A COMPARATIVE REVIEW OF DATA ENCRYPTION METHODS IN THE USA AND EUROPE

Abstract:
Data encryption is a critical aspect of modern information security, and understanding the approaches taken by different regions is vital for a comprehensive analysis. In the United States and Europe, data encryption methods vary in implementation, legal frameworks, and overall priorities. In the United States, encryption methods are primarily governed by a combination of federal laws and industry standards. The National Institute of Standards and Technology (NIST) plays a central role in recommending cryptographic standards, while the Department of Commerce oversees export controls on encryption technology. The focus in the U.S. is on balancing national security needs with individual privacy rights. The tension between law enforcement's desire for access to encrypted data for criminal investigations and the right to privacy has sparked debates and legal battles. On the other hand, Europe has taken a more privacy-centric approach to data protection. The General Data Protection Regulation (GDPR) is a cornerstone in the European Union's efforts to safeguard individual privacy rights. GDPR mandates the use of encryption to protect personal data, and failure to implement adequate measures can result in hefty fines. European countries also emphasize the importance of end-to-end encryption in communication services to ensure confidentiality. Both regions prioritize encryption, but their approaches reflect different values and legal philosophies. The U.S. tends to navigate a delicate balance between national security and individual rights, while Europe places a stronger emphasis on the protection of personal data as a fundamental right. In terms of technological implementation, the encryption algorithms adopted in both regions are often aligned with global standards. Advanced Encryption Standard (AES) is widely accepted and implemented in various sectors. However, the choice of key management and the level of regulatory oversight differ, contributing to the nuanced landscape of data protection. In conclusion, a comparative review of data encryption methods in the USA and Europe reveals the complex interplay between security, privacy, and legal frameworks. Understanding these differences is crucial for multinational organizations and individuals navigating the intricate landscape of global data protection.
 Keywords: Data, Encryption, USA, Europe, Review.


Mass Communication Strategy in Encouraging the Adoption of a Healthy Lifestyle in Society

Abstract: Healthy lifestyle has become the main focus in efforts to prevent the increasing prevalence of non-communicable diseases in society. In this context, mass communication strategies play a central role in forming awareness and encouraging the adoption of a healthy lifestyle. This research aims to explore and analyze mass communication strategies that are effective in changing people's behavior regarding healthy lifestyles. The main objective of this research is to identify the most effective mass communication strategies in encouraging the adoption of healthy lifestyles in society. In addition, this study aims to evaluate the impact of this strategy on the level of awareness and behavior change regarding various aspects of a healthy lifestyle. This research uses the literature review method as an analytical framework. Data was collected from various sources such as scientific journals, articles and books related to mass communication strategies and healthy lifestyles. Data analysis involves detailing the various mass communication strategies that have been implemented and identifying patterns of success in changing people's behavior. the use of N.Vivo analysis can help in identifying patterns, trends, and relationships between various concepts and findings that emerge from these articles. \The research results show that mass communication strategies that rely on interesting narratives, effective use of social media, and involvement of community leaders have a significant impact in encouraging the adoption of a healthy lifestyle. The ability to create community engagement and respond to local cultural dynamics also plays an important role in the success of this strategy.


Causal relationship recognition algorithm based on external semantic and contextual structural features

Abstract: Identifying the causal relationship of events plays an important role in determining the development of known events and evaluating the possible outcomes of different decisions. At present, neural network models are widely used to identify the relationships between events. Based on obtaining events, researchers distinguish the relationships between events by mining their semantics. However, due to the complexity of events and the dynamic changes in relationships between events, models often cannot fully meet the needs of accurately identifying causal relationships by only learning simple event descriptions in sentences; Moreover, focusing too much on the events themselves often leads to neglecting the structural features of statements and neglecting the impact of specific structural patterns on the relationships between events. In this article, we propose an event masking algorithm that combines external semantics to address the aforementioned issues. In this algorithm, external semantics are first introduced into the statement to enrich the information behind the event, allowing the model to mine the deep connections between events through a wider range of background knowledge; Then, the event masking module is used to enhance the model's extraction of sentence structured features, mining specific contextual representations that are unrelated to the event. The results show that compared to existing neural network algorithms, the algorithm proposed in this paper improves the F1 value of predictions on publicly available datasets by more than 4%.


Design research on an online summer school in mathematics education

Abstract: Design research in education pursues two goals simultaneously: the development of a design and a local theory that clarifies how the design can achieve the intended effects and under what conditions. The results are obtained in an iterative cyclical process including both the implementation of the design and the research on its implementation. Each cycle contains a prospective phase, in which the design is determined, and a reflective phase, in which the implementation of the design is examined. The nested connection of theory and design development leads to a reciprocal relationship between the two final products. This article introduces an example of design research from mathematics education, in which an online summer school for doctoral students was to be framed in response to the sudden lockdown situation at the outbreak of the Covid-19 pandemic. At short notice, the original summer school was moved into the virtual space of a digital conference system resulting in new framework conditions; previous design principles were adapted and a rhythmical organization of space and time was theorized and implemented. Rhythm analyses show that design decisions to realize ontological, epistemological and axiological commitments intertwined and thus fostered students’ interactive learning processes. Consequently, the suggestion is made to merge the three commitments into a three-dimensional framework concept of ethico-onto-epistemological commitments. Such a framework concept would have the function of reconciling ethical, ontological and epistemological dimensions in design research in the pursuit of robust teaching-learning processes and incorporating the ethical responsibilities of design researchers conceptually for both design and local theory.


Use of Rubber Fruit Seeds in Making Fine Artworks at SMA Negeri 1 Payaraman

Abstract: Rubber plants are very easy to find around SMAN 1 Payaraman because they are widely cultivated by the community around the school. One of the parts contained in the rubber plant is the rubber fruit seeds. Rubber fruit seeds are covered with a hard-textured seed coat, brown, and have a distinctive pattern. The inside of the rubber fruit seeds is usually processed as a food source by the people in the Payaraman area. Meanwhile, the shells of rubber fruit seeds, which have a hard, brownish texture, are only waste and no one has used them yet. The purpose of this research is to find out the process of using rubber fruit seeds in the creation of works of art. In making works of art using rubber fruit seeds, researchers are divided into several stages, namely; determining the theme, determining the type of work, determining the technique of making the work, preparing tools and materials, and making works of art based on the techniques students like. The result of this study is that students can create various works of art from rubber seeds. The types of works of art made from rubber seeds that were made by students were: paintings, key chains, flowers, wall hangings, and ant figures. Utilizing rubber fruit seeds in making works of art, can increase the knowledge of teachers and students to use various kinds of media that exist around the school so that they can become works of art which will certainly add to the selling value of a work of art.


Keynote Talk: How DSN is Contributing to Social Impact in Africa

Abstract: DSN-Data Scientists Network (formerly Data Science Nigeria) is committed to raising 1 million AI talents and building AI solutions that improve the quality of life and wellbeing of 2 billion people in an emerging market. DSN is the No 1 Artificial Intelligence learning community and solution delivery network in Africa. We won the Matthai Impact Award at Deep Learning Indaba 2019. One of DSN’s papers on the use of AI to address financial inclusion was the best poster at the 21st edition of the ACM Conference on Economics and Computation (EC’ 20), the world’s premier conference on the interface of economics and computer science, organized by the Association for Computing Machinery, New York, United States. 2 Products initiated, conceptualized and developed by DSN were selected in the 2021 Global Top 100 AI Products by UNESCO IRCAI. DSN-Data Scientists Network (formerly Data Science Nigeria) is building cross-country AI learning and research communities, developing AI solutions that enhance the quality of life and wellbeing of 2 billion people in emerging markets such as CareerTech, FinTech, RetailTech, EdTech, PlatformTech and HealthTech. DSN’s work has won academic and social impact awards across the world which has been showcased as a best practice in delivering scalable learning, research and social good solutions development. We released the 1st Artificial Intelligence and Python Book for elementary students and beginners in Africa and we recently opened a dedicated AI Research Lab Hub in Yaba, Lagos for Social Impact. Our renewed focus will see us delivering on our vision through a renewed focus on 3 core areas; A community for learning and research Product development for social impact Partnerships for solution delivery


The Effects of Chromebook Use on Student Engagement

Abstract: This action research project is intended to determine the effects of Chromebook technology on student engagement in the secondary classroom. The study involved a classroom of first-year students enrolled in a health course. These students used Chromebooks for the first time as part of the one-to-one (1:1) initiative. Chromebooks were utilized as a part of the curriculum, incorporating daily lessons, teaching tools, applications, and various activities for student learning. Data collection methods included observations, teacher journals, and field notes. On-task and off-task behaviors were noted, and the results of the data analysis showed the level of engagement involved when using Chromebooks in the classroom. Levels of engagement were explored through a six-week study in a 9th-grade classroom comprised of 28 students using Chromebook one-to-one. Engagement included focus, interest, participation in the lesson and activity, and positive social interactions between the teacher and classmates. This action research investigation indicated that incorporating Chromebook technology into lessons effectively increased student engagement and motivation.


