Peningkatkan Hasil Belajar Bahasa Indonesia Melalui Metode Role Playing

Abstract: The problem in this classroom action research is that the low value of Indonesian through the role playing method can improve the speaking skills of Class V students at SDN 219 Ara, Bonto Bahari District, Bulukumba Regency. Bulukumba. The approach used is a qualitative approach. The type of research used is classroom action research which consists of two cycles, where each cycle consists of two meetings, with activity procedures including planning, implementation, observation and reflection. The subjects in this study were teachers and fifth grade students at SDN 219 Ara, Bonto Bahari District, Bulukumba Regency. Those who are actively registered in the odd semester of the 2012/2013 academic year have 10 students, consisting of 9 boys and 1 girl. Data collection techniques used are observation, tests and documentation. The data analysis technique used is descriptive quantitative analysis. The indicators of the success of this research are the indicators of the process and results of speaking skills. The results show that with the optimal application of the Role Playing method, student learning activities increase, this can be seen in the first cycle being in the medium category because it is seen from the results of the speaking skills test at the end of the first cycle where the average score The average student has not reached the predetermined KKM and in the second cycle is in the very high category. Thus there is an increase in students' speaking skills in Indonesian subjects after the implementation of the Role Playing method can increase.


Kontribusi Latihan Passing Bawah Berpasangan dan Passing Bawah Ke Dinding Terhadap Ketepatan Passing Bawah

Abstract: This research was motivated by the inability of the volleyball players at SDN 1 Pasirjati to make down passes so that when they were about to make a pass the ball was often not directed which resulted in the ball going out of court. This research aims to determine the effect of pair bottom passing practice and bottom passing practice against a wall on volleyball bottom passing accuracy. This type of research uses quantitative research and uses pre-experimental methods. The instrument used in this research was accuracy of passing down (Brumbach) using a sample of 12 volleyball players from SDN 1 Pasirjati. The sampling technique in this research uses a total sampling technique. The research was conducted in the field at SDN 1 Pasirjati. The results of the study showed that the results of the paired down passing exercise had an average increase of 2 times, while the results of the down to the wall passing exercise had an average increase of 3 times, so the results of the down to the wall passing exercise were greater and had a significant influence on increasing the ability to accurately pass volleyball.


The Importance of Digital Technology and Clustering for Innovation in MSEs. Evidence from Secondary Data in Indonesia

Abstract: This paper explores the relationship between innovation and these two determinants in micro and small enterprises (MSEs) in the manufacturing industry in Indonesia, using secondary data. The key question in this research: is there evidence of the importance of DT and clustering for innovation capability in MSEs in Indonesia? The paper analyzes secondary data from Indonesia’s National Agency of Statistics and reviews key literature on innovation and digitalization in MSEs, and industrial cluster development in Indonesia. It is found that the number of industrial clusters of micro-, small-, and medium-sized enterprises (MSMEs) is concentrated on the island of Java, where more than 50% of the population is located, and is the most advanced region in economic development and industrialization in Indonesia. The majority of MSME clusters are in the food industry and woodworking industries. Only a very few of MSEs in the manufacturing industry use the Internet and do innovation. The type of innovation mostly is product innovation. Both relationships between MSEs doing innovation and MSEs using the internet and the total number of clusters are positive. But, statistically, only the relationship between the number of MSEs doing innovation and use of the internet is significant.


An analysis of digital literacy skills of high school students in biology

Abstract: Digital literacy is a crucial skill for understanding information from various sources, such as the use of the internet and other digital media as a learning resource. Navigating the internet is an important skill to master to keep updated with the development of information and communication technology in the era of Industrial Revolution 4.0 and to master one of the 21st-century skills. Skills that someone should have in digital literacy include internet searching, hypertextual navigation, evaluating information, and synthesizing information. This study aims to determine the digital literacy skills of tenth-grade high school students in biology class. The research method follows the quantitative descriptive method. The population and sample were 62 tenth-grade high school students. The data collection technique is in the form of questionnaires focusing on four aspects of digital literacy. The results indicate that the digital literacy of tenth-grade high school students with an average score of 44% is in the fair category. Meanwhile, the results for each aspect of the digital literacy indicator obtained different scores, namely internet searching at 47% (fair), hypertext navigation at 27% (fair), evaluating information at 40.32% (fair), and synthesizing information at 60.48% (good). This research suggests how stakeholders can issue policies as a basis for improving students' digital literacy to support the learning process focusing more on 21st-century skills.


Design of Computerized Patient Registration Application System at Tanjung Pati Health Center

Abstract: One of the shortcomings of manual patient registration systems is the procedures that affect the efficiency of service care to patients. Therefore, researchers are interested in designing a computerized patient registration application using Microsoft Access software. The type of research used is Research and Development (R&D). This research was conducted at Tanjung Pati Community Health Center, with in-depth interviews with two registration officers. The research results show that the data elements needed to design the patient registration application system, sourced from the Patient Registration Book, will be developed by creating an application system using UML modeling. The application system undergoes Black Box Testing with the result that all components run well. The application system is efficient, with an average comparison of manual patient registration time to system patient registration of 420:180 seconds for new patients and 360:33 seconds for existing patients. The application system is efficient according to the results of interviews with informants. Therefore, the design of the computerized patient registration application system using Microsoft Access meets the needs of the community health center and is efficient in terms of patient registration time. It is recommended that the patient registration application be used continuously in the medical records unit at Tanjung Pati Community Health Center.


KONSTRUKSI CITRA MUSLIMAH PADA AKUN TIKTOK @putriaqillaramadhani

Abstract: This research aims to describe and uncover the construction of the Muslimah image on the TikTok account @putriaqillaramadhani. In the digital era and the development of social media, platforms like TikTok have become important for individuals to build identities and influence others' perceptions. Muslim women use social media as a platform to express their religious beliefs, share inspiration, and spread positive messages about Islamic life. This study employs a qualitative approach to understand how the Muslimah image is represented and constructed through the content of the TikTok account. Semiotic analysis is used to analyze video content, descriptions, comments, and interactions with followers. Data are collected through observation and documentation. The results show that the @putriaqillaramadhani account actively constructs a strong and positive Muslimah image using polite and courteous language. Common topics include Islamic teachings, daily Muslimah life, and spiritual advice. The account also utilizes religious symbols such as the hijab and the Qur'an to strengthen the Muslimah identity. Additionally, it promotes positive messages about Muslimah life, such as diversity, tolerance, and empathy, by sharing personal experiences and inspirational stories with followers


Semantic Relatedness and the Efficacy of Retrieval Practice

Abstract: There is growing interest in the role that semantic memory plays in the retrieval practice (or testing) effect. We investigated how semantic relatedness impacts the efficacy of testing, relative to a restudy control, using a cued-recall paradigm with immediate correct answer feedback. High and low relatedness word pairs were created by selecting two different response words for each cue word based on associative norms. The parameter-free predictions of the dual-memory model of test-enhanced learning for low relatedness pairs were used as an empirically well-supported scaling benchmark for evaluating the effectiveness of testing for high relatedness pairs, allowing us to overcome the issue of removable interactions. Results showed a robust testing effect at both relatedness levels. For the high relatedness group, however, the testing effect was about 26% smaller than expected by the model. Nevertheless, the predicted quadratic relation between test and restudy proportion correct in the cumulative distribution held for both groups. One candidate account of those results within the dual-memory framework is that, for the high relatedness case, study and test memory strengths for tested items are positively correlated rather than independent. A simulation implementing that version of the model provided a good distribution fit for the high relatedness group. Other theoretical accounts of the semantic relatedness effect are considered.


NeVer2: Learning and Verification of Neural Networks

Abstract: Abstract NeVer2 is a cross-platform open-source tool to design, train and verify neural networks. NeVer2 integrates off-the-shelf learning libraries with our verification backend and makes their functionalities available through a graphical interface. A user can work out the structure of a neural network by dragging and dropping blocks on a canvas and then train the network by supplying a dataset source and the hyper-parameters through dialog boxes. Once the network is trained, verification amounts to (i) adding input pre-conditions and output post-conditions to the network through special blocks and (ii) starting verification in a "push-button" fashion. To the best of our knowledge, there is no other publicly available tool that integrates all such features at present. Here we provide a detailed description of NeVer2 including examples to demonstrate the seamless integration between design, training and verification. We further provide an experimental analysis on several verification benchmarks in order to show the trade-off between completeness and computability using different algorithms, including a comparison with the state-of-the-art tools α,β-CROWN and NNV for reference.


Effects of causal structure and evidential impact on probabilistic reasoning

Abstract: We compare two perspectives on base-rate neglect (Kahneman &amp; Tversky, 1973) in probabilistic judgment. The evidential impact perspective derives it from humans' focus on the impact of evidence on belief, rather than conditional probabilities. The Causal Models perspective derives it from humans' inability to integrate information that is causally opaque, as base-rates often are in such experiments. Because causal and evidential-impact relations are often concomitant and confounded, we designed an experiment that specifically teases apart their respective influence on probabilistic judgment. Our results support a combination of the two perspectives, with causal transparency influencing the degree to which one engages in evidential impact reasoning strategies.


