Distributed Ledger Technology (DLT) Regulation

Abstract: The DLT Regulation aims to introduce a pilot regime for the practical application of distributed ledger technology (DLT) in post-trade services. The DLT Regulation seeks to provide a regulatory framework for the development of DLT multilateral trading facilities (DLT MTFs) and DLT securities settlement systems (DLT SSS), including for the granting and withdrawal of specific permissions (that is, permissions granted under this new regulation would allow market participants to operate a DLT market infrastructure and provide their services across all Member States). It aims at providing a mechanism, procedures and rules for allowing market infrastructures to experiment DLT with legal certainty and flexibility.


Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Abstract: Recognizing the fundamental role of learners' emotions in the educational process, this study aims to enhance educational experiences by incorporating emotional intelligence (EI) into teacher robots through artificial intelligence and image processing technologies. The primary hurdle addressed is the inadequacy of conventional methods, particularly convolutional neural networks (CNNs) with pooling layers, in imbuing robots with emotional intelligence. To surmount this challenge, the research proposes an innovative solution—introducing a novel learning focal point (LFP) layer to replace pooling layers, resulting in significant enhancements in accuracy and other vital parameters. The distinctive contribution of this research lies in the creation and application of the LFP algorithm, providing a novel approach to emotion classification for teacher robots. The results showcase the LFP algorithm's superior performance compared to traditional CNN approaches. In conclusion, the study highlights the transformative impact of the LFP algorithm on the accuracy of classification models and, consequently, on emotionally intelligent teacher robots. This research contributes valuable insights to the convergence of artificial intelligence and education, with implications for future advancements in the field.


Efficient Path Planning Algorithm for Mobile Robots Performing Floor Cleaning Like Operations

Abstract: In this paper, we introduce an efficient path planning algorithm designed for floor cleaning applications, utilizing the concept of Spanning Tree Coverage (STC). We operate under the assumption that the environment, i.e., the floor, is initially unknown to the robot, which also lacks knowledge regarding obstacle positions, except for the workspace boundaries. The robot executes alternating phases of exploration and coverage, leveraging the local map generated during exploration to construct a STC tree, which then guides the subsequent coverage (cleaning) phase. The extent of exploration is determined by the range of the robot's sensors. The path generation algorithms for cleaning fall within the broader category of coverage path planning (CPP) algorithms. A key advantage of this algorithm is that the robot returns to its initial position upon completing the operation, minimizing battery usage since sensors are only active during the exploration phase. We classify the proposed algorithm as an offline-online scheme. To validate the effectiveness and non-repetitive nature of the algorithm, we conducted simulations using VRep/MATLAB environments and implemented real-time experiments using Turtlebot in the ROS-Gazebo environment. The results substantiate the completeness of coverage and underscore the algorithm's significance in applications akin to floor cleaning.


Information Security and Risk Control Model Based on Plan-Do-Check-Action for Digital Libraries

Abstract: To continuously and effectively control the potential risk factors of digital libraries, further improve the quality of digital library usage, ensure the smooth operation and information security of digital libraries, a security and risk factor control model for digital libraries based on the Plan-Do-Check-Action is constructed. A digital library security and risk identification model based on improved support vector machines is constructed. Based on the obtained risk identification results, a Plan-Do-Check-Action management model is implemented for information security in digital libraries. The R2L dataset in the network attack dataset is used for training and testing model performance. Then the proposed method is applied to two different digital libraries. The average accuracy of the risk identification model proposed in the research is 96.46%. The risk error of each level is maintained within 0.4%, which is within an acceptable range. The proposed method has a high recognition effect. It can be used to identify common security and risk issues encountered in digital libraries, and improve the information security performance.


Cryptographic Solutions for Data Security in Cloud Computing:

Abstract: Due to the recent explosion in the amount of data being created by various social media platforms, e-commerce websites, and other businesses, a paradigm shift from on-site data centers to the cloud is required. Concerns about privacy and secrecy have been a major obstacle to the mainstream adoption of cloud computing. The best approach to protect the confidentiality and privacy of cloud data is by using cryptographic techniques. Researchers have developed several cryptographic algorithms, but they all have lengthy, linear, predictable, memory-intensive execution times. The performance of the CPU, memory, run-time trend, and throughput of the three cryptographic schemes: Enhanced RSA (ERSA), Non-Deterministic Cryptographic Scheme (NCS), and Enhanced Homomorphic Scheme (EHS) are compared using RAsys. The experiment’s results demonstrated that NCS and EHS produced non-linear and non-deterministic run times. Again, NCS and EHS produced the lowest throughput and memory consumption for text and numeric data types when data sizes of 5n*102(KB(∈1,2,4,10,20,40) were processed. However, ERSA produced a run-time trend that was deterministic, linear, and predictable


Internet of Things (IoT) in healthcare: A systematic review of use cases and benefits

Abstract: This paper presents a systematic review of the Internet of Things (IoT) in healthcare, exploring its use cases, benefits, and challenges. Through an analysis of diverse sources, including journal articles, conference proceedings, and white papers, we identify critical applications of IoT in patient monitoring, medical supply chain management, clinical operations, and telehealth services. Our findings highlight the transformative impact of IoT on healthcare, offering improved patient care through personalized treatments and early detection, enhanced operational efficiency, and facilitated evidence-based decision-making. Despite the promising benefits, challenges such as privacy, security, and interoperability persist, necessitating further research and technological advancements. This review underscores the potential of IoT to revolutionize healthcare delivery, making it more efficient, accessible, and patient-centered.


Examining Selection Pressures in the Publication Process through the Lens of Sniff Tests

Abstract: Abstract Economics papers increasingly report balance, pre-trend, placebo, and other “sniff tests,” rejection of which is bad news for authors, undermining the credibility of their main results. We derive nonparametric bounds on the latent proportion of significant sniff tests removed by the publication process (whether by p-hacking or relegation to the file drawer) and the proportion whose significance was due to true misspecification, not bad luck. Using a hand-collected sample of nearly 30,000 sniff tests, we estimate a removal rate of over 30% for balance tests in randomized controlled trials and a misspecification rate of over 40% for other tests.


The Impact of Social, Emotional, and Educational Adjustment on Academic Performance among Male and Female Residential School Students

Abstract: Adjustment is a key component of a fulfilling and prosperous life. Achieving a balanced level of life becomes essential as we progress through life to reach a level of equilibrium of adjustment in all spheres of our lives. This holds particular significance during the student years generally and in residential settings particularly, when adjustment patterns can have a substantial influence on educational outcomes. Therefore, the current study aimed to examine the gender difference in social, emotional, and educational adjustment and its combined effect on academic achievement among boys and girls residential school students. Random sampling was used in this study with a sample size of 120 residential school students (60 boys and 60 girls), further divided based on levels of high (30 boys and 30 girls) and low (30 boys and 30 girls) adjustment patterns, studying in 11th and 12th grade at Akal Academy, Baru Sahib. The data were collected by administering an Adjustment inventory (Sinha and Singh, 1971), and GPA for obtaining academic achievement scores. Multiple Linear Regression and the ‘t' test were used to analyze the data. The findings show that boys outperformed their female counterparts in terms of social adjustment. However, there was no significant difference among boys and girls in emotional and educational adjustment. Additionally, the combined effect of social, emotional, and educational adjustment did not significantly impact academic achievement. These nuanced findings throw light on the complex interplay of adjustment patterns and suggest further investigation into its varied impact on academic achievement.


Email Spam Detection in the Age of Cyber Threats: a Comprehensive Approach Integrating Cleaning, Analysis, and Modeling

Abstract: With the escalating sophistication of cyber threats, the imperative task of safeguarding email communication from spam has become increasingly challenging. This research presents a holistic approach to email spam detection, incorporating meticulous data cleaning, in-depth exploratory data analysis (EDA), and advanced modeling techniques. The study delves into the performance evaluation of diverse machine learning classifiers, including Gaussian Naive Bayes, Multinomial Naive Bayes, and Bernoulli Naive Bayes. Exploratory visualizations, such as word clouds and bar plots, contribute to a comprehensive understanding of the textual content in spam and ham messages.


An Empirical Study of Different Reinforcement Learning Algorithms for Resource Allocation in Cloud Computing

Abstract: Regarding the increasing importance of cloud computing in modern IT architecture, it is crucial to create highly efficient resource allocation algorithms. This work conducts an empirical investigation into the utilisation of several reinforcement learning methods for optimising resource allocation in cloud computing environments. Our objective is to assess the efficiency of RL algorithms in a dynamic environment with fluctuating workloads, focusing on resource utilisation, cost effectiveness, and optimality. This research examines the effects of altering cloud settings in order to integrate theoretical reinforcement learning concepts into a practical resource management system. In the literature review, we consider the traditional resource allocation techniques and their inability to accommodate the changing demand. We additionally analyse the available research employing machine learning approaches, paying special attention to RL in cloud computing resource distribution. The methodology describes the research design, detailing the employed RL algorithms Q-learning, Deep Q Networks (DQN), and Proximal Policy Optimization (PPO). We explain the data collection procedure that involves different workloads and also situations to mimic real environments. Performance of each RL algorithm is presented in the experimental results based on the resource utilization, cost efficiency and also system responsiveness. Q-learning, DQN and PPO are being tested which provides a better understanding of their pros and cons. Discussion that follows the Interprets results of these findings bringing to light many challenges along the way as well as possible directions for future inquiry. Therefore, this research fills in the evolving landscape of cloud computing by demonstrating RL algorithms’ adaptability and effectiveness regarding resource allocation challenges under the dynamic environments.


