony huggingface dataset:
Procedural 3D Terrain Generation using Generative Adversarial Networks.

Procedural 3D Terrain generation has become a necessity in open world games, as it can provide unlimited content, through a functionally infinite number of different areas, for players to explore. In our approach, we use Generative Adversarial Networks (GAN) to yield realistic 3D environments based on the distribution of remotely sensed images of landscapes, captured by satellites or drones. Our task consists of synthesizing a random but plausible RGB satellite image and generating a corresponding Height Map in the form of a 3D point cloud that will serve as an appropriate mesh of the landscape. For the first step, we utilize a GAN trained with satellite images that manages to learn the distribution of the dataset, creating novel satellite images. For the second part, we need a one-to-one mapping from RGB images to Digital Elevation Models (DEM). We deploy a Conditional Generative Adversarial network (CGAN), which is the state-of-the-art approach to image-to-image translation, to generate a plausible height map for every randomly generated image of the first model. Combining the generated DEM and RGB image, we are able to construct 3D scenery consisting of a plausible height distribution and colorization, in relation to the remotely sensed landscapes provided during training.
DETAILING AND GENERALIZING THE MOTIVES FOR SCIENTIFIC RESEARCH OF STUDENTS OF HIGHER EDUCATIONAL INSTITUTIONS

In the article, the authors paid attention to the issues of identifying and detailing the motives of students of higher educational institutions for scientific research during their studies. Based on the generalization of the opinions of the authors who participated in the development of this problem, and the results of their own research, the authors of this article at the initial stage of the study analyzed and summarized the conclusions of scientists about the totality and detail of the above-mentioned motives. Further, the authors of this article justified a holistic set of interrelated motives that encourage students to engage in scientific research in addition to their current academic and other activities. In the process of studying the motives, the authors identified factors that represent a generalized grouping of motives for students' scientific research. Grouped motives, considered as motivational factors, can generally be considered as forces that encourage students to engage in the type of activity under consideration, namely: scientific research.The motivation factors that the authors reasonably propose to consider as determining the motivation of students for research activities are: the need for self-realization; reflection; social utility; professional interest; remuneration; social interaction; satisfaction from the process and the result. Each of the factors is justified and logically connected with the corresponding motives. This grouping is presented in tabular form and, as mentioned earlier, contains the rationale for the elements included in it.The identified factors and related motives can serve as a basis for the development of a survey material to assess students ' motivation for research activities.
Face Recognition in the Presence of Non-Gaussian Noise

A method for detecting a face and recognizing a person or a group of persons at pictures or videos that can be corrupted by non-gaussian noise using different architectures of convolutional neural networks is proposed.The technical details of building a deep learning-based face recognition system are discussed.The test results confirm the prospects of using the developed method for solving tasks of face detection and recognition.
Parental treatment methods and their relationship to social intelligence on a sample of adolescents and adolescents in the town of Beit Hanina: أساليب المعاملة الوالدية وعلاقتها بالذكاء الاجتماعي: دراسة وصفية على عينة من المراهقين والمراهقات ببلدة بيت حنينا

This study aimed to identify the method of parental treatment and its relationship to social intelligence in a sample of adolescents and adolescents in the town of Beit Hanina. To achieve this goal، the descriptive method was used by applying a questionnaire consisting of two sections، the first section of the methods of parental treatment consisting of (24) paragraphs، and the measure of social intelligence (21) paragraphs، the sample of the study consisted of adolescents and adolescents of the town of Beit Hanina، where they numbered (113). The results of the study found that there are no statistically significant differences at the level of indication. There are no statistically significant differences between parental treatment methods and social intelligence attributable to the variable sex، the variable number of brothers and sisters، the variable age group، the variable of the scientific qualification of the father and the scientific qualification of the mother، but There are statistically significant differences at the level in parental treatment methods and social intelligence due to the change of the person responsible for the adolescent. The study found that the methods of parental treatment were closer to leniency، if the average arithmetic (3.67)، and then the democratic style، and the authoritarian style. The degree of social intelligence was low and averaged 2.30. In the light of these results، the researchers recommended a number of recommendations، the most important of which are: the preparation of awareness programs and guidance for parents by educational specialists to explain the importance of the method used in raising children using the best positive methods and avoiding negative methods that may lead children to a decrease in Social intelligence. To guide the attention of officials in the field of psychological guidance to the importance of social intelligence and methods of parental treatment in the preparation of programs and training courses aimed at developing social intelligence among students، and spreading educational awareness.
Financial crisis, financial globalisation and financial development in Africa