Pengaruh Content Creation, Content Sharing, Connecting dan Community Building terhadap Keputusan Pembelian Konsumen pada 3D Stable Equestrian

Abstract: This research aims to determine the influence of content creation, content sharing, connecting and community building on consumer purchasing decisions on 3D Stable Equestrian. The research method used is a quantitative method. The data used in this research is primary data. The sampling technique uses purposive sampling technique. The sample studied in this research was 100 respondents who were 3D Stable Equestrian consumers. Based on the t test carried out, it can be concluded that partially content creation, content sharing, connecting and community building have a significant effect on consumer purchasing decisions at 3D Stable Equestrian. Content creation, content sharing, connecting and community building simultaneously have a significant influence on consumer purchasing decisions at 3D Stable Equestrian. The coefficient of determination of the content creation, content sharing, connecting and communitu building variables on the purchasing decision variable is 46,3%


Analysis and Development of a Football Scouting App based on Flutter: A Case Study of A3N

Abstract: Scouting is a crucial aspect of football, where the team's success depends heavily on the ability to select the right player. To minimize risk and optimize resources, teams can utilize the football scouting application. Unfortunately, existing applications still have limitations, especially in support of multiple platforms, and are open source. Therefore, this research is focused on developing a football scouting application that supports multiple platforms and is open source, using a single codebase-based Flutter Framework. Using Flutter, the app can be created based on just one code base, which can be compiled directly on Android and iOS platforms. The developed application has several superior features. First, the application allows the admin to insert player data into the database. Another feature is the ability to search for players based on names that users include. In addition, the application is also able to display a list of players based on the position chosen by the user. Finally, the application can display the complete data and statistics of the currently active player. Through the implementation of the selected development method, the implementation of this system can contribute significantly to supporting the scouting process in football teams. Thus, this application can increase the efficiency and effectiveness of decision-making within the scope of football scouting activities.


Dynamic Knowledge Expansion: Real-time Text Classification with Deep Convolutional Neural Networks

Abstract: Abstract Convolutional Neural Networks (CNNs) have shown exceptional prowess in text classification in the dynamic landscape of Natural Language Processing (NLP). It is difficult, however, to adapt to evolving concepts when overwhelmed with streaming data and retraining is cumbersome. We present an incremental learning solution tailored for real-world, computationally intensive applications in this paper. Using dynamic updates, we introduce a new taxonomy for text data stream classification that addresses the formidable challenge of concept drift. Using a modest dataset, we train a lean CNN. Using transfer learning, this model intelligently gleans insights from the data stream, leveraging knowledge gained from previous datasets. In order to classify data streams in real time, an online learning mechanism is deployed. A pretrained CNN is updated periodically to capture emerging features, accelerating learning even further. Our experimental results demonstrate the superiority of our approach, setting new benchmarks in performance. Our method allows NLP systems to remain agile, efficient, and state-ofthe- art in an era of relentless data flow.


Development of a mobile learning network for science with augmented reality and its impact on students’ literacy and numeracy

Abstract: The aim of the current research is to develop the Mobile Learning Network for Science application with Augmented Reality (MLNFS-AR) application on the topic of atomic structure and chemical equilibrium. The study was conducted in four public schools in Jakarta, Indonesia. The Borg and Gall development cycle was adopted in this study, including preliminary research, planning and developing learning media, and validation, evaluation, and revision. Regarding the assessment results from media experts, the mobile learning application was declared very feasible with a percentage of 91.7% for the topic of atomic structure and 87.2% for the topic of chemical equilibrium. The results of the material and language feasibility assessments were 81.6% for the topic of atomic structure and 82.3% for the topic of chemical equilibrium. The results of the media assessments for students were 92.9% for the topic of atomic structure and 89.9% for the topic of chemical equilibrium. The results of the media assessment by chemistry teachers were 97.1% for the topic of atomic structure and 94.7% for the topic of chemical equilibrium. Overall, this mobile learning medium is categorized as very feasible to use as a learning support medium. In addition, the MLNFS-AR app is effective in improving student literacy and numeracy. In sum, the mobile learning app, MLNFS-AR, can be used as a flexible learning medium, can be used anytime and anywhere, and makes it easier for students to learn chemistry.


Zero-knowledge proofs in education: a pathway to disability inclusion and equitable learning opportunities

Abstract: Abstract In the evolving landscape of global education, the significance of inclusivity and equity has never been more important. Emphasizing the United Nation Sustainable Development Goal 4, this paper explores the innovative application of blockchain-powered Zero-Knowledge Proofs (ZKPs) technology in education, with a particular focus on disability inclusion. This study introduces a novel disability management system powered by Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK). This advanced system enables educational institutions to verify the status of students with disabilities without compromising their personal information, thereby preserving their privacy and reinforcing their identity. This paper evaluates the potential operational efficiency of this prototype system against the existing costs incurred by higher education institutions in disability schemes. It also examines the system’s potential to enhance self-disclosure among students with disability, which is pivotal for their academic success. By advocating for privacy and inclusivity, this study highlights the transformative potential of ZKP in creating an educational environment where students with disabilities can comfortably disclose their needs. This approach not only protects their confidentiality but also empowers them academically, aligning with the global commitment to accessible and inclusive education.


A multi-variable service supply management portfolio model for Telecom industry

Abstract: Supply operations require some kind of classification according to the features of items. Based on the literature review and expert interview, a multi-variable service supply management portfolio model was developed to classify the procurable service items in the Telecom industry in a more realistic way with better precision. First, a multi-level relational reflective model was built based on four dimensions using the fuzzy Delphi method and the model was validated by factor analysis. Then, 50 service items were placed in 16 clusters of the portfolio model. For each category of services in the model, the pertinent supplier relationship management practices were proposed using the literature review accompanied by a focus group. The presented model contributed to the development of previous theoretical trends in supply management portfolio models by introducing a new dimension, called “pace of innovation in technology”, to those extracted from the literature review, such as ‘strategic importance of the items’, ‘supply risk’, and ‘extent of buyer captivity’. Despite the classical models targeting tangible goods and products, our model was customized for the service industry context and can be considered a starting point towards service supply market segmentation procedures according to the attributes of each service item category.


Enhancing Marketing Transparency and Trust through Blockchain Technology

Abstract: This research delved into the intersection of blockchain technology and marketing, aiming to investigate its transformative potential in enhancing transparency, trust, and reducing ad fraud. Employing a mixed-methods research design, both quantitative and qualitative approaches were employed to comprehensively address the research objectives. In the quantitative phase, a stratified random sampling technique was utilized to select participants from diverse demographic segments, including both consumers and marketing professionals. Surveys and digital analytics tools were employed to collect data on trust levels, perceptions, and customer engagement in traditional and blockchain-based marketing initiatives. Statistical analyses, including chi-square tests and ANOVA, were conducted to determine the significance of blockchain's impact. The qualitative phase involved purposive sampling of key informants for in-depth interviews, providing nuanced insights into experiences and perceptions regarding blockchain technology in marketing. Content analysis and thematic analysis were applied to marketing materials and qualitative data, respectively. The integration of findings from both phases allowed for a comprehensive understanding of blockchain's impact on marketing, emphasizing its transformative potential. Limitations, such as the challenge of capturing the rapidly evolving nature of blockchain technology, were acknowledged.


The relationship between learning motivation and learning engagement of Chinese state-funded normal university students: The mediating role of professional identity

Abstract: This study focuses on the relationship between learning motivation, career identity, and learning engagement of state-funded normal university students in Guangdong Province, China. Through intentional sampling, 1449 state-funded normal university students from 10 universities in the Guangdong Province of China were taken as the research objects. Then, reliability and validity analysis, descriptive statistical analysis, difference analysis, correlation analysis, regression analysis, structural equation analysis, the interview method, and other statistical methods are used for analysis. There is a significant positive correlation between learning motivation, professional identity, and learning engagement of state-funded normal university students, and professional identity plays an intermediary role. The results show that the learning motivation of state-funded normal university students has a significant positive effect on professional identity and learning engagement. Furthermore, the professional identity of state-funded normal university students has a significant positive effect on learning engagement; meanwhile, professional identity serves as a partial mediator between learning motivation and learning engagement of state-funded normal university students. Due to the insufficient research level of researchers, there are limited study samples, limited study variables, and limited research methods. This study will improve the quality of education and teaching, stimulate teachers' career development potential, and promote education reform and development. It also fills the research gap, deepens the study theory of learning motivation, and optimize the training mode of state-funded normal university students.