Survey on Multifaceted Impacts: Assessing the Integration of Augmented Reality (AR) and Virtual Reality(VR) in Education for Autistic Children

Abstract: This project seeks to develop a groundbreaking Augmented Reality (AR) and Virtual Reality (VR) learning platform tailored for autistic children, addressing their unique educational and social challenges. Featuring multi-user access for collaborative learning, the platform incorporates behavioral analysis to customize learning paths based on individual preferences and challenges. Immersive AR/VR environments simulate real-world scenarios, facilitating the practice of everyday life skills and social interactions in a controlled virtual space. Parental involvement is key, with features for progress tracking and guidance, aiming to empower autistic children with a personalized, inclusive, and supportive learning experience that bridges the gap between home and school. The user-friendly interface ensures accessibility, contributing to the educational empowerment of autistic children in an engaging virtual environment.


PENERAPAN MODEL PEMBELAJARAN NUMBERED HEADS TOGETHER (NHT) DALAM MENINGKATKAN HASIL BELAJAR MATEMATIKA DENGAN MATERI LUAS PERMUKAAN SERTA VOLUME KUBUS DAN BALOK DI KELAS VIII-8 SMPN 3 BIREUEN SEMESTER GANJIL TAHUN PELAJARAN 2021/2022

Abstract: This research aims to find out: Can the application of the Numbered Heads Together (NHT) learning model improve mathematics learning outcomes in the surface area and volume of cubes and blocks in class VIII-8 at SMPN 3 Bireuen? The research method used is classroom action research (PTK) which consists of 2 cycles. The research subjects were 35 students in Class VIII-8 of SMP Negeri 3 Bireuen for the 2021/2022 academic year. Data analysis uses cooperative descriptive analysis techniques by comparing initial conditions with the results achieved in each cycle, and qualitative descriptive analysis of observation results by comparing the results of observations and reflections in cycle I and cycle II. The increase in learning outcomes is shown by the percentage of completeness of learning outcomes obtained in the first cycle of student learning outcomes. Of the 35 students who took the first cycle test, 27 students (77.14%) completed the test while 8 students (22.86%) did not complete it. . In cycle II, the results obtained were that of the 35 students who took the cycle II test, 33 (94.29%) students completed the test, while 2 (5.71%) students did not complete it.


Human Capital, Education, and Economic Growth in Morocco: An Empirical Investigation

Abstract: The purpose of our research is to investigate the impact of human capital as a proxy for education on Moroccan economic growth. We mobilize a database derived from the World Bank and Penn World Table for this purpose. According to empirical results based on multiple linear models, human capital is a significant driver of economic growth in Morocco. Other control variables, such as domestic credit provided by the banking sector, savings level, trade openness, government effectiveness, and regulatory quality, also had a positive impact on growth, but their significance was not confirmed. Based on the study, corruption has a negative and significant impact on economic growth.


Development of Tools for Creating Parallel Data Mining Algorithms

Abstract: The purpose of this work is to develop tools for building parallel data mining algorithms for execution in a distributed environment. A formal model of a data mining algorithm is proposed, characterized by a representation of the algorithm in the form of a set of independent operations that change the state of the knowledge model and structural blocks that allow modifying the structure of the algorithm, including for parallel execution. A method is proposed for creating parallel algorithms for data mining, in contrast to existing ones, using a decomposition of the algorithm into thread-safe functional blocks and allowing parallelization, both by changing the structure of the parallel algorithm and by configuring its execution. A methodology is proposed for parallelizing data mining algorithms, which differs from those known in that the proposed method of creating parallel data mining algorithms taking into account the characteristics of a distributed environment is applied to sequential analysis algorithms. To create parallel data mining algorithms, software templates built on the basis of a formal model and separating the implementation of the algorithm from distributed execution tools are proposed. A library of parallel data mining algorithms has been developed for execution in a distributed environment, including the proposed templates.


Exploring the Educational Implications of Actor-Network Theory

Abstract: The purpose of this study is to examine what ANT is and to explore the educational implications of it. The characteristic of ANT is that it defines non-humans as ‘actors’ who constitute society on an equal basis with humans. A ‘black box’ refers to when actors build stable relationships and appear as if they are one object. A ‘network’ is a set of unfixed relationships between actors. ‘Translation’ refers to the process of forming a network of relationships. Translation consists of four steps: raising the issue, attracting attention, registering, and mobilizing.The educational implications of ANT are, first, to break away from the limitations of existing teaching and learning theories such as behaviorism, cognitivism, and constructivism, and to develop new teaching and learning theories necessary in the age of science and technology. Second, to move away from me-centeredness and emphasize community-centeredness. Third, ANT stresses the importance of ecological conversion education.


The Impact of Presentation Activities on College Students’ Self-efficacy

Abstract: In this study, we aim to identify the correlation between a student’s experience with presentations and self-efficacy, and to seek ways to teach the art of giving presentations in university classes. For this purpose, self-efficacy was measured before and after presentation practice for students in Presentation Techniques, a liberal arts subject at S University, and students' perceptions of the presentation experience were investigated. As a result of the survey, presentations were found to be helpful in improving self-efficacy, and were especially effective in improving social self-efficacy. These results show that presentation education can be an effective teaching method in teaching social and communication skills, and therefore, there appears to be a need to strengthen presentations in college classes.


A Study on Reading Programs in the Convergence Era : Focusing on Book-trailer Activities

Abstract: This study aims to develop the reading practicum for university students by developing an effective reading program suitable for the convergence era. Reading is a key skill that contributes to developing convergent competencies in university liberal arts education. In particular, ‘Book-trailers’ (video trailers that introduce books) are important in that they produce the results of reading using media.The contents of teaching-learning are as follows. First, once learners know the difference between letter-based text and multimodal text, they can adapt them to the studies. Next, learners experience becoming a book trailer producer and a book trailer consumer. Lastly, the learner knows the respective functions of the book trailer for promotional purposes and as a result after reading.As a result of this program, learners engaged in active reading by understanding book features and contents from a multifaceted perspective and by producing multimodal texts. Furthermore, this program is one of the solutions to solving the problem of learners' declining literacy and reading comprehension skills and can also be expanded into the reading culture industry.


‘Robot’, A Mirror for Humans : Liberal Arts Education in the Era of Technological Domination and How to Read this Era

Abstract: The winds of transition towards the ‘Fourth Industrial Revolution’ are very strong. The field of education is never outside their sphere of influence, and liberal arts education is no exception. However, it seems necessary for us to reflect differently on the fact that the trend of educational responses to adaptively accept current changes is only being strengthened. For instance, educational attempts to lead students to reflect critically and fundamentally on the prevailing trends of a particular era need to be made more actively than they are now, at least in the field of liberal arts education. This is because leading the generation who will live in a rapidly changing world to be aware of the historical context in which they are located has an educational weight that is difficult to overlook. In this regard, educational resources worth paying attention to are the redefinition discourse on the ‘human-machine’ and the preceding history of the digital age and hi-tech era symbolized by ‘robots’. The trajectories that intersect intellectual history, cultural history, and real history serve as a treasure trove of rich narratives and an important educational resource because they allow us to focus on the journey humanity has taken to reach the present stage. The field of liberal arts education, with its multidisciplinary characteristics, is the most appropriate place for these resources.


A Study on the Competence-based General Education and the Development of Convergent Thinking : Focusing on the Analysis of the Learner Centered Teaching-learning Practices Using Poster Presentations

Abstract: This paper explored the expansion of learners' convergent thinking and the practice of convergence general education through the analysis of poster presentation cases. This course is based on interdisciplinary content in the STS, and operates in a standardized flipped learning, which is learner-centered. Learners from different majors will participate in these classes, and student presentations take the form of poster presentations, consisting of a single PPT slide presented by an individual student followed by a Q&A session. In this class, students can, first, learn to integrate their majors with the unit theme through self-directed exploration, starting from an understanding of the unit's theme. Second, through the Q&A process, students can develop their convergent abilities through inquiry and interaction among their learners. At this time, the use of crosscutting themes, which allows reflection from the perspectives of humans, technology, and society, provides an opportunity to further promote the learners' convergent thinking. Thus, poster presentations utilizing crosscutting themes can be used as a teaching-learning device to cultivate the convergent abilities of learners with diverse majors from the aspects of major convergent thinking with the unit theme, as well as extended thinking through Q&A sessions. This study examined the flow of convergent thinking extended through the learners' own exploration and interaction through the analysis of the poster presentation cases utilizing crosscutting themes. This is significant in that it proposed a teaching-learning model that enables students from diverse majors to share their awareness of issues concerning humans, technology, and society, and to cultivate their abilities for connected and creative thinking.


