The Effects of L2 Chinese Learners’ Perceived Writing Anxiety on Their L1 Use within L2 Writing Performance among English Major Students in a Chinese University

Abstract: Learning to write fluently and expressively in English is widely proved to be one of the most difficult skills for Chinese EFL learners to master due to their L2 writing processes are impeded by various obstacles, among which L2 writing anxiety and their L1 use are two indispensable factors that should be accounted. Hence, the purpose of this study was to explore the relations among L2 learners&rsquo; perceived writing anxiety and the use of L1 Chinese learners within their L2 writing performance of English major students. 226 L1-Chinese sophomores of English major students enrolled in Department of Applied English at a university in China participated. A mixed methodology of (Quantitative +qualitative) approaches was employed to collect data through SLWAI (Second Language Writing Anxiety Inventory), think-aloud protocol and semi-structured interview so as to elicit the dynamic thinking process of L2 Chinese learners&rsquo; perceived writing anxiety and their L1 use within L2 writing performance on an English argumentative writing task. The findings revealed that L2 Chinese learners&rsquo; perceived writing anxiety was positively correlated with their L1 use amount but negatively correlated with L2 writing performance. However, in terms of a minor part of the participants, L2 Chinese learners&rsquo; perceived writing anxiety and their L1 use amount were positively correlated with L2 writing performance. Implications are that the study not only investigated the relations among L2 English major students&rsquo; perceived writing anxiety and their L1 use within L2 writing performance, but shed lights on the pedagogical field of L2 writing by proposing some effective measures to improve students&rsquo; L2 writing performance based on their different anxiety sources.


A Deep Learning-Based Teaching Design for High School Geography Units: Taking the Example of Landforms of the Humanistic Education Edition

Abstract: The traditional teaching methods of one-way cultivation of students can no longer meet the requirements oftalent cultivation at this stage. The issue of how to promote students from passive acceptance to the independent cognitive understanding stage (i.e. deep learning) has become the focus of geography teaching. Therefore, under the guidance of deep learning theory, this paper takes the “landforms” knowledge unit of the Humanistic Education Edition as an example, improves the classroom teaching means through the unit teaching mode, reconstructs the “landforms” teaching unit, and explores the specific teaching of high school geography unit based on deep learning. This study provides a good example and guidelines for high school geography teaching and learning.


A Systematic Literature Review of the Significance of the School of Living Tradition in the Preservation of Cultural Heritage of the Indigenous Peoples

Abstract: This systematic literature review investigates the critical role of the School of Living Traditions (SLT) and its educational approaches in preserving the cultural heritage of Indigenous Peoples (IP) in the Philippines. A set of pre-defined inclusion-exclusion criteria led to the selection of six pertinent papers, which were comprehensively analyzed and synthesized. The examination of these articles revealed recurring themes, allowing for the development of thematic constructs to address the study's objectives. The findings underscore the pivotal role of SLTs in safeguarding the profound traditions of Indigenous Peoples, thereby ensuring the transmission of cultural legacies to younger generations and empowering IP communities. Notably, the significance of SLTs lies in their ability to preserve the authentic essence of cultural practices, contributing to the empowerment of Indigenous communities. In the transmission of this indigenous knowledge, systems, and practices, pedagogical approaches within SLTs are centered on culture-based education and experiential learning. These approaches aim to facilitate meaningful learning experiences that enable individuals to derive personal significance from their acquired knowledge and apply it in real-world contexts. Consequently, this research sheds light on the instrumental role of SLTs in fostering cultural preservation and empowerment within Indigenous communities through tailored educational methodologies.


Analysis of Figurative Language Used in Nine Track Mind Album by Charlie Puth

Abstract: In writing songs, songwriters often use figurative language. In this study, analyzed the figurative language contained in the album Nine Track Mind by Charlie Puth. The purpose of this study was to determine the type of figurative language used and the meaning shown by figurative language in the songs on the album. This research is a descriptive research with qualitative and quantitative approaches. The data found on this research were analysed by theory of Knickerbocker and Reninger (1963) and supported by the theory of Wellen and Warren (1984). The results of this study showed that there were ten figurative languages used by songwriters, namely 60% hyperbole, 10% allusion, 10% repetition, 20% metaphor. In addition, the songwriter also uses connotative and denotative meanings in the album. Keywords: figurative language, song, album.


Engaging adolescents in family therapy: The role of English slang and digital communication norms in therapeutic settings

Abstract: Adolescence is a critical stage marked by rapid cognitive, emotional, and social development, often presenting challenges within familial relationships. Family therapy serves as a vital intervention to address these challenges, yet engaging adolescents in the therapeutic process can be complex. This review explores the significance of English slang and digital communication norms in facilitating rapport and effective communication between therapists and adolescents in family therapy settings. In contemporary society, English slang and digital communication have become integral aspects of adolescent culture. Adolescents frequently employ slang terms and digital platforms to express themselves, communicate with peers, and navigate their identities. However, these informal modes of communication may pose barriers in traditional therapeutic settings where formal language is often prioritized. Recognizing the importance of meeting adolescents on their own communicative terms, therapists are increasingly integrating slang and digital communication norms into family therapy sessions. By incorporating familiar language and mediums, therapists can create a more inclusive and relatable environment, fostering trust and engagement with adolescent clients. Moreover, the utilization of slang and digital communication norms in family therapy promotes authenticity and enhances the therapeutic alliance between adolescents and therapists. Adolescents feel understood and validated when therapists demonstrate an awareness and acceptance of their linguistic and technological preferences, leading to increased participation and investment in the therapeutic process. However, the integration of slang and digital communication in family therapy requires careful consideration and ethical awareness. Therapists must balance the use of informal language with maintaining professional boundaries and respect for cultural diversity. Additionally, therapists should remain mindful of the potential implications of digital communication, including privacy concerns and the blurring of therapeutic boundaries. Understanding and incorporating English slang and digital communication norms are essential for effectively engaging adolescents in family therapy. By embracing adolescents' preferred modes of expression, therapists can foster meaningful connections, facilitate communication, and promote positive therapeutic outcomes within the familial context.


Learning and adaptation of strategies in automated negotiations between context-aware agents

Abstract: This work presents the hypothesis that guided the research efforts and a summary of the contributions of the doctoral thesis '`Aprendizaje y adaptación de estrategias para negociación automatizada entre agentes conscientes del contexto'. Succinctly, the thesis focuses on agents for automated bilateral negotiations that make use of the context as a key source of information to learn and adapt negotiation strategies in two levels of temporal abstraction. At the highest level, agents employ reinforcement learning to select strategies according to contextual circumstances. At the lowest level, agents use Gaussian Processes and artificial Theory of Mind to model their opponents and adapt their strategies. Agents are then tested in two Peer-to-Peer markets comprising an Eco-Industrial Park and a Smart Grid. The results highlight the significance for the automation of bilateral negotiations of incorporating the context as an informative source.


An Analysis of Code Switching Found in Novel “The Paragon Plan”

Abstract: Bilingualism is native-like control of two languages. Has even suggested that Bilingualism commences when a person begins to understand utterances in a second language. Code-switching frequently occurs if the speakers can speak more than one language and use the languages simultaneously in the same context. This means the speakers intend to keep the utterance's meaning the same when emphasizing the purpose. In this research, the data was collected by using the observation method. The data of this study were taken from The Paragon Plan novel by Aranindy. The data was analyzed based on a theory proposed by Poplack (1980) to find out and categorize the types of code-switching. Meanwhile, the reasons for code-switching were analyzed by using the theory by Hoffman (1991). In analyzing the data, descriptive and qualitative methods were used. The types of inter-sentential switching are the most dominant in the novel The Paragon Plan with 30 data or 44.8%, followed by inter-sentential switching with a total of 24 data or 35.8%, and followed by tag switching amounted to 13 data or 19.4% of 67 data. Concerning the seven reasons for code switching, the findings indicate that only two were identified in the novel “The Paragon Plan”. Talking about particular topic is the most dominant reasons of code switching. Ultimately, code-switching in a novel can serve various narratives, character-building, and stylistic purposes. It enriches the story's linguistic and cultural landscape, making it more engaging and relatable for readers.


Temporal Analysis Of COVID-19 Cases And Recovery Trends In Panvel City: A Spline Curve Approach

Abstract: The spline curve method in mathematical modeling, was assessed across degrees for its efficacy in fitting daily cured and COVID-19 positive patient data of Panvel City. This study highlights the critical role of selecting an appropriate degree in spline interpolation for reliable disease-related dataset modeling.