The comparison of students' academic stress in biology class at public and private high schools

Abstract: Students are demanded by school assignments, and many of them feel under pressure when learning biology. This situation is a trigger for academic stress which has an impact on student achievement. This study aims to identify and compare the factors that influence academic stress on students studying biology at the senior high school level. The research method used was an ex post facto survey using a sample of 54 students with an age range between 16-18 years. Data were obtained through a questionnaire with 9 scales of answer choices and interviews. The results showed that the level of preference for biology subjects for both public and private high school students was quite good, but most students experienced academic stress with a fairly high category in terms of motivation, self-confidence, the education system, teachers, and school conditions. This research implies that teachers are expected to create learning strategies by paying attention to the psychological conditions of students so that student learning outcomes can be maximized.


Investigating the Effect of Trade Openness and Agriculture on Deforestation in Cameroon

Abstract: This study aimed to measure the effect of trade openness and agriculture on deforestation in Cameroon from 1980 to 2021 by using a fully modified ordinary least squares (FMOLS) approach. Data used are from the World Bank and FAO. The results obtained indicate that when trade openness increases, deforestation also increases, but when trade openness increases up to a certain threshold, deforestation decreases. This study also reveals that agriculture is one of the major causes of deforestation in Cameroon. Agricultural output and agricultural value-added both have a positive and significant impact on deforestation. There is an inverted curve relationship between economic growth and deforestation in Cameroon, this shows that the EKC is respected with deforestation as it is postulated that at higher levels of income, GDP turns to reduce deforestation meaning a unit change in GDP2 leads to a reduction of deforestation. We recommend the implementation of concrete actions and strict environmental policies focused on a green economy, to control the exploitation of natural resources with particular attention to the sustainable exploitation of wood. Sustainable agricultural practices should also be implemented, as well as more suitable liberal trade policies.


Design Education Methodology Using AI

Abstract: In this comprehensive research paper, the authors scrutinize the integration of Generative Artificial Intelligence (GAI) into design education methodologies with a focus on the twin objectives of enhancing creativity and operational efficiency. Leveraging an array of research methods ranging from literature reviews to case studies and empirical analyses the paper presents a well rounded exploration of how emerging AI technologies can redefine design education. Notably, the study highlights the immense potential of AI-powered tools in augmenting creativity. It asserts that GAI can act as a catalyst for spawning novel creative ideas, diversifying design variations, and expediting the exploration of complex design alternatives all within a constrained time frame. The methodology involves an objective and statistical analysis of work generated during a workshop, offering insights into the relationship between human creativity and AI-enabled design processes. Despite the palpable benefits, the study also uncovers a set of challenges hindering seamless AI integration into design curricula. These challenges range from the need for specialized, in-depth training on AI technologies for designers to mastering the nuances of prompt engineering. The paper also raises ethical considerations related to AI-generated designs, as well as flags potential biases present in AI algorithms. The authors conclude by stressing the imperative of acknowledging and addressing these challenges head-on to ensure both responsible and effective incorporation of GAI into design education.


Clustering Fake News with K-Means and Agglomerative Clustering Based on Word2Vec

Abstract: Fake News on digital platforms is a major problem in this digital age. Many people want to find methods to detect Fake News. This research looks at a way to group Fake News articles using K-Means and Agglomerative Clustering techniques, using the semantic representations from Word2Vec embeddings. The researchers use natural language translation methods and advanced machine learning to improve the accuracy and efficiency of Fake News detection. The study involves getting meaningful features from textual data, turning them into vector representations using Word2Vec, and then applying clustering algorithms to sort similar articles. The methodology aims to improve the most recent state of the art in Fake News detection, helping to create more reliable and robust tools to fight misinformation in the digital age, In the comparative analysis of clustering metrics, K-Means clustering exhibits a Purity Score of 88.09% and an Adjusted Rand Score of 58.03%. Conversely, Agglomerative Clustering with the Ward method yields a Purity Score of 85.13% and an Adjusted Rand Score of 49.36%.The Purity Score of 88.09% for K-Means suggests a strong ability to form clusters where the majority of data points share the same true class. Agglomerative Clustering with Ward, though slightly lower at 85.13%, also demonstrates effective class separation within clusters. When considering the Adjusted Rand Score, which accounts for chance and measures the agreement between true and predicted labels, K-Means significantly outperforms Agglomerative Clustering with Ward. The scores are 58.03% and 49.36%, respectively


Using mHealth for Primary Prevention of Dementia: A Proof-of-Concept Study on Usage Patterns, Appreciation, and Beliefs and Attitudes Regarding Prevention

Abstract: Background: Health- and lifestyle factors account for a substantial part of all dementia cases, which opens the opportunity for primary prevention. However, the required behavioral change is complex and involves targeting multiple risk factors. mHealth interventions can potentially contribute to improving motivation in a low-cost and scalable way. Objective: To explore usage patterns, appreciation, and beliefs and attitudes regarding dementia risk reduction during the use of the MyBraincoach mobile app. Methods: Participants were community-dwelling middle-aged adults from the Netherlands and used either the standard (education) or extended (education+motivational triggers) app version for three months. Two panel studies were combined in this paper. Chi-square tests, t-tests and linear mixed models were used, adjusted for age, sex, and education. Results: Of all participants (n = 299, 50.2% male), 167 (55.9%) had installed the app. The most reported reason for non-use was technical problems (47%). Those who used the app were at baseline already more positive about dementia risk reduction than those who did not use the app. Of all users who completed the evaluation (n = 102), 78.4% (n = 80) stated that the app provided a positive approach towards brain health and 80.4% (n = 82) felt better informed. Younger (<60y) and lower educated participants evaluated the app most positively. Conclusion: Usage of the app was low, but users showed more positive beliefs and attitudes regarding dementia risk reduction. Most users evaluated the app positively and stated to have gained knowledge on the topic. Improving the use of the app must keep high priority in future studies.


Pengembangan Media Pembelajaran Berbasis Augmented Reality (AR) pada Mata Pelajaran IPAS Kelas VIII SMP

Abstract: Augmented Reality (AR) learning media is a new learning innovation that displays interesting 3D animation visuals and makes complex and abstract concepts more real so that it triggers students' interest and curiosity in learning, so it can be used as an alternative in learning problems. This study aims to develop learning media for IPAS Class VIII Junior High School in order to produce valid media that is practical and effective for use by teachers and students This type of research is a development research (Reseacrh an Development) with the 4D (Four-D) development model. The average score for media validity assessment using Likert scale percentage presentation with media assessment by validator I is 4.93 and the percentage of 98% is categorized as "very valid", validator II is 4.73 with a percentage of 94% categorized as "very valid". And the average score of the material validator is 4.79 with a percentage of 95% categorized as "very valid". The research was conducted in 4 stages, the first stage by conducting curriculum analysis, learner analysis, and concept analysis. Then the initial product development was carried out by making flowcharts and storyboards and then making learning media with the Aseemblr edu application. Furthermore, the validity test stage is carried out by media and material validators, the last one is conducting a practicality trial and effectiveness trial for VIII grade junior high school students.


Research on a Feature Enhancement Extraction Method for Underwater Targets Based on Deep Autoencoder Networks

Abstract: The low-frequency line spectrum of the radiated noise signals of hydroacoustic targets contains features describing the intrinsic properties of the target that make the target susceptible to exposure. In order to extract the line spectral features of underwater acoustic targets, a method combining image processing and a deep autoencoder network (DAE) is proposed in this paper to enhance the low-frequency weak line spectrum of underwater targets in an extremely low signal-to-noise ratio environment based on the measured data of large underwater vehicles. A Gauss–Bernoulli restricted Boltzmann machine (G–BRBM) for real-value signal processing was designed and programmed by introducing a greedy algorithm. On this basis, the encoding and decoding mechanism of the DAE network was used to eliminate interference from environmental noise. The weak line spectrum features were effectively enhanced and extracted under an extremely low signal-to-noise ratio of 10–300 Hz, after which the reconstruction results of the line spectrum features were obtained. Data from large underwater vehicles detected by far-field sonar arrays were processed and the results show that the method proposed in this paper was able to adaptively enhance the line spectrum in a data-driven manner. The DAE method was able to achieve more than double the extractable line spectral density in the frequency band of 10–300 Hz. Compared with the traditional feature enhancement extraction method, the DAE method has certain advantages for the extraction of weak line spectra.


What Observables Are Needed for Precision Data-Enabled Learning of Inverse Operators?

