Reviewerly/exHarmony huggingface dataset:
Digital fluency – a dynamic capability continuum

The impact of digital technologies on the ways people work, learn and live has been debated and researched for half a century. Digital literacy approaches have recurrently been the focus of educational and industry learning; however, current framings of digital literacy are not sufficient to support students’ digital capability development, nor do static digital literacies reflect the dynamic and contextual nature of digital capabilities. A digital capability continuum that fluidly moves between digital foundation skills, digital literacies and digital fluency is a more robust model for education. By unpacking the digital capability continuum and responding to both learning and curriculum paradigms, this paper expands on an earlier framework (Coldwell-Neilson, 2020), the decoding digital literacy framework, as well as building on our research and academic experiences, to inform higher education. A key agenda is that the higher education sector frames digital fluency as a mindset and an attitude. The model and framework underscore that capabilities need to be flexible and transferable across technologies, disciplines and the world of work.
Strategic Implementation of AWS Security Services: A Focus on Best Practices, SSM and Secrets Manager

Purpose: As organizations increasingly migrate to the cloud, ensuring robust data security becomes paramount. This technical paper explores the purpose, methodology, findings, and unique contributions in cloud security, focusing on AWS Systems Manager (SSM) and Secrets Manager. The objective is to provide a comprehensive guide for engineers and architects seeking to enhance data security in their cloud environments.&#x0D; Methodology: The comparative study conducted involves analyzing the features, capabilities, and integration possibilities of AWS SSM and Secrets Manager. Real-world use cases, best practices, and security measures associated with these services are explored through a methodology that includes hands-on exploration, case studies, and a survey of existing literature to distill critical insights.&#x0D; Findings: The paper uncovers a rich landscape of security measures facilitated by AWS SSM and Secrets Manager. Findings highlight the strengths and limitations of each service, emphasizing the importance of their integration for a holistic security approach. Automated rotation of credentials, encryption options, IAM policies, and monitoring strategies emerge as critical findings, contributing to the overall understanding of secure cloud data management.&#x0D; Unique contributor to theory, policy, and practice: This work uniquely provides a detailed comparative analysis that empowers engineers and architects to make well-informed decisions. The paper offers insights into the nuances of AWS SSM and Secrets Manager, enabling professionals to tailor their security strategies based on specific use cases and requirements. The focus on real-world scenarios and identifying best practices make this contribution practical and applicable, serving as a valuable resource for those navigating the complex landscape of cloud security engineering and architecture.
REVIEWS OF ACOUSTICAL PATENTS

The purpose of these acoustical patent reviews is to provide enough information for a Journal reader to decide whether to seek more information from the patent itself. Any opinions expressed here are those of the reviewers as individuals and are not legal opinions. Patents are available via the internet at the USPTO website.
Efficient Selection Methods in Evolutionary Algorithms

Evolutionary algorithms mimic some elements of the theory of evolution. The survival of individuals and the possibility of producing offspring play a huge role in the process of natural evolution. This process is called a natural selection. This mechanism is responsible for eliminating poor population members and gives the possibility of development for good ones. The evolutionary algorithm - an instance of evolution in the computer environment also requires a selection method, a computer version of natural selection. Widely used standard selection methods applied in evolutionary algorithms are usually derived from nature and prefer competition, randomness and some kind of ``fight'' among individuals. But computer environment is quite different from nature. Computer populations of individuals are usually small, they easily suffer from a premature convergence to local extremes. To avoid this drawback, computer selection methods must have different features than natural selection. In the computer selection methods randomness, fight and competition should be controlled or influenced to operate to the desired extent. Several new methods of individual selection are proposed in this work: several kinds of mixed selection, an interval selection and a taboo selection. Also advantages of passing them into the evolutionary algorithm are shown, using examples based on searching for the maximum α-clique problem and traditional TSP in comparison with traditionally considered as very efficient tournament selection, considered ineffective proportional (roulette) selection and similar classical methods.
An algorithm for optimal control of a closed labor market system at a given time interval

