Reviewerly/exHarmony huggingface dataset:
Gender, Cybersecurity &amp;amp; Fraud

This paper explores how issues and malfeasances relating to cybersecurity and cyber fraud differ based on gender, primarily focusing on women in relation to use of mobile-phone centric digital financial services (DFS) in developing countries in Africa and South Asia.We find that women within the scope of our study may be more susceptible than men to malfeasances and other concerns related to cyber risks and cyber fraud as a result of inequalities and gender gaps that exist within developing countries. Women consistently ranked behind men with regard to access to, use of and experience with information and communication technologies, ownership of mobile hardware and DFS. Women were perceived to possess lower digital and language literacy rates and subjected to limiting social, cultural, religious and legal barriers. These factors often tied them to roles in private spaces, such as the family home. As such, access to important peer knowledge networks which disseminate timely information about cybersecurity and fraud issues was limited, which often occur in public spaces (such as the workplace, in the marketplace and social settings.) As a result, women’s overall cyber awareness and cyber hygiene levels would likely to be lower than men, including their capabilities to combat social engineering related fraud which appears to present a formidable challenge in developing countries.Presently, a paucity of gender-disaggregated data exists and/or is available publicly related to information and communications technology and cybersecurity. Findings from relevant available data were supplemented by interviews conducted with regional and local experts and consultants in the field.With faith in the security of financial systems and process being the mainstay of user adoption and use of services, we find that the distinct lack of quantitative and qualitative research on gender differences in susceptibility to fraud, social engineering attacks and cyber-attacks is a siren cry for additional focus and funding of this important issue by academic institutions, donor communities, and think tanks. Furthermore, mobile network operators, digital financial services providers and related governmental authorities and agencies are encouraged to increase their outreach to women, especially in rural areas, to increase their levels cyber awareness and hygiene and to address rapidly increasing cybercrime.
Certain aspects of the law enforcement practice of taking preliminary interim measures in accordance with article 144.1 of the Civil Procedure Code of the Russian Federation

The article examines the features of preliminary interim measures for disputes on the protection of copyright and associated intellectual property on the Internet and specific aspects of law enforcement practice of their adoption Article 1441 of the Civil Procedure Code of the Russian Federation. The authors note that the peculiarity of applications for preliminary protection of copyright and (or) associated intellectual property rights (except for the rights to photographic works and works obtained by methods similar to photographs on the Internet) is the possibility of their submission to the Court by filling out a unique electronic form, posted on the official website of the Moscow City Court, and signing with an exclusively enhanced qualified electronic signature belonging to an individual authorized to act on behalf and in the interests of the applicant (as by a power of attorney, and by the post held in the case of representing the interests of a legal entity). The documents’ requirements to be submitted by applicants simultaneously with the application for insufficient protection of copyright and (or) associated intellectual property rights (mandatory requisites) are noted, as well as the legal consequences of failure to submit such documents or their non-compliance with the established requirements. The authors conclude that the copyright holder has the right to demand restrictions on access to information resources that distribute not only objects of copyright and (or) associated intellectual property rights but also the information necessary for obtaining them using information and telecommunications networks. This is because posting information on the sale of audiovisual works on websites without the copyright holder&amp;apos;s consent, including links for downloading them, is one of the forms of distribution of such objects. The article discusses the peculiarity of trademark rights protection, including cases of its illegal use on a website on the Internet, and also expresses an opinion on the possibility of claiming the protection of rights to stylized artistic images that are not a trademark by the procedure provided for in Article 1441 of the Civil Procedure Code of the Russian Federation, even if their illegal use consists in placing photographs of relevant artistic images, drawings on the website page without the consent of the copyright holder.
Some Cool Utility Packages

Python has an amazing ecosystem for data science work, starting from numerical analysis to advanced deep learning or reinforcement learning, with statistical modeling and visualization thrown in as well. A great open-source culture keeps new and exciting developments coming and thriving. Data scientists can learn, contribute code, share their experience, help debug, and support each other in this environment. There are some predominant libraries and packages in this ecosystem that are used by almost all data scientists in their daily job. However, there are also some little-known Python packages that can help you do common data science jobs faster and more efficiently. They are not general-purpose large projects like NumPy or pandas. Instead, they focus on some niche aspects of similar data science tasks and do them well. This chapter focuses on some of these hidden gems.
No Re-entrancy Guard: A novel approach for mitigating all types of Re-entrancy bugs and evaluating its efficiency