Abstract: Abstract The advent of easy access to large amount of data has sparked interest in directly developing the relationships between input and output of dynamic systems. A challenge is that in addition to the applied input and the measured output, the dynamics can also depend on hidden states that are not directly measured. In general, it is unclear what type of data, such as past input and or past output is needed, to learn inverse operators (that predict the input needed to track a desired output for control purposes) with a desired precision. The main contribution of this work is to show that, irrespective of the selected model, removing the hidden-state dependence and achieving a desired precision of inverse operators require (i) a sufficiently-long past history of the output and (ii) sufficiently-precise estimates of the output's instantaneous time derivatives that are necessary and sufficient for linear systems, and under some conditions, for nonlinear systems. This insight, about the required observables (output history and derivative) for removing the hidden-state dependence and achieving precision, is used to develop a data-enabled algorithm to learn the inverse operator for multi-input multi-output square systems. Simulation examples are used to illustrate that neural nets (with universal approximation property) can learn the inverse operator with sufficient precision only if the required observables, identified in this work, are included in training.


Analysis of Human activities on Smart Devices Using Riak-TS

Abstract: In this paper we have implemented Riak TS which is a time series-based database.It is a key valuebased database and has time as important parameter.During the implementation of the project we have understood the installation process, loading the data and also analyzing the data using Riak TS.By doing complex querying we learnt how time plays a crucial role in understanding the data and analyzing them to visualize.


Computer Technologies as a Method of Forming Students' Information Skills in the Process of Learning

Abstract: The article investigates the problem of formation of future teachers' readiness to innovative activity, the role of computer technologies as a method of formation of students' information skills in the learning process. Based on the stated and analyzed interpretations of the authors about the concept of "innovations in education", the research position to consider innovation as an important element of education support in a state of dynamic development, adequate to the demands of today's society as a phenomenon of collective or formed individual professional creative activity of a teacher was formulated. On the example of the course, in which a systematic theoretical, methodological and practice-oriented training of students is carried out, the experience of formation of readiness of future teachers to innovative activity is described, the leading forms of pedagogical activity are presented. According to the results of the study it is concluded that the formation of the future teacher's readiness for innovative activity contributes to the identification of the conceptual position of the specialty, the identification of development resources and innovation (including potential) and their critical evaluation, creating conditions for finding innovative solutions in education. This competence is an important part of the professionalism of the today's teacher. Also in the article the role of computer technologies as a method of formation of students' information skills in the learning process is studied; e-learning tools as a basic step of realization of digital pedagogical technologies, the structure of the author's system of preparation of university teacher to the application of e-learning tools in professional activity is given.


The Role of Current Computer Technologies in Developing Professional Competence of Music Teachers: A Model of a Personalized Educational Environment

Abstract: The importance of the article lies in the fact that the profession of school music teacher today needs to create its own updated competence model of personal educational environment taking into account digital transformation and computerization of general education. The information competence of the music teacher, the issues of his professional development in working with music and computer technologies are taken into account. Analyzed the possibility of building a personal educational environment in the era of postmodern (including electronic) and their application in further professional activities. The author analyzes the possibility of introducing modern computer technology as a resource for the formation of musical needs of the teacher of the educational institution. In addition to professional competencies, some general requirements for music teachers today, which can be grouped into four groups: general professional, communicative, behavioral and analytical. The article presents a model of university music education in the postmodern era; ways of acquiring professional competence of a music teacher are considered; the stages of creating a model of personal educational environment of a music teacher in the postmodern era are described; the role of professional competence of a music teacher in the postmodern personal educational environment model is defined; the role of current music and computer technologies (MCT) in creating a model of personal educational environment of a music teacher was determined.


EASE: Energy Optimization through Adaptation – A Review of Runtime Energy-Aware Approximate Deep Learning Algorithms

Abstract: This survey provides an overview of the state-of-the-art in runtime adaptive Approximate Computing (AxC) for Deep Learning (DL) algorithms, highlighting the challenges and opportunities in the field. The survey covers a broad spectrum of applications, including medical applications, computer vision, and natural language processing. Various power-constrained platforms, such as System-on-Chips (SoCs), Application Specific Integrated Circuits (ASICs), and Field Programmable Gate Arrays (FPGAs), are explored for their utilization in implementing runtime adaptive AxC. The survey explores various techniques, such as dynamic quantization, adaptive pruning, and low-rank approximations, offering a detailed discussion of their advantages and disadvantages. Specifically, in some surveyed research works, the runtime approximation is achieved through the utilization of machine learning algorithms, with a notable emphasis on Reinforcement Learning (RL). These approaches aim to realize runtime conditions and exploit them appropriately. By providing insights into the advancements and trends in runtime adaptive AxC, this survey serves as a valuable resource for researchers and practitioners interested in this rapidly evolving area of computing. This survey conducts an in-depth investigation into the application, challenges, and scope of runtime adaptive AxC techniques, aiming to mitigate energy consumption while preserving acceptable levels of accuracy in DL models. Our primary focus lies on Convolutional Neural Networks (CNNs), with an emphasis on their application in diverse domains. In striving for comprehensiveness, the survey encompasses selected research works that extend beyond CNNs, including alternative DL models like Recurrent Neural Networks (RNNs). our scope of applications, focuses on CNNs; however, to make a comprehensive survey, we cover some surveyed research works that contain other DL models, such as RNNs. It also highlights the importance of considering specific application requirements and available resources when choosing the appropriate technique.


Backtracking CTE Pathways: Identifying and Investigating Pathways and Critical Junctures in Two-Year Information Technology Programs

Abstract: Abstract This NSF Advanced Technological Education (ATE) research and development project aims to design and test a Backtracking Technique for understanding the pathways students take through college and into careers in science, technology, engineering, and mathematics (STEM) and career and technical education (CTE), with the focus of this project on information technology (IT). The project gathers data about current and former students who started in the same cohort, includes institutional research data (e.g., grades, demographics, course-taking) and merges these data with employment data from surveys and lived experiences obtained from interviews. These data are analyzed to identify potential pathways and critical junctions that may lead to student success or other outcomes. The research team is led by a doctoral granting institution and a community college, and includes four additional community colleges that collectively serve rural and urban student populations. In this paper we share the potential of the Backtracking Technique to generate contextualized career pathway data for institutions and create visualizations that can aid in institutional decision-making through a study pilot. The pilot is an initial effort to test the project's aims of integrating institutional data with phenomenological data to model student progression through post-secondary STEM programs. The analysis will identify and verify influencers that support or hinder student success. Quantitative data analyses will consist of descriptive and comparative methods, which will be verified and informed by open coding and thematic analysis of the qualitative data. We share how the systematic investigation of institutional and phenomenological data used in the Backtracking Technique has the potential to: (1) generate practical knowledge about academic/career pathways in information technology for use by stakeholders; (2) identify and examine relationships among these pathways, students experiences, and psychosocial factors; and (3) add to the analytical methods available to institutional research professionals to document, investigate, and visualize student pathway information using data dashboards. This ATE project has great potential to transform the technician preparation for the advanced technology fields that drive the nation's economy.


Cybersecurity for Everybody - A Multi-Tier Approach to Cyber Security Education, Training, and Awareness in the Undergraduate Curriculum

Abstract: Abstract Almost every career encompasses some form of security and today's students must be introduced to various aspects of security to be effective in their career and daily lives. South Carolina State University (SC State University) has an ongoing Target Infusion Proposal grant from National Science Foundation (NSF) to address the problem of the lack of awareness and participation in cyber security since 2019. The project vision is to create a successful model of institution wide reform for undergraduate cybersecurity education at SC State University using instruction, internships, and seminars. The student should be able to evaluate, make decisions, and take responsible actions in the context of cyber security. This project uses a multi-tier approach to increase capacity in cyber security education, training, and awareness in the undergraduate curriculum The objective of this poster presentation is to share our experiences with various project activities. The findings presented in this paper may be used by interested parties involved in STEM curriculum.


Creating Employer-Driven Information Technology Skill Standards, the Process, and the Results