The ratio of supply and demand in the labor market is an important criteria for the effectiveness of the functioning of the economic system at the macro level. Improving the effectiveness of employers' personnel policy requires the development of new control tools and forecasting of dynamic labor market processes at the macro and micro levels. The problem of optimal control of the previously proposed closed labor market system is considered. The problem of optimal control of a system described by a system of nonlinear differential equations is formulated in general form. Aim. Finding piecewise constant control values at which the price-quality ratio in the labor market will be maximum over the next three years. Materials and methods. According to the concept of the model, the subjects of the labor market are divided into three categories depending on the amount of demand for their labor: subjects of high, low and medium qualifications. A hypothesis has been adopted according to which vacant places in the labor market are considered to be occupied by low-skilled subjects. Due to the previously proven uniqueness of solving the problem of optimal control of a closed labor market, a greedy algorithm for finding the maximum value of the price-quality ratio in the labor market is justified. Using Wolfram Mathematica, according to the method found earlier, the formulated optimal control problem was solved at each time half-interval. Results. Control values have been obtained at which the price-quality ratio in the labor market of the village of Sylva in the Perm Territory will be maximum over the next three years. The results substantiate the recommendation to introduce stricter requirements for applicants for vacant positions at the city-forming enterprise of the village of Sylva in the Perm Territory.
Deepfake Creation and Detection of Multimedia Data

As the increasing number of deepfake content poses a growing threat to multimedia integrity, this paper proposes a robust deepfake detection approach based on a hybrid architecture. The proposed framework combines the power of Residual Networks (ResNet) for spatial feature extraction and Long Short-Term Memory (LSTM) with Convolutional Neural Networks (CNN) for temporal dependency modeling. The ResNet component captures complicated patterns in facial and contextual information, whereas the LSTM-CNN module identifies dynamic facial expressions and movements across multiple frames. Transfer learning strategies are used to improve model generalization by combining pre-training on a large dataset with fine-tuning on deepfake data. Experimental evaluations on a variety of deepfake datasets show superior accuracy, precision, and recall, demonstrating the hybrid architecture's efficiency in dealing with the evolving challenges posed by more advanced deepfake generation techniques. In conclusion, our novel deepfake detection framework employs an effective combination of ResNet and LSTM-CNN, demonstrating a promising solution that not only advances the state-of-the-art in multimedia forensics but is also resistant to adversarial attacks. This hybrid model, which effectively combines spatial and temporal information, has the potential to significantly improve the accuracy and reliability of deepfake detection systems in the face of emerging digital threats.
An Emotional Artificial Intelligence Based Attendance System for Class Room

Attendance through a camera using Artificial Intelligence (AI) and Deep Learning (DL) is a mod-ern approach to monitoring and tracking attendance in various settings such as schools, offices, and man-ufacturing facilities. It uses computer vision techniques and deep learning algorithms to automatically detect and identify individuals in an image or video captured by the camera. The process typically in-volves training the system on a dataset of labelled images of the individuals who will be taking attend-ance. This training data can include images of the individual's face, iris, or fingerprints, depending on the specific approach used. Once the system is trained, it can then use this knowledge to recognize these in-dividuals in new images captured by the camera. When an individual is recognized, the system can log their attendance in a database or other record-keeping system. This can be done in real-time, allowing for immediate tracking of attendance, or it can be done at a later time for batch processing. However, AI has been expanding its horizon and face recognition with deep learning techniques can be augmented with emotion recognition as well. Sometimes, students feel really sad and overwhelmed by their school work and other responsibilities. They might feel like they can't keep up or that they are not good enough. When these feelings last for a long time, it's called depression. Depression can make it hard for students to do their school work, be with friends and family, or even get out of bed in the morning. When students are feeling really sad and hopeless, they might think about hurting themselves or ending their lives. This is called suicide. It's important to know that suicide is preventable and there are people who can help. It's important to take care of our mental health, just like we take care of our physical health. If you ever feel sad or overwhelmed, it's important to talk to someone you trust and get help. One way to prevent suicide is to detect if someone is feeling sad and hopeless, which can be a sign of depression. One way to do this is by using a CCTV camera and AI and DL technology to analyze the person's facial expression, body language, and speech patterns. Emotional AI and DL can be a powerful tool in detecting depression but it's not a substitute for a professional diagnosis. It's always advisable to consult with a mental health pro-fessional if you suspect that you or someone you know may be struggling with depression.
Impact of Stress on Eating Behavior among College Students