Creating an Interactive Model for Simulation-Based Learning Environments

Abstract: The purposes of this paper are to explore some of the important characteristics of the key elements of simulation and debriefing learning and expand our understanding of the critical interactions between these elements by introducing an Active Engagement Model to be used as a framework for creating a simulation learning environment (SLE) that fosters learner active engagement. A major learning methodology in healthcare education that is quickly gaining traction is simulation. The special qualities of simulation learning assist students strengthen their clinical reasoning abilities and, more significantly, provide a bridge between the classroom and real patient care. Learner active engagement is a critical requirement for effective learning during simulation and debriefing, which tasks educators to design simulation learning environments that foster learner active engagement. To foster learner active engagement, the educator and the learner must develop a dyadic relationship of trust, openness, sharing, and safety. The formation of this dyadic relationship implies that the learner has engaged in the learning environment. Educators should also understand the difference between a challenging environment and one that is perceived as threatening. A challenging environment is one where the learner has multiple opportunities to make critical decisions. The simulation literature lacks significant discussion of how the elements of the simulation learning environment can be used to create a learning environment that encourages active engagement in the learning process. From the information gathered through a literature search in CINHAHL, PubMed, and Psychology and Behavioral Sciences databases, this article describes the critical elements of effective simulation learning. The educator/learner dyad is the functional entity of the paradigm, and it is made up of three components: the learner, the educator, and the environment. These components must work together effectively. After the teacher-student pair is established, every component of the model works together to create a productive virtual learning environment.


Access to Digital Justice

Abstract: Chapter 7, Access to Digital Justice, asks the central question: how accessible is online justice? This chapter explores how those who administer justice, those who provide advice and those who use the online justice system experience it. In doing so, we explore how the use of technology in the justice system is shaped by, and may reshape, people’s orientations and sensibilities towards law and technology. We use our data, in this chapter, to explore how consciousness of how people think and feel about the law relates to their capability of acting upon it.


DISCRIMINANT ANALYSIS IN DETERMINING THE DIFFERENTIATING FACTORS OF BLENDED LEARNING SUCCESS IN EDUCATIONAL INSTITUTIONS

Abstract: Blended learning has become an increasingly popular approach in the educational field because it combines traditional/conventional learning with digital technology-based learning. However, the success of implementing blended learning in educational institutions can be influenced by various different factors depending on the characteristics of the educational institution. Therefore, this study aims to analyze the differentiating factors that influence the success of blended learning in educational institutions. The discriminant analysis method in this study was used to identify and determine the differentiating factors that distinguished the high success blended learning group from the low success group. Research data was collected from various educational institutions that have implemented blended learning in their curricula. The results of the analysis show that there are several significant differentiating factors between the high and low success groups in applying blended learning. Some of these factors include Implementation Standards and Guidelines, Infrastructure and Technology mastery, Learning Strategy, Human Resources, Learning Content and Learning Environment. This research makes an important contribution to the development of more effective blended learning implementation strategies in educational institutions. By understanding the differentiating factors that influence success, educational institutions can increase their efforts to create a conducive learning environment and improve the quality of student learning outcomes.


EFEKTIVITAS PENGGUNAAN MEDIA PEMBELAJARAN DI ERA DIGITAL DI SEKOLAH SMK N 1 PERCUT SEI TUAN

Abstract: The use of learning media in the digital era opens wide doors for global education access. With technology, learning can be tailored to individual learning styles, increasing engagement and providing a wider variety of resources. This creates a learning environment that is dynamic and responsive to student development. This article aims to broaden the insight of prospective educators so they can choose which learning media are effective to use in this digital era. The research method used by the researchers is a descriptive qualitative method by conducting interviews and document studies. Effective learning media is a means of exchanging information between educators and students so that students can absorb lessons that are communicated clearly and concisely, thereby improving learning outcomes. Choosing the right media is one of the key factors for success in planning and implementing lessons. Effective use of learning media can increase student interaction during the learning process. Apart from that, students are happy with this educational media because it can maximize the quality of their learning outcomes


Status of Science Laboratories in Secondary Basic Education Public Schools in the Division of Davao Del Sur, Philippines

Abstract: The science laboratory became a substantial learning environment for science education, especially in realizing the K–12 science curriculum’s goals, which focus on applying and demonstrating scientific knowledge. Thus, this study aimed to determine the compliance rate of science laboratories in the division of Davao del Sur, Philippines based on DepEd Order No. 48 series of 2006 which entails the prescribed science laboratories in basic education and DepEd Order No. 118 series of 2009 which stipulates the science laboratory equipment and materials. The research examined six schools in three economic categories to assess their science laboratory compliance rate. This study involved creating four matrices containing the prescribed laboratory facilities, equipment, materials, and required quantities for each equipment and material. Results revealed that the compliance rate of the sampled schools in terms of their facilities is 69.04% (good), the equipment is 65.59% (good), the materials are 43.33% (fair), and the compliance with the required quantity is 50.39% (fair). Based on the result of the study, it can be concluded that some schools in the division of Davao del Sur are lacking with the prescribed science laboratory. Thus, it is recommended that the Department of Education, Davao del Sur division to conduct a general assessment of all secondary schools to determine the status of science laboratories’ physical infrastructure, equipment, and materials.


EXPLORING THE SIGNIFICANCE AND CONTRIBUTION OF VOCATIONAL EDUCATION & TRAINING IN MEETING THE INDUSTRIAL DEVELOPMENT NEEDS IN CHINA

Abstract: Education is widely recognized as one of the most invaluable assets in the world. Educational attainment is categorized into diplomas, bachelor’s degrees, and graduate studies. From the standpoint of stakeholders, the competencies necessary for individuals graduating with varying levels of education have been cultivated. Vocational training encompasses educational programs or courses explicitly targeting acquiring the required skills for a specific job role or industry. On the contrary, vocational education emphasizes equipping students with the necessary skills and knowledge for occupations, often at the expense of traditional, unrelated academic subjects. The primary objective of this research is to examine the role of vocational education and training in China and its impact on the country’s industrial advancement. Researchers will employ qualitative methodologies to interview pertinent stakeholders, encompassing graduates of vocational education institutions, administrative staff, and lecturers. They will also use additional secondary data about vocational education and training in China. Education is widely recognized as one of the most invaluable assets in the world. Educational attainment is categorized into diplomas, bachelor's degrees, and graduate studies. From the standpoint of stakeholders, the competencies necessary for individuals graduating with varying levels of education have been cultivated. Vocational training encompasses educational programs or courses targeting acquiring skills essential for a specific job role or industry. On the contrary, vocational education emphasizes equipping students with the vital skills and knowledge required for occupations, often at the expense of traditional, unrelated academic subjects. The primary objective of this research is to examine the role of vocational education and training in China and its impact on the country's industrial advancement. Researchers will employ qualitative methodologies to interview pertinent stakeholders, encompassing graduates of vocational education institutions, administrative staff, and lecturers. They will also use additional secondary data about vocational education and training in China.


Towards Confidential Chatbot Conversations: A Decentralised Federated Learning Framework

Abstract: The development of cutting-edge large language models such as ChatGPT has sparked global interest in the transformative potential of chatbots to automate language tasks. However, alongside the remarkable advancements in natural language processing, concerns about user privacy and data security have become prominent challenges that need immediate attention. In response to these critical concerns, this article presents a novel approach that addresses the privacy and security issues in chatbot applications. We propose a secure and privacy-preserving framework for chatbot systems by leveraging the power of decentralised federated learning (DFL) and secure multi-party computation (SMPC). Our DFL framework leverages blockchain smart contracts for participant selection, orchestrating the training process on user data while keeping the data local, and model distribution. After each round of local training by the participants, the blockchain network securely aggregates the model updates using SMPC, ensuring that participants’ raw model parameters are not exposed to others. The global model is encrypted and stored in hypermedia protocols such as the InterPlanetary File System. Participants decrypt the global model updates using their private keys to further fine-tune their models. Iterative training rounds are executed through the blockchain network, with participants updating the model collaboratively using SMPC. Experiments show that our approach achieves comparable performance to centralised models while offering significant improvements in privacy and security. This article presents a ground-breaking solution to privacy and security challenges in chatbots, and we hope our approach will foster trust and encourage broader adoption of chatbot technology with privacy at the forefront.


Application of Random Forest in Modeling the Prevalence of Depression among Murang’a University of Technology Students

Abstract: Around the world, depression is a prevalent mental illness and it affects the way people think, feel, talk and conduct their daily activities. The associated stigma often leads to misdiagnosis, posing risks such as disability and suicide. The study employed random forest algorithm to model the prevalence of depression among Murang’a University of technology (MUT) students. A sample of 1448 students from the different schools at the university participated in the study by completing questionnaires on sociodemographic and other factors associated with depression. The questionnaires were administered through social media platforms. Participants were selected using proportionate stratified random sampling and simple random sampling to ensure that a representative sample was chosen from each school. The data gathered was examined using descriptive and inferential statistics. Depression was measured using the Patient Health Questionnaire scale (PHQ-9). Using a cut-off point of 10, 25.97% students had depressive symptoms. This comprised of 19.61% moderate symptoms and 6.35% severe symptoms. The confusion matrix criteria were used to assess the performance of random forest in modeling depression prevalence among MUT students. Metrics for random forest included, accuracy (0.9868), sensitivity (0.95), specificity (1.00), positive predictive value (1.00), and negative predictive value (0.9824). Implementing targeted interventions founded on identified risk and protective factors and exploring the long-term outcomes of these interventions would contribute to the evolving field of mental health research within academic settings.