Abstract: Abstract The "IT Skill Standards 2020 and Beyond" National Science Foundation Advanced Technological Education (ATE) project has created a set of future-facing, employer-led and verified Information Technology (IT) skill standards for six of the most critical contemporary IT job clusters. The job clusters of focus were identified by roughly 100 IT Thought Leaders (mostly CTOs, CIOs, and futurists responsible for keeping their companies in business), and over 250 employers have participated to create the standards. Per a re-convening of Thought Leaders, the project is shifting to developing sets of skills (at a level that is more than awareness but less than a full skill standard) that can be layered onto all or almost all technical jobs. The project is in the process of developing the layering skill set for cybersecurity and will also focus on a range of skill sets from Artificial Intelligence and Machine Learning to Augmented Reality and Virtual Reality in the near future. The project will compare and contrast at least one skill set with the full skill standards. It has been over 17 years since comprehensive national IT skill standards were updated (Northwest Center for Emerging Technologies, 2003). Because of the fast-changing nature of IT careers, the 2003 standards are no longer useful to employers, educators, or potential IT workers. New skill standards were sorely needed. Educators use skill standards to create relevant, aligned curriculum to prepare students for the workforce. Employers use skill standards to improve communications about job openings to hire the most qualified candidates. Students and graduates use skill standards to better understand the realities of the IT job market. The job clusters of focus for these skill standards were/are Infrastructure Management and Engineering, Technical Support, Technical Project Management, Data Analytics and Predictive Modeling, Data Management and Engineering, and Software Development. Each Skill Standard package includes a prioritized future-facing list of Knowledge, Skills, Abilities, and Tasks employers want future graduates to know or be able to do; Key Performance Indicators for the prioritized Tasks; Student Learning Outcomes (SLOs) for each of the KSAs, as well as levels of Employability skills employers desire for each job cluster. The six Skill Standards are posted at http://itskillstandards.org. Notably, the Texas Skill Standards System (TSSS) has recognized the first five sets of skill standards and is distributing them across the state. The sixth job cluster will be considered by TSSS in December, 2021, and is expected to be recognized as well. Moreover, the project is reaching out to other states for similar adoptions and recognition while at the same time holding introductory training sessions for educators to help them to apply the skill standard SLOs. In addition to the actual skill standards artifacts, the project is sharing the process used to energize employer leadership. This process identified the future-facing skill standards as well as inspired a modified process now in use for developing the skill sets that can be layered onto other technical fields. The "IT Skill Standards 2020 and Beyond" ATE project continues to build on a successful business engagement model developed by a National Science Foundation-funded National Convergence Technology Center of Excellence that covers Infrastructure and Communications. This model – known as the "BILT" (Business and Industry Leadership Team) – puts business and industry leaders in a co-leadership role that allows them to make continuous recommendations on program curriculum to ensure it aligns with workforce needs. The goal is to teach students the skills they need to know to get hired. The National Convergence Technology ATE Center of Excellence's BILT – which includes IT experts from national companies from across the US – convenes quarterly to discuss emerging industry trends and provide program guidance. Each May, the BILT uses an electronic voting system to rank and update a list of IT knowledge areas that entry-level IT workers need to know prior to discussing the rankings synchronously. The updated list is shared with 85+ colleges around the country that are members of the national ATE center of excellence's community of practice. Those schools use the updated list with their local BILTs to keep curriculum aligned with workforce needs in their areas. Important elements of the "IT Skill Standards 2020 and Beyond" ATE project include the processes used for developing both skill standards and skill sets as well as a pilot for closed crowd-sourcing approach to keep the standards current far beyond their publication.


Panduan Praktis untuk Meningkatkan Kinerja Keuangan UMKM: Implementasi Sistem Akuntansi Microsoft Access pada Lamoela Bar untuk Optimalisasi Laporan Keuangan

Abstract: The financial management application implemented for Lamoela Bar has brought innovation in the management of micro, small, and medium-sized businesses (MSMEs) in the handicraft industry. With an initial focus on security through a login menu, the app provides restricted access to authorized users. Various app menus, including transaction types, sales, cash transactions, and general ledger, offer easy navigation and business activity recording. The transaction types allow recording of various transaction categories, while the sales menu facilitates efficient product sales recording. The cash transaction menu provides real-time monitoring of cash flow, and the general ledger helps organize transaction records into a ledger. With an intuitive design, this app not only provides efficiency in management but also offers an accurate overview of Lamoela Bar's financial performance. It is expected that the implementation of this app will increase productivity and support sustainable growth for this MSME in the dynamic business world.


Edtech and Equity in Panama: Mobile Technology for Levelling the Learning Playing Field

Abstract: Access to quality education in the Republic of Panama is a topic of growing concern. The COVID-19 pandemic served to highlight the importance of this issue and its connection with information and communications technology. As remote learning assumed a critical role during the global pandemic, education technology (edtech) became a familiar concept and a determinant of families’ abilities to access instruction for their school-aged children. The situation prompted complex questions about the political economy of edtech, digital inequality, and the future of learning. This chapter examines edtech and equity in Panama, focusing on mobile technology as a potential means to levelling the learning playing field. It discusses two collaborative low-tech projects to propel basic skills acquisition for children in their early years of schooling that were implemented nationwide in 2020 while schools were closed. It links the knowledge generated from the study of these projects to the broader discussion of global forces that shape education delivery and local responses that work to drive equity and inclusion.


Image Captioning: A Comprehensive Review

Abstract: Abstract: The Image Caption Generator is an intriguing project that bridges the domains of computer vision and natural language processing, aiming to automatically generate descriptive text for images. In the last decade, considerable progress has been made, yet key challenges in optimising Convolutional Neural Networks (CNNs) for precise image feature extraction and refining Long Short-Term Memory (LSTM) networks for coherent text generation persist. Challenges include overfitting, limited context understanding, and difficulties in modelling long-range dependencies. Solutions have emerged in the form of attention mechanisms, which help focus on relevant image regions, and advanced LSTM architectures like the use of Gated Recurrent Units (GRUs) to improve sequential modelling. These innovations have significantly enhanced the overall performance, leading to more accurate and contextually relevant image captions, ultimately advancing the synergy between computer vision and natural language processing in the realm of image captioning


Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning

Abstract: Until recently, the question of the effective inductive bias of deep models on tabular data has remained unanswered. This paper investigates the hypothesis that arithmetic feature interaction is necessary for deep tabular learning. To test this point, we create a synthetic tabular dataset with a mild feature interaction assumption and examine a modified transformer architecture enabling arithmetical feature interactions, referred to as AMFormer. Results show that AMFormer outperforms strong counterparts in fine-grained tabular data modeling, data efficiency in training, and generalization. This is attributed to its parallel additive and multiplicative attention operators and prompt-based optimization, which facilitate the separation of tabular samples in an extended space with arithmetically-engineered features. Our extensive experiments on real-world data also validate the consistent effectiveness, efficiency, and rationale of AMFormer, suggesting it has established a strong inductive bias for deep learning on tabular data. Code is available at https://github.com/aigc-apps/AMFormer.


Verifiable evaluations of machine learning models using zkSNARKs

Abstract: In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results, whether over task accuracy, bias evaluations, or safety checks, are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on black-box model outputs. This work presents a method of verifiable model evaluation using model inference through zkSNARKs. The resulting zero-knowledge computational proofs of model outputs over datasets can be packaged into verifiable evaluation attestations showing that models with fixed private weights achieve stated performance or fairness metrics over public inputs. These verifiable attestations can be performed on any standard neural network model with varying compute requirements. For the first time, we demonstrate this across a sample of real-world models and highlight key challenges and design solutions. This presents a new transparency paradigm in the verifiable evaluation of private models.


The THREE CENTERS OF EDUCATION AS INSTITUTIONS FOR THE DEVELOPMENT OF LEARNING THEORY

Abstract:
This research is aiming at elaborating the three educational centers which is a concept that gradually and integrated assumes educational responsibility for children. These three centers include education in the family environment, education in the school environment, and education in the community environment. The three centers responsible for education are required to work together comprehensively. This research uses a literature review approach combined with the researcher's reflection method. The literature method utilizes research articles published in various current scientific journals, while the reflection method is based on the knowledge and experience of researchers in recognizing, identifying and responding to student phenomena in various educational institutions. The family environment, which is the first environment and is considered the foundation of a child's education and the beginning of the education that will be pursued later, also plays a major role in personality formation. The school environment is the second environment that children face. Education in the school environment has a foundation, objectives, content, methods and other requirements which are arranged systematically in the form of a curriculum. Therefore, schools are considered the foundation of knowledge. Meanwhile, in society, children will be aware of a number of educational and teaching theories that have been taught in previous educational institutions. Then they will actualize and involve themselves in the community environment.
 Keywords: three centers of education, development, learning theory


CCG Promulgation and Firms' Accounting Performance: A Case of an Emerging Economy

Abstract:
Purpose – This study examines the impact of the code of corporate governance (CCG) before and after promulgation-2002 in Pakistan. The impact of the enhanced (updated) corporate governance code on business performance is also examined in this study, both before and after the 2012 modification.
 Design/methodology/approach – It's a longitudinal study based on 20 years of firm observations, from 1999 to 2018, of 202 listed companies in Pakistan. This study applies a two-sample t-test to compare means of control and dependent variables before and after the promulgation and revision of the corporate governance code to evaluate the impact on firms' performance. In furtherance, regression analysis under the fixed effect model has been conducted to evaluate the impact on dependent variables, i.e., The indicators of a company's accounting success return on equity, profit margin, return on assets, return on return on capital employed, and earning per share.
 Findings – According to empirical studies, adopting a corporate governance code has a favorable and statistically significant impact on the company's success. The study also reveals the updated corporate governance code's positive and statistically significant effect on business performance.
 Research limitations/implications – This study enriches the literature on the role of the code of corporate governance firms accounting performance nexus, strengthening firms' practices. The stakeholder's protection has application to sustainable development practices in the emerging environment.
 Originality/value – The distinction from prior studies is examining the impact of code based on a longitudinal period of twenty years and evaluating promulgation and revision to provide evidence that either change enhances the firm performance.