Project of implementation strategies formation by alternation in education media técnico profesional

Abstract: Technical-vocational education is one of the fundamental pillars that governs the educational system, and its strengthening is a task assumed by the Ministry of Education through the implementation of the National Policy on Technical-Vocational Training implemented through the National Strategy for Technical-Vocational Training. There are multiple challenges involved in carrying out actions to promote and generate instances of participation and decision-making that enable the involvement of all actors to articulate the educational system and economic development. In this sense, professional technical high schools with a specialty in preschool care assume a strategic and preponderant role in the education sector, since the young people who study the specialty put into practice what they have learned in classrooms and workshops allow them to effectively insert themselves into the world of work, access their professional practice and insert themselves into higher education. And, in most cases, to be an agent of change at the family level to improve the socioeconomic reality of their homes. For this reason, Chile needs to have technicians in the care of middle schools that will allow it to address the challenges of its own development and its growing participation in the preschool care system, with demands of greater added value, dynamic and challenging. In this scenario, alternation training will not only strengthen the specific technical competencies of the specialty, but also the capacity for innovation, entrepreneurship, teamwork, and a set of transversal competencies that will prepare students to work in multiple areas of education.


Fast and Secure Image Encryption System Using New Lightweight Encryption Algorithm

Abstract: The rapid evolution of network technologies, where computer vision and the Internet seamlessly merge, poses significant challenges in safeguarding end-user data. To address the growing need for secure data in smart environments, lightweight and efficient security solutions are in high demand. Notably, the Trivium, a well-known encrypt stream cipher (eSTREAM) project, has been the target of several attacks. This paper proposes an innovative approach for fast and highly secured image encryption using byte scrambling and a modified version of the Trivium algorithm. The Henon map is utilized to generate random numbers for permutation, reducing time and increasing security. Performance is measured on 100 colour images with different several tests. The results present a fast and robust security system for the transmission process. It is more efficient than the traditional Trivium method. The encrypted-decryption time is reduced by 1:24 times, making it a quick security system for surgical telepresence and multimedia visuals.


An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework

Abstract: The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.


Exploring Multidimensional Embeddings for Decision Support Using Advanced Visualization Techniques

Abstract: As artificial intelligence has evolved, deep learning models have become important in extracting and interpreting complex patterns from raw multidimensional data. These models produce multidimensional embeddings that, while containing a lot of information, are often not directly understandable. Dimensionality reduction techniques play an important role in transforming multidimensional data into interpretable formats for decision support systems. To address this problem, the paper presents an analysis of dimensionality reduction and visualization techniques that embrace complex data representations and are useful inferences for decision systems. A novel framework is proposed, utilizing a Siamese neural network with a triplet loss function to analyze multidimensional data encoded into images, thus transforming these data into multidimensional embeddings. This approach uses dimensionality reduction techniques to transform these embeddings into a lower-dimensional space. This transformation not only improves interpretability but also maintains the integrity of the complex data structures. The efficacy of this approach is demonstrated using a keystroke dynamics dataset. The results support the integration of these visualization techniques into decision support systems. The visualization process not only simplifies the complexity of the data, but also reveals deep patterns and relationships hidden in the embeddings. Thus, a comprehensive framework for visualizing and interpreting complex keystroke dynamics is described, making a significant contribution to the field of user authentication.


Research on the Resilience Evaluation of Urban Rail Transit Construction Organization Based on the Cloud Matter-Element Model: A Case Study of Nanchang West Station

Abstract: In the construction of urban rail transit projects, the disturbance of equipment, sudden failure, rainstorms, and other emergencies may bring serious safety risks. Resilience theory emphasizes the ability of the system to resist, adapt, absorb, and learn from risks in the whole process before, during, and after the occurrence of risks. It is introduced into the safety management of construction organization of urban rail transit projects to describe the ability of urban rail transit projects to cope with risks in the whole process of dealing with construction risks. This study defines the connotation of the resilience of the project construction organization and uses the literature frequency statistics method to determine the resilience evaluation indexes. The game theory combination weighting method is used to determine the index weights, and the cloud matter element model is used to establish the evaluation model of construction organization resilience of urban rail transit projects. Taking Nanchang West Station of Phase 1 Project of Nanchang Line 2 as an example, the validity and accuracy of the model are verified. The results show that the resilience grade of the construction organization of the project is “higher resilience,” which is consistent with the actual survey situation, and the evaluation model is reasonable. In addition, in the evaluation results, the key indexes and risk indexes of the project are determined, and the safety management measures of the construction organization of the project are put forward according to the key indexes and risk indexes.


Advances in Architectures for Deep Learning: A Thorough Examination of Present Trends

Abstract: This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.


CYBERSECURITY DYNAMICS IN NIGERIAN BANKING: TRENDS AND STRATEGIES REVIEW

Abstract:
This paper provides an in-depth review of the cybersecurity dynamics within the Nigerian banking sector, emphasizing recent trends and strategic approaches to address emerging challenges. As a review paper, it synthesizes existing literature, reports, and case studies to offer a comprehensive understanding of the current cybersecurity landscape in Nigerian banks. The focus is on identifying the predominant cyber threats, analyzing the sector's response strategies, and evaluating the effectiveness of these measures in the context of Nigeria's unique socio-economic and regulatory environment. Our analysis reveals a notable escalation in cyber threats, particularly phishing, ransomware, and insider attacks, which have been intensified by the rapid digital transformation in banking services. The review identifies key factors contributing to these challenges, such as the increasing sophistication of cybercriminals, the digital literacy gap among customers, and the evolving nature of cyber threats. It also examines the strategic responses of Nigerian banks, including the adoption of advanced security technologies, enhanced staff training, and collaboration with government and international cybersecurity bodies.The paper concludes that Nigerian banks have made significant strides in fortifying their cybersecurity defenses. However, it also highlights the need for more robust regulatory frameworks, increased customer awareness initiatives, and a shift towards more integrated and proactive cybersecurity strategies. The findings of this review underscore the critical need for continuous evolution and investment in cybersecurity measures to effectively counter the dynamic and complex nature of cyber threats in the Nigerian banking sector.
 Keywords: Cybersecurity, Cybersecurity Dynamics, Nigerian Banking Sector, Digital Transformation, Cyber Threats, Phishing Attacks, Ransomware, Insider Threats, Regulatory Framework, Central Bank of Nigeria (CBN), Compliance Challenges, Security Technologies, Cybersecurity Awareness, Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology.


A Text Analysis Method for Student Learning Feedback on Network Teaching Platform Based on Natural Language Processing

Abstract:
With the emergence and end of the COVID-19, online learning has become an irreplaceable way of learning. In order to promote the improvement and enhancement of online curriculum resources and increase the learning effect of students, the content of curriculum evaluation is an important reference for the direction of curriculum improvement. Therefore, this article focuses on the student learning feedback of course resources. Firstly, through data collection algorithms, effective evaluation information is crawled, and then based on the collected information, the course evaluation text is annotated and classified, forming a reasonable corpus. Finally, through feature collection and sentiment analysis algorithms, sentiment analysis is performed on the evaluation content, effectively distinguishing between positive and negative evaluations, and guiding teachers to improve the course content.

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Robust Zero-Watermarking by Circular Features and 1-D NRDPWT Transformation

Abstract:
This paper introduces a secure and robust zero-watermarking framework that leverages the advantages of zero-watermarking, ensuring non-destructive modification of original images and unlimited capacity. The proposed method enables robust watermark embedding while preserving the original image. It employs a novel feature extraction approach using circular areas based on image radius, enhancing feature resilience. Additionally, applying one-dimensional non-recursive discrete periodized wavelet transform (1-D NRDPWT) converts feature values into phi, contributing to enhanced stability and robustness. Enhanced security is achieved through the use of Shuffle and Pseudo-Random Number Generator (PRNG). Experimental results, evaluated using metrics such as Bit Error Rate (BER) and Normalized Correlation (NC), validate the exceptional performance of this watermarking technique. These findings underscore the framework&rsquo;s robustness, security, reliability, and integrity against both general and geometric noise attacks, making it a secure and robust solution for modern digital image copyright protection. In summary, our method offers an effective defense against various noise attacks while ensuring the highest watermark quality without compromising the original image. It is a significant advancement in copyright protection applications.