This study unites two streams of research by simultaneously focusing on the impact of financial globalisation on financial development and pre- and post-crisis dynamics of the investigated relationship. The empirical evidence is based on 53 African countries for the period 2004-2011 and Generalised Method of Moments. The following findings are established. First, whereas marginal effects from financial globalisation are positive on financial dynamics of activity and size, corresponding net effects (positive thresholds) are negative (within range). Second, while decreasing financial globalisation returns are apparent to financial dynamics of depth and efficiency, corresponding net effects (negative thresholds) are positive (not within range). Third, financial development dynamics are more weakly stationary and strongly convergent in the pre-crisis period. Fourth, the net effect from the: pre-crisis period is lower on money supply and banking system efficiency; post-crisis period is positive on financial system efficiency and pre-crisis period is positive on financial size.
A Violation Information Recognition Method of Live-Broadcasting Platform Based on Machine Learning Technology

With the development of the live broadcast industry, security issues in the live broadcast process have become increasingly apparent. At present, the supervision of various live broadcast platforms is basically in a state of human supervision. Manpower supervision is mainly through user reporting and platform supervision measures. However, there are a large number of live broadcast rooms at the same time, and only relying on human supervision can no longer meet the monitoring needs of live broadcasts. Based on this situation, this study proposes a violation information recognition method of a live-broadcasting platform based on machine learning technology. By analyzing the similarities and differences between normal live broadcasts and violation live broadcasts, combined with the characteristics of violation image data, this study mainly detects human skin color and sensitive parts. A prominent feature of violation images is that they contain a large area of naked skin, and the ratio of the area of naked skin to the overall image area of the violation image will exceed the threshold. Skin color recognition plays a role in initial target positioning. The accuracy of skin color recognition is directly related to the recognition accuracy of the entire system, so skin color recognition is the most important part of violation information recognition. Although there are many effective skin color recognition technologies, the accuracy and stability of skin color recognition still need to be improved due to the influence of various external factors, such as light intensity, light source color, and physical equipment. When it is detected that the area of the skin color in the live screen exceeds the threshold, it is preliminarily determined to be a suspected violation video. In order to improve the recognition accuracy, it is necessary to detect sensitive parts of the suspected video. Naked female breasts are a very obvious feature in violation images. This study uses a chest feature extraction method to detect the chest in the image. When the recognition result is a violation image, it is determined that the live broadcast involves violation content. The machine learning algorithm is simple to implement, and the parameters are easy to adjust. The classifier training requires a short time and is suitable for live violation information recognition scenarios. The experimental results on the adopted data set show that the method used in this article can effectively detect videos with violation content. The recognition rate is as high as 85.98%, which is suitable for a real-life environment and has good practical significance.
Indian Currency Recognition and Authentication

It is very difficult for blind people to recognize or classify the Indian Currency into 10, 20, 50, 100, 200, 500 and2000 rupees, therefore this project aims to classify the image into respective class of currency note.This is done using TensorFlow object detection which not only classify the image but also helps to localize/detect the currency in image provided by the user. Thus, the recognition part is done using object detection. Furthermore, the project also aims to authenticate the Indian currency means it helps to classify the image into real currency or fake currency. This is very much helpful because it helps to detect the fake currency and stop the flow of the same in market. Indian Currency Authentication is done using deep learning that is using Image Classification and Mobilenet model of image classification is used. The authentication is done using the watermark ofMahatma Gandhi which is present in each and every currency notes.So, each part is implemented using somewhat different approach.
Best Practice in Multi-factor Authentication

Multi-factor Authentication (MFA) often referred to as Two-factor Authentication (2FA), which is a subset of MFA, is the practice of implementing additional security methods on top of a standard username and password to help authenticate the identity of a user and increase the security of data. This chapter will investigate the problem with username and password logins, the different types of authentication, current best practice for multi-factor authentication and interpretations about how the technology will grow in the upcoming years.
Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