Solutions To Improve the Quality of Managing Online Teaching Activities at Vocational Education Institutions in The Context of Globalization

Abstract: This research aims to identify management solutions to quality improve online teaching activities at vocational education institutions (VEI) in the COG. In addition, identifying contiguous principles plays an important role in the process of evaluating solutions. Providing solutions will clarify theoretical and practical issues in managing online teaching activities (MOTA) at VEI. This study has the participation of 203 people, who work in the field of state vocation management of education, teachers, and staff teams who are working in several VEI. Specifically, 22 managers, and 181 lecturers and staff at vocational education establishments. A questionnaire system was determined for the survey process. By quantitative analysis of data collected during the survey; combined with theories and research results of colleagues, the comments and assessments given will contribute to accurately determining solutions to improve the quality of MOTA at vocational educational institutions in the COG. The study provides empirical evidence to evaluate the necessity and feasibility of the solutions, thereby providing accurate solutions to improve the quality of MOTA activities at vocational educational institutions in the COG. This study has identified principles in identifying solutions; at the same time, the proposed solutions will clarify the theoretical aspects of quality management activities and management of online teaching activities at VEI in the COG. The solutions provided will help VEI apply them in the general school management process and the management of online teaching activities in response to the requirements of globalization for vocational education.


WRITING COMPARISON/CONTRAST PARAGRAPH WITH BLOCK STYLE

Abstract: A comparison/contrast paragraph can be organized into two ways known point-by-point, and block model. The most of fifth semester students of English department at Nias Raya University, however, prefer to organize their writing with block model. The reasons for choosing the model were the purposes of this research. This research was carried out by using qualitative descriptive. The data were obtained through unstructured questionnaire. The result of this research showed that there are three reasons of the students in using block model in organizing the comparison/contrast paragraph; the first is easier to be organized; the next is good for the shorter paragraph; and the last is the similarities or differences between the compared subjects is understood easily. Hence, block organization for a comparison/contrast paragraph is the easiest way for the students to construct their paragraph to be interested writing rather than other style. Therefore, this information can be a consideration for the lecturer in teaching the type of the paragraph.


Fluxonium qubits in a flip-chip package

Abstract: The strong anharmonicity and high coherence times inherent to fluxonium superconducting circuits are beneficial for quantum information processing. In addition to requiring high-quality physical qubits, a quantum processor needs to be assembled in a manner that minimizes crosstalk and decoherence. In this paper, we report on fluxonium qubits packaged in a flip-chip architecture, where a classical control and readout chip is bump bonded to the quantum chip, forming a multichip module. The modular approach allows for improved connectivity between the qubits and control and readout elements, and separate fabrication processes. We characterize the coherence properties of the individual fluxonium qubits, demonstrate high-fidelity single-qubit gates with 6-ns microwave pulses (without DRAG), and identify the main decoherence mechanisms to improve on the reported results.


Auto-Encoding Variational Bayes

Abstract: This paper employs the Auto-Encoding Variational Bayes (AEVB) estimator based on Stochastic Gradient Variational Bayes (SGVB), designed to optimize recognition models for challenging posterior distributions and large-scale datasets. It has been applied to the mnist dataset and extended to form a Dynamic Bayesian Network (DBN) in the context of time series. The paper delves into Bayesian inference, variational methods, and the fusion of Variational Autoencoders (VAEs) and variational techniques. Emphasis is placed on reparameterization for achieving efficient optimization. AEVB employs VAEs as an approximation for intricate posterior distributions.


Development of Learning Blocket Media for Vocabulary at A2 Level

Abstract: The research aims to determine the process of making learning media using Blooket media for vocabulary learning and to describe the results of making learning media using Blooket for vocabulary learning. This research is a compilation study. This research uses the Richey and Klein model. There are three steps in this research, which are divided into three steps: (1) planning, (2) production, and (3) evaluation. The research results show that learning materials on the topic of family history were created using data collected from existing data sources during the research. Blooket's media about family history is packaged well, practical, interesting and effective so that students can easily understand the material and use it as a learning tool.


Website Based Attendance Information System at Jasdam II Sriwijaya

Abstract: Attendance is mandatory for employees. Jasdam II Sriwijaya still uses manual methods when carrying out the attendance process. Therefore, we need an employee attendance system that can manage data so that it can be done quickly, efficiently and accurately. The result of this independent study is a website-based employee attendance information system which includes managing attendance information. The method used is Waterfall which is arranged in five stages, namely 1) needs analysis, 2) system design, 3) writing program code, 4) program testing, 5) program implementation and maintenance. The subject of this service is a website-based attendance information system at Jasdam II Sriwijaya. So the result obtained is that the attendance process becomes more efficient. After carrying out this service activity, it has a positive impact on the employee attendance system.


Exploring the human digestive system through ARSIPERSIA augmented reality insights

Abstract: As times evolve, numerous advanced technologies emerge to support human activities. Augmented reality, a technology that integrates virtual objects with real-world elements in the physical environment, is particularly noteworthy. In the field of education, augmented reality is widely applied to enhance the interactivity of the teaching and learning process. This study endeavours to create an application focused on the human digestive system, with the aim of presenting digestive organs in three dimensions. Unlike traditional learning media, which typically feature two-dimensional images or videos of digestive organs, the Augmented Reality Human Digestive System (ARSIPERSIA) application addresses the challenge of visualizing authentic human digestive organs. Unity 3D and Vuforia were employed in the development of this application. The application underwent comprehensive testing through two methodologies: black box testing and usability testing. The results of the black box testing affirm the effective functionality of the application, covering tasks such as image target scanning, information display, and interactive quizzes. Usability testing findings indicate that ARSIPERSIA facilitates students in visualizing human digestive organs, provides relevant information, enhances comprehension of the digestive system, and sparks students' interest and motivation through captivating learning media.


Exploring the potential adoption of Metaverse for Higher Education: Structural Equation Modelling Approach in a Business School context

Abstract: The evolution of the Metaverse in Higher Education (HE) in Mexico can potentially enhance pedagogy, develop skills, and improve the learning experience. This research aims to estimate the factors guiding the adoption of the Metaverse in a Business School context in Mexico. A survey analysis was conducted to gauge insights from stakeholders on the Metaverse in HE. A total of 117 respondents, comprising students, professors, and staff members, completed the 21-item questionnaire. To determine the relationship between data, a quantitative analysis was performed using a structural equation model (SEM), and a path analysis was computed to illustrate the relationship among the adoption components of the UTAUT model. The study found no correlation between age, gender, and previous experience toward adopting the Metaverse. On the other hand, performance expectancy, effort expectancy, social influence, and facilitating conditions significantly impact the stakeholders' attitudes toward adopting this technology. The R2 coefficient of 94% obtained explains the model's accuracy. The research reveals that a practical learning exercise in the Metaverse improved student knowledge acquisition. Implementing this model is to be at the service of concerned HE authorities to create an adequate environment for adopting the Metaverse in Universities in Mexico.


Abstract: Nonprofit Communications ReportVolume 22, Issue 3 p. 7-7 Valuable Partnerships Find an Editing Buddy to Increase Accuracy First published: 06 February 2024 https://doi.org/10.1002/npcr.32359Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume22, Issue3March 2024Pages 7-7 RelatedInformation


Analysis of Autism Children's Classifications in Elementary School

Abstract: This research was conducted with the fundamental aim of carrying out a careful analysis of the classification of children with autism in the elementary school environment. Using the case study method, the research approach includes consideration of various aspects, including cognitive characteristics, behavior and social interactions. Data was collected through observations, interviews with teachers, as well as examination of related documents, providing a strong basis for in-depth analysis. With a focus on a deeper understanding of the varied characteristics of children with autism at the elementary school level, the main aim of the research was to classify these children based on their cognitive abilities, behavior and level of social interaction. It is hoped that the results of this research can be used as a basis for designing educational programs that are more appropriate to the individual needs of each autistic child. The benefits are not only limited to improving learning effectiveness, but also include supporting social integration in the elementary school environment. This research involved subjects of children who had autism and attended elementary school. Study participants included children with varying cognitive characteristics, behavior, and levels of social interaction. The results highlight significant variation in the characteristics of children with autism in elementary school, allowing classification based on their cognitive abilities, behavior and level of social interaction. Some children show a positive response to certain learning methods, while others require a more individualistic approach. It is hoped that the classification of research results will provide a solid basis for improving educational programs, increasing learning effectiveness, and supporting the social integration of children with autism in the elementary school environment.