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Effects of Assistance Media on Active Learning and Creative Thinking in Online Learning

Abstract: Online learning is the best learning strategy during the Covid-19 pandemic. However, it has obstacles for students who are still learning to adapt to internet technology. This research aims to determine the effect of using online learning strategies on students' active learning and creative thinking. The subjects of this study were 52 students of the Indonesian Language Education Study Program at the Indonesian Catholic University of Saint Paul. They are taught using the Moodle application. The experimental class was taught using media assistance in the form of WhatsApp, while the control class was not taught. Data collection uses tests and observation sheets. Data analysis used ANOVA with the help of SPSS version 27. The results showed that 1) there were significant differences in student learning activities in online learning with media assistance and students without media assistance; 2) there is no significant difference in creative thinking between students who are taught using media and students who are not given media assistance. Based on the results, findings and conclusions, suggestions will be explained for implementing future learning and further research. Especially the creation of an online learning environment for students who are still adapting to advances in internet technology.


APLIKASI SIFAT GOLONGAN ULU AL-ALBAB DAN FASIK DI ERA SOCIETY 5.0

Abstract: This study discusses the application of the ul? al-alb?b and wicked groups in the era of society 5.0 based on QS. al-Ra'd/13:19-25. This research is a library research using primary and secondary data with interpretation and sociology approaches. The results of this study are first, that the ul? al-alb?b group in QS. al-Ra'd/13:19-25 has several characteristics, namely taking lessons from everything, fulfilling promises, connecting something that is ordered to be connected, fearing painful reckoning, being patient, establishing prayer, giving alms, and repaying evil with good. Second, the wicked group in the verse are breaking promises, breaking something that is ordered to be continued, and doing damage. The three forms of application of the ul? al-alb?b group in QS. al-Ra'd/13:19-25 in the era of society 5.0, among others, using the internet wisely, as a means of connecting, as a means of giving alms, and as a means of spreading goodness. Meanwhile, the application of the wicked groups in this verse in the era of society 5.0 includes using the internet for immorality, breaking social relations or hospitality, and using it to do damage. The urgency of this research is to provide understanding to Muslims about the characteristics of ul? al-alb?b and wicked, so that they can be used to face the era of society 5.0.


A hybrid neural network based on the small-world network for inner speech recognition

Abstract: This study is devoted to the identification of human inner speech using an electroencephalogram (EEG), where inner speech refers to an individual's subjective experience of language, disconnected from discernible audible articulation. The core aim of this system is the rapid and precise classification of signals via human inner speech, thus facilitating enhanced control and interaction functionalities. The research entails a comprehensive analysis of 10 volunteers' brain activity across 128 channels from OpenNeuro's Inner speech dataset. A hybrid neural network, which incorporates the small-world network structure, is employed to model neural activity within the brain. This approach outperforms random chance and aligns with current research expectations.


Bearing fault diagnosis based on online symbolic aggregation approximation and streaming deep discriminant analysis

Abstract: Abstract Rolling bearings are one of the important components of many industrial equipment, and the timely detection of faults in these bearings significantly contributes to ensuring equipment safety. To achieve real-time diagnosis of bearing faults, this paper proposes an online fault diagnosis framework that utilizes online symbolic aggregation approximation (SAX) and streaming deep discriminant analysis. This method uses the moving window approach to segment the vibration data collected online, and then uses SAX for symbolic representation.The obtained icons are input into stacked denoising convolutional autoencoder (SDCAE) for&#xD;classification. The model consists of several denoising convolutional autoencoders (DCAEs) and a linear discriminant analysis (LDA) module. To accommodate online data changes, while fixing the main structure of the network, the streaming method is used to update the parameters of LDA. Compared with other traditional approaches for diagnosing bearing faults, the proposed method exhibits distinct advantages. Specifically, it utilizes a stable neural network model, updates classifier parameters in real-time, and demonstrates superior computational efficiency. The application results demonstrate the effectiveness of the proposed method.


Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Abstract: Abstract Underwater images can be captured either with the help of light waves or sound waves. Images that are taken underwater typically are not of optimum quality as they suffer from issues such as low contrast, blurring of detail, colour distortion, and greenish tones. Several physical processes that take place in the aquatic environment, such as light absorption, refraction, and scattering, are responsible for the existence of such degradation in underwater images. To address these challenges, numerous researchers have put forth a range of cutting-edge techniques for enhancing and restoring such degraded underwater images, with the aim of addressing these issues. These techniques primarily focus on improving visibility and enhancing the level of detail. To achieve this, we propose a method that performs White Balancing in the LAB colour space to remove the bluish-greenish tones present in the image. Next, we enhance the contrast by first converting the RGB image into HSV (Hue, Saturation, Value) and HLS (Hue, Lightness, and Saturation) colour spaces and then by using the S & V channels in HSV and L & S colour channels in HLS, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE). To control the brightness of the enhanced image, we apply Gamma Correction. Lastly, by using the method Dark Channel Prior (DCP), we separate the image’s red channel from the RGB colour space and perform the dehazing operation to get the final enhanced image. We have conducted a comprehensive qualitative analysis of our proposed approach as well as existing techniques, evaluating them objectively and subjectively through metrics such as Peak Signal-to-Noise Ratio (PSNR), Root-Mean-Square Error (RMSE), Structural Similarity (SSIM), and the Underwater Colour Image Quality Evaluation metric (UCIQE).&#xD;


Riemannian quantum circuit optimization for Hamiltonian simulation

Abstract: Abstract Hamiltonian simulation, i.e., simulating the real time evolution of a target quantum system, is a natural application of quantum computing. Trotter-Suzuki splitting methods can generate corresponding quantum circuits; however, a faithful approximation can lead to relatively deep circuits. Here we start from the insight that for translation invariant systems, the gates in such circuit topologies can be further optimized on classical computers to decrease the circuit depth and/or increase the accuracy. We employ tensor network techniques and devise a method based on the Riemannian trust-region algorithm on the unitary matrix manifold for this purpose. For the Ising and Heisenberg models on a one-dimensional lattice, we achieve orders of magnitude accuracy improvements compared to fourth-order splitting methods. The optimized circuits could also be of practical use for the time-evolving block decimation (TEBD) algorithm.


Depth_ ASPP_ UNet++: A novel 2D ESPI phase unwrapping based on convolutional neural network

Abstract: Abstract Phase unwrapping is a key step in optical metrology and physical optics to obtain accurate &#xD;phase distributions. In practice, phase images obtained from electronic speckle pattern &#xD;interferometry (ESPI) exhibit diverse and complex morphology, with significant shape &#xD;variations and non-uniform densities among different individuals. This takes challenges for &#xD;accurately extracting phase information and unwrapping the phase. With the progress of deep &#xD;learning technology in optical image processing, real-time performance and accuracy have &#xD;become concerned issues. In this paper, an ESPI phase unwrapping method based on &#xD;convolutional neural network (CNN) UNet++ is proposed. The proposed network combines &#xD;the depthwise separable convolution (DSC), atrous spatial pyramid pooling (ASPP), defined &#xD;as Depth_ ASPP_ UNet++. In this model, the use of DSC improves network computational &#xD;efficiency and provides better feature representation capability. In addition, ASPP is &#xD;introduced to pay more attention to the phase information of the phase image, and then obtain &#xD;better phase unwrapping results. The experimental results show that our proposed method can &#xD;obtain excellent results, especially with various of variable density, different noise levels, and &#xD;different speckle sizes.


A simplified Mølmer-Sørensen gate for the trapped ion quantum computer

Abstract: Abstract We discuss how to simplify the Mølmer-Sørensen (MS) gate which is used for the trapped ion quantum computer. The original MS gate is implemented by illuminating two ions with bichromatic coherent light fields separately at the same time. In this paper, we propose a method for transforming a separable state of two ions into one of the Bell states by illuminating the two ions with monochromatic coherent light fields individually and this point is the advantage of our scheme over the original MS gate. The length of the execution time of our proposed gate is comparable to that of the original MS gate, however, numerical calculations show that our proposed gate is weakly sensitive to thermal fluctuations of the phonons. By giving another example of a simple two-ion gate that can generate entanglement but is strongly vulnerable to thermal fluctuations, we show that our simplified MS gate is more marked than usual.


Addressing the Need for Interculturality in Cybersecurity Education

Abstract: This paper addresses the need for incorporating global virtual team (GVT) projects into cybersecurity education curricula in an effort to develop students’ understanding of different cultures and hone their abilities to work across multiple time zones, communicate using digital communication platforms as well as improve their virtual project and time management skills. An example of a GVTs project, Virtual Business Professional, is presented in order to illustrate how collaborative online international learning (COIL) can be embedded into IT-related coursework. It is the authors’ intention to encourage instructors and administrators at institutions of higher learning to support and carry out transdisciplinary GVT projects in order to best prepare graduates for the challenges of the 21st century global workplace.


Evaluation of AI Models to Update Cybersecurity Curriculum

Abstract: This study explores the performance of several Large Language Models (LLMs) across different facets of Cybersecurity Modules. Using prompt engineering, this work evaluates publicly available LLMs for their ability to assess the suitability of secure coding topics based on learning outcomes, categorize these topics following OWASP standards, and generate up-to-date examples for curriculum use. The findings would highlight the transformative role that LLMs would play for future advancements in Cybersecurity education.


