Reviewerly/exHarmony huggingface dataset:
Organization of the process of professional socialization of the younger generations in Canada and New Zealand

The article actualizes the problem of organizing the process of professional socialization of the younger generations in Canada and New Zealand. A number of effective mechanisms and approaches implemented in English-speaking countries are described, while focusing on the fact that a feature of the organization of the process of professional socialization in Canada was the shift in the focus of career guidance to the opportunities of school graduates through the content of the concept of “life strategy” to consciously make a professional choice. In New Zealand, professional socialization is associated with the professional activities of the teacher, which is implemented in schools with a focus solely on the development of entrepreneurial activity and the involvement of the younger generation in business. The relevance of this topic is determined by its relatively low level of study in the Russian pedagogy, including comparative pedagogy. The purpose of the article is to identify and characterize the problem of organizing the process of professional socialization of the younger generations in Canada and New Zealand. The authors analyze the main directions and describe a number of successful initiatives implemented in these countries that contribute to the successful organization of the process of professional socialization of the younger generations. Methodology and research methods. An analysis of the scientific literature on the organization of the process of professional socialization of the younger generations in the two countries we studied showed the importance of this type of organization of the process of professional socialization of the younger generations. Research results. The article emphasizes that in the third decade of the 21st century, the concept of “life strategy”, implemented in Canada, allows the younger generation to make a conscious professional choice. Unlike Canada, the organization of the process of professional socialization of the younger generations in New Zealand is mainly focused on the development of entrepreneurial activity and the involvement of the younger generation in business. Conclusion. The study of the organization of the process of professional socialization in Canada and New Zealand is of great theoretical and practical importance for preparing the younger generations for life and professional activity. The study of the positive experience of these countries may be important for understanding, enriching and the possibility of its use in practice in Russia, taking into account the cultural characteristics of our country.
Detection of pedophilia content online: A case study using Telegram

Users of social media can consume a wide range of subjects and information, including pornography. Pornography usage as well as the difficulties linked with this sort of content have increased over time, particularly among teens. Now, another sort of pornographic content is popular: child pornography (CP). Controlling online CP has always been a difficult task for the international community. The introduction, growth, and use of information and communication technologies coincide with an increase in illegal activities. In terms of cyberspace, children’s huge online presence as well as the emergence of CP as a business compel all governments to enact tough legislation and unite worldwide to combat this issue. Is social media assisting in the dissemination of this content? To understand this issue further, a study conducted in 2021 using Telegram data on the consumption of CP followed by a series of analyses show Telegram’s potential effect on the spread and consumption of this type of content.
A Comparative and Performance Analysis of Similarity Metrics in Recommender System Based on Hadoop Framework

With exponential growth of information available on web, there is a need for a personal assistance to customers for finding the best item out of the large set of items. There is also need for finding the best item having highest popularity. The personal assistance which will assist the user is the Recommender System. It is a software tool that uses knowledge discovery techniques to produce personalized recommendations. The majority of the recommendations are based on the machine learning algorithm and techniques which uses a standard data set for producing predictions. With tremendous growth of customers and items in the recent years, there is a key challenge for the recommendation system to produce quality recommendations based on the similarity of the users. There also lies a great challenge for the recommendation system to produce quality output when data set is huge, reduce latency, group users having similar interest and perform recommendations in seconds for millions of customers and products. Thus there is a need for new recommendation technologies which will produce quality output to users having similar tastes in a best possible manner. In this paper we present a comparative analysis of various similarity metrics used in recommendation system for clustering the users based on hadoop framework. The experimental results show that spearman rank correlation performs the best among the other similarity metrics based on three evaluation metrics.
ATUALIZAÇÕES NA DISTROFIA MUSCULAR DE DUCHENNE

Objective: Conduct a narrative review of the literature on the updates and main aspects of Duchenne Muscular Dystrophy such as etiologies, diagnosis and new therapies.Data source: 29 articles from the MEDLINE and Pubmed databases, in English and Portuguese, between the years 2010 and 2020.The selection occurred using the following keywords: "Duchenne Muscular Dystrophy", "Duchenne Diagnosis", "Duchenne Clinical Evolution", "Duchenne Drug Therapy".The research looked at epidemiology, etiology, clinical presentation, diagnosis and treatment of Duchenne Muscular Dystrophy.Data synthesis: Many research on Duchenne Muscular Dystrophy is available.New therapies are in the initial phase of study and may mean better management of the disease, but the prognosis remains reserved.Conclusion: Early diagnosis and management is essential for a better quality of life for patients with Duchenne Muscular Dystrophy because it allows better management of atrophies.
Improvement of noisy images filtered by bilateral process using a multi-scale context aggregation network