Transition from Mythoverse Imaginary to Metaverse : Exploring the Liberal Arts Educational Value of Mythology in the Context of Literary Narratives

Abstract: This research examines the evaluative and future-oriented predictions of the 21st century, defined as the ‘Century of Technology’, from a humanistic perspective, foreseeing its advancement propelled by sophisticated science and technology. In particular, this study aims to examine the cultural acceptance patterns regarding the concept of the ‘metaverse’, which is commonly referred to as a representation of digital technology emphasized at the beginning of the 21st century, from both humanistic and educational perspectives. Given the current lack of a clear definition, the study highlights the need for the intervention of humanistic reasoning, as the metaverse is generally understood either as 3D digital technology or represented as a ‘virtual world’. At the current juncture, as the implementation of the virtual world in the metaverse is perceived through character creation games or role-playing games such as ‘Second Life’, this study seeks to raise questions about the playful imagination that is projected onto the metaverse. Examining the phenomenon where internet users connect to the virtual world of the metaverse, placing a central focus on creating an ‘avatar’ or hero representation, the study aims to explore the inherent presence of imaginative desires, specifically the ‘human creative desire’, manifested in mythical worlds. With the advent of the metaverse era and the connection to virtual worlds, the human desire to create avatars reflects similarities to the ancient expression of the human creative desire in mythical worlds. The study also interprets how literary and artistic attempts of past centuries have echoed and intersected with this desire. Through this comparison, it was possible to recognize the significant influence of the ‘mythoverse’, or mythological imagination, in implementing the virtual world of the metaverse. Additionally, the study considered that if human life transitions to virtual worlds in the near future, the wellspring of humanistic knowledge, namely ‘myth’, could have a meaningful impact. Despite the technological advancements of the 21st century, the transition and realization from mythology to history, reality to dreams, the real world to virtual worlds, and the mythoverse to the metaverse transcend space and time. By emphasizing the value of mythology as a cultural heritage for humanity and highlighting the need for applying mythology in humanities education, this study aims to underscore the significance of myth in the cultural assets of humanity.


Research on the Interdependent Relationship between Teaching Methods and Technology : Focusing on the Internal Communication between Instructors and Learners

Abstract: This study examines that even in an era when technology is essential to education, the essence of liberal arts education lies in the instructor's consideration and internal communication with learners. Technical skills are essential in limited physical and group learning environments, and technology and teaching methods are interdependent. By empirically observing the period of transition from non-face-to-face to face-to-face, we confirmed the interdependence between teaching methods and the university's LMS in H University's liberal arts writing class. Technology functions to sustain and promote the internal relationship between instructor and learner, and internal communication is achieved through the instructor's consideration for the learner. When a sense of trust is formed through the instructor's consideration, learners can focus on writing independently, even in a group learning environment where they do not receive attention. The university's LMS is used for hybrid education where instructors and learners maintain internal communication and communicate using online technology and offline consideration. However, the interdependent relationship with technology in education is meaningful when instructors aim for internal communication through consideration as humanization.


Two-Stream Network for Korean Natural Language Understanding

Abstract: This study pioneers a dual-stream network architecture tailored for Korean Natural Language Understanding (NLU), focusing on enhancing comprehension by distinct processing of syntactic and semantic aspects. The hypothesis is that this bifurcation can lead to a more nuanced and accurate understanding of the Korean language, which often presents unique syntactic and semantic challenges not fully addressed by generalized models. The validation of this novel architecture employs the Korean Natural Language Inference (koNLI) and Korean Semantic Textual Similarity (koSTS) datasets. By evaluating the model's performance on these datasets, the study aims to determine its efficacy in accurately parsing and interpreting Korean text's syntactic structure and semantic meaning. Preliminary results from this research are promising. They indicate that the dual-stream approach significantly enhances the model's capability to understand and interpret complex Korean sentences. This improvement is crucial in NLU, especially for language-specific applications. The implications of this study are far-reaching. The methodology and findings could pave the way for more sophisticated NLU applications tailored to the Korean language, such as advanced sentiment analysis, nuanced text summarization, and more effective conversational AI systems. Moreover, this research contributes significantly to the broader field of NLU by underscoring the importance and efficacy of developing language-specific models, moving beyond the one-size-fits-all approach of general language models. Thus, this study is a testament to the potential of specialized approaches in language understanding technologies.


Impelentasi Kegiatan Bumi Gelisah dalam Menumbuhkan Minat Baca Siswa SDN Kondangjaya III

Abstract: The aim of this research is to find out whether the Restless Earth program (Wednesday Thursday School Literacy Movement) can cultivate students' interest in reading at SDN Kondangjaya III. The research method used is descriptive qualitative. Data collection techniques include observation, interviews, and documentation. Data analysis techniques are carried out in three action steps: data reduction, data presentation, and conclusion. The results show that the Restless Earth program can cultivate students' interest in reading with positive support from the school and the role of teachers who are able to motivate students. The Restless Earth activity is carried out every Wednesday and Thursday for 15 minutes before learning begins. The books used are non-curricular books. Facilities provided by the school include the school library, reading corner, posters, and pictures related to reading activities.


Penerapan Aplikasi Klasifikasi Hukum Tajwid Menggunakan Image Processing

Abstract: Tajwid is an important science that regulates the way of reading the verses of the Al-Qur’an properly. Learning Tajwid means knowing the meaning that corresponds to the correct recitation. Learning to read the Al-Qur’an tends to be done traditionally in a place of learning or by calling a teacher to the house. Learning in this way has some drawbacks, such as the limited availability of trained and competent teachers because not all areas have sufficient access to these teachers. Dependence on schedules and locations can be a constraint for students with limited mobility or busy schedules. The role of the teacher is still important in learning tajwid, especially in providing effective explanations, guidance, and feedback. However, to overcome these shortcomings, integration with independent and technology-based learning methods can help improve the accessibility, flexibility, and quality of tajwid learning. The classification of tajwid laws using image processing allows users to see the results of inputting images of verses of the Al-Qur’an into the type of detected nun sukun tajwid and how to recite it. The initial stage of this system in detecting tajwid laws from uploaded images is the input of images by users, which can be done in two ways, namely by directly taking pictures using a smartphone camera or uploading images from the gallery. This is followed by the OCR process to detect the Arabic text contained in the image and provide diacritics for that Arabic text. Finally, letter classification is carried out after nun sukun and classification of tajwid laws contained in accordance with the detected letters after nun sukun. This system has an accuracy rate of 92.18% from the classification results that have been carried out.


E-Dharmagita Learning Model Innovation with Mobile and Multimedia Technology

Abstract: Purpose: Yadnya ceremony is a sacred sacrifice performed with sincere and wholehearted devotion by Hindus to God, spiritual leaders or teachers, fellow humans, the ancestors, and Bhuta_Kala. The implementation of the Yadnya ceremony is_usually accompanied by a spiritual chant of the Hindus known as Dharmagita. Nowadays, a lot of young generations do not know and are even more indifferent to the Dharmagita. Using books as a source of Dharmagita information is less attractive since there are no audio examples or recordings of correct songs that can be listened to. The Android-based e-Dharmagita application was built to overcome these problems to facilitate the younger generation in getting more interesting information about Dharmagita.Methods: e-Dharmagita was constructed with the support of mobile and multimedia technology. In addition, the information presented was related to the Yadnya Ceremony by implementing a complex Tree Algorithm.Results: This research produced an e-Dharmagita Application, of which user acceptance testing using the PSSUQ Method resulted in a score incudes in the excellent category. Therefore, the application could be accepted well in the implementation the Information Technology in Balinese Culture and Hinduism, especially the innovation of a more modern and attractive Dharmagita learning model.Novelty: The Dharmagita learning model using a mobile application is a new model that has not existed before.


Effective Strategies for Blended Teaching of Elementary School Mathematics Based on the Smart Education Platform

Abstract: The continuous development of educational technology has made the smart education platform an important part of modern teaching and learning, especially in elementary school mathematics teaching, which continues to show great potential. Smart education platforms have been described as revolutionary tools that have dramatically changed traditional teaching and learning methods through integrated technologies such as artificial intelligence, big data analytics, and cloud computing, and these platforms not only provide rich and diverse teaching resources but also support personalized learning paths and interactive learning experiences, thus effectively improving teaching efficiency and learning quality. The integration of smart education platforms with traditional offline teaching not only meets the requirements of the reform of the teaching mode but also optimizes the learning process of students and improves the learning effect. Based on this, this paper elaborates on the effective strategies of blended teaching of elementary school mathematics based on the wisdom education platform from different perspectives.