THE EFFECTS OF ONLINE CHINESE CRITICAL THINKING CURRICULUMS ON STUDENTS IN MAINLAND CHINA

Abstract: The newly released Language Curriculum Standards for Compulsory Education by the Ministry of Education of mainland China emphasises the importance of developing critical thinking skills, and the value of related online curriculums is receiving increasing attention. Many studies have shown that essential thinking education develops students' creativity and self-diversity. However, it is necessary to note that this concept may need to be more idealised. A qualitative study was conducted using a case study approach to investigate further. The study involved semi- structured interviews and manual thematic coding with three teachers of online critical thinking curriculums. The results highlight that online essential curriculums of thinking have value, but the teacher, the students, the model of instruction, the teaching content, and the learning platform influence their effectiveness.


Information technologies in teaching

Abstract: With increasing knowledge and technological progress of society; our country requires learning skills that could help it keep pace with the development of science and technology. Educational systems in a community and consequently education will not be able to separate from other social institutions, national and international interactions widely known in the global village. But the fundamental problem is that what strategies should be adopted so that education systems in developing countries do not only follow developed countries but grow and progress base on their own needs in the path of progress. The paper discusses and explains the possibilities of using information technologies in which the students of the Academy of Technical - Art Professional Studies in Belgrade participated. The questionnaire contained four questions: students' opinion on the importance of ICT for learning and acquiring new knowledge; students' possession of ICT equipment; to what extent the Internet is used for teaching and learning purposes and time use of the device.


CONSTRUCTION OF A TEACHING STRATEGY MODEL FOR CULTIVATING HIGHER-ORDER THINKING SKILLS IN COLLEGE STUDENTS

Abstract: In the era of the knowledge economy, the intensification of the technological revolution and international competition have triggered a rethinking of the core competencies of talent in the 21st century by countries. Many countries have shifted their focus on talent development from specialized knowledge and skills to training in complex tasks and information-processing thinking. In higher education, guiding students to transition from lower-order thinking, such as memorisation and understanding of knowledge to higher-order thinking, such as analysis, synthesis, and evaluation of various information is a vital aspect of the new era's innovative teaching model and the enhancement of talent cultivation. This study was conducted face-to-face interviews with 12 teachers with practical experience cultivating higher-order thinking skills using purposive sampling. The collected interview data were analysed and coded using nvivo software and qualitative research methods, constructing five teaching strategy models for cultivating higher-order thinking skills in college students: scaffolding instruction, heuristic instruction, project-based instruction, collaborative learning, and diversified assessment feedback.


Integration of Management and Accounting Information Systems and Their Impact on Creativity and Excellence in Providing Banking Services, An Applied Study in Banks Operating in Jordan

Abstract: The study aimed to investigate the impact of the integration of management and accounting information systems on creativity and excellence in providing banking services, as it addressed the concept of management and accounting information systems and the impact of their integration in achieving the advantage of excellence and creativity in providing banking services in banks operating in Jordan. The study population was (17) banks operating in Jordan, whether commercial or Islamic, and a questionnaire was designed and distributed to the community, with (5) questionnaires for each bank. The senior management targeted the Finance, Planning and Training Department, with (105) questionnaires, of which (98) were retrieved.) Form. Using statistical methods and the statistical analysis program (SPSS), it was found that (95) questionnaires were suitable for analysis. The study hypothesis was proven that there is a statistically significant effect of the impact of integration between management and accounting information systems that leads to creativity and excellence in providing banking services. The study recommended the need to increase attention to integration between management and accounting systems because of their effective role in completing operations and transactions in markets that have adopted the application of technology in all commercial transactions. As well as increasing interest in adopting strategic planning programs for future performance, studying the challenges it faces in performing its work, and developing programs that will increase the efficiency and effectiveness of performance.


Developing Macbox Media in Teaching English Grammar for Indonesian Students

Abstract: This research aims to develop a learning media namely, Macbox. This media is a kind of flashcard built with the appropriate concept of matching box. This research is a Research and Develoment (R&D) research with four steps, 1) Definition, 2) Design, 3) Development, 4) Dissemination. Interview, observation and test were used to obtain the data. The data results show that the macbox media was suitable for use by students. It can be seen from the validation from the material and media experts. Besides, in the media trial, the pretest results the average score in the control class is 48,02. Meanwhile in the experimental class the average value is 48,47. Then in the posttest score the average score in the control class is 62,76. Meanwhile in the experimental class the average value is 75,41. The data show that it increased students’ grammar mastery who taught using macbox media. Thus, it can be concluded that the development of macbox media is very feasible and effective to used in teaching grammar.


Studying the peculiarities of the universities students’ motivation for research activities

Abstract: Introduction. The article substantiates the need for up-to-date knowledge about the state and possibilities of university students’ research activities, taking into account the necessity to increase the importance of such activities for education and personal development of future professionals in the field of science. The purpose of the article is to present the research results (2023) reflecting the problems of involving students into research activities and ways of solving these problems, as well as the results of investigating students’ motivation at the Herzen State Pedagogical University in Russia. The innovative model of preparing highly qualified scientific and academic staff is characterized. Materials and Methods. The research methodology is based on the ideas of a personality-centered approach. Theoretical and practical methods of scientific research were applied: theoretical analysis, synthesis, classification; the online questionnaire, which revealed various aspects of students’ motivation to participation in research activities, and mathematical methods of data processing. Results. The authors presented three groups of results: - the review and analysis of studies conducted in the 2000s by Russian and international researchers on various aspects of engaging university students in research activities; - the review of current research investigations conducted in Russian universities, reflecting practice of improving university students’ research activities in various regions of the country; - the results of the study conducted in 2023 at the Herzen State Pedagogical University of Russia - a survey of students majoring in the following fields: Education (Education science); Psychology (psychological sciences); Psychology and Education which reflect the key positions of organizing students’ research: the engagement of students in educational activities; motivation for research activities; satisfaction with research experience; the degree of participation in the student research society; self-assessment of the research skills; assessment of existing conditions for research activities and accessibility to them at the university. Conclusions. Students will choose research activities as a basis for their professional formation if research is considered to be a leading activity in the higher educational institution; where external and internal motives of involving students in research are taken into account and regulatory and research support of students in their scientific self-determination is manifested. One of the possible forms of creating such conditions is the model of a joint master’s and doctoral-level degree program.


Research into the capacities of the global digital environment for obtaining cultural and conceptual information at institutional and individual levels

Abstract: Introduction. The article examines the problem of using the global digital environment as a resource of information about the concepts of German linguistic culture. The purpose of the article is to study the functions of the digital environment, identify its capacities for institutional and individual familiarization with cultural-conceptual information, justify the criteria for selecting media texts, and summarize recommendations for using the environment as a source of information about linguistic concepts. Materials and Methods. The study of the digital environment was conducted during two semesters in 12 groups of undergraduates studying German at MGIMO University of the Russian Foreign Ministry. During the research, the following methods were used: survey and study of written and oral statements of students in the process of their immersion into the digital environment, analysis and systematization of the accumulated experience in teaching the concepts of German culture. Results. The article summarizes the experience of researching the global digital environment as a resource of cultural and conceptual information. The functions of the environment used in teaching a foreign language are determined: informational, educational, motivational,and diagnostic. The prospects for institutional and independent familiarization with information about key concepts of culture are identified. The criteria for selecting media texts used as a resource of cultural-conceptual information are substantiated: the availability of data on the concepts being studied, compliance with the value guidelines of Russian society, relevance and novelty of materials, matching with the level of foreign language proficiency of international relations students. Conclusions. The authors have revealed the capacities of the digital environment for obtaining cultural and conceptual information at institutional and individual levels. Implementing the research provisions in teaching practice will contribute to the effective use of the virtual cultural and linguistic space and improve the quality of language and professional preparation of international relations professionals.


Characteristic features of designing the system of students’ practical training in the context of education transformation

Abstract: Introduction. The article investigates the problem of enhancing students’ ability and willingness to perform professional tasks. The research aim is to identify the characteristic features of practical training design in the context of educational system transformation. Materials and Methods. In order to solve the research tasks, we used the following methods: questionnaire survey of students (n=159) and teaching staff (n=98), observation of the educational process, analysis of laws and regulations as well as scholarly literature on the subject, and focus group discussions of employers, students, and practical training supervisors. Results. We identified core components of practical student training and comprehensively analysed this concept. The analysis revealed the characteristic features of practical training design during education transformation. Furthermore, the study substantiated the viability of modular and step-by-step designs of the practical training system aimed at the development of students’ ability and willingness to perform professional tasks. Conclusions. We consider the characteristic features of practical training design to be the basis of effective educational design aimed at the development of students’ professional competencies and strengthening the tendencies of practice-oriented learning.