Impact of a Cybersecurity Work-Related Course on Students' Career Thoughts and Attitudes

Abstract: This article proposes a research study conducted at Murray State University Cybersecurity and Network Management program to investigate the impact of work-related experiential learning on college students' career thoughts and attitudes within the context of cybersecurity career development. The Cybersecurity and Network Management program introduced the CNM 518 course based on the Public Infrastructure Security Cyber Education System (PISCES) project that offers practical, hands-on experiences. The proposed research project slated for Spring 2024, aims to assess how this work-related experiential learning influences students' career thoughts and attitudes, using the Career Thoughts Inventory as a measurement tool. This research project emphasizes the importance of reflective learning within CNM 518 and aims to contribute empirical evidence on the impact of work-related experiential learning on students' career thoughts and how such learning experiences positively influence the career decision-making processes and, subsequently, the broader field of cybersecurity education.


Assessing the Effectiveness and Security Implications of AI Code Generators

Abstract: Students, especially those outside the field of cybersecurity, are increasingly turning to Large Language Model (LLM)-based generative AI tools for coding assistance. These AI code generators provide valuable support to developers by generating code based on provided input and instructions. However, the quality and accuracy of the generated code can vary, depending on factors such as task complexity, the clarity of instructions, and the model’s familiarity with the programming language. Additionally, these generated codes may inadvertently utilize vulnerable built-in functions, potentially leading to source code vulnerabilities and exploits. This research undertakes an in-depth analysis and comparison of code generation, code completion, and security suggestions offered by prominent AI models, including OpenAI CodeX, CodeBert, and ChatGPT. The research aims to evaluate the effectiveness and security aspects of these tools in terms of their code generation, code completion capabilities, and their ability to enhance security. This analysis serves as a valuable resource for developers, enabling them to proactively avoid introducing security vulnerabilities in their projects. By doing so, developers can significantly reduce the need for extensive revisions and resource allocation, whether in the short or long term.


What Is Interesting and Relevant About Cybersecurity?

Abstract: Cyber attacks are a common feature of current news and many of them are the result of easy to avoid vulnerabilities in software. It is imperative that students graduating from an undergraduate Computer Science (CS) curriculum understand the consequences of vulnerable code. When developing lessons and assignments, it would be useful to have a sense of students’ attitude toward cybersecurity and appreciation of the need to write secure code. This paper describes an analysis of the results of a survey of students in core CS courses at our large public university, in which students answer free response questions about what they find interesting and relevant about cybersecurity. The survey was conducted in Fall 2022 and repeated in Spring 2023 after cybersecurity interventions were introduced into several core CS courses. We performed a Natural Language Processing (NLP) analysis of the free response answers to determine the overarching themes in the responses. We found that the most prevalent topics students are interested in are cryptography and penetration testing, and did not change over the two semesters. In answer to the question about the relevance of studying cybersecurity, we found that as students progress through the curriculum, what students find relevant moves from protecting their personal data to its importance in job duties and writing secure programs. When developing lessons and assignments, it may be helpful to introduce cryptography or penetration testing to engage students. Also, students should be taught early and often about the relevance of cybersecurity in their future job duties.


Controlled bidirectional remote implementations of partially unknown quantum operations

Abstract: Abstract By blending the ideas of controlled teleportation, bidirectional teleportation and remote implementation of quantum operations, we investigate the bidirectional transmission of quantum operations under the control of one supervisor. Firstly, we provide a method for generating a class of multi-particle entangled states, including ﬁve-particle and seven-particle entangled states, using a nine-particle entangled state as an example. Then using our constructed ﬁve-particle entangled state as a quantum channel, we propose a new controlled bidirectional protocol for remotely implementing partially known quantum operations (PUQOs for short) of one qubit within the two restricted sets satisfying speciﬁc conditions. Subsequently, exploiting a nine-particle entangled state as the quantum channel, we extend it to the case of two-qubit quantum operations which come from eight restricted sets. Replacing two PUQO of two qubits by a PUQO of one qubit and a PUQO of two qubits, we put forward another new asymmetric controlled bidirectional protocol for remotely implementing PKQOs via a seven-particle entangled state as the quantum channel. In these protocols, by using universal recovered operations performed by the receivers, the senders can simultaneously exchange their PUQOs as long as the controller collaborates, where each PUQO acts on the corresponding unknown state owned by the remote recipient.&#xD;


Unsupervised Clustering on Paleochannel Detection with Curvature-Based Attribute

Abstract: Abstract This research focuses on the application of advanced seismic data processing techniques and unsupervised clustering algorithms to accurately identify and map paleochannels in the high-resolution Maui seismic dataset. Pre-processing steps were applied to enhance data quality, followed by the calculation of a positive curvature attribute used as input for multiple clustering algorithms. The study evaluates four state-of-the-art unsupervised clustering methods and employs Principal Component Analysis (PCA) for result refinement and differentiation. By analysing the effectiveness of the algorithms, we demonstrate that Fuzzy C-means and Birch provide optimal segmentation of paleochannels. This automated approach not only overcomes the challenges of noisy data but also opens avenues for similar applications in seismic interpretation. The research underscores the significance of the unsupervised method in facilitating efficient resource exploration, emphasizing the integration of PCA to enhance segmentation accuracy and reduce human intervention. This study contributes to the sustainable management and extraction of natural resources, underscoring the potential of advanced techniques in remote sensing analysis.


THE EFFECTIVENESS OF THE JOLLY PHONICS METHOD ON LETTER RECOGNITION ABILITY IN 5-YEAR-OLD CHILDREN THROUGH LETTER CARD MEDIA

Abstract: Children aged 0-6 years are a fundamental educational period because development at this age determines development at the next age, which is determined by various stimuli provided from an early age. This research aims to determine the effectiveness of the jolly phonics method in improving the ability to recognize letters in five year old children using letter cards at TK X Surabaya. This type of research is experimental research in the form of Single Subject Research (SSR) using an ABAB design. The subject of this research was one of the TK X students in Surabaya. Data collection techniques in this research used pretest and posttest. The data obtained was analyzed using comparative descriptive. The results of the research show that the grades obtained by children have increased from before. At the first baseline the score was 2, and after the first intervention the child's score increased to 4. At the second baseline measurement the child obtained a score of 6, and after the second intervention the child's score increased to 8. Based on the research results, the use of the jolly phonics method is efficacious in improving children's letter recognition skills through letter cards for Kindergarten X Surabaya children.


Research on Teaching Practice of English for Science and Technology Based on Multimedia Technology

Abstract: The purpose of this paper is to study the practice of multimedia technology in the teaching of scientific and technical English. With the rapid development and popularisation of computer technology and the deepening of educational concepts, multimedia-assisted English teaching has become one of the most important foreign language teaching modes at present. This study firstly analyses the impact of multimedia technology on English teaching, secondly faces the advantages and challenges of multimedia technology in the teaching process, and finally explores the use and effect of multimedia technology in scientific and technological English teaching based on the data of the survey and research. The results of the study show that for college English teachers in colleges and universities, they should fully explore the advantages of multimedia technology in the process of using it and use it skillfully in teaching practice, which will have a positive effect on improving the quality of English teaching and deepening the reform of college English teaching.


A Geospatial Verification Method for Severe Convective Weather Warnings: Implications for Current and Future Warning Methods

Abstract: Abstract Legacy National Weather Service verification techniques, when applied to current static severe convective warnings, exhibit limitations, particularly in accounting for the precise spatial and temporal aspects of warnings and severe convective events. Consequently, they are not particularly well-suited for application to some proposed future National Weather Service warning delivery methods considered under the Forecasting a Continuum of Environmental Threats (FACETs) initiative. These methods include Threats-In-Motion (TIM), wherein warning polygons move nearly continuously with convective hazards, and Probabilistic Hazard Information (PHI), a concept that involves augmenting warnings with rapidly updating probabilistic plumes. A new geospatial verification method was developed and evaluated, by which warnings and observations are placed on equivalent grids within a common reference frame, with each grid cell being represented as a hit, miss, false alarm, or correct null for each minute. New measures are computed, including false alarm area, and location-specific lead time, departure time, and false alarm time. Using the 27 April 2011 tornado event, we applied the TIM and PHI warning techniques to demonstrate the benefits of rapidly updating warning areas, showcase the application of the geospatial verification method within this novel warning framework, and highlight the impact of varying probabilistic warning thresholds on warning performance. Additionally, the geospatial verification method was tested on a storm-based warning dataset (2008-2022) to derive annual, monthly, and hourly statistics.