PERBANDINGAN KRITERIA DECISION TREE PADA PENGETAHUAN MASYARAKAT PADA PEMILIHAN UMUM PRESIDEN INDONESIA

Abstract: In the Presidential election, gen z as a new voter, must know in advance who the presidential candidate will be in the 2024 election as well as the election process, because if voters do not know and do not understand, it will cause the wrong choice will result in their votes cannot be used even to abstain. What factors cause milennials and gen z generations to not know about elections can be determined using a decision tree. Therefore, in this study, a questionnaire was given to millennials and gen z generation to find out whether voters know the presidential candidate to be elected. The data from the questionnaire is processed to become training data and testing data with a ratio of 70:30. Then measure the accuracy level using the C4.5 algorithm with a comparison of splitting criteria, namely gain ratio, information gain and gini index. By knowing the right splitting criteria, the decision tree model can help overcome the problem of overfitting in the data. Overfitting occurs when the model is too complex in memorizing training data, thus failing to generalize well to read new data. The calculation results show the difference in accuracy values between Gain ratio, Information gain and Gini index, namely 81.67%, 83.33% and 83.33%. It can be concluded that for the use of Algorithm C4.5 splitting criteria Gain ratio and Gini index have the same accuracy value for accuracy measurement in this study.


Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

Abstract: Abstract: The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included.


Does board structure influence employee retention?: A systematic literature review

Abstract: Employee Engagement (EE) has become a crucial element for the success of any organization. While psychology has conducted extensive research on EE, accounting has yet to give it the attention it deserves. This systematic literature review (SLR) was conducted to explore the linkage between corporate governance (CG) practices and factors that drive employee engagement (EED) in greater detail. The review identified several key factors influencing EED: board composition, audit committees, top leadership expertise, network power, other stakeholders’ supervision, and above-board application. In particular, the study revealed that effective CG practices have positive EE consequences, such as increased staff morale, job satisfaction, and productivity, and decreased employee turnover rates. The study underlined the importance of an employee-centric culture that encourages empowerment and fosters a sense of belonging in achieving maximum business success. It suggests that companies must create a work environment that promotes EE, provides opportunities for growth and development, recognizes employee contributions, and fosters open communication and collaboration. By leveraging these insights, companies can enhance their business operations and achieve maximum success by fostering a culture of EE and empowerment. The study concludes that businesses prioritizing EE as a core value can create a sustainable competitive advantage, increase their reputation, and attract and retain top talent.


A Systematic Review of Augmented Reality In Mathematics Education In The Last Decade

Abstract: The goal of the study is to examine a comprehensive review of research involving augmented reality (AR) in mathematics teaching during the previous 10 years. For this purpose, the research was conducted using a systematic review technique. It synthesized a set of 60 articles from 2012 to 2021. Two researchers used content analysis to analyze the data. According to the findings, the majority of publications were published in 2019, with studies mostly aiming at assessing the influence of AR applications on mathematical achievement and exposing viewpoints on AR applications used in mathematics education. Also, secondary school students were mostly preferred as participants. Furthermore, most of the studies found that mathematics teaching with AR applications had a positive impact on academic standing and the teaching process, and that AR applications positively affected attitudes in mathematics lessons, increased the motivation of individuals towards mathematics lessons, and improved three-dimensional thinking skills.


Fostering Responsible Behavior Online- Relevance of Cyber Ethics Education

Abstract: The role of ethics becomes even more significant given the huge advent and increase of cyber-criminal activities in cyberspace. It has become an urgent necessity to curb the menace of unethical practices in cyberspace and contribute ethical guidelines to a safe and secure digital environment. To minimize the growth of cybercrimes and unethical issues online, users must understand how existing societal norms and ethics can be extended according to the challenges posed by ethical dilemmas in cyberspace. Hence, understanding the importance of cyber ethics in daily life has become essential than ever before. Cyber ethics implies the rules of social engagement and responsibility in cyberspace; it is really about social responsibility in cyberspace and it is aimed at inculcating knowledge of responsible behavior in man when using the online environment. This paper is an attempt to provide an overview of the relevance of cyber ethics education in fostering responsible behavior online.


The optimal scaling factor based on Secant Method for image watermarking using the hybrid DTCWT-DCT domain

Abstract: This paper proposes an implementation of the secant optimization method on the watermarkembedding in DTCWT domain. An objective function combining PSNR and NC (The Normalized Correlation)values calculated for a set of common attacks is used in the optimization process. The aim is to enhance the tradeoff between robustness and imperceptibility against the various image processing attacks via an automatic andcareful selection of the scaling factor using optimization. The watermark is inserted in the high energy bands ofDCT-transformed sub-vectors, which were generated from the DTCWT coefficient decomposition of the hostimage. Experimental results performed on a set of 512×512 greyscale images, show better results than theexisting schemes for most common image attacks such as compression, low-pass filtering and noise addition.


A bibliometric analysis of the of cybersecurity policy research

Abstract: The cybersecurity policy is a fundamental framework for developing a robust defense strategy for an organization's digital landscape. It involves various critical steps, including classifying information based on sensitivity, implementing strict access control, encrypting data in transit and at rest, continuous security awareness training, an incident response plan for addressing security breaches, deploying network security measures such as firewalls and intrusion detection systems, and physical security protocols to protect data centers and infrastructure. The purpose of this study is to answer the following questions: How are articles on Cybersecurity Policy classified? What are the latest study trends in Cybersecurity Policy analysis? What are the most frequently published research topics? What are the potential areas for future research in Cybersecurity Policy analysis? This research used a quantitative-descriptive technique. This bibliometric analysis explores the extensive and dynamic domain of cybersecurity policy, examining academic outputs from various dimensions. Utilizing standardized methodologies, including Mendeley software, VOSviewer, and PoP, this study navigates the complex terrain of academic literature. The findings reveal major topics such as 'Cybersecurity policy,' 'Network security policy,' and 'Cybersecurity' emerge, illuminating both well-explored areas and less developed fields such as 'National cybersecurity' and 'Cyberspace.' These unexplored aspects present intriguing avenues for future research.


The Development of Financial Statement Fraud Research: A Bibliometric Analysis

Abstract: This research aims to present and explain the development of research related to financial statement fraud by reviewing bibliometric analysis taken from the Scopus database for the period 1979 – 2023. The method used is a bibliometric analysis approach using VOSviewer and Microsoft Excel to analyze data frequency. The results of this research show that the research topic of financial statement fraud will be most researched in 2023 with 60 articles, the journal that publishes the most topics related to financial statement fraud is the Journal of Financial Crime. The country that publishes the most on the topic of financial statement fraud is the United States. Variables that often appear related to the topic of financial statement fraud are corporate governance, earnings management, crime, and the fraud triangle. Brazel, Hermanson, and Jones are the authors who write most frequently or extensively about financial statement fraud.


ANALISIS KEPUASAN PENGGUNA WEBSITE ORLANSOFT MENGGUNAKAN METODE WEBQUAL 4.0

Abstract: Orlansoft website is an ERP (Enterprise Resource Planning) solution that unifies business operations into a single system that integrates and optimizes business processes and provides real-time critical information for all entities and office locations from a single source. PT Multifortuna Sinardelta, in its business processes, uses the Orlansoft website. The quality of the website greatly affects the level of user satisfaction itself. The higher the quality of a website, the more users will access the website. So far, there is no appropriate method and way to measure user quality of the Orlansoft website. This research examines the extent of user satisfaction in using website services. The Webqual 4.0 method has been successfully applied to similar research with website quality measurement and helps to understand the factors that affect user satisfaction, with three measurement categories including usability, information quality and service interaction quality. From the test results, the calculated F value = 11.536 with a significance of 0.0000011. In this study, the calculated F value is 11.536> F table 2.81 and the significance value is 0.0000011 <0.01, thus it can be concluded that variables X1 (usability quality), X2 (information quality), and X3 (service interaction quality) have a significant and positive effect on variable Y (user satisfaction). This is evidenced by the results of the analysis which gives positive results for each variable on the dependent variable.


