Reviewerly/exHarmony huggingface dataset:
THE IMPACT OF VIDEO GAMES ON COGNITIVE DEVELOPMENT

Video games and internet gaming have been more popular with individuals of all ages in recent years.Over the course of the previous 50 years, the number of people who play games on a regular basis has risen from a few thousand to an estimated 2.2 billion people globally.Most studies on how these games influence the brain have focused more on the detrimental than the beneficial consequences, such as games that make players feel powerful emotions like aggression, fear, and wrath.Video games have been connected to gamer lassitude, poor performance at work and school, as well as dismal social behavior and relationships, according to studies that have shown how addictive they can be.Recent research has shown the positive effects of playing these games on human development in general.Additionally, studies have shown that playing these games has a positive impact on a variety of crucial cognitive abilities, such as multitasking, self-empowerment, control, and quick and precise decision-making.This article uses data from studies of the selected population to compare and contrast the cognitive abilities of gamers with those of non-gamers in areas including response speed, task completion under time constraint, and multitasking.The findings from these studies might be useful in assessing how internet use and video games affect key aspects of human cognition.
Cybersecurity Risk Assessment and Management for Organizational Security

Organisations are constantly at risk from cyberattacks in today's digital environment, which can seriously harm their finances, reputations, and operational capabilities.Organisations must take a proactive approach to managing cybersecurity risk in order to protect sensitive data and systems.In order to strengthen organisational security, this research study focuses on the ideas, methodology, and best practises of cybersecurity risk assessment and management.The significance of comprehending the threat landscape, doing risk assessments, putting risk mitigation techniques into practise, and creating an all-encompassing cybersecurity management framework are discussed in the article.The results emphasise the importance of a comprehensive and flexible strategy to cybersecurity that incorporates technology solutions, personnel awareness and training, incident response planning, and ongoing monitoring to maintain strong organisational security.
Life-Saving Lullabies: The conception, development and adaptation of a cultural heritage tool for good

On 9 May 2019, President Edgar Lungu of Zambia declared maternal and prenatal deaths a Public Health emergency with 10-15 women per week losing their lives due to preventable causes. The objective of the Life-Saving Lullabies project is to define and develop a zero-cost, innovation service strategy that is responsive to current maternal child health needs (MCH). The study is supported by St John Zambia (SJZ) and their volunteers who provide MCH services on behalf of the Ministry of Health. Until now, the potential for extending the functional purpose of lullaby lyrics as a methodological tool for delivering essential knowledge has been overlooked. The rationale for using song has evidence to support it. Studies show that singing lullabies promotes mother/ baby attachment (Creighton et al., 2013) and reduces prenatal stress and anxiety (Carolan, 2012). This study explores the potential of song as a culture heritage tool for imparting critical MCH knowledge and skills. Central to this, is our human-centred design approach (Design Council, 2005) that foregrounds the everyday experiences of young women. The research demonstrates the value of cultural heritage tools in enabling non-professionals as an untapped resource. A total of 31 MCH songs have been created.
CRUD application on Library Management system

The library management system is a web-based CRUD (Create, Read, Update, Delete) application built using Python and Django framework.The system allows librarians to manage the library's collection of books, users, and borrow history.Users can browse the library catalog, borrow and return books, and view their borrowing history.The application's core features includes authentication and Authorization system to secure the application.Create, Read, Update, Delete (CRUD) operations on books, users, and borrow history, Ability to search and filter books by author, title, and category.Book borrowing functionality, including due dates and notifications, Ability to view borrowing history and overdue books, Reporting and statistics for librarians to analyze usage and trends.The system is built using Django's Model-View Template (MVT) architecture, which separates data management, user interface, and control logic.The database used is PostgreSQL, which offers reliable and scalable storage for the library's data.The system's user interface is designed to be user-friendly and responsive, with a modern and clean design.The application is responsive, allowing users to access it from any device, including desktops, tablets, and smartphones.In conclusion, the library management system is a comprehensive and efficient solution for libraries looking to manage their collections and improve user experience.With its robust features and user-friendly interface, the application will help librarians save time and effort, while users will benefit from a seamless borrowing and browsing experience.
Research on Intelligent Information Model of Evaluating Program for Oral English Teaching Using Artificial Intelligence Technology