Changes To the Management Control Function Due to The Impact of The Covid-19 Pandemic

Abstract: Since the division of management into individual functions, controlling has – despite some turbulences – been an integral part of management theory. The Covid-19 pandemic broke out in the first half of 2020 and hit the work of perhaps every manager hard. Managers had to adapt their functions, including controlling, because the crisis threatened not only the existence of the organisation but also the lives of its members. Crises also bring opportunities for the wise, and the changes they bring can become permanent. However, a literature review of academic papers revealed that this opportunity was rarely taken and that the authors paid little attention to changes in controlling. Based on a questionnaire survey of 425 organizations operating in the Slovakia (conducted in the spring of 2022), this paper presents case studies of 17 companies on how the controlling function has changed for a number of their managers due to the pandemic. The sample consisted of those companies from which more than one manager responded. Compared to the overall sample, the case study companies did not report a single significant change in function, but the proportion of larger changes was almost double that of all businesses analysed. Although in many cases managers have made changes to control and will retain these changes, the results suggest that this management function has seen only minor, or no changes and the crisis of the Covid-19 pandemic did not turn into an opportunity to optimize it.


Developing a Trustworthy Cloud Service Framework for Cloud Computing Security

Abstract: Cloud computing is quickly becoming an essential platform for sharing infrastructure, software, apps, and corporate resources. Cloud computing has many advantages, but users still have a lot of questions about the dependability and safety of cloud services. Concerns about the hazards associated with the possible exploitation of this technology to undertake criminal operations might threaten the undeniable success of cloud computing. To ensure happy customers, the cloud model must prioritize safety, openness, and dependability.Its main purpose is data security, which concerns everyone contemplating cloud services. A cloud-based assault protection system will safeguard data, communications, and information.According to studies, the recommended technique is successful, however updating tags and blocks when data is amended requires computation and communication expenses. Scalability, data secrecy, and decentralized double encryption improve security.The proposed method employs cloud servers for computation-intensive tasks and protects data content by depriving data owners and users of privilege information. Also ensures responsibility. Sharing health data on the cloud is feasible, cost-effective, efficient, adaptive, and better for individuals. This"Advanced Encryption Standard with Lightweight Cipher-text-Identity and Attribute-based Encryption" (AES-lightweight CP-ABE) aims to protect sensitive data.


Identification of Security Issues and Finding their Solution in Cloud Computing

Abstract: The advent of Cloud Computing has simplified on-demand access to IT services including data storage and administration. In addition, it seeks to secure systems and make them functional. With these benefits, there are significant security constraints for cloud providers. When it comes to cloud computing, one of the biggest obstacles is ensuring the safety of data and services. Considering this, several solutions have been put into place to boost cloud security by keeping an eye on everything from resources to services to networks to identify and stop intrusions as soon as they occur. The term "Intrusion Detection System" (IDS) refers to an improved technique used to regulate network traffic and identify abnormal activity. This paper presents the identification of Security Issues and Finding their Solution in Cloud Computing using machine learning techniques including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Multi-Layer Protocol (MLP). This model is trained and evaluated using NSL-KDD dataset. The experimental findings show the highest accuracy of 93.5% with the use of SVM model. As a result, the achieved results demonstrate strong performance concerning Accuracy, Precision, Recall, and F1-Score when compared to recent studies.


Exploring Colonial Heritage (EXCOTAGE) Application as a Mobile Technology-Based History Learning Education

Abstract: This research focused on the history learning concerns among 11th-grade Social Science students at MAN Kota Pasuruan, a senior high school. To support research activities, the instruments included a questionnaire to measure the effectiveness of the product. The questionnaire was distributed to validators and users, specifically the 11th-grade students of social science at MAN Kota Pasuruan. The participants in this study were selected using purposive sampling for validation and simple random sampling for testing. During the implementation, this research used the ADDIE research and development model. The product effectiveness analysis technique developed by Nunuk Suryani, Achmad Setiawan, and Aditin Putria was used for analysis of the collected data. Based on the comparison results, it is evident that the developed product is truly valuable, appealing, and aligned with the current advancements in learning technology. Meanwhile, in the effectiveness testing, it was concluded that the product has been highly effective based on expert or user assessments.


Mobile Applications in Otology: A Scoping Review

Abstract: This scoping review aims to comprehensively synthesize current research on the use of mobile applications in otology, identify research gaps, and assess the feasibility of mobilebased applications in this field. The aim is to support and direct future research studies on harnessing the potential of mobile technology in otology. Following the PRISMA-ScR guidelines, a literature profiling approach was adopted using the Google Scholar search engine from September 2022 to January 2023. Only articles published in English between 2012 and 2023 were included, resulting in a final sample size of 32 papers. The results revealed an increasing focus on the remote delivery of healthcare, with the majority of the articles originating from the United States and authored by medical doctors. Two main categories were identified within the 32 selected journals: audiology and smartphone otoscopy. A total of 124 mobile applications were identified in these journals, with 48 applications for hearing tests and 27 applications for smartphone otoscopes being the most commonly utilized. Mobile applications have the potential to serve as valuable tools in the field of otology, especially in the context of smartphone hearing tests and smartphone-enabled otoscopes. However, further research is needed to assess their effectiveness, accuracy, and appropriate usage.


The Role of Technology in Improving Public Sector Performance in Nigeria

Abstract: Over the years, the public sector in Nigeria has faced various challenges, including bureaucracy, corruption, and inefficiencies in service delivery, which have dampened the efficiency of the public sector, especially in revenue generation. However, the rapid advancement of technology globally, the increasing rise of globalization, and the contagion effect across the globe have led to the adaptation of technology to a great extent in developing economies. Nigeria, as a developing economy, relies on the Western world for most of its technology, which is accompanied by a huge cost of the transfers, and the country is left with the dilemma of whether technology truly promotes public sector efficiency and growth. Against this backdrop, this study seeks to investigate the impact of technology on public sector efficiency in Nigeria. The study employed monthly data ranging from 2010 to 2022 and was extracted from the database of the Central Bank of Nigeria. The independent variables are Information, Communication, and Technology which served as a proxy for Technology, while Foreign Direct Investment, Exchange Rate, and Inflation were also included as the control variables in the model. While public sector efficiency which is proxied by public revenue represent the dependent variable. The result of the unit root showed that the variables are stationary at levels and first difference, hence the use of the ARDL model which established a short-run relationship among the variables. The result, in the long run ARDL showed that technology had a positive and significant impact on public sector efficiency. The study, therefore, recommends that the Government needs to intensify efforts in the process of acquisition and implementation of technology in the public sector to improve revenue generation for the Government and, invariably, output. A large portion of government budgetary allocations should be channeled more toward adequate development of IT infrastructures to increase efficiency. Ultimately, the Monetary and Fiscal Authorities should collaborate in churning out policies that will improve economic parameters such as exchange rate, interest rate, Inflation, output, etc.


Education in the Age of Artificial Intelligence

Abstract: The age of artificial intelligence (AI) is just around the corner. This article presents the AI-based technologies that represent a significant change for the learning and teaching process. The article describes the potential of personalised learning, automated assessment, chatbots, predictive models, intelligent robots, and virtual and augmented reality for education, by reviewing scientific literature. Nowadays, knowledge of these technologies is essential for teachers. In this article we conducted a qualitative survey involving teachers. The aim of our survey is to assess the perceptions of educators about the use of AI in education. Our results show that educators are open to these technologies regardless of generation and discipline. The study summarizes the appropriate use of these technologies, the role of teachers, their attention to students and their active communication, as only this can lead to effective education in the age of artificial intelligence.


Media Article Text Analysis in the Context of Distance Education: Focusing on South Korea

Abstract: The impact of COVID-19 pandemic is far-reaching, encompassing the social, economic, and psychological aspects of the society. . In order to prevent the spread of COVID-19, various countries, including South Korea, have entered into long-term home care and distance learning systems. However, distance learning experiments conducted in many countries have raised whether face-to-face education can be replaced by distance learning. Therefore, this study analyzed public opinion, social perception, and field trends based on media reports on remote learning. For this purpose, 2,600 articles from 11 newspapers and four broadcasters related to remote learning were collected in this study. Based on this data, keyword trend analysis, topic modeling analysis, semantic network analysis, and emotion analysis were performed. This study provides valuable insights into the impacts and perceptions of remote learning during the COVID-19 pandemic. It highlights the complexities of remote education, from influencing educational policies to affecting students' and parents' emotional states. The findings underscore the potential of remote learning in driving educational innovation while also pointing out its role in widening educational disparities. This research offers essential perspectives for shaping future educational strategies in crisis situations, emphasizing a balanced approach to harness the benefits of remote learning while mitigating its challenges.


Real-Time Transcriptionist Based on Artificial Intelligence to Facilitate Learning for People with Hearing Disabilities in Virtual Classes

Abstract: Schools have historically been ill-prepared to cater to the needs of deaf students at the elementary and secondary levels. This leads to communication difficulties that impact the learning process for each individual. During the recent COVID-19 pandemic, educational institutions for deaf students faced difficulties in providing effective teaching to children and youth. It is important to emphasize that education is fundamental for all individuals, without exception, as acquiring literacy skills enables them to lead a more fulfilling life. In this context, our research aims to investigate how the use of a computer tool can enhance communication for deaf students in a virtual environment. The methodology used involved the use of a checklist to gather data from each participant’s evaluation. The post-test yielded favorable results, thanks to the statistical analysis employed in the research. In conclusion, it has been determined that a real-time transcriber facilitates learning, leading to improved educational outcomes for deaf students.