Effectiveness of Speedy English Grammar Application on Students’ Grammar Mastery in Simple Past Tense

Abstract: The objective of this research is to examine the effectiveness of Speedy Grammar application on students' grammar mastery in simple past tense at the twelfth grade of a vocational school in Banten. This research employed a quasi-experimental method with achievement tests in collecting the data. Achievement tests included pre-test and post-test. This research was conducted at the twelfth grade of a vocational school in Banten. The samples of this research were 40 students that consisted of 20 students of experimental and 20 students of control class. The experimental class used Speedy English Grammar and the control class used English Grammar and Test application. The data were analyzed by using statistical of program SPSS 22. Based on the result of data analysis, the result showed that mean of post-test both experimental and control class different are 76.70 and 70.45 and for the result of t-test obtained sign score 0.000 ? ? = 0.05. The result indicated the alternative hypothesis was accepted and the null hypothesis was rejected. It is concluded that Speedy English Grammar application was effective in improving students' grammar mastery in simple past test at the twelfth grade of a vocational school in Banten.


Effect of Weight Control through Swimming Exercise on Oxygen Radicals and Heart Rate Changes in Overweight Women

Abstract: The purpose of this study was to determine the effect of weight control through swimming exercise on oxygen radicals and heart rate in overweight women and to use it as data for a health promotion program. BRThe subjects of the study were 24 overweight women, divided into 12 SEG (Swimming Exercise Group) and 12 CG (Control Group), and compared and analyzed body weight and oxygen radicals between groups and before and after. The swimming exercise program was performed three times a week for 12 weeks, exercise time was 50 minutes, and exercise intensity was 50-75% HRR. Results 1. In the swimming exercise of overweight women, body weight showed a significant difference in the exercise group, a main effect was observed in the measurement period, and there appeared to be an interaction effect between period and group. 2. In the swimming exercise of overweight women, active oxygen showed a significant difference in the exercise group, a main effect was observed in the measurement period, and there appeared to be an interaction effect between period and group. 3. In the swimming exercise of overweight women, heart rate showed a significant difference in the exercise group, a main effect was observed in the measurement period, and there appeared to be an interaction effect between period and group. Therefore, swimming exercise in overweight women is effective in weight loss and also provides positive improvements in oxygen radicals and heart rate changes. Accordingly, it is believed that it is possible to provide exercise guidelines to improve health through swimming exercise for women who are overweight, and if they proceed considering the intensity and time of long-term exercise, they will live a prosperous life.


Evaluating ICT Competencies of Higher Education Faculty:A Comprehensive Quantitative Model after the Pandemic

Abstract: This study aimed to enhance the Information and Communication Technology (ICT) skills of faculty members in a higher education institution. The faculty’s limited proficiency in ICT impeded the integration of technology instruments across different topics. A training program was devised, executed, and assessed to tackle these problems within six months. Online courses were developed for participants under the training’s criteria. Quantitative data collection involved administering a pre-test and post-test, which were subsequently analyzed using descriptive statistics. The assessed competencies encompassed the utilization of computers in instructional activities and the development and implementation of educational initiatives. The findings indicated that 75% of the faculty did not typically include technology in their instructional methods, whereas 41% recognized the potential of technology to enhance and innovate teaching practices. Only 66.67% of the participants possessed knowledge of fundamental components of computers, while the same percentage was aware of basic peripherals. Additionally, 58.33% of the participants demonstrated proficiency in installing computer programs. The program sought to enhance the educational quality and showed positive modifications in formulating strategies for integrating ICTs into the curriculum. The results showcased the efficacy of the training program in improving teaching skills in the field of information and communication technologies (ICTs), enhancing the teaching methods of faculty members, and providing the opportunity to deliver courses either fully online or in a blended format.


The Influence of Technology in the World of Education

Abstract: This study investigates the influence of technology in the world of education. With the rapid advancement of digital technology, its impact on educational practices and outcomes has become increasingly significant. Through a comprehensive review of existing literature, this research explores the multifaceted influence of technology on various aspects of education, including teaching methods, learning experiences, student engagement, and educational outcomes. The study examines how technology has transformed traditional classroom settings, facilitating the adoption of innovative teaching strategies such as flipped classrooms, blended learning, and personalized instruction. Additionally, it investigates the role of educational technology tools such as learning management systems, interactive whiteboards, and educational apps in enhancing instructional delivery and facilitating student learning. Furthermore, the research explores the broader implications of technology in education, including its potential to democratize access to education, bridge learning gaps, and foster lifelong learning. It also addresses challenges associated with technology integration, such as digital divide, privacy concerns, and the need for teacher professional development. Moreover, the study discusses emerging trends in educational technology, such as the use of artificial intelligence, virtual reality, and gamification, and their potential to revolutionize educational practices and outcomes. By synthesizing empirical evidence and theoretical frameworks, this research provides insights into the transformative power of technology in education and its implications for educational policy and practice. Ultimately, this study contributes to a deeper understanding of the influence of technology in the world of education and highlights the opportunities and challenges associated with its integration into teaching and learning environments. It serves as a foundation for future research and informs evidence-based strategies for leveraging technology to enhance educational outcomes and promote lifelong learning.


Application of Inventory Information Systems as a Work Practice Management Strategy in Improving Technology Learning in Education Management

Abstract: This study aims to investigate the application of Inventory Information Systems (IIS) as a work practice management strategy to enhance technology learning in education management. The research explores how the integration of IIS into educational institutions' management systems can improve the efficiency and effectiveness of technology learning processes. Through a comprehensive literature review, this study examines the potential benefits of employing IIS, such as streamlining inventory management, optimizing resource allocation, and facilitating data-driven decision-making in educational settings. Additionally, the research evaluates the challenges and limitations associated with implementing IIS in education management, including infrastructure requirements, staff training needs, and data security concerns. By analyzing case studies and empirical evidence, this study seeks to provide insights into best practices for integrating IIS into education management to foster enhanced technology learning outcomes. The findings of this research contribute to the development of strategies aimed at leveraging inventory information systems as a tool for advancing technology learning in educational institutions.


THE IMPLEMENTATION OF THE COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC) MODEL IN TEACHING ANALYZING THE STRUCTURE AND LANGUAGE OF DESCRIPTIVE TEXTS

Abstract: This study aims to determine if the Cooperative Integrated Reading and Composition (CIRC) model is effective in teaching class VII students at SMP NU Kaplongan Indramayu about the structure and language of descriptive texts. The researchers chose the CIRC model because it is relevant and helpful in teaching students about descriptive texts. The study was conducted at NU Kaplongan Middle School, with a total of 204 students from 6 classes. The sample for the study included 34 students from class VII E and 34 students from class VII F. The data collected included initial and final tests from both the experimental and control classes. The results of the study are as follows. The t-value value for both research groups is 5.892, while the t-table value is 1.996. Therefore, the t-value is greater than the t-table value, indicating that the alternative hypothesis stating the significant influence of the CIRC learning model on the ability to study descriptive texts is accepted. Additionally, the CIRC learning model brings about significant changes in teaching methods and student learning. It promotes active, creative, effective, and enjoyable learning experiences. It also helps students develop problem-solving skills and encourages teamwork and mutual respect.


PENGARUH GAYA HIDUP DAN KELOMPOK REFERENSI TERHADAP KEPUTUSAN PEMBELIAN LAPTOP GAMING MERK ACER ( Studi Kasus Pada Komunitas Game Online di Kota Surabaya Timur )

Abstract: This research aims to investigate the influence of lifestyle and reference groups on purchasing decisions for Acer brand gaming laptops. This research uses a quantitative approach with a survey method through primary data collection or questionnaires. The respondents of this research are members of the kill the last community in East Surabaya. This research sample consisted of 90 respondents selected randomly. The data collected will be analyzed using multiple linear regression techniques and hypothesis testing to test the hypothesis of this research. The research results show that the reference group has a significant influence on the decision to purchase an Acer Brand Gaming laptop. In the current era of globalization, it causes an increase in population and technological developments. Human lifestyles change along with technological developments, resulting in a modern, digital and instantaneous human lifestyle. This causes the need for technology products to become a primary need, one of which is the need for laptops. The increase in people's lifestyles and knowledge from the reference group causes people to become more critical in choosing the laptop products offered, giving rise to competition between laptop companies in getting sympathy, in order to get purchasing decisions from consumers. The Acer company was founded by Stan Shih who comes from Taiwan. Acer laptops are one of the laptops that have always been the choice of people in the city of Surabaya


ANALISIS SENTIMEN ULASAN APLIKASI SHAZAM DI GOOGLE PLAY STORE MENGGUNAKAN SUPPORT VECTOR MACHINE

Abstract: Shazam is a popular music recognition app on the Google Play Store. Shazam allows users to discover new songs and identify songs that are playing around them. In addition, it provides song lyrics, music videos, and song recommendations. User reviews give an idea of how users perceive the application, be it positive or negative. In this study, sentiment analysis was conducted on Shazam user reviews on the Google Play Store. The researchers used a Support Vector Machine (SVM) model to classify user reviews into two sentiments, namely positive and negative. Results show that SVM has 84% accuracy in predicting the sentiment of Shazam reviews. Furthermore, it can be shown that Shazam gets five times more positive responses than negative responses. Therefore, SVM is better at predicting positive reviews than negative reviews. This research can help interested parties to understand how users perceive the app as a whole.