Evaluating the functions of the teacher-producer for preparing prospective economists in the modern context of education system digitalization

Abstract: Introduction. The authors explore the problem of enhancing the communication culture of prospective economists in the context of digital transformation and interaction with artificial intelligence systems. The purpose of the article is to identify the role of the teacher-producer in organizing teacher-student interaction in the system of economic education (secondary school-university), as well as to evaluate the functions of pedagogical production necessary to improve the communicative culture of future economists in the era of digitalization. Materials and Methods. The Pareto principle is used as the methodological basis of the study, according to which 20% of teaching resources provide 80% of the obtained educational result. The sociological survey was conducted among students and academic staff of economic faculties at the Financial University under the Government of the Russian Federation, Astrakhan State University, State Social and Humanitarian University (Kolomna) and schoolchildren of specialized classes (gymnasium 19, Omsk). Results. The analysis of empirical data demonstrates the relevance of the role of the teacher-producer in the economic education system in the era of digitalization, as well as the positive attitude of the younger generation towards the prospects for the introduction of artificial intelligence. The functions of pedagogical production are highlighted, taking into account the digital specifics of modern economic education. Using the Pareto principle confirms that mastery of the functions of pedagogical production (information, communication, organizational) in the system of economic education, constituting about 20% of the component composition of pedagogical functions, determine 80% of the educational result in terms of increasing the level of communicative culture of future economists. Conclusions. The authors revealed and described the functions of pedagogical production are highlighted, taking into account the digital specifics of modern economic education.


Artificial Intelligence and Customer Experience: Key Takeouts From Telecoms Sector in Zimbabwe

Abstract: The purpose of this study was to glean key learnings from the use of artificial intelligence on customer experience with emphasis on Zimbabwean telecoms companies. The study employed qualitative research design, using semi structured interviews with customers, staff, and management of the telecoms companies in Zimbabwe. To unearth the key themes and trends and insights between artificial intelligence and customer experience thematic analysis was applied. The research evaluated the models employed by the Zimbabwean telecoms companies in transforming the customer experience landscape and hurdles they face in moving customers to the new channels. The challenges experienced in the new trajectory were explicitly highlighted. Case studies of other telecoms companies who explored the same avenue were chronicled. The various merits and skepticism of artificial intelligence on customer experience were clearly evacuated paving way for future research areas to completely harness the potential in the discipline of customer experience and its contribution to competitiveness.


On the transitivity of Logical Consequence without Assuming Monotonicity

Abstract: We generalize Ripley’s results on the transitivity of consequence relation, without assuming a logic to be monotonic. Following Gabbay, we assume nonmonotonic consequence relation to be inclusive and cautious monotonic, and figure out the implications between different forms of transitivity of logical consequence. Weaker frameworks without inclusiveness or cautious monotonicity are also discussed. The paper may provide basis for the study of both non-transitive logics and nonmonotonic ones.


Thematic Analysis through Artificial Intelligence (AI)

Abstract: Thematic analysis, a well-enforced qualitative analytic method, is likely to continue evolving with the adoption of AI technologies. This how-to report does not delve into the details of thematic analysis itself, as there are ample existing studies on the topic. Instead, it acknowledges the potential impacts, dynamics, and pitfalls of AI in thematic analysis while offering valuable advice, particularly for novice analysts, on how to incorporate and document AI tools in each phase of a thematic analysis. The author underscores the importance of not allowing AI to overshadow the analyst's critical evaluative and interpretive skills but instead supporting the use of AI as an aid in thematic analysis, enhancing the depth and breadth of analysis, provided certain criteria are adhered to. This approach ensures that AI serves as a complementary tool, augmenting rather than replacing human analytical inquiry.


Adiabatic elimination in the presence of multiphoton transitions in atoms inside a cavity

Abstract: Various approaches have been used in the literature for eliminating nonresonant levels in atomic systems and deriving effective Hamiltonians. Important among these are elimination techniques at the level of probability amplitudes, operator techniques to project the dynamics on to the subspace of resonant levels, Green’s function techniques, the James’ effective Hamiltonian approach, etc. None of the previous approaches is suitable for deriving effective Hamiltonians in intracavity situations. However, the James’ approach does work in the case of only two-photon transitions in a cavity. A generalization of the James’ approach works in the case of three-photon transitions in a cavity, but only under Raman-like resonant conditions. Another important approach for adiabatic elimination is based on an adaptation of the Markov approximation well-known in the theory of system–bath interactions. However, this approach has not been shown to work in intracavity situations. In this paper, we present a method of adiabatic elimination for atoms inside cavities in the presence of multiphoton transitions. We work in the Heisenberg picture, and our approach has the advantage that it allows one to derive effective Hamiltonians even when Raman-like resonance conditions do not hold.


The COVID-19 Tweets Classification Based on Recurrent Neural Network

Abstract: Due to its extensive use in both public and commercial contexts, sentiment analysis on Twitter has recently received much attention, particularly concerning tweets about COVID-19. Information about COVID-19 has been widely spread over social media, resulting in various views, opinions, and emotions about this pandemic, significantly impacting people's health. It is exceedingly challenging for the authorities to find these rumors on these public platforms manually. This paper proposes a framework for text classification using the RNN model and its updates, such as LSTM, BiLSTM, and GRU. This study aims to determine the best recurrent network model for handling cases of Twitter data classification. We utilized Twitter data relevant to COVID-19 and the lockdown with four classification classes (sad, joy, fear, and anger). In addition, this study aims to prove whether GloVe pre-trained word embedding can increase the accuracy of model predictions. The training and testing datasets were split into 80% and 20%, respectively. Therefore, in this experiment an early stopping technique was used with a limit of 15 epochs and a minimum delta of 0.01, meaning that training will be stopped if there is no improvement of 0.1% accuracy after 15 epochs. We used the f1-score average to measure the accuracy of the classification task results. The test results show that the BiLSTM model with GloVe word embedding yields the best f1-score compared to other models. Moreover, in all model testing, the f1-score value of the 'fear' class displays the highest accuracy compared to other classes.


PENGARUH PENGGUNAAN APLIKASI TIKTOK TERHADAP POLA PIKIR KREATIF REMAJA (STUDI PADA SMK NEGERI 3 KOTA BEKASI JURUSAN TEKNIK KOMPUTER DAN JARINGAN)

Abstract: TikTok application is currently very popular and has a pretty good influence as a place to express yourself. The TikTok application can be used to sharpen creative minds when viewing interesting content. Therefore the research is interested in discussing this matter further. This research is discussed using the theory of new media or new media. This study uses a quantitative method with a positivism paradigm. In this study, data was obtained by distributing questionnaires to students of SMK Negeri 3 Bekasi, TKJ Department, class of 2022-2023. The sampling technique is by purposive random sampling. The data analysis technique used by researchers in this studywas one sample Kolmogrov Smirnov with the help of the SPSS 22.0 program. The results of the research can be seen in the simple linear regression test with the results from the R square test it is known that the use of the tiktok application has an effect of 0.083. This states that the use of the tiktok application affects the creative mindset of teenagers at SMK Negeri 3 Bekasi City, Department of Computer and Network Engineering by 8.3%. The results can be explained that tcount > rtable then it is determined that Ho is rejected statistically significant. It can be seen from the results of the t test, the significant value is less than 0.05 (0.000 <0.05) meaning that there is a strong influence between the independent variable of the tiktok application and the dependent variable of a creative mindset. So it can be concluded from this study that the use of the TikTok application has an influence on the creative mindset of adolescents at SMK Negeri 3 Bekasi majoring in Computer and Network Engineering.


Beam dynamics corrections to measurements of the muon anomalous magnetic moment

Abstract: The Muon $g-2$ experiment at Fermilab is making progress towards its physics goal of measuring the muon anomalous magnetic moment with the unprecedented precision of 140 parts per billion. In April 2021, the collaboration published the first measurement based on the first year of data taking. The second result is based on the second and third years of data taking combined. We discuss the corrections to the anomalous spin precession signal due to beam dynamics effects being used to determine the anomalous spin precession frequency for the second result.


Enhancing Students' Motivation and Learning Outcomes in Thematic Learning Using the Discovery Learning Model for Grade V SDN 31 Kinali

Abstract: This research aims to determine the increase in student motivation and learning outcomes in thematic learning using the Discovery Learning model for class V SDN 31 Kinali. This research aims to determine the increase in student motivation and learning outcomes in thematic learning using the Discovery Learning model for class V SDN 31 Kinali. Research subjects 18 students where 10 male students and 8 female students. This research was conducted in two cycles. In each cycle there are activities of planning, implementing, observing, and reflecting. Data collection techniques used observation and tests. The technique for checking the validity of the data used is source triangulation, namely comparing and re-checking the trustworthiness of data obtained through different times and tools by comparing the researcher's data with the learning results. The results of research using the Discovery Learning model can increase student motivation and learning outcomes. In cycle I, there were 5 students who completed with a percentage of 28%, while there were 13 students who did not complete with a percentage of 72%. In cycle II the number of students who completed was 15 people with a percentage of 83%, while there were 3 students who did not complete it with a percentage of 17%.