Engineering Students' Acceptance of Augmented Reality Technology Integrated with E-Worksheet in The Laboratory Learning

Abstract: The use of augmented reality (AR) technology in the field of education has emerged as a rapidly growing trend. However, there is an urgent need for more comprehensive research to determine the reactions of engineering students and their acceptance of this technology in laboratory learning. This study investigates the acceptance of integrated augmented reality with e-worksheet (IARE-W) among engineering students in the laboratory learning (IARE-W) among engineering students the electrical machines course (EMC). This research empirically uncovers the factors that influence it based on the technology acceptance model (TAM), specifically perceived ease of use (PEU) and perceived usefulness (PU). Acceptance is indicated by students’ attitudes toward the use. A survey-based quantitative research study using questionnaires was conducted to collect data, involving 102 students in the field of industrial electrical engineering. The partial least squares structural equation modeling (PLS-SEM) analysis was used to analyze the research data. The results demonstrated that engineering students had a highly positive attitude toward the use of the IARE-W in the EMC. Additionally, both PEU and PU had a positive and significant direct effect on engineering students’ attitudes toward using IARE-W. Furthermore, PEU also had a significant and positive indirect effect through PU as a mediating variable. These findings have significant implications for the development of engineering education and the integration of AR technology in laboratory learning contexts. The results of this study underscore the importance of taking into account PEU and PU in the design, development, and implementation of the IARE-W.


Risk Assessment and Distribution of Heavy Metals in Water and Sediments along Ibelebiri Axis of Kolo Creek, Ogbia Local Government Area, Niger Delta Region of Nigeria

Abstract: Pollution arising from uncontrollable combustion processes, including artisanal refineries are common in communities through which Kolo Creek traverses. Previous studies have centered on the effect of oil and gas production on Kolo Creek at shell flow station at Imiringi. Therefore, the aim of this study is to investigate the risk and distribution of heavy metals (Zn, Ni, Cr, Cu, Pb, Co, and Cd) in water and sediments along Ibelebiri axis of Kolo creek in Ogbia L.G.A., Niger Delta region of Nigeria. Water and sediment samples were collected at locations upstream, within and downstream at Ibelebiri community, and also from a control site. Generally, heavy metal concentrations showed a decreasing trend of Cu˃Zn˃Pb˃Cr˃Cd˃Co˃Ni for water samples. The mean concentration of these metals: Ni, Cr, Zn and Cu recorded in this study was below international recommended limits. But, Pb, Cd levels in locations LC1, LC3 and LC4 are above the international recommended limits. The heavy metals in the sediments showed a decreasing trend of Zn˃Pb˃Cu˃Co˃Cr˃Ni˃Cd. The concentrations of these metals Cd, Pb, Ni, Cr, Cu and Zn are below the permissible limit set by Department of Petroleum Resources (DPR). However, Co values at locations LC3 and LC4 are slightly above the DPR target value of 20 mg/kg for sediments. Using the geoaccumulation index (Igeo) classification, the sediments across the four locations are uncontaminated with Cd, Cr, Cu, Zn, Co and Ni. But, the sediments in locations LC1, LC3 and LC4 are moderately contaminated with Pb. Similarly, the contamination factor (CF) classification, revealed low contamination of Cd, Cr, Cu, Zn, Ni and Co across the four locations; moderate contamination of Pb in the four locations. Finally, principal component analysis (PCA) identified two major factors.


Characterisation, axial anisotropy, and formation conditions of celestine minerals from the Jabal Eghei (Nuqay) late Neogene – Pleistocene volcanic province, southeastern edge of the Sirt Basin, southern Libya: Constraints on the mineralogical geothermometer – ERRATUM


Proving the existence of Euclidean knight’s tours on n × n × ⋯ × n chessboards for n < 4

Abstract: The Knight's Tour problem consists of finding a Hamiltonian path for the knight on a given set of points so that the knight can visit exactly once every vertex of the mentioned set. In the present, we provide a 5-dimensional alternative to the well-known statement that it is not ever possible for a knight to visit once every vertex of $C(3,k):=\{\underbrace{\{0,1,2\} \times \{0,1,2\}\times \cdots \times \{0,1,2\}}_\textrm{\textit{k}-times}\}$ by performing a sequence of $3^k-1$ jumps of standard length, since the most accurate answer to the original question actually depends on which mathematical assumptions we are making at the beginning of the game when we decide to extend a planar chess piece to the third dimension and above. Our counterintuitive outcome follows from the observation that we can alternatively define a 2D knight as a piece that moves from one square to another on the chessboard by covering a fixed Euclidean distance of $\sqrt{5}$ so that also the statement of Theorem 3 in [Erde, J., Golénia, B., & Golénia, S. (2012), The closed knight tour problem in higher dimensions, The Electronic Journal of Combinatorics, 19(4), #P9] does not hold anymore for such a Euclidean knight, as long as a 2 × 2 × ⋯ × 2 chessboard with at least 27 cells is given. Moreover, we construct a classical closed knight's tour on $C(3,4)-\{(1,1,1,1)\}$ whose arrival is at a distance of 2 from $(1,1,1,1)$, and then we show a closed Euclidean knight's tour on $\{\{0,1\}\times\{0,1\}\times\{0,1\}\times\{0,1\}\times\{0,1\}\times\{0,1\}\times\{0,1\}\}\subseteq \mathbb{Z}^7$.


On Preventing and Mitigating Cache Based Side-Channel Attacks on AES System in Virtualized Environments

Abstract: Cloud computing aims to cut costs through a reduction in spending on equipment, infrastructure, and software by applying the multi-tenancy feature. Despite all the benefits of multi-tenancy, it is still a source of risk in cloud computing. Cloud adoption may be hampered by security concerns if suitable cloud-based security solutions are not available. Moreover, virtualization that enables multi-tenancy, considered one of the main components of a cloud, introduces major security risks and does not offer appropriate isolation between different instances running on the same physical machine. In this paper, we present a preliminary idea that may support the development of new countermeasures for a particular type of threat, namely cache-based side-channel attacks that target cache memories in virtualized environments. Attackers specifically target virtual machines in this type of attack to create many side channels and gather sensitive data. Additionally, this research offers preliminary concepts to aid in developing of solutions or defenses that enable us to identify unusual activity that could point to attacks associated with multi-tenancy, as well as security measures that preserve the benefits of multi-tenancy while lowering security concerns.


Towards Achieving the Sustainable Development Goals (SDGs): The Impact of Debts on Income Inequality in Africa

Abstract: The Agenda for Sustainable Development provides a framework for the creation of a better and sustainable future for all. The Sustainable Development Goals (SDGs) focus on addressing global challenges, including income inequality. Among others, the SDGs also commit countries to work towards reducing income inequality and as such, lowering income inequality is one of the key issues in Africa. However, given the high levels of debt on the continent, reducing income inequality could be a challenge. Majority of the literature examines the link between debts and economic growth with little attention paid to the impact of debts on income inequality. Moreover, not much is known on whether the relationship between debt and income inequality is non–linear. This study therefore contributes to the existing research efforts by investigating the threshold effects of debts using data from 24 African countries by relying on the sample splitting and threshold estimation approach. The results reveal that, while debts generally dampen income inequality, unbridled debt accumulation above the estimated thresholds does not decrease income inequality, indicating that the income inequality–reducing impact holds at lower levels of debt–to–GDP ratio. This evidence is insensitive to different indicators of income inequality and debts. Findings from this study show how increasing indebtedness contributes to widening income inequality in Africa.


Enhancing digital competencies in schools: The role of ICT coordinators

Abstract: This article discusses the growing importance of digital competencies in education, specifically focusing on the role of technical infrastructure in schools and staff support strategies. The discussion aims at the introduction of the ICT coordinator role in Czech Republic schools. Analysis indicates that schools with an ICT coordinator exhibit improved technology access for students and teachers, not solely determined by computer quantity per pupil. Noteworthy differences lie in the flexibility and availability of technology, with ICT coordinator-aided schools having more mobile computers, mobile labs, and more vital facilitation of BYOD methods. In conclusion, while recognizing the partial nature of school technology equipment data, the article concludes that training teachers and implementing the ICT coordinator role positively impact ICT technology use in schools. Recommendations based on Czech Republic research include establishing the ICT coordinator position, providing financial incentives, and reducing direct teaching loads. Future research should track changes in ICT equipment during ICT coordinators’ tenures and explore technology deployment dynamics, emphasizing collaboration between ICT coordinators, school management, and individual teachers.