Communication Prototype for Post-Stroke Patients Using Electrooculography (EOG)

Abstract: The background of this prototype is meant for post-stroke patients who have difficulties owing to trouble completing daily activities, particularly speaking with others. They have trouble communicating, which reduces their quality of life after a stroke. The goal of developing novel communication aids for post-stroke patients is to make it easier for post-stroke patients to communicate with caretakers. Electrooculography (EOG) signals generated by the movement of the eye muscles during eye glances collected by the MaM Sense sensor are employed in post-stroke communication aids. The microcontroller processes the method's command signal, which is subsequently displayed on a 20 x 4 Character LCD. For the four communications utilized, the MaM Sense sensor reading was successful 80.5% of the time. Thus, communication tools for post-stroke patients can help them communicate while also assisting caretakers in understanding the patient's wants. Improvements will be made in the design and wireless technologies for EOG signal recovery in the future.


Pengaplikasian Media Pembelajaran Kotak Suara Pemilihan Umum Untuk Meningkatkan Minat Belajar Siswa di SMPN 5 Karawang Barat

Abstract: Learning media is a means of communication for a teacher in conveying learning material, and is something that needs to be considered by the teacher. This is because through interesting and creative learning media it will stimulate students' interest in learning so that students will take part in learning enthusiastically. The purpose of this research is to attract student learning interest in the Civics learning process in class by using a new learning media, namely through the general election ballot box on the material Sovereignty of the Unitary State of the Republic of Indonesia and collaborating with subject teachers. The researcher uses a qualitative descriptive research method, which is a research method that describes the condition of objects in a real and natural way. In this study, the learning media of the general election ballot box as an independent variable binds to the variable of student learning interest. Data collection techniques were carried out in the form of direct observation in class, interviews with teachers and students and giving questionnaires to students. The results showed that class IX students tended to be active and able to receive learning material well on the subject of Sovereignty of the Unitary State of the Republic of Indonesia by implementing general election simulations. The purpose of this general election simulation is for students to understand the actual material, not only knowing the material but by practicing it directly to provide a clearer understanding of the material being taught


Nursing Educational Program for Improving Older Persons' Hydration Status

Abstract: Background: Dehydration is a serious public health concern occurred due to the imbalancebetween water intake and loss which increased in the climate change and Egypt's hot weather, leadto the morbidity and mortality rate raise among the older persons. Aim: To evaluate the effect ofnursing educational program for improving older persons' hydration status. Subjects and Method:One group pre-post intervention research design. Setting: The study conducted at the (internalmedicine and urology) clinics at the Assuit University hospitals. Sample: Total number of 130older persons was included. Study tools: Contained four tools; Tool (I): Consisted of four parts;personal data, El-Gilany scale for measurement of socioeconomic status, health condition andknowledge of older persons regarding dehydration. Tool (II): Dehydration screening. Tool (III):Hydration knowledge scale and Tool (IV): Hydration facilitators and barriers attitude. Results:Mean ± SD and (range) of participant age: 69.28±7.65 (60-91). It was found that, more than half(56.9% &53.1% respectively) of them were male and had very low socioeconomic status. Therewas statistically significant effect of the educational program on the older persons' dehydrationstatus; also there was improvement of older person's hydration & dehydration knowledge andattitude of older persons about hydration facilitators and barriers after the application of theeducational program. Conclusion and recommendations: These results support that the nursingeducational program improved the hydration status, hydration & dehydration knowledge andattitude toward hydration facilitators and barriers among older persons. Continuous educationalprograms about hydration status of older persons are recommended


Technological tools for training by competencies in distance education in the university environment

Abstract: In recent years, distance education has become a very useful educational practice in the university environment, motivated by the development achieved by technologies. The objective of this study was to analyze the technological tools available to improve training by competencies in distance education of the Faculty of Business Sciences at the Domingo Savio Private University, Bolivia. The research was descriptiveexploratory and propositional, with a non-experimental design. The sample was made up of 370 students. A questionnaire was used with multiple choice and dichotomous questions. The results indicated insufficiencies in the use of the Virtual Learning Environment by teachers and students and technologies are not used appropriately in teaching activities. It is concluded that (49%) of the students affirm that there is a lack of adaptability, (67%) of the teachers acknowledged having good management of the educational platforms used in the distance modality. A lack of interest and demotivation of the students is confirmed (76%), 64% of students recognized that they appropriately use the technological tools used in teaching. The need for continuous training of teachers is highlighted, since 66% did not receive adequate training from the university that allows them to strengthen digital knowledge and skills to develop innovative teaching activities in the distance modality.


Linguistic Support of Information Systems and Processes

Abstract: The article examines the process of dictionary data design, which are sets of lexical units with specific information about them. The methods for creating indexing and search dictionaries are described, which enable efficient organization of search in textual data using specialized indexes.


The Effect of Coaching Leadership on Employee Voice Behavior: The Mediating Role of Thriving at Work and the Moderating Role of Person-Supervisor Fit

Abstract: This study has two purposes. First, it aims to verify the mediating role of thriving at work in the relationship between coaching leadership and employee voice behavior. Second, it looks at the moderating role of person-supervisor fit in terms of the influence of coaching leadership. Data collected from 174 supervisor-subordinate pairs was used for the analysis. The results show that coaching leadership was positively related to thriving at work, and thriving at work was positively related to voice behavior. In particular, thriving at work mediated coaching leadership and voice behavior. The positive relationship between coaching leadership and thriving at work was weaker with a better person-supervisor fit, and the moderated mediation effect was also weaker in this scenario. Based on the results, the limitations of this study are acknowledged, and theoretical and empirical implications are suggested, as well as a fruitful future direction of research.


PhishScanner : A Web-Based Tool for Detection of Phishing Websites Using Machine Learning

Abstract: Phishing is a common attack that tricks an individual to reveal personal information through fake websites. The main goal is to steal important data, including logins for social media accounts, bank account numbers, passwords, and usernames. The attacker uses his/her website that is visually and semantically similar to the actual website. Over the years there have been multiple attacks of phishing and many people have lost huge sums of money by becoming victims of these attacks. Attackers frequently employ fake emails or websites to trick victims into clicking hazardous links or downloading malicious attachments. The victim's login details and account information may be accessible to the attacker, thanks to these acts. The complexity of phishing attacks has increased, making it more challenging for the typical user to tell whether a link in an email or a message is authentic or not. To detect these types of websites there are different methods such as blacklist approach, content based approach etc. Detecting attacks through traditional methods such as blacklists can be unreliable as it has become simpler to register new domains, and maintaining a comprehensive and current database is difficult. An internet user can interact with a web application through a graphical user interface by running it on a web server and accessing it through a web browser. Therefore, we developed a web application whose primary objective is to detect Phishing URLs by utilizing advanced machine learning models that analyze features extracted from the website. After reviewing various classification algorithms, we decided on adopting the Random Forest Algorithm because of its high accuracy of 0.965. Random Forest Algorithm is a well-known algorithm for Machine Learning that predicts using an ensemble of decision trees. It has proven to be effective in a wide range of classification tasks.


Information Literacy in the Artificial Intelligence Sphere

Abstract: Artificial intelligence (AI) is a disruptive technology that has the potential to revolutionise how information is accessed, processed, and utilised. AI has the capability to personalise learning experiences, provide tailored recommendations, and enhance information retrieval processes, thereby contributing to the development of critical information literacy (IL) skills among library users. This study aims to examine the impact of AI on teaching and improving IL in higher education by determining the use and approaches used for IL provisioning in academic libraries. It also examines how IL pedagogy and librarian roles have evolved in response to new learning settings and AI technologies.


KEY ASSESSMENT TEST MODIFICATION PROJECT PRINCIPLES AND PRACTICES OF LANGUAGE TESTING

Abstract: This article presents data collection about the key features, principles and practices of language testing by modifying an available test to be valid, reliable, and practical for the target learner, including both theoretical and practical pictures of the process. Starting with studies on the target research, this work contains the learner assessment profile, the critique of an existing test, modified version of the chosen test together with appendix (material).