In order to evaluate the quality of college English teachers' oral classroom teaching, this article proposes a method of using artificial intelligence technology to evaluate the quality of oral English classroom teaching, establishes a teaching quality evaluation model based on SOFM neural network, and proposes two types based on deep neural networks.A more noise-robust scoring algorithm for acoustic models."Listening" is an important way for learners to acquire information, and the process also includes refining and reprocessing the information when acquiring information."Speaking" is a direct way of information feedback, and the process includes the organization and reconstruction of information.(Goodness of Pronunciation), and the performance is improved by 7.6%.
Natural language processing and multimedia applications in finance

The innovation of multimedia apps has accelerated in recent years. Developing technology and techniques enable the construction of massive multimedia archives and the construction of new intervention techniques. This chapter presents an overview of multimodal applications that extensively use natural speech.Conventional multifunctional systems often do not save explicit reconstructions of user input and merely manage mode integrating in an introductory manner. This chapter demonstrates how expanding techniques and conceptual representation frameworks established for natural linguistic structures can aid in resolving some of these issues.It examines strategies for developing autonomous multimedia instructional systems based on lessons acquired while creating machine-creating synthesizers. Finally, it contends that using natural language technology can result in a quantitative upgrade of existing approaches for following form and interpretation.
Multi-attribute Vehicle Recognition with Feature Fusion Using Muti-Gate Unit

Vehicle multi-attribute recognition is a critical technology in modern transportation systems. It can help automatically recognize and classifies vehicles based on attributes such as manufacturer, model, color, and type. This information is helpful for a variety of downstream applications, including traffic surveillance, intelligent transportation, and vehicle tracking. Currently, the benchmark performance of image recognition models continues to improve with the ongoing development of deep learning technology. However, most recognition models still have room for improvement in tasks involving the simultaneous recognition of multiple attributes. In this paper, we propose a new method for enhancing the performance deep learning models in multi-attribute recognition tasks. Our method is inspired by the idea of Muti-Gate Mixture of Experts (MMoE) network, which can effectively mix models of different scales of features for the recognition of different attributes. We also design a loss function that combines information entropy and contrastive loss to enhance the accuracy of fine-grained image recognition in multi-task scenarios with limited datasets. We apply our method to a vehicle multi-attribute recognition model and demonstrate through results the effectiveness of our proposed method in improving deep learning models for multi-attribute recognition tasks.
Classification of Mammogram Images by Using SVM and KNN

Breast cancer is a fairly diverse illness that affects a large percentage of women in the west. A mammogram is an X-ray-based evaluation of a woman's breasts to see if she has cancer. One of the earliest prescreening diagnostic procedures for breast cancer is mammography. It is well known that breast cancer recovery rates are significantly increased by early identification. Mammogram analysis is typically delegated to skilled radiologists at medical facilities. Human mistake, however, is always a possibility. Fatigue of the observer can commonly lead to errors, resulting in intraobserver and interobserver variances. The image quality affects the sensitivity of mammographic screening as well. The goal of developing automated techniques for detection and grading of breast cancer images is to reduce various types of variability and standardize diagnostic procedures. The classification of breast cancer images into benign (tumor increasing, but not harmful) and malignant (cannot be managed, it causes death) classes using a two-way classification algorithm is shown in this study. The two-way classification data mining algorithms are utilized because there are not many abnormal mammograms. The first classification algorithm, k-means, divides a given dataset into a predetermined number of clusters. Support Vector Machine (SVM), a second classification algorithm, is used to identify the optimal classification function to separate members of the two classes in the training data
Self-advocacy among university students

The current research aims to know: Self-advocacy among university students.In order to achieve the objectives of the research, the two researchers chose the research sample from the students of the University of Babylon, according to the simple random method, five colleges, two humanities and three scientific colleges, and applied the Thomson equation to extract the sample size, and the result was that the research sample reached (378) male and female students were distributed according to the method of random stratification with a proportional distribution.The self-advocacy measurement tool and Harris definition (Harris, 2009) and its self-advocacy measurement tool, which consists of (48) items distributed over five domains, were applied to the research sample.On the research sample, and after conducting the statistical analysis and extracting the validity and reliability values of the scale, the research reached the following results: University students have self-advocacy.In light of this, the researchers reached a set of proposals and recommendations.
Multi-Factor Biometric Authentication Framework to User for Securing Digital Transaction