Blockchain applications are powered by smart contracts which perform crypto exchanges according to the policies set by developers. These transactions are free-of-conflict and transparent. Though at the end of day these are all computer programs which means they are not immune from bugs. In this paper We focus on most common and deadly vulnerability called re-entrancy, which has caused numerous DAO and DeFi attacks costing millions to organizations and end-users alike. We have researched all sub-types of re-entrancies and hence proposed a novel solution to mitigate them all by ensuring all state changes happen before calling external smart contracts and using function modifiers to apply mutual exclusion lock like protocol to prevent it. Moreover, we have also compared my solution with that of other solutions been proposed on scale of their gas cost efficiency.
Webinars for ecological education and advancement of sustainable development concepts in libraries

For efficient ecological education and advancement of sustainable development goals (STGs), the librarians need special knowledge in ecology and professional competencies of searching thematic information, organizing educational activities, and systematic information exchange. These goals are efficiently achieved through thematic webinars for librarians in ecological education organized by Russian National Public Library for Science and Technology. The RNPLS&amp;T hosts the webinars since 2012, and since 2017, does it regularly and systematically. The author examines the role of the webinars in advancing this library activity vector.
Face Detection and Recognization System

Face recognition from image or video is a popular topic in biometrics research. Many public places usually have surveillance cameras for video capture and these cameras have their significant value for security purpose. It is widely acknowledged that the face recognition have played an important role in surveillance system as it doesn’t need the object’s cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. As human face is a dynamic object having high degree of variability in its appearance, that makes face detection a difficult problem in computer vision. In this field, accuracy and speed of identification is a main issue.The goal of this paper is to evaluate various face detection and recognition methods, provide complete solution for image based face detection and recognition with higher accuracy, better response rate as an initial step for video surveillance. Solution is proposed based on performed tests on various face rich databases in terms of subjects, pose, emotions, race and light.
Transfer Learning to Detect Age From Handwriting

Handwriting analysis is the science of determining an individual’s personality from his or her handwriting by assessing features such as slant, pen pressure, word spacing, and other factors. Handwriting analysis has a wide range of uses and applications, including dating and socialising, roommates and landlords, business and professional, employee hiring, and human resources. This study used the ResNet and GoogleNet CNN architectures as fixed feature extractors from handwriting samples. SVM was used to classify the writer’s gender and age based on the extracted features. We built an Arabic dataset named FSHS to analyse and test the proposed system. In the gender detection system, applying the automatic feature extraction method to the FSHS dataset produced accuracy rates of 84.9% and 82.2% using ResNet and GoogleNet, respectively. While the age detection system using the automatic feature extraction method achieved accuracy rates of 69.7% and 61.1% using ResNet and GoogleNet, respectively
The Content of Social Competencies of a Lawyer Contained in the Text-Requirements of Russian Employers for the Training of Students within the Framework of Higher Professional Education

The article deals with the competence-based approach to education in its social aspect from the standpoint of training students-future lawyers. The author emphasizes the importance of introducing a competence-based approach in higher professional education, based on an analysis of the works of both domestic and foreign authors and regulatory documents, since there is a need to provide the education system with a more fully, personally and socially integrated result of education as today it is represented by complex, multi-component personal quality of a person which, in addition to knowledge, skills and abilities, also involves the experience of personal responsibility and independent activity. Accordingly, the understanding that the competence-based education results is of utmost importance today and it should serve as a basis within modern education system. Social competence is also considered as an integral part of the social and professional competence of a professional lawyer in the international sphere. Russian legal firms, in addition to the requirements for professional skills, require specialists to be socially competent, which determined the analysis of the content of the job requirements for the lawyers. On the basis of the method of content analysis, a study of the texts-requirements of Russian legal firms was conducted, as a result of which the requirements for social competencies and skills of social interaction were identified, and a ranked list of these requirements was compiled, which allows to identify the most and least demanded skills of social interaction required by the Russian law firms.
Complex Object Detection Using Light-Field Plenoptic Camera

Identifying objects with a lot of complex features in an uncontrolled scene is a challenging task in the image processing research field. This article presents the application of robust and invariant feature descriptors together with an optimal, iterative and probabilistic methodology. These methods let to achieve an application of object detection through a homographic transformation matrix. This work is built on the Raytrix R42 Plenoptic camera, for which the camera calibration process is introduced. The results present a homographic transformation that relates spatial information of the reference object in an uncontrolled scene. This approach put forward an efficient performance that overcomes bad lighting and occlusion problems.
AI Versus IA