Enhancing the Zebra Optimization Algorithm with Chaotic Sinusoidal Map for Versatile Optimization

Abstract: In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA)is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibitsgreat optimization capabilities, but the addition of CSM increases its potential even more. This addition greatlystrengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks.CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions,including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM toinvestigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shownby CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatilitythrough multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOAbecomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficultoptimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is arelatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership andfollowing, in which members of the population—zebras in this case—cooperate to solve optimisation issues in thebest possible ways


Factors Affecting Student Satisfaction and Dissatisfaction in a Higher Education Institute

Abstract: In the current competitive environment, one of the most critical issues that organizations deal with is awareness of customer satisfaction and their opinions regarding the organization's performance, products, and services. Organizations that focus their primary activities on customer satisfaction have a competitive advantage. According to systems thinking, academic institutions are also among the organizations whose survival is closely tied to the satisfaction of their students (customers), and therefore, satisfaction is a factor that must be given greater attention than ever before. The main goal of this research is to identify the factors affecting the satisfaction and dissatisfaction of students at a higher education institute, which is examined in three dimensions: the teaching staff, the administrative staff, and the management staff of the institution. Based on interviews conducted with students, the results indicate a significant level of dissatisfaction with the educational dimension and the teaching methods of the professors. Additionally, in the administrative dimension, dissatisfaction is more concentrated in the areas of welfare services and university facilities. In the administrative dimension, failure to fulfil responsibilities correctly has led to student dissatisfaction.


Shape and orientation classification of objects based on their electromagnetic signatures using convolutional neural networks

Abstract: Abstract This study addresses the classification of objects using their electromagnetic signatures with Convolutional Neural Networks (CNNs) trained on noiseless data. The singularity expansion method (SEM) was applied to establish a compact model that accurately represents the ultra-wideband scattered field of an object, independently of its orientation and observation angle. To perform the classification, we used a CNN associated with a noise-robust SEM technique to classify different objects based on their characteristic parameters. To validate this approach, we compared the performance of the classifier with and without SEM pre-processing of the scattered field for different noise levels and for object sizes not present in the training set. Moreover, we propose a procedure that determines the direction of the receiving antenna and orientation of an object based on the residues associated with each complex natural resonance. This classification procedure using pre-processed SEM data is promising, especially when generalizing to object sizes not included in the training set.


Penyuluhan Bahaya Narkoba Di Kalangan Remaja Khususnya Pelajar SMKN 1 Kisaran

Abstract: The purpose of this community service activity is to educate students at SMK Negeri 1 Kisaran on how to avoid drug abuse. drug abuse. This community service activity used an counseling strategy; all activities were planned and conducted with 25 student representatives from each grade and level. The comparison results between pre-test and post-test showed that the PKM can improve the knowledge of participants about the dangers of drugs and drug abuse prevention efforts; the number of participants who were able to mention the types of drugs, the dangers of drugs, and efforts to prevent drug drug abuse prevention efforts increased from 30% to 95%, and the average score reached 87 out of the maximum score. reached 87 out of the maximum score.


Enhanced Multi-Class Newsgroup Document Classification Using N-Gram Approach

Abstract:
With the advancement of technology, consumption of data and information over web is increasing day by day. This has led to rising dependence of our lives on internet data at personal and professional fronts. Being a populous nation with high internet penetration, India is a huge market in digital media. The adaptability to online news, has created a large number of text news data which requires classification into categories for further applications. To address the work for efficient news document classification in the field of computational linguistics, we have proposed a novel approach for Newsgroup classification by combining tokens with a context window size of one, two and three known as unigram, bigram and trigram respectively.
 In our research work, we explored the relevance of trigram by experimenting over different collection of n-grams using three supervised classifiers namely Perceptron, Nearest Centroid, and Random Forest. In the experiment, we found that inclusion of trigram along with unigram and bigram achieves the best result in terms of F1-macro score and accuracy among all three classifiers. Perceptron shows the best results in comparison with Random Forest and Perceptron classifiers, with an accuracy of 0.846945 and an F1-macro score of 0.83055. Significant time was spent on the validation of our efficient approach with the existing literature.


The Role of Community Health Nurses in Promoting Occupational Health and Safety Awareness within Facilities by Embracing Modern Distance Learning Techniques

Abstract: Overall, accessibility, engagement, and cost-effectiveness can all be significantly impacted by the use of remote learning for occupational health and safety training. In order to make significant progress toward raising occupational health and safety awareness within facilities, it is critical to deal with any potential roadblocks and implement plans that will optimize its effectiveness. Using remote learning for occupational health and safety training of facility staff presents both potential benefits and challenges with regard to raising safety awareness and improving occupational health. Community health nurses play a critical role in improving health and wellness in local communities (CHNs). Workplace health and safety is another area where this is pertinent, particularly for smaller businesses located in impoverished areas where access to occupational health professionals may be limited. By utilizing contemporary remote learning technology, CHNs can serve as effective advocates for and teachers of personnel on critical safety procedures. Thus, the purpose of this study was to examine the role of community health nurses in promoting occupational health and safety awareness within facilities by embracing modern distance learning techniques.


Clean and Healthy Living Behavior Education to improve the knowledge of Elementary School Students

Abstract: Clean and healthy living behavior is the main foundation for maintaining individual health and preventing the spread of disease. Education about clean and healthy living behaviors can be done in various ways, such as through counseling, campaigns, the development of educational materials, collaboration with health centers, training, the active role of schools, the use of social media, exemplary examples, and measurement and evaluation. Educational materials that are arranged in an attractive and easy-to-understand manner are the main instruments in this effort. Collaboration with medical personnel, involving health centers, and the active role of schools are recognized as important supporting factors. The use of social media is also integrated as an effective means of reaching a wider audience. By combining these approaches, it is hoped that people can better understand and adopt clean and healthy living behaviors in their daily lives. Evaluation of the effectiveness of educational programs needs to be done to ensure their success and impact on community knowledge and behavior.


RANCANG BANGUN APLIKASI E-VOTING DI SMK NURUL HUDA PETIR BERBASIS WEB DENGAN SENSOR RADIO FREQUENCY IDENTIFICATION (RFID)

Abstract: Information technology is developing rapidly in all aspects of life and until now its application has created conditions conducive to community work. One of the activities that can now take advantage of information technology is e-voting in the election of the chairman and deputy chairman of the organization. SMK Nurul Huda Petir has more than 200 students. The democratic process of selecting the head of this school organization is still used manually. There will be many obstacles, namely data security risks, which will reduce learning time, and require large implementation costs. The problems that arose created an information system called e-voting. E-voting is a system for creating ballots, casting, counting, and displaying ballot results, and generating and maintaining audit trails electronically and digitally. This research aims to speed up, improve security, and reduce implementation costs in the process of election activities at Nurul Huda Petir Vocational School. The application development process-voting uses the method Waterfall with Unified Modelling Language (UML) for system design. System e-voting its build-based web with Framework Codeigniter and added an RFID sensor (Radio Frequency Identification). The results of this research found that the application of e-voting can reduce 80% of implementation costs, improve voice data security, and speed up the process of collecting and calculating voice data.


KLASIFIKASI KEMATANGAN BUAH ALPUKAT MENTEGA BERDASARKAN FITUR WARNA MENGGUNAKAN SUPPORT VECTOR MACHINE

Abstract: The ripeness level of the avocado greatly affects the shelf life of the fruit and also determines the taste of the fruit. Determining the proper ripeness of a fruit will play an important role in increasing the nutritional value and ripeness of avocado affects the quality of avocado oil. Classification of fruit maturity manually has many limitations because it is influenced by human subjectivity so that the application of digital image processing needs to be used.This study aims to classify avocado fruit ripeness which is divided into three categories, namely Unripe, Ripe, and Rotten. This research is intended to facilitate the classification of ripeness in fruit through its color. This study used avocado fruit as an experimental material which was carried out using the SVM (Support Vector Machine) method. The colors of the various avocados and in different positions and conditions of light contrast are used as data to classify the types of avocados. Data collection was taken from the selected Keagle Dataset of 150 data. The data is grouped into two data sets, namely dataset-1 (120 training data and 30 test data) and dataset-2 (90 training data and 60 test data). The image will be converted to grayscale to facilitate the classification process and look for HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue) values to classify the fruit. The results from dataset-1 has an accuracy rate of 86%, 90% precision, and 86% Recall, while dataset-2 has an accuracy rate of 85%, 84% precision, and 85% Recall.


SISTEM INFORMASI PENGELOLAAN TOUR DAN TRAVEL BERBASIS WEB APP PADA CV. KENKEN AGENCY

Abstract: CV. Kenken is a service company in the city of Serang in managing tour and travel reservations still using the manual system. There are many problems including management of tour booking data and WEB-Based Travel On CV. Kenken Agency, still using a ledger, boking data collection and departure schedule is not well recorded, difficulties in tracking documents sent. The method used is the waterfall method, the design tool uses the UML flow map. While the implementation uses the PHP programming language and MySQL data base. With the web-based tour and travel information system on CV. Kenken Agency, is expected to overcome the problems that exist in the CV. Kenken Agency. Therefore, the recording of documents is neat, the departure schedule is well organized, the latest prices, boking and schedules are well recorded.