The recent growth of digital transactions over the internet is high. This is the newest topic which is to be implemented as well as need to study related with the security. The development of technology we need to improve and build the safe authentication scheme over the internet. The safe authentication scheme is protecting the user valuable information against security threats. While performing digital transactions over the internet, the protection is turn into high importance and demand for authentication is more necessary than before. In the traditional authentication there are so many problems such as username, password &amp; so on. Also, intruders will crack various ways of hacking user information, passwords including brute force attack, shoulder surfing, dictionary attacks, etc.&#x0D; This paper is useful to performing a secure authentication scheme using different fusion techniques for digital transactions. The propose system approach here combines user details as like user name, password, OTP and biometrics (traits as like fingerprint, face &amp; iris) fusion verification for a more valid user authentication. Our proposed point of view has been initiating to improve security benefits for various forms of invasion &amp; authentication layers turn on password-based attacks.
PROFESSIONAL COMPETENCY PROFILE OF DESIGNING MATHEMATICS LESSON PLANS FOR GRADE 3 IN THE DIRECTION OF STEM EDUCATION

In the current era of Industrial Revolution 4.0, the development speed of science and technology in the direction of STEM education is increasing day by day; the amount of scientific knowledge is produced at an increasingly rapid rate; Occupational structure in society is also changing rapidly... requiring people to have enough capacity to adapt the world context and the needs of society. Those competencies can be formed and developed through the STEM education model. In this article, we propose the process and illustrate the lesson plan for Maths subject based on theoretical research on STEM education and the reality of STEM teaching Mathematics in Grade 3 in some primary schools in Vietnam.
Use of Bluetooth contact tracing technology to model COVID-19 quarantine policies in high-risk closed populations

Containment measures in high-risk closed settings, like migrant worker (MW) dormitories, are critical for mitigating emerging infectious disease outbreaks and protecting potentially vulnerable populations in outbreaks such as coronavirus disease 2019 (COVID-19). The direct impact of social distancing measures can be assessed through wearable contact tracing devices. Here, we developed an individual-based model using data collected through a Bluetooth wearable device that collected 33.6M and 52.8M contact events in two dormitories in Singapore, one apartment style and the other a barrack style, to assess the impact of measures to reduce the social contact of cases and their contacts. The simulation of highly detailed contact networks accounts for different infrastructural levels, including room, floor, block, and dormitory, and intensity in terms of being regular or transient. Via a branching process model, we then simulated outbreaks that matched the prevalence during the COVID-19 outbreak in the two dormitories and explored alternative scenarios for control. We found that strict isolation of all cases and quarantine of all contacts would lead to very low prevalence but that quarantining only regular contacts would lead to only marginally higher prevalence but substantially fewer total man-hours lost in quarantine. Reducing the density of contacts by 30% through the construction of additional dormitories was modelled to reduce the prevalence by 14 and 9% under smaller and larger outbreaks, respectively. Wearable contact tracing devices may be used not just for contact tracing efforts but also to inform alternative containment measures in high-risk closed settings.
Deep-Neural Networks as feature extractors and monolithic neural networks as classifiers, for classification of uterine cervix cancer cases