The study explores the impact of stress on eating behavior among college students, noting that stress can lead to unhealthy dietary choices. The transition from school to college life is highlighted, emphasizing the influence of factors like parental expectations, peer pressure, and adjusting to a new environment on students' eating habits. The research, conducted in Pune, Maharashtra, involved a cross-sectional questionnaire study with 247 participants.The results indicate a variety of dietary behaviors, with differences based on living arrangements. Notably, 39.3% of participants reported undereating during stress, and coping strategies varied, including phone use and emotional eating. Lack of social activities and poor academic performance were identified as common stressors. The study suggests a need for college support and interventions, advocating for mandatory psychological counseling and stress management seminars to promote healthier eating habits among students, especially those in hostels or living alone.In conclusion, the study explores the correlation between stress and dietary habits among students, revealing diverse patterns influenced by living conditions. Recognizing the impact of living environment on long-term health, the study emphasizes the need for college support and interventions to address unhealthy eating habits, particularly among hostel-dwelling and solitary students. Advocating for mandatory psychological counseling in colleges and conducting seminars on stress management and healthy eating habits, the study emphasizes the importance of initiating stress management before college to safeguard students' ability to adopt and maintain healthy dietary practices for long-term well-being.
The Issue of Malaysian Art Culture Inspired for Fashion Design Course

Malaysia is a multi-ethnic and multi-religious society country with a rich artistic and cultural background. This diversity contributes to significant inspiration in creating cultural heritage such as clothing, dancing, art sculptures, weapons architecture and diets for the civilization. Therefore, the study on the influence of Malaysian art culture towards Fashion Design students’ inspiration was conducted on Malaysian Polytechnic second to fifth-semester diploma students majoring in Fashion Design and Clothing. The objective is to facilitate the student by measuring the tendency to be inspired by suitable Malaysian art culture characteristics elements and principles while maintaining its authenticity during producing and developing the research for their fashion design portfolio. The study is quantitative and implemented a set of questionnaires as its research instrument that was distributed to 92 respondents as its data collection techniques. It was found that 96% of them agreed with the design framework proposed during the design development process to achieve the purpose of the syllabus. The overall result will be used to upgrade the guidelines and improvements in the syllabus to achieve the conditions of sustainability in Quality Education Practices (SDG4) and Decent Work and Economic Growth (SDG8) as recommended in the SDG17 plan.
Curriculum and Pedagogy in Indian Knowledge System: A Relevance Educational Practice for Today's Educational Scenario

India has had a rich tradition of education and learning since ancient times, especially during the Renaissance period, which was the golden age of Indian culture. The principle of karma holds great importance in the Indian education system and has evolved during the transition. The four Vedas, namely, Rigveda, Yajurveda, Samaveda and Atharvaveda were configured through ideals, practices and conduct. In the Vedic period, two methods of teaching were prevalent, first, the oral/verbal method, and second, the method based on thinking (contemplation). Bloom's taxonomy defines three domains: learning, cognitive, affective, and psychomotor. Ancient education systems are also based on three domains, developing higher- order learning by building lower-level cognitive skills. Ancient to modern education. The current Higher Education 2020 also suggests a multidisciplinary approach on similar lines. The Vedic period saw teaching being student-centred. Listening, understanding and discussion method of oral work, reasoning used gave rise to generalization and application of knowledge. Santiniketan which follows the philosophy of Naturalism in its curricular dealings. Many other experiments of Gurukul's system are being explored in different parts of the country. It becomes imperative that we learn from our past and adopt ways of designing our curriculum and pedagogy to meaningfully address societal needs. What are the academic subjects from the past that can be included in the present day curriculum? How can the curriculum be based on Indian teachings? What instructional strategies can be integrated from the past? Through the answers to these questions, we can prepare better citizens for an advanced society by introducing an educational practice relevant to today's educational scenario. Because in ancient times, educational practices included such subjects which have relevance even today.
Are The Sudanese Banks Financially Sound?