APPLICATION OF AUGMENTED REALITY IN MOBILE BASED HIJAIYAH LETTER RECOGNITION APPLICATIONS USING MULTIMEDIA DEVELOPMENT METHODS LIFE CYCLE

Abstract: Islamic religious education is an important aspect of Muslim life, and learning Arabic and Hijaiyah letters is an integral part of Islamic education. The use of augmented reality (AR) technology in educational setting is rapidly increasing. This study aims to apply the AR concepts to the creation of mobile Hijaiyah letters using Multimedia Development Life Cycle (MDLC) methodology. The application of AR in this application allows users to interactively learn each hijaiyah letter. Through a mobile device camera, users can see hijaiyah letters that appear virtually on the device screen. The development of this application is based on the needs of users in understanding hijaiyah letters in a more attractive and interactive way. Thus, this application is expected to facilitate users, especially children, to learn hijaiyah letters more effectively.


An Evaluation of Selected Newspapers' Reporting on Hate Speeches during the 2015 Presidential Election in Nigeria

Abstract: Election stakeholders in Nigeria continue to express worry over the growing trend of hate speech, particularly during the 2015 general elections. The influx of advertisements, political innuendos, and propaganda messages in some chosen individuals who were in the forefront of delivering hate speeches in Nigeria prior to the 2015 general elections increased the socio-political climate. With regard to the 2015 Nigerian presidential election, this study aims to investigate the effects of hate speech from a few media reports. Secondary data are heavily cited in this study such as textbooks, official reports and publications, Internet resources, journals, among others. As for analysing the various data gathered, content analysis was performed. This study found that hate speech coming from particular media reports contributed to electoral-related violence and mistrust among Nigerians. Consequently, the electoral process' credibility is affected. This study recommended, among other things, that election stakeholders, in particular INEC, CSOs, Elections Observers and Monitors, should also keep an eye on the media, particularly native language radio stations that utilise hate speech to fan the ember of ethnic and religious separation.


RANCANG BANGUN PERANGKAT RUMAH CERDAS PENCEGAHAN KEBAKARAN BERBASIS IOT

Abstract: Various technologies, including the Internet of Things (IoT), aim to streamline system functionalities. IoT finds application in diverse sectors, notably Smart Homes, enhancing household tasks and bolstering security via internet-connected devices. This study investigates IoT's utilization in detecting different types of smoke—combustion smoke from various sources like cigarettes, paper, and gas. The trials involved testing the MQ2 sensor's efficacy in detecting smoke at varying distances. In testing this device the author conducted several trials on several types of combustion smoke, cigarette smoke, paper combustion smoke and gas. In these trials, the authors obtained several tests of distance differences in detecting smoke using the MQ2 sensor. The devices made in this study have been adjusted to the purpose of the study. The important conclusion of this study is the need to understand the specifications of the device at the beginning of the study, so as not to use 2 (two) microcontrollers


A Novel Method for Hill Cipher Encryption and Decryption Using Gaussian Integers Implemented in Banking Systems

Abstract: Cryptographic tools like Hill's encryption algorithm protect digital data. In this work, we present a novel Hill cipher security method that utilizes Gaussian integers from number theory. Using these intriguing mathematical entities to disguise plaintext values dramatically boosts assault resistance and duration. This research includes a three-pass protocol for encryption and decryption without key exchange, ensuring a safe, efficient, and dependable solution. Pandas is used for efficient data processing, and Numpy for computational tasks, notably matrices. Hill cipher-based encryption and decryption can be utilized in real life. It also demonstrates how to save Pandas DataFrame data to Excel. This strategy assures progress in cryptography. Uniquely designed for banking, it emphasizes its applicability and possible influence on present financial systems.


Does digital inclusive finance promote regional green inclusive growth?

Abstract: Based on the construction of theoretical foundations, this study uses panel data from 30 provinces in China from 2011 to 2021, and employs regression analysis and mediation mechanism tests to explore the relationship between digital inclusive finance and regional green inclusive growth. The findings indicate that digital inclusive finance significantly enhances the level of regional green inclusive growth, and the results remain significant after addressing endogeneity issues and conducting robustness tests. The mechanism test reveals that digital inclusive finance effectively promotes regional green inclusive growth by improving the efficiency of financial services in supporting the real economy.


Integration of ICT for Research and Administrative Work by Teacher Educators in Secondary Stage Teacher Education Institutes

Abstract: The study explored the best practices by Teacher Educators in integrating ICT into Research and Administrative work. Understanding teacher educators’ use of ICT and its influence is central to determining technology’s role and effectiveness during their career. Thus, teacher educators should be assisted and encouraged to develop positive attitudes in learning and to apply ICT in their Profession. The findings showed that both young and experienced teacher educators showed readiness to use ICT in their career.


Monocular depth estimation for 2D mapping of simulated environments

Abstract: This article addresses the problem of constructing maps for 2D simulated environments. An algorithm based on monocular depth estimation is proposed achieving comparable accuracy to methods utilizing expensive sensors such as RGBD cameras and LIDARs. To solve the problem, we employ a multi-stage approach. First, a neural network predicts a relative disparity map from an RGB flow provided by RGBD camera. Using depth measurements from the same camera, two parameters are estimated that connect the relative and absolute displacement maps in the form of a linear regression relation. Based on a simpler RGB camera, by applying a neural network and estimates of scaling parameters, an estimate of the absolute displacement map is formed, which allows to obtain an estimate of the depth map. Thus, a virtual scanner has been designed providing Cartographer SLAM with depth information for environment mapping. The proposed algorithm was evaluated on a ROS 2.0 simulation of a simple mobile robot. It achieves faster depth prediction compared to other depth estimation algorithms. Furthermore, maps generated by our approach demonstrated a high overlap ratio with those obtained using an ideal RGBD camera. The proposed algorithm can find applicability in crucial tasks for mobile robots, like obstacle avoidance, and path planning. Moreover, it can be used to generate accurate cost maps, enhancing safety and adaptability in mobile robot navigation.


Solving the problem of preliminary partitioning of heterogeneous data into classes in conditions of limited volume

Abstract: In the context of the formation of heterogeneous data that differ significantly in nature, even of a small volume, it becomes necessary to analyze them for decision-making. This is typical for many high-tech industrial fields of human activity. The problem can be solved by bringing heterogeneous data to a single view and then dividing it into clusters. Instead of searching for a solution for each data element, it is proposed to use the division of the entire set of normalized data into clusters, and thereby simplify the process of isolating the cluster and making a decision on it. The essence of the proposed solution is the automatic grouping of objects with similar data into clusters. This allows you to reduce the amount of analyzed information by combining a lot of data and perform mathematical operations already for the cluster. When splitting, it is proposed to use the theory of fuzzy logic. The possibility of such an approach is due to the fact that different objects always have several characteristics by which they can be combined. These signs are often not obvious and are poorly formalized. A hierarchical modification of the AFC fuzzy clustering method based on the operation (max- min) of the fuzzy similarity ratio is proposed. The basic concepts and definitions of the proposed method of automatic partitioning of a set of input data, a step-by-step scheme of the corresponding cluster procedure are considered. The efficiency of the proposed method is demonstrated by the example of solving the problem of forming a traffic flow. A numerical experiment has shown that the developed algorithm allows you to automatically analyze heterogeneous data and stably divide them into classes. The application of the proposed modification allows for the preliminary partitioning of data into clusters and allows reducing the volume of analyzed data in the future. There is no need to consider the objects in each case separately.


An improved performance of RetinaNet model for hand-gun detection in custom dataset and real time surveillance video

Abstract: The prevalence of armed robberies has become a significant concern in today’s world, necessitating the development of effective detection systems. While various detection devices exist in the market, they do not possess the capability to automatically detect and alarm the presence of guns during robbery activities. In order to address this issue, a deep learning-based approach using gun detection using RetinaNet model is proposed. The objective is to accurately detect guns and subsequently alert either the police station or the bank owner. RetinaNet, the core of the system, comprises three main components: the Residual Neural Network (ResNet), the Feature Pyramid Network (FPN), and the Fully Convolutional Networks (FCN). These components work together to enable real-time detection of guns without the need for human intervention. Proposed implementation uses a custom robbery detection dataset that consists of gun, no-gun and robbery activity classes. By evaluating the performance of the proposed model on our custom dataset, it is evident that the ResNet50 backbone architecture yields outperforms for the accuracy in robbery detection that reached in 0.92 of Mean Average Precision (mAP). The model effectiveness lies in its ability to accurately identify the presence of guns during robbery activities.


Computational prediction in the problem of stereo image identification

Abstract: The paper examines the issues of increasing the efficiency and reliability of stereo image identification through computational prediction of the position and size of the uncertainty zone in which the desired correspondence point is known to be located. A control point is selected on one of the stereo images, for which it is necessary to find a correspondence point on the second stereo image. Based on the known parameters of the stereoscopic television system and the coordinates of the control point, using the mathematical apparatus proposed in the work, the coordinates of the boundaries of the uncertainty zone on the second stereo image are calculated. The second point of correspondence is found by the search procedure by comparing identical small areas with centers in the control point on the first stereo image and in the points of the uncertainty zone on the second; the comparison is made according to the criterion of minimum quadratic mismatch of intensities. The necessary a priori information for implementing the method is the maximum heights of the relief displayed on stereo images. The ratios of linear dimensions on a flat relief and on an image formed according to the principle of central projection were obtained. Relationships have been obtained that make it possible to obtain, by calculation, the coordinates of the correspondence points and the stereoscopic mismatch for stereo images of a flat relief. For stereo images of a volumetric relief, calculation formulas are obtained for determining the boundaries of the zone of uncertainty in the second stereo image within which the search for the point of correspondence is carried out. The correctness and performance of the obtained relationships are confirmed by computer modeling. Limiting the size of the search area by means of calculated prediction of the uncertainty zone makes it possible to reduce the computational and time costs of the search procedure. Due to this, the efficiency of identifying stereo image points increases and the likelihood of false identification decreases.