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for de-noising noisy CCTV images. Data-store is used tomanage our dataset, which is an object or collection of data that are huge to enter in memory, it allows to read, manage, and process data located in multiple files as a single entity. The CAN architecture provides integral deep learning layers such as input, convolution, back normalization, and Leaky ReLu layers to construct multi-scale. It is also possible to add custom layers like adaptor normalization (µ) and adaptive normalization (Lambda) to the network. The performance of the developed CAN approximation operator on the bilateral filtering noisy image is proven when improving both the noisy reference image and a CCTV foggy image. The three image evaluation metrics (SSIM, NIQE, and PSNR) evaluate the developed CAN approximation visually and quantitatively when comparing the created de-noised image over the reference image.Compared with the input noisy image, these evaluation metrics for the developed CAN de-noised image were (0.92673/0.76253, 6.18105/12.1865, and 26.786/20.3254) respectively
Expanding Aesthetics

This paper seeks to expand traditional aesthetic dimensions of design beyond the limits of human capability in order to encompass other species' sensory modalities. To accomplish this, the idea of inclusivity is extended beyond human cultural and personal identities and needs, to embrace multi-species experiences of places, events and interactions in the world. This involves drawing together academic perspectives from ecology, neuroscience, anthropology, philosophy and interaction design, as well as exploring artistic perspectives and demonstrating how these different frames of reference can inspire and complement each other. This begins with a rationale for the existence of non-human aesthetics, followed by an overview of existing research into non-human aesthetic dimensions. Novel aesthetic categories are proposed and the challenge of how to include non-human aesthetic sensibility in design is discussed.
Designing for Non-Humans

Hwang is a registered architect in New York State who develops constructed environments that incorporate wildlife habitats.Her projects include "Bat Tower," "Bat Cloud," "Habitat Wall," "Bower."Her latest project, "To Middle Species, With Love," was installed as part of Exhibit Columbus, and is on display
Handling Events in PyQt

Since GUIs need to perform tasks, the widgets, windows, and other aspects of the application need to be able to react to the events that occur. Whether caused by the user or by the underlying system, the events, and possibly data, need to be delivered to their appropriate locations and handled accordingly.
PRESCHOOL CHILDREN’S SENSITIVITY FORMATION BY MEANS OF THEATRICAL GAMES

The article highlights the issue of preschool children’s sensitivity formation by means of theatrical games. Emphasis is placed on the signs of this quality, and it is determined that preschool age is a sensitive period for its formation.&#x0D; It is noted that theatrical and play activities are a priority type of the life experience display and a leading means of preschool children’s moral education. The range of types of theatrical games is presented, and the possibilities of their introduction into the educational process of preschool educational institutions are characterized.&#x0D; Emphasis is placed on the value of involving preschoolers in theatrical play activities as spectators and direct participants in the action, in particular, as actors, musicians, choreographers, decorators, illuminators, etc.&#x0D; The stages of preparatory work for preschoolers’ theatrical and play activities are determined. The pedagogical potential of theatrical games based on the content of literary works for sensitivity formation as a preschool child’s key quality is revealed.
Docverifer

Abstract: In this digital world, everything is digitalized but still, some practices are a bit unvarying, one of those is the certificate verification process. The foremost important documents are certificates for graduates from universities and other educational institutions. However, it is costly and time-consuming to check certificates using traditional methods. This paper aims to introduce a theoretical model, which can give the possible infusion for the issue and verification process of any type of document. We need to consider strong points on blockchain apart from finance, those are tracking, supply chain, etc. In this model, we are using blockchain and distributed storage. There are many functions such as hash, public and private key cryptography, digital signatures, peer-to-peer networks and work evidence in blockchain technology. In This model, we are using IPFS for distributed storage (peer-to-peer) everything in ipfs is globally available with the appropriate hash generated by ipfs, for this reason, we added multi-layer security at the Client-side, as an encryption mechanism. To Protect the documents uploaded by the issuer, we enabled an encryption mechanism at the client This Dapp (decentralize) has been divided into two parts one is the document issuer and the other one is the verifier.
Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks

Scheduling under uncertainty is an area of interest in artificial intelligence. We study the problem of Dynamic Controllability (DC) of Disjunctive Temporal Networks with Uncertainty (DTNU), which seeks a reactive scheduling strategy to satisfy temporal constraints in response to uncontrollable action durations. We introduce new semantics for reactive scheduling: Time-based Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We present a tree search approach to determine whether or not a DTNU is R-TDC. Moreover, we leverage the learning capability of a Graph Neural Network (GNN) as a heuristic for tree search guidance. Finally, we conduct experiments on a known benchmark on which we show R-TDC to retain significant completeness with regard to DC, while being faster to prove. This results in the tree search processing fifty percent more DTNU problems in R-TDC than the state-of-the-art DC solver does in DC with the same time budget. We also observe that GNN tree search guidance leads to substantial performance gains on benchmarks of more complex DTNUs, with up to eleven times more problems solved than the baseline tree search.
Deteksi Intrusi Pada Basis Data Menggunakan Random Forest

More services have been made online in recent years, more and more data is being stored virtually. This important and confidential data becomes an easy target for criminals in the era of digitalization. Database security becomes very necessary to keep data safe. Attacks can come from outside or from within, attacks caused by insiders are the second biggest threat after hacking. Conventional security has not been able to detect anomalies from internal users. This can be anticipated using an intrusion detection mechanism. This mechanism has previously been applied to networks and hosts. However, some actions that are harmful to the database are not necessarily harmful to the network and hosts so that intrusion detection on the database becomes extra security to defend the database from intruders. This system uses the Random Forest algorithm which includes supervised learning to detect anomalous transactions. The dataset used is a transaction log containing 773 records and 9 attributes. Anomalies are determined based on the threshold value of 3 attributes, namely operation, object and field name. The test uses 6 different trees, 10, 20, 40, 60, 80 and 100. The results of the test on 762 records and 5 attributes used, the Random Forest algorithm has the highest accuracy value on the number of trees 80 and 100 which have a test time difference of 0 .03 seconds. In the dataset used, the optimum number of trees is found at number 80 with an accuracy value of 99.56% and an execution time of 0.13183 seconds.
General Strategy for the Synthesis of Rare Sugars via Ru(II)-Catalyzed and Boron-Mediated Selective Epimerization of 1,2-trans-Diols to 1,2-cis-Diols

Key words ruthenium catalysis - boronic acid - sugars - epimerization - hydrogenation
Yakov Alexandrovich Ponomarev: some results of scientometric analysis of his creative heritage

The article discusses the prospects for the use of scientometrics in historical-psychological research. The authors refer to the scientific heritage of Ya. A. Ponomarev and apply some methods of scientometric analysis to his scientific works, which are presented in the Scientific electronic library and according to which various RSCI indicators are calculated, as well as to queries in the Yandex Internet search engine related to his personality and scientific work . The authors stated the number of indexed publications by Ya. A. Ponomarev, the number of citations of his works, the Hirsch index, his place according to the Hirsch index and the number of citations in the rating of psychologists. The number of Internet users' requests for several key phrases related to his personality and his ideas in the field of psychology of creativity and methodology of psychology was revealed, and the reasons for changes in the dynamics of Internet requests associated with the name of Ya. A. Ponomarev are examined. The analyzed indicators reveal logicalscientific (the interest of modern researchers is expressed in the number of citations, and the Hirsch index reflects the impact on various branches of psychology) and social-historical (interest from various Internet users (specialists from different professional fields, students, etc.) is expressed in the number of requests in Internet search engines) factors of demand for personality and creativity of Ya. A. Ponomarev at the present stage of development of psychological science. New possibilities of analyzing the scientific creativity of a scientist and his contribution to specific areas of scientific knowledge using scientometric methods are noted.
A Light-Weight Scheme for Detecting Component Structure of Network Traffic

The rapid development of network services not only expands the scale of Internet traffic, but also diversifies the types of traffic. In this work, we design a light-weight compromise scheme to meet the management requirements of large-scale and business sensitive scenarios. The proposed scheme regards the mixed traffic as a whole and directly analyzes the component structure for it. It converts the structural and attribute features into a traffic profile by encoding, embedding and mapping. Then the traffic profile is used to infer the component structure based on CNN. The proposed scheme has no need to perform flow-by-flow classification, it is not limited to the “quantity” balance of traffic, but also considers the types of traffic in each link. Based on the experiments with actual dataset, the results show that the proposed scheme can infer component structure for mixed traffic quickly and accurately.
Soft Computing in Decision Support Systems