With the advent of deep neural networks, application of machine learning in multidisciplinary problems enhanced many folds. Many unsolvable problems previously sought as complex to compute are now made solvable by deep neural network techniques. Problems like protein folding by Alpha-fold and Alpha-Go are prime examples.&#x0D; In this study six well known convolutional neural networks are applied for the classification of uterine cervix cancer cases for both seven class and two class classification. A primary dataset was also created by collecting raw slide samples form the leading medical institutes. The machine learning techniques do require set of well-crafted feature values representing the ground truth. Many times, these features fail to represent the ground truth. The deep neural networks can extract all the relevant features itself and those extracted features are used for final classification. In this work the convolutional neural networks are used for extraction of features which are the used for training shallow neural networks. The shallow neural networks used are Levenberg Marquardt neural network, One Step Secant and Scaled Conjugate gradient descent. The results indicated that among the 6 convolutional neural networks the ResNet50 is best and among the three shallow neural network Levenberg Marquardt is best for both seven and two class classification. The duo (ResNet50 and Levenberg Marquardt) produced a classification accuracy of 82.92%. Among all the classes of diagnosis, class 7 has the best F-value followed by class 1, whereas class 4 has the lowest F- value followed by class 5 and class 2. Lowest F-value indicates maximum misclassification. For two-class classification, duo (ResNet50 and Levenberg Marquardt) produced classification accuracy is 94.77%. The F-value of both the classes is above 92% for all the combination of CNN and shallow neural network.&#x0D; The results do conclude that the deep neural networks can easily classify the cases of cervical cancer with notable accuracy, without feature extraction.
A digital signature scheme based on problem of solving polynomial congruence in residue class modulo n

Many digital signature schemes have been proposed based on different mathematical problems. Some of them are based on factoring into primes, discrete logarithm problem, elliptic curve discrete logarithm problem, lattice problem, multivariate quadratic problem etc. In this work a signature scheme is proposed which security is based on problem of solving polynomial congruence modulo some positive integer. In proposed scheme degree of polynomial is not fixed, so degree can be changed to increase security. It is also efficient because in all the phases modulo addition and multiplication are used and no need to calculate higher exponent
METHODOLOGICAL ASPECTS OF DIALECTS OF EPISTOMOLOGICAL INTEREST (ON THE EXAMPLE OF MATHEMATICS)

Methodological aspects of dialects and education epistemological interest to mathematics are considered in this article. The latter is defined as the management of the process of its forming (developing). The problem being research now is especially relevant today because it is the cognitive interest that can be considered as an indicator of personality development. The result of the experiment indicates that a teacher of Mathematics experiences certain difficulties in solving set tasks.
THEORETICAL ASPECTS OF FORMING PROFESSIONAL AND LEGAL COMPETENCE OF CADETS AT A DEPARTMENTAL EDUCATIONAL ORGANIZATION

The article reveals the theoretical aspects of forming professional and legal competence of cadets at a departmental educational organization. It examines methodological approaches to solving this problem, clarifies and concretizes the conceptual and categorical framework, proposes a theoretical model for forming cadets’ professional and legal competence at a departmental educational organization, which allows you to systematize and holistically present the essence of this process, describes criteria-based assessment tools to determine the formation of this phenomenon in students.
Broad Federated Meta-Learning of Damaged Objects in Aerial Videos

We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge-to learn damaged objects in aerial videos.A meta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning.Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system.The study results indicated that the object classification accuracy is up to 90% and the average time cost in damage detection is only 0.277 s.Consequently, the broad federated meta-learning system is efficient and effective in detecting damaged objects in aerial videos.
3D Geography Course Using AR: The Case of the Map of Greece

Augmented reality (AR) is utilized alongside game-based learning to increase student engagement in blended learning environments. This paper describes an AR-based edugame designed to improve learning the geography curriculum in Greek elementary schools. The game utilizes two interaction techniques: via a virtual hand model and via a touch screen. Moreover, 3D objects represent prefectures and geographic regions to provide a more extensive presentation of a map. The game is a puzzle-like game with an additional phase of matching names to the puzzle pieces. Players move the 3D objects to complete the puzzle and choose provinces to continue the game by locating counties. Furthermore, this paper investigates the impact of Felder Silverman learning style and tests learning outcomes with the use of AR-based edugame as a learning object.
A Reference Architecture for Supporting Multidimensional Big Data Analytics over Big Web Knowledge Bases: Definitions, Implementation, Case Studies

This paper focuses attention on the problem of supporting multidimensional big data analytics tools over Big Web Knowledge Bases. These big data repositories are generated from web systems built on top of big datasets, as in several real-life examples such as web interfaces to social networks, e-government systems, smart city tools (e.g., for weather forecasting), and so forth. The problem of extracting actionable knowledge insights from big web knowledge bases is a relevant challenge now. In line with this emerging research field, the paper proposes models, issues and research trends in the context of multidimensional big data analytics over big web knowledge bases, along with a reference architecture that impersonates this paradigm.