A method of storing vector data in compressed form using clustering

Abstract: The development of the machine learning algorithms for information search in recent years made it possible to represent text and multimodal documents in the form of vectors. These vector representations (embeddings) preserve the semantic content of documents and allow the search to be performed as the calculation of distance between vectors. Compressing embeddings can reduce the amount of memory they occupy and improve computational efficiency. The article discusses existing methods for compressing vector representations without loss of accuracy and with loss of accuracy. A method is proposed to reduce error by clustering vector representations using lossy compression. The essence of the method is in performing the preliminary clustering of vector representations, saving the centers of each cluster, and saving the coordinate value of each vector representation relative to the center of its cluster. Then, the centers of each cluster are compressed without loss of accuracy, and the resulting shifted vector representations are compressed with loss of accuracy. To restore the original vector representations, the coordinates of the center of the corresponding cluster are added to the coordinates of the displaced representation. The proposed method was tested on the fashion-mnist-784-euclidean and NYT-256-angular datasets. A comparison has been made of compressed vector representations with loss of accuracy by reducing the bit depth with vector representations compressed using the proposed method. With a slight (around 10 %) increase in the size of the compressed data, the absolute value of the error from loss of accuracy decreased by four and two times, respectively, for the tested sets. The developed method can be applied in tasks where it is necessary to store and process vector representations of multimodal documents, for example, in the development of search engines.


Profile of ICT Literacy in Science Learning Assisted by Google-Sites for Junior High Students

Abstract: Education in the era of revolution must utilize the application of ICT in science learning which can refer to 21st- century skills is ICT literacy. The purpose of this study aims to see the effect of Google sites-based science learning on the profile of ICT literacy of junior high school students. The method used is quantitative research by giving post-tests to two different classes that have implemented the learning The two classes consisted of the experiment class and the control class. The sampling technique used was cluster random sampling. The results obtained were then analyzed and discussed descriptively quantitative. The results show that the application of Google sites-based learning can affect the achievement of students' ICT literacy profile by obtaining an average score of high categories in the experiment class and medium category in the control class. Based result of study, there is a significant influence of the use of Google-sited based in science learning on ICT literacy in junior high school students


QR Code Use and Identification Problems in Tourism

Abstract: The integration of technology with tourism increases its efficiency and improves the tourist experience, especially through QR codes and NFC technologies, which are already widely used. The number of research related to digitization and the sustainability of tourism is increasing, and the focus is shifting towards ICT, AR and VR. Mobile services like QR codes are crucial in tourism, improving service availability and user experience. The article analyzes the importance of QR codes and image processing in logistics and tourism. The focus is on the technological foundations, recognition, and decoding methods of QR codes, highlighting the role of artificial intelligence. The article discusses in detail the application of QR codes in tourism, along with technological challenges and development opportunities. It also presents future research directions and the potential effects of QR codes on tourism.


A Systematic Literature Survey on Electronic Health Record System Using Blockchain

Abstract: Abstract-Compared with traditional paper-based medical records , Electronic health records (EHRs) are widely used because of their efficiency, secu- rity, and reducing data redundancy.The Electronic Health Record (EHR) system is a major technolog- ical development in healthcare that has completely changed how patient data is collected, stored, and used. Through the use of cutting-edge digital tech- nologies, the EHR system offers a safe, centralized location for all patient data. Healthcare personnel may easily access, edit, and share patient informa- tion thanks to its user-friendly interfaces, which helps them make more informed clinical decisions and provide better treatment.The system developed using the Ethereum network using Ganache. The measured approach is to store patients data and execute functions in a decentralized system using blockchain smart contracts. To further protect pa- tient privacy and adhere to privacy laws, the system places a high priority on data security and uses strong authentication and encryption mechanisms. Keywords-Blockchain, Decentralized, Ethereum, Healthcare, Smartcontacts,


A Systematic Survey on E-VOTING SYSTEM USING BLOCKCHAIN TECHNOLOGY

Abstract: Abstract–An electronic voting system based on blockchain technology, addressing the challenges of traditional paper ballots and digital voting methods. The system aims to provide security, integrity, transparency, and privacy for voters. It evaluates various blockchain frameworks and proposes a novel e-voting system that can be particularly useful for small-scale elections within corporate environments. The implementation relies on Ethereum’s smart contracts, with development and testing facilitated by the Truffle framework and Ganache as the Ethereum client for testing. It also highlights the historical challenges in developing secure electronic voting systems that balance fairness and privacy with transparency and flexibility. The paper explores the application of blockchain technology in addressing these issues, emphasizing the potential of distributed ledger technology for hosting nationwide elections securely and cost-effectively. The paper also underscores the significance of blockchain technology in ensuring anonymity, privacy, verifiability, and fairness in the voting process. Keywords- Blockchain, Decentralized, Ethereum, E-Voting, Smart Contracts.


A Survey Paper on Automation of Attendance System through Facial Recognition

Abstract: Abstract—In recent times almost, everything around us is being automated in some way just to make life easier for us. With the use of machine learning and deep learning we can allow non- living objects to program themselves to self-learn from the available data and perfect itself in the process. The process of recording attendance in educational institutions and workplaces has traditionally relied on manual methods, which are time- consuming, error-prone, and lack the level of security and accuracy desired in today’s digital age. This research presents a technological solution to these challenges. This technology offers a range of compelling advantages. It eliminates the need for manual attendance tracking, reducing the potential for errors and administrative workload. Moreover, it enhances security by ensuring that only authorized individuals can mark their attendance. Real-time data is available for monitoring and generating attendance reports, offering valuable insights into punctuality and attendance patterns. This research provides a comprehensive overview of the system’s architecture, algorithms, and the underlying technology, along with a discus- sion of the ethical and privacy considerations associated with face recognition. By implementing this innovative technology, educa- tional institutions and organizations can streamline attendance management, increase operational efficiency, and contribute to a more secure and productive environment. Keywords: Face recognition, LBPH, Haar-cascade, face detection, Viola n Jones, openCV, Adaboost


Comparative Analysis of Sentiment in Original and Summarized Tweets: Leveraging Transformer Models for Enhanced NLP Insights

Abstract: This paper investigates the sentiments of Twitter users towards the emergent topic of ChatGPT, leveraging advanced techniques in natural language processing (NLP) and sentiment analysis (SA). Our approach uniquely incorporates a dual setting for sentiment analysis: one analyzes the sentiments of original, full-length tweets, while the other first condenses these tweets into succinct summaries before performing sentiment analysis. By employing this dual approach, we are able to offer a comparative analysis of sentiment assessment pre- and post-text summarization, exploring the accuracy and reliability of the summarized sentiments. Central to our methodology is the application of Transformer models, specifically ProphetNet, which facilitates a deeper and more nuanced understanding of the original text. Unlike traditional methods that rely on keyword extraction and aggregation, our approach generates coherent and contextually rich summaries, providing a novel lens for sentiment analysis. This research contributes to the field by presenting a comprehensive study comparing sentiment analysis outcomes between original texts and their summarized counterparts, and examining the effectiveness of different NLP techniques, namely NLTK and the Transformer-based ProphetNet model. The findings offer valuable insights into the dynamics of sentiment analysis in the context of social media and the efficacy of state-of-the-art NLP technologies in processing complex, real-world data.


Analysis of the Impact of Dataset Quality on Task-Oriented Dialogue Management

Abstract: Task-oriented dialogue systems have become crucial for users to interact with machines and computers using natural language. One of its key components is the dialogue manager, which guides the conversation towards a good goal for the user by providing the best possible response. Previous works have proposed rule-based systems, reinforcement learning, and supervised learning as solutions for correct dialogue management; in other words, select the best response given input by the user. This work explores the impact of dataset quality on the performance of dialogue managers. We delve into potential errors in popular datasets, such as Multiwoz 2.1 and SGD. For our investigation, we developed a synthetic dialogue generator to regulate the type and magnitude of errors introduced. Our findings suggest that dataset inaccuracies, like mislabeling, might play a significant role in the challenges faced in dialogue management.