A Comprehensive Exploration of the Jordan Tourism Board's 2023 Strategic Planning, News, and Social Media

Abstract: The tourism industry is one of constant changesin its nature, as it operates within a world that has become increasingly globalized with vast opportunities through the digital age. The study is anchored in two core research questions, determine the prevailing themes and narrations about 2023 news and posts that are related to Visit Jordan and secondly to find out how those narrations were constructed and what implications does it have for Jordan’s tourism industry. Content analysis of Visit Jordan’s news articles and social media posts in 2023 concerning major attractions and forms of tourism in Jordan is another topic that the study explores. However, cultural aspects emerge as the main focus of this type of tourism including such sites as Jerash, Petra, etc. Besides, architectural and heritage, natural tourism, adventure, outdoor tourism, festive and religious tourism, events, and seasons tourism, plus the diversification of Jordan’s provision.


Learning Framework for Joint Optimal Node Placement and Resource Management in Dynamic Fog Environment

Abstract: Background: With recent improvements in fog computing, it is now feasible to offer faster response time and better service delivery quality; however, the impending challenge is to place the fog nodes within the environment optimally. A review of existing literature showcases that consideration of joint problems such as fog node placement and resource management are less reported. Irrespective of different available methodologies, it is noted that a learning scheme facilitates better capability to incorporate intelligence in the network device, which can act as an enabling technique for superior operation of fog nodes. Objective: The prime objective of the study is to introduce simplified and novel computational modelling toward the optimal placement of fog nodes with improved resource allocation mechanisms concerning bandwidth. Method: Implemented in Python, the proposed scheme performs predictive operations using the Deep Deterministic Policy Gradient (DDPG) method. Markov modelling is used to frame the model. OpenAI Gym library is used for environment modelling, bridging communication between the environment and the learning agent. Result: Quantitative results indicate that the proposed scheme performs better than existing schemes by approximately 30%. Conclusion: The prime innovative approach introduced is the implementation of a reinforcement learning algorithm with a Markov chain towards enriching the predictive analytical capabilities of the controller system with faster service relaying operations a. a This article is an extension of our paper entitled “Computational Framework for Node Placement and Bandwidth Optimization in Dynamic Fog Computing Environments" presented at INDICON- 2022, CUSAT, 24-27 November 2022.


Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework

Abstract: Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. The recent surge of interest in MORL has led to diverse studies and solving methods, often drawing from existing knowledge in multi-objective optimization based on decomposition (MOO/D). Yet, a clear categorization based on both RL and MOO/D is lacking in the existing literature. Consequently, MORL researchers face difficulties when trying to classify contributions within a broader context due to the absence of a standardized taxonomy. To tackle such an issue, this paper introduces multi-objective reinforcement learning based on decomposition (MORL/D), a novel methodology bridging the literature of RL and MOO. A comprehensive taxonomy for MORL/D is presented, providing a structured foundation for categorizing existing and potential MORL works. The introduced taxonomy is then used to scrutinize MORL research, enhancing clarity and conciseness through well-defined categorization. Moreover, a flexible framework derived from the taxonomy is introduced. This framework accommodates diverse instantiations using tools from both RL and MOO/D. Its versatility is demonstrated by implementing it in different configurations and assessing it on contrasting benchmark problems. Results indicate MORL/D instantiations achieve comparable performance to current state-of-the-art approaches on the studied problems. By presenting the taxonomy and framework, this paper offers a comprehensive perspective and a unified vocabulary for MORL. This not only facilitates the identification of algorithmic contributions but also lays the groundwork for novel research avenues in MORL.


Analysis of English Reading Teaching Strategies based on the Thematic Meaning in Senior High Schools

Abstract: Thematic meaning inquiry provides a new way of thinking and direction for English reading teaching. The degree of exploring the meaning of the theme influences the depth of students’ discourse comprehension, the height of students’ thinking development and the effect of students’ language learning. Combined with senior high school English teaching cases, this paper discusses how to explore the thematic meaning through in-depth interpretation of texts. Under the guidance of the activity-based approach to English learning, this paper expounds an effective approach to reading teaching design based on thematic meaning exploration, aiming to lead students to read texts in depth, explore the thematic meaning and connotation of texts, and highlight the educational value of English subjects.


Characteristics and Development Trends of Network Mass Incidents in Colleges and Universities

Abstract: With the leapfrog progress of mobile internet technology, the network mass incidents in colleges and universities are increasingly concerned by many parties. The characteristics of network mass incidents in colleges and universities include the particularity of participants, the diversity of inducement causes, emotionalization and irrationalization, the non-confrontational nature of event and the extensive range of impact. With social development, the potential trends of future network mass incidents in colleges and universities include the probability and quantity of occurrences will further increase, the breadth and intensity of impact will be further enhanced, the organizational form will become more complex, the coping strategies of management department will affect the development trend of the event, the increase in uncertainty brought by the development of new technologies.


Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large…

Abstract: <strong> Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language&nbsp;Models </strong> Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI&nbsp;Systems Author: Vaibhav Khobragade (0009–0009–8807–5982) <strong> Introduction </strong> Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like


Teachers’ Learning Reinforcement: Effects on Students’ Motivation, Self Efficacy and Academic Performance

Abstract: Teachers play an important role in shaping the educational experiences and achievements of students. As education continues to evolve, it is essential to explore innovative approaches that can enhance teaching effectiveness and improve student outcomes. This study was conducted to determine the teachers’ learning reinforcement and its effects to student’s motivation, self-efficacy and academic performance. A total of thirty five (35) classroom teachers and two hundred sixty (262) Criminology students of Southern Capital Colleges participated in the study during the academic year 2022-2023. Weighted Mean, Standard Deviation, Frequency Count and Percentage, and Spearman Rho Correlation Coefficient were the statistical tools used in the study. The teachers consistently employed various reinforcement strategies, demonstrating their commitment to supporting students’ educational experiences. Students exhibited positive self-efficacy beliefs, indicating confidence in their abilities to succeed and overcome challenges. It also displayed satisfactory levels of motivation, showing a genuine interest in challenging course content and a focus on deep understanding. Furthermore, there were significant relationships between teachers’ learning reinforcement and students’ self-efficacy, motivation, and academic performance. Effective learning reinforcement positively influenced students’ beliefs in their abilities, motivating them to persist and excel in their studies. An emphasis on the importance of implementing effective learning reinforcement in educational settings is paramount to enhance students’ educational experiences and promote academic success.


Exploring the Latest Trends in Artificial Intelligence Technology: A Comprehensive Review

Abstract: Artificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.


SAACCESS (SIMPAN ARSIP DENGAN ACCESS) APPLICATION DEVELOPMENT AT 11TH STATE SENIOR HIGH SCHOOL SURABAYA

Abstract: Archives are the center of memory in every activity in the office. Archives function as a memory center, proof of the existence of the institution, and for the benefit of other institutions. Therefore, there must be good management or management of archives from creation to shrinkage. The main obstacle is the decreasing physical letter storage space, but it’s not possible to build a special building for physical letter storage space. Using the type of research with R&D (Research and Development) and prototype development research methods. Development of this archival application uses a Microsoft Access-based application. This research goes through a research procedure starting with information gathering, designing the product quickly, building a prototype, and the final step is that the application is ready to use with an evaluation that has been carried out by experts. The subject is 2 peoples from 11th Senior High School Surabaya administration staff. The trial was conducted in December 2023 and January 2024. The application feasibility results based on material expert validators and media expert validators get a score of 96%. While the results of application feasibility based on administrative staff get a score of 88.8%, which shows the interpretation of “Very Feasible”. The development of Microsoft Access-based digital archiving applications uses a prototype development model. The SAAccess application received a feasibility score of 96% from both the material expert validator and the software developer. SAAccess also received a score of 88.8% from the administrative staff of 11th Senior High School Surabaya. It can be concluded that SAAccess is effectively used to manage letters in 11th Senior High School Surabaya digitally.


Measuring the quality of the search activity

Abstract: As Search & Rescue practitioners and trainers delivering to experienced full-time and volunteer searchers, a subject discussed with those who have been responsible for the coordination, management, or command and control of lost and missing person incidents is how do we measure the quality of the search activity that has been carried out on our (and our missing person’s) behalf by the assets that have been identified, briefed, tasked and de-briefed by us?


Analysis of Pepsi Brand Advertising Implementation Using #Betterwithpepsi for Fast Food Brands

Abstract: Pepsi's recent advertisement campaign, which features the tagline #BetterWithPepsi, was launched in honor of National Burger Day. The campaign has been promoted across all social media platforms and showcases portraits of fast-food packaging that incorporate Pepsi's signature colors into the branding of popular fast-food chains such as Burger King, McDonald's, and Wendy's. This advertisement has been analyzed using semiotic theory by Roland Barthes, who suggests that the use of the advertisement is akin to a collaboration or combination between fast food and drinks that complement each other, such as Pepsi. The tagline #BetterWithPepsi implies that the public will consume food and drinks that are always together, thereby creating a mindset that associates the consumption of food with the use of Pepsi. The advertisement's use of semiotics is evident in the incorporation of Pepsi's colors into the branding of other fast-food chains, which creates a visual association between the two. This association is further reinforced by the tagline, which suggests that the consumption of food is incomplete without the addition of Pepsi. The advertisement's effectiveness can be attributed to its ability to tap into the public's desire for convenience and the need for a complete dining experience. By positioning Pepsi as an essential component of the fast-food experience, the advertisement creates a sense of loyalty and brand recognition among consumers.