This study attempts to investigate whether the banks operating in Sudan are solvent and financially sound as well as to examine whether there are roots for a banking panic. The research is also intended to determine whether there are candidate bankrupt banks. The study employs quantitative and qualitative research methods and utilizes both secondary and primary data and covers the eight-year period 2013-2020. The annual audited financial reports of banks published for the period under study represent the source of the secondary data and the primary data is collected through questionnaires distributed to depositors. The sample comprises 30 banks out of a total population of 37 banks. Also, responses from 416 participants in the questionnaire are considered for constructing the depositors’ confidence index. To test the hypotheses a number of quantitative models, namely, univariate financial ratios models, Ahmed (2003) Z-score model, Altman (2002) emergent markets Z-score model, and depositor’s confidence index (DCI) model are utilized. The statistical results of three out of the four models, namely, the univariate financial ratios model, Ahmed (2003) Z-score model, and depositor’s confidence index, document that banks operating in Sudan are financially unsound and financially distressed and none of those banks is thoroughly healthy. However, the results of EM Z-score model show that banks operating in Sudan can be categorized as healthy and financially sound and that there are no roots for a banking panic in the country.
Application of the AIGC Large Language Model in College English Writing Teaching

In the present era of the internet, the use of artificial intelligence is gradually permeating into higher education, especially in foreign language teaching. The co-teaching model of artificial intelligence is becoming an irreversible trend. This paper deeply studies the AIGC large language model and uses the GPT-4 version to design a university English writing class and use it for auxiliary teaching after much experimentation. The author designs the class around the theme of “travel” according to the unit teaching objectives and lesson teaching objectives. Using the dialogue mode and text analysis function in the language model, the Prompt that meets the classroom needs is designed and implemented in each link of the classroom writing teaching. The process gradually builds scaffolding to train students’ abilities to absorb, select, analyze, and integrate information. After writing, AI is used for evaluation, and the language model’s usage in each link realizes the human-computer collaboration in classroom teaching. Through practical teaching, the author concludes that the AIGC large language model has a helpful role in improving undergraduates’ English writing ability. It can effectively enhance students’ ability to organize information, and the students can quickly summarize titles and write an article outline. The language model significantly improves students’ word spelling, grammar structure, paragraph development, article layout, and logical structure.
"Heterogeneity and Criticality in Neural Networks: A Computational Model of the Griffiths Phase"

This article presents a computational exploration into the dynamics of neural networks, focusing on the manifestation of heterogeneity and critical behavior akin to the Griffiths phase observed in condensed matter physics. Drawing inspiration from the rich tapestry of neural activities within the human neocortex, we develop a simplified model to investigate how networks with diverse propensities for critical behavior can exhibit complex, adaptive functionalities. Utilizing an Erdős–Rényi random graph as the foundation, we introduce a proportion of nodes designated as "critical," possessing a higher likelihood of activation, to simulate the nuanced interplay between different regions of a theoretical brain network. Through iterative simulations, we examine the activation patterns over time, paying close attention to the influence of critical nodes on the overall network dynamics. The model integrates fundamental principles from network theory and neuroscience, offering insights into the potential mechanisms underlying the brain's remarkable ability to process information efficiently, adapt to new stimuli, and maintain robustness in the face of perturbations. Our findings highlight the importance of heterogeneity and criticality in enhancing the computational capabilities of neural networks, providing a conceptual bridge between theoretical physics and cognitive neuroscience. This work contributes to a deeper understanding of the brain's operational principles and opens avenues for further research into the applications of criticality and Griffiths phases in artificial intelligence and neural network design.
Lesson Learned from the Initial Phase of the Development Process of CEFR-Based Uttaradit Rajabhat University Test of English Proficiency

Background and Aims: The development of the CEFR-Based Test of English Proficiency enriches the broader field of language assessment. By sharing valuable insights into the complexities of aligning assessments with international frameworks. Thus, this academic article aims to present the lesson learned during the initial phase of the development process of the CEFR-Based Uttaradit Rajabhat University Test of English Proficiency (URU-TEP). Linking the URU-TEP to the Common European Framework of Reference for Languages (CEFR), widely used as a reference in English as a Foreign Language (EFL) education, is crucial if test results are to be interpreted under the CEFR levels. Methodology: This study used the method of studying documents and related research, using thematic analysis and presenting descriptive information. Results: the finding is divided into four sections: the current state of the English proficiency test at URU, the process of developing the five-skill URU-TEP based on the CEFR, the process of obtaining test validity and reliability, and opportunities and challenges. Conclusion: The essence of the discovery is to enhance understanding in various areas. of the English proficiency testing process at Uttaradit Rajabhat University It also emphasizes consideration of the validity and reliability of the test to support quality and improve the examination process in the future.