Deep Learning based Smart Image Search Engine

Abstract: Abstract: This paper introduces a new reverse search engine integration into content-based image retrieval (CBIR) systems that employs convolutional neural networks (CNNs) for feature extraction. It generates global descriptors using pre-trained CNN architectures such as ResNet50, InceptionV3, and InceptionResNetV2. It retrieves visually similar images without depending on linguistic annotations. Comparative analysis against existing methods, such as Gabor Wavelet, CNN-SVM, Metaheuristic Algorithm, etc., has been tested, and it proves the superiority of the proposed algorithm, the Cartoon Texture Algorithm, in CBIR. As the Internet sees an exponential growth of different data types, the importance of CBIR continues to grow. In order to efficiently retrieve images, solely relying on image features while ignoring metadata is exactly what we need. As such, this paper is a reminder of the need for CBIR in this changing world. They showed that CBIR continues to be quite effective in the age of the Internet. Their proposed model for CBIR, which integrates ResNet-50-based feature extraction, a neural network model trained on different image datasets, and clustering techniques to make retrieval fast, provides a significant improvement in accuracy and efficiency for content-dependent image retrieval. This methodology is likely to be very useful as we work with the increasingly huge data of vision and beyond on the Internet. It provides a good basis for an effective image search and retrieval system


Classifying Opinions and Sentiments on Social Networking Sites using Machine Learning Classifiers

Abstract: Abstract: People now publish evaluations on social media for any product, movie, or location they visit as a result of the Web's rapid development. Customers and product owners can both benefit from the reviews posted on social media in order to assess their offerings. Compared to unstructured data, structured data is simpler to analyze. The reviews are mostly available in an unstructured format. Aspect-Based Sentiment Analysis extracts from the reviews the features of a product and then calculates sentiment for each feature. Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone expressed in a piece of text, such as a sentence, paragraph, or document. Machine leaning classifiers are used to classify sentiments. Machine learning classifiers cannot process raw text so raw text needs to be converted into vectorized form. Feature extraction techniques are used to convert raw text to numerical form also called vectorized data. In present research, four feature extraction techniques with five different machine learning classifiers namely, SVM, Logistic Regression, Naïve Bayes, Random Forest, and KNN are applied to classify sentiments associated with tweets. Two online twitter data sets containing tweets about product reviews and tweets about people's thoughts on public policy are selected for experimentation. In the experiments done, it has been found that the SVM classifier using TFIDF and HFE shows better performance as compared to other classifiers. Using the feature sets, 97% accuracy and 98% F1- score is achieved in the aspect category prediction task.


Studi Literatur Mengenai Prediksi Kepuasan GrabFood Menggunakan Machine Learning

Abstract: The rapid growth of food delivery applications, such as GrabFood, has revolutionized the way consumers access and enjoy their favorite meals. Ensuring customer satisfaction is crucial for these platforms to maintain their competitive edge and foster customer loyalty. If many customers are not satisfied with the service or anything provided by GrabFood, it is possible that they will leave the application soon or later. This research is aimed to review some literature related to customer satisfaction of the food delivery application with the use of machine learning. From this review, one can have more information and insight on how machine learning can help to find the satisfaction level of the customers. The method which is used in this research is literature review which reviews the collection of literature. Varied literature with different variables has been resulted from this research. Many of them use different methods of machine learning.


A Perceptive Mobile Program to Analyze Finger Posture on the Piano using Machine Learning and Object Detection

Abstract: People from various professions are involved in the production of anime, including directors, animation directors, character designers, and voice actors/actresses. Specifically, the role of an animation director is of importance in the realm of animation. The animation director serves as the unifying force in shaping the animation's style by meticulously reviewing and redrawing the key animations provided by the key animators. The aim of this study is to develop a redrawing system using GLCIC to reduce the workload on animation directors when redrawing original key animation. Specifically, this study devised a system that employs GLCIC to analyze and learn the distinctive drawing styles of individual animation directors from images of their work and subsequently apply those styles to the conversion process. In the experiment, we asked people whose hobby is drawing to experience the developed system, and conducted a qualitative evaluation using a questionnaire and a quantitative evaluation using KLM analysis. As a result, we found that there were issues with ease of modification and UI. Additionally, the KLM analysis revealed that improving the system could reduce work time by a quarter. In the future, we think to improve the system with the aim of increasing work efficiency.


WorkTune: A Music-Assisted Writing Efficiency Evaluation and Promotion Platform using Artificial Intelligence and Natural Language Processing

Abstract: In a world where music accompanies various tasks, our paper addresses the challenge of understanding the impact of background music on work efficiency. The background problem centers on the lack of precision in existing studies, overlooking individual preferences and work types. Our proposed solution is a Python-based application that evaluates an individual's work efficiency while listening to different music genres [1]. The user-friendly interface incorporates features like music category selection, login options, and real-time statistics tracking [2][3]. Challenges, such as diverse user interactions and limited data, were addressed through a feedback channel for continuous improvement. The application underwent experiments, including regression model evaluations for essay grading and SVM parameter tuning [4]. Results indicated superior performance, emphasizing the relevance of ensemble learning and optimal parameter selection. This application provides a nuanced understanding of how background music influences work efficiency, offering a personalized approach that people can leverage for enhanced productivity and satisfaction in various work scenarios.


An Optimized Writing Efficiency Tool with Integrated Gamification and Work Interval Timer for Enhanced Essay Productivity and AI Advice

Abstract: both productivity and writing quality. Students often struggle with overwhelming tasks, slow progress, and lack of motivation during the writing process. Our proposal combines the Pomodoro Technique, gamification, and an AI-powered writing quality checker to offer a holistic solution [8]. The Pomodoro Technique breaks down tasks, while gamification elements, including a playful chicken character, make writing enjoyable. An AI-based writing quality checker provides real-time feedback on grammar, sentence structure, and clarity [9]. Challenges, such as balancing interactivity and efficiency, were addressed through thoughtful design decisions. Experimentation involved applying the application to various writing scenarios, showcasing its adaptability. Results demonstrated enhanced productivity, improved writing quality, and increased user satisfaction. This comprehensive approach addresses the shortcomings of existing methodologies and provides users with a valuable tool to navigate the challenges of essay writing efficiently and effectively.


Domain Adaptation Regularized LayoutLM with Automatic Domain Discovery from Topic Modelling

Abstract: In this paper, we propose using domain adaptation to improve the generalizability and performance of LayoutLM, a pre-trained language model that incorporates layout information of a document image. Our approach uses topic modelling to automatically discover the underlying domains in a document image dataset where domain information is unknown. We evaluate our approach on the challenging RVL-CDIP dataset and demonstrate that it significantly improves the performance of LayoutLM on this dataset. Our approach can be applied to other NLP models to improve their generalization capabilities, making them more applicable in real-world scenarios, where data is often collected from a variety of domains.


An Efficient Domain-Independent Approach for Supervised Keyphrase Extraction and Ranking

Abstract: We present a supervised learning approach for automatic extraction of keyphrases from single documents. Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings. The ranking component of our proposed solution is a fairly lightweight ensemble model. Evaluation on benchmark datasets shows that our approach achieves significantly higher accuracy than several state-of-the-art baseline models, including all deep learning-based unsupervised models compared with, and is competitive with some supervised deep learning-based models too. Despite the supervised nature of our solution, the fact that does not rely on any corpus of “golden” keywords or any external knowledge corpus means that our solution bears the advantages of unsupervised solutions to a fair extent.


Impact of 5G on Internet of Things and its Challenges

Abstract: In this era of rapid technological growth, the Internet of Things (IOT) refers to a system of interrelated, internet-connected objects that are able to collect and transfer data over a wireless network without human intervention. We see the IOT as billions of smart, connected things (a sort of ―universal global neural network in the cloud) that will encompass every aspect of our lives, and its foundation is the intelligence that embedded processing provides. The IOT is comprised of smart machines interacting and communicating with other machines, objects, environments and infrastructures. As a result, huge volumes of data are being generated, and that data is being processed into useful actions that can ―command and control things to make our lives much easier and safer—and to reduce our impact on the environment. The creativity of this new era is boundless, with amazing potential to improve our lives. The following thesis is an extensive reference to the possibilities, utility, applications and the evolution of the Internet of Things. The rollout of commercial 5G cellular networks has commenced, marking a significant milestone in the adoption of 5G and the Internet of Things (IoT). This adoption is propelled by various factors, such as rising consumer and enterprise demands, coupled with the availability of cost-effective devices. Notably, substantial investments by operators in 5G technology, spectrum allocation, and infrastructure development, alongside the establishment of global standards, are key drivers fuelling growth and generating heightened market interest in the IoT. The substantial investments made by operators in 5G technology, spectrum allocation, and infrastructure, coupled with the adoption of global standards, play a pivotal role in stimulating growth and generating heightened market interest in the Internet of Things (IoT). The ongoing evolution of 5G mobile cellular networks, derived from existing 4G frameworks, underscores their adaptability to diverse use cases, both present and future-oriented. With a forward- looking approach, 5G infrastructure is poised to address current demands like smart energy applications while proactively accommodating forthcoming innovations such as autonomous vehicles. In navigating this technological transition, mobile operators face the challenge of ensuring that their networks remain adept at supporting a spectrum of present and future use cases. Strategic investment management is imperative for operators to seamlessly transition their networks to 5G while upholding the continuity of service for their customers Key Words: Scalability, safety, security, rapid growth, interoperability, devices, interconnections, 5G with Internet of things, healthcare, smart city