ORGANIZATION OF INDEPENDENT EDUCATION IN HIGHER EDUCATIONAL INSTITUTIONS USING MOBILE LEARNING SYSTEMS

Abstract: This article discusses the possibilities, types, methods and possibilities of using mobile educational systems in organizing independent learning for students of higher educational institutions. Today's foreign experience in using mobile educational systems has been studied.


Agency Banking and Poverty Reduction in Plateau State

Abstract: This study delves into the potential of agency banking to alleviate poverty in Plateau State, a region primarily reliant on agriculture and grappling with a high poverty rate, along with a sizable unbanked population. The research aims to investigate the correlation between agency banking and poverty reduction in Plateau State. Employing a descriptive survey design, the Taro Yamane formula was used to select a sample of 360 participants who were First Bank Plc agents. Quantitative analysis techniques, including descriptive statistics (percentages, mean, and standard deviation) and multiple linear regressions with SPSS 25.0, were employed to test the study's hypotheses. The results of the statistical analysis revealed that the income of agents has a significant impact on poverty reduction. Furthermore, the study found that the transaction volume of agents also significantly contributes to poverty reduction in Plateau State. However, it is important to note that the educational level of agents did not show a significant effect on poverty reduction in the state. As a result, the study recommends that banks operating in Plateau State should seize the opportunity presented by agency banking to reach the rural population, aligning with the Central Bank of Nigeria's (CBN) National Financial Inclusion Strategy (NFIS). This strategy emphasizes targeting women, rural areas, youth, Northern Nigeria, and micro, small, and medium-sized enterprises (MSMEs) to achieve a high level of financial inclusion, ideally reaching a 99% financial inclusion rate by 2030. In addition to promoting agency banking, the study suggests creating awareness of financial literacy in rural areas. This awareness campaign aims to educate the long-standing unbanked population on the benefits of banking services facilitated through agent banking, thereby fostering a more inclusive financial landscape.


Mediating Effect of Access to Electronic Payment System on Currency Redesign Policy and SMEs Performance in Northeast-Nigeria

Abstract: This paper investigated the mediating role of access to electronic payment system (APS) on the relationship between currency redesign policy (CRP) and the performance of SMEs (PERF) in selected states (Adamawa, Bauchi, Gombe, and Yobe) in north-eastern Nigeria. The study used quantitative research approach and a multi-stage sampling technique was used to generate 300 responses using a research questionnaire. The partial least square structural equation modeling (PLS-SEM) technique was used to analyze data while the research model was examined using the disjointed two-stages approach. The study found a positive and significant mediating effect between CRP and PERF through APS. Equally, the direct paths were all found to be positive and significantly related. Meaning that not only does CRP affect PERF directly, but it also has an indirect effect on PERF through APS. This provided support for linking CRP and PERF through APS, and CRP and PERF, which doubles for validating the research model on the reflective-formative basis. Therefore, businesses are encouraged to utilize the usefulness of APS for doing transactions, which will double for achieving better PERF under the current CRP regime. This will have implication as the government is reintroducing the CRP as one of the measures of addressing inflation in Nigeria. On this note, the study concluded that APS is the most crucial factor in the implementation of CRP. Although, this cannot be generalized due to the concentration of the study in one region of the country, but future studies are encouraged to extend the scope of research to establish a basis of generalization.


The Role of Government Policy Programme in the Growth and Development of MSMEs in Nigeria

Abstract: This research work is focused on understudying the role of government policy programmes in the growth and development of micro small and medium enterprises with a view of highlighting its relevance in contributing to the overall economic growth of the Nigerian economy from the standpoint of both the micro and macro-economic analysis. The policy in review is the national enterprise programme which serves as a vehicle for the promotion and development of MSMEs. However, this policy agenda is challenged in view of the multifaceted needs of MSMEs. The theoretical framework used for the study is the systems theory which conceives a system as comprising of different components or parts that are interdependent, integrated and function as a whole. Methodologically the research adopts an expost-facto research design which utilizes survey method to collect relevant data. Using inferential statistical approach hypotheses were drawn and tested using Chi-square method in seeking the correlation between the variables. Conclusions were drawn on the subject matter to suggest that government policy programmes have significant effect on micro, small and medium scale enterprises as they are instrumental in increasing output and mobilizing the resources that engenders the sustainability of the sustainable of any economy. Recommendations were made based on the findings for adequate funding and support services for MSMEs in addition to the proper management and the timely release of funds for MSMEs.


Survey of Secure Data Access Control Models in Cloud computing for EHR

Abstract: Today cloud computing is the first choice of many individuals and different organizations. Computing services offered by different CSP’s are available as per need and the requirement. Most of the healthcare organizations are using cloud services for transmitting, storing and processing electronic health records (EHR) of patients. Patient’s health record is sensitive and there is possibility of violation of privacy by CSP’s. Data access control is one of the best mechanisms to address security issues in cloud computing and ensure the safety, reliability of the system and privacy of the user data. Existing surveys on access control mechanisms in cloud computing focus on all traditional centralized models. However, decentralized access models like block chain with traditional access control model is also used now days for ensuring privacy of EHR. This paper takes the detailed of reviews such existing access control mechanisms in cloud computing, based both types of access control models. Also provides comparisons on each model’s advantages and limitations, and discusses the challenges of, and future research direction for access control.


Setting up classroom libraries in rural areas: The case of Mogodumo circuit in Limpopo

Abstract: There is an urgent need to establish classroom libraries, especially in rural areas where learners hardly come across any reading material. The purpose of this study was to investigate how teachers set up classroom libraries in rural areas of the Mogodumo circuit in the Limpopo province of South Africa. The study was qualitative in nature. Fifteen English First Additional Language (EFAL) teachers from 12 schools participated in the study. Observation and semi-structured interviews were employed to collect data. It was found that while all participants recognised the importance of reading for their learners, only 7 of the 15 teachers managed to establish classroom libraries. The study revealed that teacher self-efficacy (TSE) played a crucial role in setting up classroom libraries, given the serious challenges of funding, resources and support experienced by rural schoolteachers. Teachers with high TSE managed to establish classroom libraries by using their own time, money, and resources, while those with low TSE failed to establish classroom libraries. We recommend that school managers should make time and resources available to rural schoolteachers to enable them to establish classroom libraries.


Unified voice analysis: speaker recognition, age group and gender estimation using spectral features and machine learning classifiers

Abstract: Predicting speaker's personal traits from voice data has been a subject of attention in many fields such as forensic cases, automatic voice response systems, and biomedical applications. Within the scope of this study, gender and age group prediction was made with the voice data recorded from 24 volunteers. Mel-frequency cepstral coefficients (MFCC) were extracted from the audio data as hybrid time/frequency domain features, and fundamental frequencies and formants were extracted as frequency domain features. These obtained features were fused in a feature pool and age group and gender estimation studies were carried out with 4 different machine learning algorithms. According to the results obtained, the age groups of the participants could be classified with 93% accuracy and the genders with 99% accuracy with the Support Vector Machines algorithm. Also, speaker recognition task was successfully completed with 93% accuracy with the Support Vector Machines.


OPTIMIZATION OF RETENTION POND IN FLOOD CONTROL EFFORTS IN PEKANBARU (URBAN FLOOD SYSTEM IMPROVEMENT PROJECT)

Abstract: This research aims to optimize the function of retention ponds as part of the urban flood control improvement project in Pekanbaru. Evaluation was conducted on the capacity and efficiency of retention ponds, with a focus on spatial distribution, sustainability, and environmental impact. The findings of this study provide a foundation for formulating concrete and data-driven improvement recommendations. The spatial distribution of retention ponds is crucial in understanding their effectiveness in mitigating flood risks. Evaluation of the actual capacity of retention ponds provides an overview of their ability to accommodate excess rainwater, while efficiency identifies factors for enhancement and improvement. Improvement recommendations include increasing capacity, implementing advanced technology, and developing data-driven solutions. Involving stakeholders, including local government, research institutions, and the community, is a strategic step to ensure successful implementation. The application of sensors and geographic information systems (GIS) can enhance the effectiveness of retention pond operational management. Sustainability of solutions is realized through routine maintenance, performance monitoring, and multi-stakeholder collaboration. This research significantly contributes to urban flood management in Pekanbaru by providing concrete, data-driven recommendations for the retention pond system.


UTILIZATION OF THE ANDROID APPLICATION SI-RAJA BUMIL (ESTIMATED FETAL WEIGHT OF PREGNANT WOMEN) IN MONITORING FETAL GROWTH IN MIDWIFE AT INDEPENDENT PREMISES IN TANJUNGPINANG CITY

Abstract: Estimated fetal weight is useful for tracking fetal growth in the womb, allowing for the early detection of aberrant fetal growth. With the advancement of technology, Android users can be used as a channel for health education information. The objective of this study was to look at how utilizing the Android application Si-RAJA Bumil (Estimated Fetal Weight of Pregnant Women) affects pregnant women’s understanding of fetal growth monitoring. This study applied a quasi-experimental design with two groups pre-testing and post-testing. Samples of 60 pregnant women were collected using the Simple Random Sampling approach and divided into two groups: the intervention group and the control group. The Midwife Independent Practice in Tanjungpinang City collected data in September 2023. Wilcoxon and Mann-Whitney tests were used to analyze data. The knowledge value of pregnant women differs significantly between groups who do not utilize the Si-RAJA Bumil android application and groups that do, with a p-value of 0.003. Pregnant mothers are expected to be able to monitor their foetus’ growth and development using technological media such as the Si-RAJA Bumil application.