What is the current state of artificial intelligence (AI) in the world of scholarly communication? What impact does AI have on the practices and strategies of publishers, libraries, information technology companies, and researchers? What exactly is AI and what are those in the realm of scholarly communication actually thinking about it and doing with it? This Charleston Briefing seeks to provide some answers to these very important questions, offering both general essays on AI and more specific essays on AI in scholarly publishing, academic libraries, and AI in information discovery and knowledge building. The essays will help publishers, librarians, and researchers better understand the actual impact of AI on libraries and publishing so that they can respond to the potentially transformative impact of AI in a measured and knowledgeable manner. Charleston Briefings: Trending Topics for Information Professionals is a thought-provoking series of brief books concerning innovation in the sphere of libraries, publishing, and technology in scholarly communication. The briefings, growing out of the vital conversations characteristic of the Charleston Conference and Against the Grain, will offer valuable insights into the trends shaping our professional lives and the institutions in which we work. With Chapters By: Daniel W. Hook And Simon J. Porter Catherine Nicole Coleman And Michael A. Keller James W. Weis And Amy Brand Ruggero Gramatica Haris Dindo Todd A. Carpenter
Using Specific Shock Filters and Decomposition of Single Values in Dual Mode to Reconstruct the Picture with Super Resolution

Super resolution (SR) algorithms are commonly used for resolving hardware limitations on resource sensitive imaging systems. This paper incorporates a single SR approach (SISR) into the wavelet domain, which retains the contrast and edge data at the same time. Our algorithms use a concept of geometric duality by way of covariance-dependent interpolation to create an original hight resolution unknown (HR) image. Advanced SISR wavelet technologies increase performance by substituting the low-frequency sub-band for the signal data of higher quality. The solution proposed is to fix the low frequency subband with a single wavelet (SWT). Interpolation of Lanczos is the result of shifting sub- and high frequency pictures. The interpolated subbands are filtered using diffusion-based shock filters, which function in both directions. All filters are reverse SWT fused with the intention of producing the final HR picture.
Orleans Dashboard

The Orleans Dashboard is a community-contributed project which adds a real-time dashboard to Orleans, allowing you to get a visual insight into the performance of your application.
So Much Information, So Little Screen Space: Assessing the usability of hierarchical data visualizations in Tableau

The purpose of this usability study was to determine the most effective of three ways to display hierarchical data using the interactive data visualization software, Tableau. Often, data visualizations contain large amounts of important information that users need to be able to manipulate and interpret. Viewing hierarchical data in an interactive data visualization software like Tableau has the advantage of allowing dynamic selection of the hierarchical level of detail of results displayed. This enables improved understanding and exploration of the material. However, individuals using such software do not necessarily have knowledge of a dataset and/or the data visualization software, resulting in an inability to fully investigate data relationships. It is therefore critical that research be conducted to determine which data presentation styles promote intuitive navigation within the data visualization. A within-subjects usability study was conducted to examine the most effective of three ways to display hierarchical data within a designated area of a Tableau visualization. Three distinct visualizations of hierarchical data were randomly shown to participants. Each visualization was bordered by identical contextual information with the centrally placed hierarchical data varying. One condition showed the data relying on filters. A second condition showed the data relying on users to expand and collapse the level of detail with scrolling. A third condition showed the data as a drill-down chart that only expands the level of detail selected by the user. Metrics of user-response time, the accuracy of responses to assessment questions, the subjective rank of usability for each data visualization, and open-ended user feedback were examined. Results are discussed.
Secure image watermarking using MLDWT and SVD for secure medical data transmission model in healthcare systems

In this paper, primarily a novel medical image water marking&apos;s obsolete to embed a watermark picture, a new watermarking approach because of Multilevel discrete wavelet transform (MLDWT) and singular value decomposition (SVD) is implemented, in which changing most incredible upsides of the cover image in the MLDWT province offers remarkable resilience against typical attacks. As a result of the MLDWT implementation, the algorithm has a high PSNR and correlation coefficient of the watermarked image. The outcomes demonstrated that the MLDWT-SVD perspective is more effective than the existing spatial domain, and DWT drew on methods.
Computer Vision Based Attendance Management System