The complexity of modern decisions has caused organizations to become increasingly dependent on advanced intelligent technologies to quickly process large amounts of data and make informed decision.Decision support system (DSS) is a computerbased system that helps in the process of decision making.It is designed to support better-informed decision-making.It is used to support determinations, judgments, and courses of action in an organization.Intelligent decision support systems based on soft computing have attracted the attention of researchers and practitioners in a wide range of disparate areas from engineering to business.This paper is an introduction on the applications of soft computing in decision support system.
Industrial ‘ROBOT’ Taxation System

Technology and Humankind have mutually coexisted in this world for decades. However, the world is changing fast and our technology is changing faster; the dynamics of this relationship between humans and technology is feared by many to change soon. The ‘ROBOT apocalypse’ as seen in science fiction can soon become reality for some aspects of our world, in particular, the job market. How do we save our jobs and therefore save our lives from such an event? Is this event even going to happen or is this just yet another false alarm? The legal applications for such an event are relatively unexplored and this is exactly what this paper delves into. We start off my understanding what such an event could look like by drawing parallels from history where similar speculation took place about our relationship with technology, i.e. the Industrial Revolutions. We discover how our current situation is much different from history and how we face a huge challenge to come up with public policy solutions and regulations to combat such an event. This paper takes up this challenge and discusses some ways in which technology governance can be changed and how a system of taxation might be our best bet to protect jobs while also not hindering the growth and progress of our society. The idea of such a tax has been discussed quite a lot, however, major concerns over its implementation have halted this discussion. This paper seeks to breathe a new life into the discussion by clearing up many concerns about such a taxation system and laying down foundations for a new governance system in the best interest of our society.
A Study on Super Resolution of SRCNN according to Early Up-sampling Method

Super resolution is a technology that converts low-resolution images into high-resolution images, and super resolution technologies using deep learning have recently been studied. The SRCNN model was first studied as a deep learning-based super-resolution technology, and the SRCNN model performs early up-sampling and learning using interpolation to perform super-resolution. In this paper, the best performance early up-sampling interpolation was investigated through the performance comparison of the early up-sampling method. Through this, it was found that the bicubic interpolation method derived good performance from up to 35% to at least 17% compared to other interpolation methods.
PRETCO: An English Test for Vocational and Technical College Students

In China, vocational and technical colleges account for more than half of its institutions of higher education.For most students in these colleges, English is compulsory.This creates one of China's largest English learning bodies.The paper deals with the Practical English Test for Colleges (PRETCO), a nation-wide standardized English test specially designed for non-English majors of vocational and technical colleges.Specifically, it first focuses on its purpose, length, and administration.Then, it describes its development and introduces its components and format at length.It also describes test takers' performance in the test.Finally, it provides an overall evaluation of the test.
Hybrid Singular Value Decomposition Based Alpha Trimmed Meanmedian Filter in Eliminating High Density Salt and Pepper Noise from Grayscale Image

The use of images has increased over the previous decade and they have the potential to be effective communication tools, similar to social media. In the world of social media, uploading visual information or images looks to be becoming more popular. The appearance of noise disturbs the original information in image. Thus, it is necessary to remove the noise before using the image for subsequent task. There is a lot of filtering techniques that have been introduced to remove noise in the image. The approaches for image restoration are based on mathematical model of image deterioration. In this thesis, singular value decomposition (SVD) and alpha trimmed mean median filter (ATMMF) is proposed as a new method for removing salt and pepper noise in digital image. The basic principle behind this method is that it starts with noise detection and then moves on to a filtering strategy. For noise detection, SVD is used to detect salt and pepper noise and replacement of noisy pixels using ATMMF. The experimental process was performed with 12 sample of grayscale images that was provided with a variable salt and pepper noise density ranging from 10% to 90% in order to compare the proposed method to other widely used methods. Afterwards, PSNR ans SSIM were taken as the quality measurement. The proposed filtering technique is simple to use and implement. Experimental results show that the proposed method has successfully reduced salt and pepper noise in high noise density. It outranks all the previous filtering method in terms of visual effects and quantitative measure results.