EFFECTIVENESS OF E-POSYANDU HEALTH (EPOK) APPLICATION ON KNOWLEDGE AND SKILLS OF POSYANDU CADRES IN MONITORING GROWTH AND DEVELOPMENT OF TODDLERS

Abstract: Posyandu cadres are community elements that play an important role in the implementation of Posyandu activities. The cadre’s role includes monitoring the growth and development of children under the age of five. To do their job effectively, they must have strong monitoring knowledge and skills in order to detect complications or problems in toddlers early on. So that appropriate intervention and counseling can begin immediately. The ePoK application is an innovative smartphone application that helps cadres monitor the growth and development of toddlers. The objective of this study was to determine the effectiveness of the ePoK application in terms of posyandu cadres’ knowledge and skills in monitoring toddler growth and development. This study was a quasi-experiment with a one-group pretest and posttest design. This study was carried out in Tanjungpinang City. A total of 60 posyandu cadres responded. Respondents were given the ePoK Application to use for one month. Knowledge was assessed using a questionnaire, and skills were assessed using a checklist. The results showed that respondents’ knowledge and skills increased both before and after using the application (11.55 and 5.78, respectively). There was also a significant difference in the knowledge and monitoring skills of respondents before and after using the ePoK Application with a p value <0.05 (0.000<0.05). The conclusion of this study is that the use of the ePoK application is proven to increase the knowledge and skills of posyandu cadres on monitoring the growth and development of toddlers.


A Survey on Path Planning Algorithms for Unmanned Aerial Vehicles Using Bio-Inspired Optimization Techniques

Abstract: The rapid development of unmanned aerial vehicles (UAVs) and UAV-based applications has been increased in the recent past due to the advancement in software and electronics industry. Use of UAVs are considered to be a very efficient and useful platform that can deeply monitor the critical infrastructures around the geographical areas. UAVs are also useful for data collection through different wireless sensor networks. Based on the collected data, an optimal path can be formed. Bio-inspired algorithms are inspired from the principles of the biological evolution of nature. The recent trends tend to employ the bio-inspired optimization techniques that are best-suitable for handling strenuous optimization problems. In this chapter, the authors investigate different bio-inspired algorithms for the UAV path planning over the last decade. They compared the working principles, key features, advancements, and limitations of different path planning algorithms. Furthermore, the challenges and future research scopes are also discussed and summarized.


There’s a Strong Wind a’Blowing re Security Disclosures: Lessons Learned from SolarWinds — An analysis of the significance of the SolarWinds case for companies with making security statements on websites and in public filings

Abstract: Abstract For years now, we have been advising clients to be cautious about including overly broad, or overly detailed, statements in their online privacy policies, noting that the FTC has in the past interpreted such statements as being representations to the market - such that false or misleading statements made therein could constitute misrepresentations. The Securities Exchange Commission complaint last year filed against SolarWinds Corp. and Timothy G. Brown (between July 2017 and December 2020, the Vice President of Security and Architecture and head of the Information Security group, and as of January 2021, the Chief Information Security Officer, for SolarWinds Corp.) has now made it clear that the same risk applies to public statements regarding the state of security of an organization. The SEC complaint raises additional related issues, however: namely, to what extent are organizations required to detail instances of non-compliance with their security policies in their public security disclosures; and to what extent should software companies be held to a higher standard of cybersecurity disclosure. This article proves the background to the SEC complaint (I.); provides a overview of the SEC complaint’s critique of the online security statement published by SolarWinds, and the security disclosures in the securities filings of SolarWinds (II.); outlines the challenges posed by the SEC approach in the SEC complaint and the resulting blowback, in particular from chief information security officers (III.); highlights three key lessons learned from the SEC complaint (IV.); and concludes (V.) with a warning as to what this means for organizations publishing online security statements.


A Study on College Students’ Learning Attitude During Synchronous and Asynchronous Video-Based Group Activities for English Presentation — A Secondary Publication

Abstract: The purpose of the study is to explore college students’ perceptions/attitudes during synchronous and asynchronous group activities. The survey comprised a 10-item, 5-point Likert-type questionnaire and open-ended questions. In the study, two groups, a synchronous group and an asynchronous group, participated in four activities during one semester. For the data collection, the researcher surveyed 116 students who are in their 1st year of college and collected their final tests, an oral presentation. The findings are as follows. Firstly, between the two groups, the synchronous group showed higher mean scores for online group participation. Secondly, the synchronous group also indicated higher mean scores for English learning, speaking confidence, and interest in learning English. Lastly, both groups mentioned communication with team members, improvement in English speaking and pronunciation, increased self-confidence, and other elements as advantages of group work. In contrast, disadvantages included technology problems, decreased self-confidence, inconveniences when meeting with team members online, and others. The implications for the study are: firstly, online group activity can reduce isolation and increase interaction; secondly, the online video-based platform, Flipgrid, can help students improve English speaking skills by observing their own or peers’ videos; lastly, instructors can selectively use the synchronous and asynchronous group activities to suit their educational needs.


A Probe into the Integration Concept of Blended Teaching Mode of College English in Application-Oriented Universities

Abstract: Blended teaching is the product of the organic combination of classroom teaching and online learning. The practice of college English teaching in application-oriented colleges shows that the construction of the integration concept of blended teaching mode will play a good role in promoting the quality of college English teaching. The organization and implementation of the blended teaching mode of college English should pay more attention to the integration of all elements of college English core literacy and online teaching resources, and establish a sense of integration in teaching concept, content, mode, and evaluation, so as to further promote the course reform of college English and realize the goal of cultivating English talents with emphasis on application practice.


A Study on Perception Survey of Preservice Elementary Teachers on Teaching Methods in Astronomy — A Secondary Publication

Abstract: This study was conducted on 115 students from four classes of 2nd-year intensive courses at B University of Education. Preservice elementary teachers were surveyed on what teaching methods they were aware of regarding the subject of elementary science astronomy. Recognition data of 80 people from whom the questionnaire was collected were analyzed. For this study, a 5-day survey was conducted. Preservice elementary teachers complained of difficulties in teaching methods in the astronomy area of the science department. The purpose of this study was to find out what difficulties preservice elementary teachers have in teaching elementary science astronomy topics and to explore more efficient teaching methods for teaching astronomy topics. The topic of the survey was set by discussing with the preservice elementary teachers about what kind of survey to use in teaching the subject of elementary science astronomy. There are many topics for elementary science astronomy, but two questionnaires were prepared focusing on the unit on the Earth and the moon: “What does the Earth look like?” in Unit 4 (1/10) of the 3rd year, 1st semester; in Unit 2 (1/11) of the 1st semester of the 6th grade, it was set as “What does the moon look like?” The surveys candidly described how to teach the subject of astronomy to elementary school students by mobilizing all the background knowledge of preservice elementary teachers. The results of these surveys were visualized and displayed using NetMiner as a language analysis method, and the contents of the responses to the actual surveys by preservice elementary teachers were described and interpreted. Based on these results, preservice elementary teachers tried to suggest a more efficient teaching method for the subject of elementary science astronomy. In addition, basic procedures and methods for lecturing on the subject of elementary science astronomy were presented. A more efficient teaching method for teaching elementary science astronomy subjects to preservice elementary teachers was suggested.


A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation

Abstract: Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges. First, huge feature distinction between support and query images causes knowledge transferring barrier, which harms the segmentation performance. Second, limited support prototypes cannot adequately represent features of support objects, hard to guide high-quality query segmentation. To deal with the above two issues, we propose self-distillation embedded supervised affinity attention model to improve the performance of few-shot segmentation task. Specifically, the self-distillation guided prototype module uses self-distillation to align the features of support and query. The supervised affinity attention module generates high-quality query attention map to provide sufficient object information. Extensive experiments prove that our model significantly improves the performance compared to existing methods. Comprehensive ablation experiments and visualization studies also show the significant effect of our method on few-shot segmentation task. On COCO-20i dataset, we achieve new state-of-the-art results. Training code and pretrained models are available at https://github.com/cv516Buaa/SD-AANet.