In many educational institutions, student participation in academics is a major concern. In crowded classrooms, handling attendance sheets manually takes a long time. This paper introduces and measures an automated student attendance approach that can be used in closed classes and captures classroom pictures with a PC. A protocol which takes into account the quantity of images per subject used in the gallery is used to test several facial recognition algorithms based on learned and handcrafted features. We used the Hear Cascade and Local Binary Pattern Histogram (LBPH), two deep learning methods, with only 1 enrolment image per subject achieving around 95% accuracy. We believe that our automated student attendance system based on facial recognition will save time for both teachers and students while still preventing fake attendance.
The Workings of Quantum Computing: Quantum Physics for Information Technology Students and Professionals

This paper will be about the life of the quantum computer. Specifically, it will cover how quantum technology was used and developed in the past, and how it is currently used and developed in the modern day. Then it will talk about, how those concepts are applied to make quantum computing possible. After that, it will see how quantum computing is applied to and attempting to be applied to normal business ventures and people’s everyday lives. Finally, it will cover what companies are at the forefront of quantum computing research and how close we are to the point that a quantum computer attains a consistently higher performance than the world’s best supercomputers.
The physical load of the Human body during Motion with BP Neural Network

Background: Unreasonable tasks will increase the person’s physical load, leading to safety accidents and occupational diseases. To ensure a reasonable physical load and improve the operational efficiency of the person as far as possible, it is necessary to predict and evaluate the physical load of workers in real-time.Objective: A prediction model of the physical load intensity of the human body based on a neural network was established, and its effectiveness was verified.Methods: Twelve volunteers completed four movements walking, jogging, climbing, and jumping. The surface electromyography (sEMG) on the left and right sides of the rectus femoris and biceps femoris was measured, and the motor posture of volunteers was obtained by Vicon, the joint torque, maximum muscle activity, and muscular force parameters were calculated based on the reverse dynamic model of human motion. The sEMG eigenvalue and mechanical load parameters in different postures were considered input and output, respectively, and 80% of all data were used as the training set and the rest as the validation set.Results: In this study, we found that the hip joint, knee joint, and ankle joint have a sizeable joint torque during movement, in which the joint torque of the ankle joint is the largest and twice human body weight at its peak. Besides, a larger muscle load occurs at the beginning and end of contact between the human foot and the ground, and the muscle strength of the rectus femoris was significantly higher than that of the biceps femoris (p&lt;0.05). The number of neurons in the input layer, an output layer, and a hidden layer of the model is 32, 13, and 12, respectively. This study found that the prediction error of maximum muscle activity was 6.4%. The average prediction error of joint torque was 8.7%, and the prediction error of the muscular force of the rectus femoris muscle was no more than 9.5%. This model can reasonably predict the physical load of the human body.Conclusions: A workload evaluation model based on the BP neural network was established in this research, which can analyze the biomechanics of the human body in motion and judge the human body’s physical load effectively according to the EMG signal.Application: This model can measure the body load of soldiers and firefighters in real-time during task training and provide a reference for task design.
A TRAJECTORY TRACKING METHOD OF PARALLEL MANIPULATOR BASED ON KINEMATIC CONTROL ALGORITHM, 273-279.

A trajectory tracking method based on a dynamic recursive fuzzyneural network (DRFNN) proportional–integral–derivative (PID)controller and an improved artiﬁcial ﬁsh-swarm algorithm (IAFA)is proposed to control a 6-DOF parallel manipulator.
A BERT Model-Based Sentiment Analysis on COVID-19 Tweets

In the past few decades, the growth of data on the Internet has increased significantly, and even today, tons of data get generated with each passing day. The World Wide Web has become a great source of e-learning, sharing ideas, and interchanging school of thoughts and views. Internet community sites like Twitter, Facebook, and Instagram have gained immense attraction and have gathered a huge pool of daily active users over the past few decades, as they provide a medium to exchange or express their opinions or ideas about everything and anything. Many studies have been conducted on the topic of sentiment analysis, particularly on Twitter data. Sentiment analysis is effective for analyzing data in tweets where suppositions are very unstructured, varied, and positive, negative, or neutral. This research examines the evaluation of coronavirus-related Twitter data. We looked into the feelings of a large number of tweets gathered by Web scraping of tweets using various hashtags. The experimental results were presented using the bidirectional encoder representations from transformers (BERT) model, which was compared to the performance of traditional classification models like stochastic gradient descent, Naive Bayes, random forest, decision tree, logistic regression, and XG Boost. With an overall accuracy of 95.12%, the BERT model outperformed all other standard models.