&lt;span lang="EN-US"&gt;Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.&lt;/span&gt;
Hybrid FWF Model For Gaussian Noise Reduction From Images

In this paper, the image efficiency is improved by using hybrid model of wiener’s filter and fuzzy filter. It’s a challenging task to remove Gaussian noise (GN) from an image and to protect the picture edges. The Fuzzy - Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results presented that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques
Eye-movement control during learning and scanning of English pseudoword stimuli: Exposure frequency effects and spacing effects in a visual search task

Wang et al. (Attention, Perception, and Psychophysics, in press, 2021) reported a Landolt-C learning and scanning experiment. In a learning session, they simulated exposure frequency effects successfully by training participants to learn target Landolt-C clusters with different exposures. The rate of learning high-frequency (HF) targets were greater than that of learning low-frequency (LF) targets. In a subsequent scanning session, participants were required to scan text-like Landolt-C strings to detect whether any pre-learnt target was embedded in the strings. The Landolt-C strings were displayed under different spacing formats (i.e., spaced format, unspaced format, and unspaced shaded format). However, the simulated exposure frequency effect did not occur in the scanning session. Wang et al. argued one straightforward reason for this might be because participants failed to maintain the memory of pre-learnt target to the scanning session. In the current study, we employed the same learning and scanning paradigm to investigate whether exposure frequency would occur in a target search task by using easier learning materials – pseudoword stimuli. The learning of pseudoword stimuli was much more successful than Landolt-C stimuli. Interestingly, however, we found a very different rate of learning effect such that the rate of learning LF targets was greater than HF targets. To our surprise, we did not find any influence of exposure frequency on eye movements during scanning even when participants were able to identify pre-learnt pseudowords in strings. Learning rate effect, exposure frequency effects, and saccadic targeting during the scanning of strings under different spacing formats are discussed in this paper.
The Construction of Disciplinary Knowledge and the Dilemma of Literary Education

Current knowledge of literary disciplines is a system of discourse constructed according to scientific principles and formal methods, which contradicts literature as a metaphorical and poetic activity.The manifestation of literary education is the closure of education and the reduction of the integration between literature and social practice.The inherent contradiction between Enlightenment modernity and aesthetic modernity is the root cause of these phenomena, so the way out for literary education should start from aesthetic modernity.Specifically, it is to improve the tendency of scientific and formalized dominance of the literary discipline and to bring back the literary nature of literature, for which the current cultural studies provide ready insights and direct influence.
Hybrid Model with Time Modeling for Sequential Recommender Systems.

Deep learning based methods have been used successfully in recommender system problems. Approaches using recurrent neural networks, transformers, and attention mechanisms are useful to model users' long- and short-term preferences in sequential interactions. To explore different session-based recommendation solutions, this http URL recently organized the WSDM WebTour 2021 Challenge, which aims to benchmark models to recommend the final city in a trip. This study presents our approach to this challenge. We conducted several experiments to test different state-of-the-art deep learning architectures for recommender systems. Further, we proposed some changes to Neural Attentive Recommendation Machine (NARM), adapted its architecture for the challenge objective, and implemented training approaches that can be used in any session-based model to improve accuracy. Our experimental result shows that the improved NARM outperforms all other state-of-the-art benchmark methods.
The construction of the Hilbert genus fields of real cyclic quartic fields

Let $p$ be a prime number such that $p=2$ or $p\equiv 1\pmod 4$. Let $\varepsilon_p$ denote the fundamental unit of $\mathbb{Q}(\sqrt{p})$ and let $a$ be a positive square-free integer. In the present paper, we construct the Hilbert genus field of the real cyclic quartic fields $\mathbb{Q}(\sqrt{a\varepsilon_p\sqrt{p}})$.
Bayesian Knowledge Tracing for Navigation through Marzano’s Taxonomy