Clustering longitudinal ordinal data via finite mixture of matrix-variate distributions

Abstract: In social sciences, studies are often based on questionnaires asking participants to express ordered responses several times over a study period. We present a model-based clustering algorithm for such longitudinal ordinal data. Assuming that an ordinal variable is the discretization of a underlying latent continuous variable, the model relies on a mixture of matrix-variate normal distributions, accounting simultaneously for within- and between-time dependence structures. The model is thus able to concurrently model the heterogeneity, the association among the responses and the temporal dependence structure. An EM algorithm is developed and presented for parameters estimation. An evaluation of the model through synthetic data shows its estimation abilities and its advantages when compared to competitors. A real-world application concerning changes in eating behaviours during the Covid-19 pandemic period in France will be presented.


Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches

Abstract: To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches.A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches were used to identify the major factors that contribute to asthma.A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found the top six risk factors for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index.Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma, and thus provide insights into the disease pathogenesis.


Joint Sparse Locality Preserving Regression for Discriminative Learning

Abstract: Ridge Regression (RR) is a classical method that is widely used in multiple regression analysis. However, traditional RR does not take the local geometric structure of data into consideration for discriminative learning and it is sensitive to outliers as it is based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> -norm. To address this problem, this article proposes a novel method called Joint Sparse Locality Preserving Regression (JSLPR) for discriminative learning. JSLPR not only applies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm on both loss function and regularization term but also takes the local geometric structure of the data into consideration. The use of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm can guarantee the robustness to outliers or noises and the joint sparsity for effective feature selection. Taking the local geometric structure into consideration can improve the performance of the feature extraction and selection method when the data lie on a manifold. To solve the optimization problem of JSLPR, an iterative algorithm is proposed and the convergence of the algorithm is also proven. Experiments on four famous face databases are conducted and the results show the merit of the proposed JSLPR on feature extraction and selection.


Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile Environments

Abstract: The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.


pFedEff: An Efficient and Personalized Federated Cognitive Learning Framework in Multiagent Systems

Abstract: With the increase in data volume and environment complexity, real-world problems require more advanced algorithms to acquire useful information for further analysis or decision-making. Cognitive Learning effectively handles incomplete information, and multi-agent systems can provide enough data for analysis. Inspired by Distributed Machine Learning, Federated Learning has become an efficient framework for implementing Cognitive Learning algorithms in multi-agent systems while preserving user privacy. However, traditional communication optimizations on the federated learning framework suffer from either large communication volumes or large accuracy loss. In this paper, we propose pFedEff, a personalized federated learning framework with efficient communication that can reduce communication volume and preserve training accuracy. pFedEff uses two magnitude masks, two importance masks, and a personalized aggregation method to reduce the model and update size while maintaining the training accuracy. Specifically, we use a pre-training magnitude mask for approximated regularization to reduce the magnitude of ineffective parameters during training. We also use a post-training magnitude mask to eliminate the low-magnitude parameters after training. Furthermore, we use uploading and downloading importance masks to reduce the communication volume in both upload and download streams. Our experimental results show that pFedEff reduces up to 94% communication volume with only a 1% accuracy loss over other state-of-the-art federated learning algorithms. In addition, we conduct multiple ablation studies to evaluate the influence of hyperparameters in pFedEff, which shows the flexibility of pFedEff and its applicability in different scenarios.


Cryptosystem for IoT Devices With Feedback Operation Modes Based on Shared Buffer and Unrolled-Pipeline Techniques

Abstract: This paper presents a multichannel cryptosystem based on the proposed shared buffer techniques which enhance security for several Internet of Things (IoT) sensors and devices by using block cipher with feedback operation modes. Information security and its performance are both important characteristics for modern networking devices. In cryptography, the block cipher, for example, advanced encryption standard (AES), using feedback operation modes can enhance the security level but, unfortunately, the encryption cannot be performed in parallel owing to data dependency. Therefore, the pipeline and unrolling techniques are not applicable to increase the throughput of hardware designs. Under this situation, the round-based architecture is popular and regarded as an area-efficient solution. However, this method inherently limits its throughput. The proposed cryptosystem aims to provide many resource-constrained IoT devices with high-speed centralized encryption service to enhance their security levels, which are applicable for various scenarios, such as vehicular network, home network and network function virtualization (NFV)/software-defined networking (SDN) IoT. Beyond that, the proposed design introduces the shared buffer technique based on linked lists and presents a novel queuing structure to enhance the memory utilization so that it can reduce 72.9% memory requirement of the naïve implementation while achieving the same speedup. According to the implementation result, an aggregate throughput of 130.91 Gbps for encrypting 10 IoT devices in cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can be achieved on TSMC 40nm. The area efficiency of this work significantly outperforms the state-of-the-art works.


Does Qubit Connectivity Impact Quantum Circuit Complexity?

Abstract: Some physical implementation schemes of quantum computing can apply two-qubit gates only on certain pairs of qubits. These connectivity constraints are commonly viewed as a significant disadvantage. For example, compiling an unrestricted n-qubit quantum circuit to one with poor qubit connectivity, such as a 1D chain, usually results in a blowup of depth by O(n2) and size by O(n). It is appealing to conjecture that this overhead is unavoidable—a random circuit on n qubits has Θ(n) two-qubit gates in each layer and a constant fraction of them act on qubits separated by distance Θ(n). While it is known that almost all n-qubit unitary operations need quantum circuits of Ω(4n/n) depth and Ω(4n) size to realize with all-to-all qubit connectivity, in this paper, we show that all n-qubit unitary operations can be implemented by quantum circuits of O(4n/n) depth and O(4n) size even under 1D chain qubit connectivity constraint. We extend this result and investigate qubit connectivity in three directions. First, we consider more general connectivity graphs and show that the circuit size can always be made O(4n) as long as the graph is connected. For circuit depth, we study d-dimensional grids, complete d-ary trees and expander graphs, and show results similar to the 1D chain. Second, we consider the case when ancillary qubits are available. We show that, with ancilla, the circuit depth can be made polynomial, and the space-depth trade-off is not impaired by connectivity constraints unless we have exponentially many ancillary qubits. Third, we obtain nearly optimal results on special families of unitaries, including diagonal unitaries, 2-by-2 block diagonal unitaries, and Quantum State Preparation (QSP) unitaries, the last being a fundamental task used in many quantum algorithms for machine learning and linear algebra.


ATRC: An Anonymous Traceable and Revocable Credential System Using Blockchain for VANETs

Abstract: With the number of smart vehicle drivers increasing rapidly, privacy-preserving identity management methods in Vehicular Ad-hoc Networks (VANETs) become more imperative and receive much attention from researchers. Since identity leakage or a single point of failure may result in serious consequences in the VANET, the decentralized anonymous credential (DAC) could be a potential approach to construct a robust network. However, the supervision under decentrilized environment could be a necessary and troublesome in the VANETs. In this paper, we present an anonymous traceable and revocable credential system using blockchain, called ATRC, built over a generalized group signature. The underlying group signature not only meets the anonymity requirement but also makes the users master their identities. What's more, to tackle the privacy leakage in the revocation process, we employ the Merkle tree to construct a whitelist, which trades off the efficiency and the privacy. Finally, we gives the comparison and experiment performance to show our scheme holds lightweight on the user side and has fewer computation costs in the show and revocation phase.


Learning from Incorrectness: Active Learning with Negative Pre-training and Curriculum Querying for Histological Tissue Classification

Abstract: Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.


Application of deep learning-based ethnic music therapy for selecting repertoire

Abstract: With the advancement of modern medical concepts, the beneficial effects of music on human health have gradually become accepted, and the corresponding music therapy has gradually become a new research direction that has received much attention in recent years. However, folk music has certain peculiarities that lead to the fact that there is no efficient way of selecting repertoire that can be carried out directly throughout the repertoire selection. This paper combines deep learning theory with ethnomusic therapy based on previous research and proposes a deep learning-based approach to ethnomusic therapy song selection. Since the feature extraction process in the traditional sense has insufficient information on each frame, excessive redundancy, inability to process multiple frames of continuous music signals containing relevant music features and weak noise immunity, it increases the computational effort and reduces the efficiency of the system. To address the above shortcomings, this paper introduces deep learning methods into the feature extraction process, combining the feature extraction process of the Deep Auto-encoder (DAE) with the music classification process of Gaussian mixture model, which forms a new DAE-GMM music classification model. Finally, in terms of music therapy selection, this paper compares the music selection method based on co-matrix and physiological signal with the one in this paper. From the theoretical and simulation plots, it can be seen that the method proposed in this paper can achieve both good music classifications from a large number of music and further optimize the process of music therapy song selection from both subjective and objective aspects by considering the therapeutic effect of music on patients. Through this article research results found that the depth of optimization feature vector to construct double the accuracy of the classifier is higher, in addition, compared with the characteristics of the original optimization classification model, using the gaussian mixture model can more accurately classify music, the original landscape “hometown” score of 0.9487, is preferred, insomnia patients mainly ceramic flute style soft tone, without excitant, low depression, have composed of nourishing the heart function.