The Mediating Effect of Problem-based Learning on the Relationship between Technology Integration and Student Engagement in Mathematics

Abstract: This research aimed to determine whether problem-based learning significantly mediates the relationship between technology integration and the engagement of first-year college students. A quantitative approach with descriptive and correlational designs was employed. The respondents of the study were 133 first-year college students in three state colleges in the Division of Davao del Norte. They were chosen through stratified random sampling. Mean, Pearson-r, regression, and mediation analysis were used to analyze the data, which were obtained through the use of three adapted questionnaires. The findings revealed that technology integration is manifested in the students’ learning. In mathematics, student engagement is observed, and problem- based learning is evident among the students. It is also found out that there is a significant relationship among technology integration, problem-based learning, and student engagement. Additionally, problem-based learning partially mediated the relationship between technology integration and student engagement. The findings suggested that CHED officials should encourage and support the integration of technology into the mathematics curriculum to foster increased student engagement. School administrators can work to maximize student engagement by focusing on the integration of technology and employing problem-based learning methods. Teachers may integrate technology into their lessons and incorporate problem-based learning, and college students may consider integrating technology into their learning environment. Future researchers should explore the effects of technology integration and problem-based learning on student engagement in mathematics.


Mobile Adaptive Routing Algorithm for Road-Aware Infrastructure-Assisted Communication in Cognitive Internet of Vehicles

Abstract: The Internet of Things (IoT) is expanding the capabilities of traditional vehicular ad-hoc networks (VANET) into the Internet of Vehicles (IoV). However, there are a few challenges that need to be addressed in order to enhance the intelligence of IoV, leading to the development of a new evolving technology called Cognitive Internet of Vehicles (CIoV). In this study, we propose a road-aware infrastructure-assisted and vehicle-to-vehicle (V2V) adaptive routing system to select the most efficient route for delivering data from the source to the destination vehicle, with the aim of reducing data delivery delay. The performance is measured and evaluated based on packet delivery ratio (PDR), average end-to-end (E2E) delay, and normalised routing overhead using MATLAB. By comparing the proposed mobile adaptive routing algorithm (MARA) with existing protocols, it has been examined and found to outperform the existing ones in terms of performance.


Android-Based Mobile Learning Application Using App Inventor on Computer Operating System Material: The Development and Validity Study

Abstract: Mobile learning apps are widely acknowledged for their effectiveness in enhancing learning results. This study aims to develop and validate an Android-based mobile learning app for computer operating system principles. Using the user-friendly App Inventor platform known for visual programming, it integrates interactive modules and multimedia for diverse learning styles. The study adopted a Research and Development approach following the ADDIE model (analysis, design, development, implementation, and evaluation). The research was conducted at Universitas Negeri Padang and Universitas Muhammadiyah Muara Bungo and involved 10 participants. The analysis of questionnaire-based data conducted by media and material experts categorized the Android-based learning media as "Appropriate", achieving an over-all average of 82.51% and 82.29%, respectively. Meanwhile, student evaluations rated this media as "Feasible" with a score of 82.72%. In conclusion, the development of this Android-based mobile learning application for computer operating system material demonstrates significant potential as an innovative educational tool. The validation process highlighted its potential effectiveness and offered valuable recommendations to enhance its educational value. These findings make a meaningful contribution to the ongoing discourse surrounding the integration of technology-driven learning approaches into traditional education, benefiting educators and developers seeking to create impactful mobile learning experiences.


FEATURES OF THE DEVELOPMENT OF MATHEMATICAL IDEAS OLDER PRESCHOOL CHILDREN WITH INTELLECTUAL DISABILITIES

Abstract:
This article is devoted to the theoretical analysis of the problem of the development of mathematical concepts of older preschoolers with intellectual disabilities. The peculiarities of thinking as a cognitive process of the individual are analyzed. The basis of the thinking process is always analysis and synthesis. Analysis is a necessary condition for the scientific interpretation of facts. It requires completeness, depth and accuracy. This is the first stage of studying any phenomenon. Synthesis, as a thinking process, can occur at different levels in human activity, starting from a simple mechanical connection of parts of a whole to the creation of a scientific theory based on the generalization of individual facts and research materials. It can be carried out both on the basis of perception and on the basis of memories and ideas. Although opposite in nature, the operations of analysis and synthesis are actually closely related.
 The psychological features of older preschool children are analyzed. During this period of development, the child’s cognitive sphere is significantly enriched and developed; perception becomes more conscious and purposeful; voluntary and involuntary attention, the ability to analyze and the ability to reproduce what was previously learned develop; there is a transition from visual-active to visual-figurative thinking; verbal and logical thinking begins to develop; generalized ways of thinking are formed: comparison, search for similarities, differences, classification, analysis, combination; the grammatical structure of the native language is mastered.
 The psychological characteristics of mathematical representations of children with intellectual disabilities are provided. Such children in older preschool age have no desire to engage in intellectual games, there is an increased interest in mobile, non-targeted games. The pace of perception is slowed down, the volume is narrow. They can hardly distinguish the main or general thing in the picture, in the text, grasping only individual parts and not understanding the internal connection between the parts, they often confuse graphically similar letters, numbers, objects. All mental operations (analysis, synthesis, comparison, generalization, abstraction) are not sufficiently formed. A distinctive feature of thinking is uncriticalness, the inability to independently evaluate one’s work.
 The methodological foundations of the empirical study of the development of mathematical concepts of older preschoolers with intellectual disabilities are substantiated. The obtained indicators of the formation of mathematical representations of older preschoolers with intellectual disabilities are experimentally analyzed; specifics of combinatorial and conceptual thinking of children with intellectual disabilities; indicators of the development of thinking operations of children of older preschool age.


A Machine Learning Guided Path for Optimal Literature Review

Abstract: This paper introduces a novel machine learning framework to address the challenge of optimizing literature research by identifying the optimal path. To create dataset and ensure the versatility of the solution for different applications, we developed an online scraping tool designed to extract articles from ResearchGate based on a specific search query. The proposed machine learning model leverages contextual embeddings and graph theory, translating intricate scholarly work into informative steps for one to go wider rather than deeper in their research. By employing a Christofides approximation of the Traveling Salesman Problem algorithm, our model efficiently navigates through more than 1000 article embeddings. We prove that the resulting path not only accelerates the knowledge gaining process, but also evidently diversifies the findings. Moreover, we evaluated multiple PDF reader libraries to arrive at the most suitable one for the purpose. This adaptability allows the framework to be applied not only to scraped articles, but also to those stored as PDF files, giving an option for multiple data sources. In conclusion, this paper presents a transformative approach for literature research optimization, equipping researchers with a potent tool to efficiently explore articles.


A Micro-class Teaching Data Retrieval Method of Business English Based on Network Information Classification

Abstract: In order to quickly extract the required micro-class teaching data of business English in the fragmented network information environment, a micro-class teaching data retrieval method of business English based on network information classification is offered. This way, it constructs a network information classification method on the basis of the optimised SVM model, and the parameters of the SVM model are optimised and trained by the improved artificial bee colony algorithm. After optimising the function of the SVM method, the method classifies the online teaching information of various business English micro-class teaching; In the classified network information set, the targeted retrieval method of network teaching resources according to big data technology is adopted to obtain the teaching data with the highest similarity to the user's retrieval data by clustering and complete the targeted retrieval of micro-class teaching data. The experimental results show that the retrieval delay of the proposed method for micro-class teaching data retrieval of business English is less than 1s, the number of correct retrievals is relatively high, and there are few wrong retrieval phenomena.


Implementation of Virtual Laboratory of Electronic Commerce Into University Course

Abstract: Plenty of information and communication technologies are frequently employed in educational activities to improve the opportunities for students to gain knowledge and experience, including the virtual laboratory. The virtual laboratory is quite often implemented into educational process in many science, technology, and engineering courses at the university level of education. However, the use of virtual laboratory in the field of electronic commerce education is rather rare. In our educational practice, a virtual laboratory of electronic commerce was designed and implemented within the course of electronic entrepreneurship. This paper presents the design of the virtual laboratory of electronic commerce and provides an outline of its integration into the university course's teaching process. The study examines the learning progress claimed by the students who participated in the most current edition of the course delivered utilizing this virtual laboratory. The findings revealed improvement of course participants' experience and abilities in the field of electronic entrepreneurship in e-commerce.