In this paper we propose a theoretical model of an ITS (Intelligent Tutoring Systems) capable of improving and updating computer-aided navigation based on Bloom's taxonomy.For this we use the Bayesian Knowledge Tracing algorithm, performing an adaptive control of the navigation among different levels of cognition in online courses.These levels are defined by a taxonomy of educational objectives with a hierarchical order in terms of the control that some processes have over others, called Marzano's Taxonomy, that takes into account the metacognitive system, responsible for the creation of goals as well as strategies to fulfill them.The main improvements of this proposal are: 1) An adaptive transition between individual assessment questions determined by levels of cognition.2) A student model based on the initial response of a group of learners which is then adjusted to the ability of each learner.3) The promotion of metacognitive skills such as goal setting and self-monitoring through the estimation of attempts required to pass the levels.One level of Marzano's taxonomy was left in the hands of the human teacher, clarifying that a differentiation must be made between the tasks in which an ITS can be an important aid and in which it would be more difficult.
Destylization of text with decorative elements.

Style text with decorative elements has a strong visual sense, and enriches our daily work, study and life. However, it introduces new challenges to text detection and recognition. In this study, we propose a text destylized framework, that can transform the stylized texts with decorative elements into a type that is easily distinguishable by a detection or recognition model. We arranged and integrate an existing stylistic text data set to train the destylized network. The new destylized data set contains English letters and Chinese characters. The proposed approach enables a framework to handle both Chinese characters and English letters without the need for additional networks. Experiments show that the method is superior to the state-of-the-art style-related models.
Binary Log Analysis on MySQL to Help Investigation Process Against Database Privillege Attacks

Abstract—Database is an important part in managing information, because a database is a collection of data that is processed to produce information. because of the importance of the database, many crimes are directed to attack the database, both attacks against access rights or attacks against the data itself. My SQL is a Database Management System (DBMS) that provides several facilities, one of which is the logging facility. Binary Log is a type of database log in the form of binary digits that contains some information including the record of the time of the transaction, the user who made the transaction and the order in the transaction. With the Binary Log, it can be seen when the transaction occurred, who made the transaction and what transaction occurred in the database. The recording of transactions in the Binary Log can be used as one way to carry out an investigation process in the event of an attack on the database. In this study the focus is on analyzing transaction records in binary logs, namely when, who, dam and what information can be taken from the Binary Log. The output of this research is a table of binary log investigation results and its relation to database attacks.
Supervised Learning Based Peripheral Vision System for Immersive Visual Experiences for Extended Display

Video display content can be extended to the walls of the living room around the TV using projection. The problem of providing appropriate projection content is hard for the computer and we solve this problem with deep neural network. We propose the peripheral vision system that provides the immersive visual experiences to the user by extending the video content using deep learning and projecting that content around the TV screen. The user may manually create the appropriate content for the existing TV screen, but it is too expensive to create it. The PCE (Pixel context encoder) network considers the center of the video frame as input and the outside area as output to extend the content using supervised learning. The proposed system is expected to pave a new road to the home appliance industry, transforming the living room into the new immersive experience platform.
Building user interfaces in e-learning resources

The article discusses a pressing problem of improving the quality of e-learning and considers a special approach to e-learning resources graphic interfaces layout based on the Successive Approximation Model of instructional design. This approach allows the learner to influence the attainability of learning goals, which implements the principle of humanization and individualization of e-learning.
Challenges to Digital Education in a Situation of Social Crisis

The present article aims to examine the state of mobile learning in Bulgaria, presenting the policy framework in which the education processes themselves take place, and also to show the various platforms and software products that form the basis of mobile learning.Online platforms greatly facilitate and support mobile learning, as they connect individuals regardless of distances or situations.The analysis shows that mobile learning has a place in Bulgarian education and its quality is at relatively high level.Its specifics are related to the technological security, the technical and methodological training and qualification of teachers, as well as the responsibility of families.A specific case is analyzedthe situation with Covid-19 and how the Bulgarian education system is being restructured in the direction of mobile learning under the influence of the new pandemic conditions.Methodology is based on qualitative methods, under the national project "Digital Media Literacy in the context of "Knowledge Society": state and challenges‖, № КП-06-Н25/4, 2018, funded by National Science Fund -Bulgaria.The main conclusion is that Covid-19 puts Bulgarian education in a situation that requires comprehensive and universal mobile learning, which is in line with the modern knowledge society.