A Literature Survey on BIO-PULSE AI based Medcare App

Abstract: The MedCare Chatbot signifies a significant leap forward in healthcare technology, providing users with individualized medical guidance and prescription suggestions. Going beyond its primary functions, this inventive chatbot incorporates a pioneering feature centered on the identification of skin diseases through image processing techniques. Utilizing the capabilities of artificial intelligence and computer vision, the MedCare Chatbot can analyse images of skin conditions submitted by users, furnishing precise diagnoses and treatment recommendations. This paper details the design, implementation, and assessment of this advanced functionality seamlessly integrated into the MedCare Chatbot. Employing a dual strategy of symptom-based prescription advice and image-based skin disease detection, the MedCare Chatbot aims to enhance healthcare accessibility and empower users to actively manage their well-being


Secure Data Transfer using Video Steganography

Abstract: Steganography, a wellestablished practice of covering messages inside standard ones, has found restored significance in the mechanized age, encompassing various mediums like pictures, message, sound, and dynamically, accounts. With the climb of mechanized video correspondence worked with by open taking care of programming, video steganography has emerged as a basic space, wanting to embed data cryptically while staying aware of video quality. This study presents a sharp video steganography plot that unequivocally embeds limited data inside moving things perceived through object recognizable proof, utilizing focus repeat sub-gatherings to safeguard visual decency. Through quantitative and emotional evaluations, the proposed plot shows predominant execution to the extent that intangibility and strength against disturbance attacks, outflanking existing methodologies. Likewise, a flexible steganography approach custom fitted for HEVC accounts is proposed to direct bitrate addition and mutilation storing up. This approach utilizes thought net and PU fragment modes, close by Issue Cross section Code (STC) steganography coding and convolutional cerebrum associations, to overhaul the visual quality and bitrate execution. Preliminary outcomes confirm the feasibility of the proposed computation, showing better perceptual quality through cutting-edgeg-edge strategies. In the end, careful testing against steganalysis techniques upholds the security of the proposed contrive, featuring its significance in working with covert correspondence and security affirmation in electronic video settings


Eigenphase distributions of unimodular circular ensembles

Abstract: Abstract Motivated by the study of Polyakov lines in gauge theories, Hanada and Watanabe Hanada and Watanabe [1] recently presented a conjectured formula for the distribution of eigenphases of Haar-distributed random SU(N) matrices (β = 2), supported by explicit examples at small N and by numerical samplings at larger N. In this letter, I spell out a concise proof of their formula, and present its orthogonal and symplectic counterparts, i.e. the eigenphase distributions of Haar-random unimodular symmetric (β = 1) and selfdual (β = 4) unitary matrices parametrizing SU(N)/SO(N) and SU(2N)/Sp(2N), respectively.


Abstract: Nonprofit Communications ReportVolume 22, Issue 3 p. 8-8 Useful Writing Resources Enhance Your Writing Potential With AI Tools First published: 06 February 2024 https://doi.org/10.1002/npcr.32361Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume22, Issue3March 2024Pages 8-8 RelatedInformation


AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a  Single High-Resolution Image

Abstract: The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper is to propose a framework that is learnt from the source domain with sufficient labeled trees and is adapted to the target domain with only a limited number of labeled trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a hierarchical feature extraction scheme to extract robust features from the source and target domains. It also consists of three subnets: two for extracting self-domain attention maps from source and target domains respectively and one for extracting cross-domain attention maps. For the latter, an attention-to-adapt mechanism is introduced to distill relevant information from different domains while generating tree density maps; a hierarchical cross-domain feature alignment scheme is proposed that progressively aligns the features from the source and target domains. We also adopt adversarial learning into the framework to further reduce the gap between source and target domains. Our AdaTreeFormer is evaluated on six designed domain adaptation tasks using three tree counting datasets, ie Jiangsu, Yosemite, and London; and outperforms the state of the art methods significantly.


IMPLEMENTASI METODE JIGSAW DALAM MENINGKATKAN KETRAMPILAN KOMUNIKASI LISAN SISWA SMALB MATA PELAJARAN PAI DI SLB NEGERI TULUNGAGUNG

Abstract: Basically, every human being cannot be separated from interacting with other humans. As social creatures, humans always try to build relationships with others. However, some people are limited in communication (have special needs), as we know, students at Special Schools (B-Deaf) are limited in one of their senses, namely hearing, which causes communication to be less than fluent. Student deafness concerns individual hearing conditions, namely the student's inability to perceive verbal communication or other sounds that are adjusted to a certain frequency and intensity. By implementing the jigsaw method in PAI learning, it is hoped that it will be able to provide solutions in oral communication skills. The aim of this research is to find out how the implementation of the jigsaw method improves the oral communication skills of SMALB Tulungagung students. This research uses a qualitative method with a descriptive type. Descriptive research type is used as a procedure to propose problem solving by highlighting the state of the object being studied. Data collection took place from 01 August to 15 October 2023 at the Tulungagung State Special School. The results of the research showed that there was an increase in the oral communication skills of Tulungagung State High School students through the implementation of the jigsaw method in PAI subjects. The weakness of this research is that it does not cover all subjects so this research cannot yet be generalized and needs to be studied by further researchers


Training-Free Consistent Text-to-Image Generation

Abstract: Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to consistently portray the same subject across diverse prompts remains challenging. Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects or add image conditioning to the model. These methods require lengthy per-subject optimization or large-scale pre-training. Moreover, they struggle to align generated images with text prompts and face difficulties in portraying multiple subjects. Here, we present ConsiStory, a training-free approach that enables consistent subject generation by sharing the internal activations of the pretrained model. We introduce a subject-driven shared attention block and correspondence-based feature injection to promote subject consistency between images. Additionally, we develop strategies to encourage layout diversity while maintaining subject consistency. We compare ConsiStory to a range of baselines, and demonstrate state-of-the-art performance on subject consistency and text alignment, without requiring a single optimization step. Finally, ConsiStory can naturally extend to multi-subject scenarios, and even enable training-free personalization for common objects.


A new track finding algorithm based on a multi-dimensional extension of the Hough Transform

Abstract: We introduce a new pattern recognition algorithm for track finding in High Energy Physics Experiments based on an extension of the Hough Transform to multiple dimensions. A remarkable property of this algorithm is that the execution time is simply proportional to the total number of the hits to be processed, making it particularly attractive for high occupancy situations. The algorithm needs to be trained using a sufficiently large set of simulated tracks. The same track finding algorithm can be used for very different detector geometries and only the set of simulated tracks used for training needs to be changed. The particular structure of the algorithm also lends itself naturally to parallel hardware implementations which, combined with its intrinsic flexibility, should provide a most powerful tool for triggering at future colliders.


Video Super-Resolution for Optimized Bitrate and Green Online Streaming

Abstract: Conventional per-title encoding schemes strive to optimize encoding resolutions to deliver the utmost perceptual quality for each bitrate ladder representation. Nevertheless, maintaining encoding time within an acceptable threshold is equally imperative in online streaming applications. Furthermore, modern client devices are equipped with the capability for fast deep-learning-based video super-resolution (VSR) techniques, enhancing the perceptual quality of the decoded bitstream. This suggests that opting for lower resolutions in representations during the encoding process can curtail the overall energy consumption without substantially compromising perceptual quality. In this context, this paper introduces a video super-resolution-based latency-aware optimized bitrate encoding scheme (ViSOR) designed for online adaptive streaming applications. ViSOR determines the encoding resolution for each target bitrate, ensuring the highest achievable perceptual quality after VSR within the bound of a maximum acceptable latency. Random forest-based prediction models are trained to predict the perceptual quality after VSR and the encoding time for each resolution using the spatiotemporal features extracted for each video segment. Experimental results show that ViSOR targeting fast super-resolution convolutional neural network (FSRCNN) achieves an overall average bitrate reduction of 24.65 % and 32.70 % to maintain the same PSNR and VMAF, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 4 s segments using the x265 encoder, when the maximum acceptable latency for each representation is set as two seconds. Considering a just noticeable difference (JND) of six VMAF points, the average cumulative storage consumption and encoding energy for each segment is reduced by 79.32 % and 68.21 %, respectively, contributing towards greener streaming.


Influencer Identification on Link Predicted Graphs

Abstract: How could one identify a potential influencer, or how would admissions look like in a University program for influencers? In the realm of social network analysis, influence maximization and link prediction stand out as pivotal challenges. Influence maximization focuses on identifying a set of key nodes to maximize information dissemination, while link prediction aims to foresee potential connections within the network. Given the complexity of these tasks, especially in large-scale networks, we propose a novel algorithm, The Social Sphere Model, tailored for weighted networks. This algorithm uniquely combines link prediction techniques with influence maximization strategies to effectively identify future vital nodes. Our approach leverages two distinct contagion models, offering a promising solution with lower computational demands. This advancement not only enhances our understanding of network dynamics but also opens new avenues for efficient network management and influence strategy development.


Retrieval Augmented Cross-Modal Tag Recommendation in Software Q&A Sites

Abstract: Posts in software Q\&A sites often consist of three main parts: title, description and code, which are interconnected and jointly describe the question. Existing tag recommendation methods often treat different modalities as a whole or inadequately consider the interaction between different modalities. Additionally, they focus on extracting information directly from the post itself, neglecting the information from external knowledge sources. Therefore, we propose a Retrieval Augmented Cross-Modal (RACM) Tag Recommendation Model in Software Q\&A Sites. Specifically, we first use the input post as a query and enhance the representation of different modalities by retrieving information from external knowledge sources. For the retrieval-augmented representations, we employ a cross-modal context-aware attention to leverage the main modality description for targeted feature extraction across the submodalities title and code. In the fusion process, a gate mechanism is employed to achieve fine-grained feature selection, controlling the amount of information extracted from the submodalities. Finally, the fused information is used for tag recommendation. Experimental results on three real-world datasets demonstrate that our model outperforms the state-of-the-art counterparts.