Analyzing Variances in User Story Characteristics: A Comparative Study of Stakeholders with Diverse Domain and Technical Knowledge in Software Requirements Elicitation

Abstract: Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories, Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories. Keywords: User story, Agile Software Development, Requirements Elicitation, Stakeholder, Domain Knowledge, Process Innovation Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories, Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories. Keywords: User story, Agile Software Development, Requirements Elicitation, Stakeholder, Domain Knowledge, Process Innovation


Sentiment Analysis on a Large Indonesian Product Review Dataset

Abstract: Background: The publicly available large dataset plays an important role in the development of the natural language processing/computational linguistic research field. However, up to now, there are only a few large Indonesian language datasets accessible for research purposes, including sentiment analysis datasets, where sentiment analysis is considered the most popular task. Objective: The objective of this work is to present sentiment analysis on a large Indonesian product review dataset, employing various features and methods. Two tasks have been implemented: classifying reviews into three classes (positive, negative, neutral), and predicting ratings. Methods: Sentiment analysis was conducted on the FDReview dataset, comprising over 700,000 reviews. The analysis treated sentiment as a classification problem, employing the following methods: Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), LSTM, and BiLSTM. Result: The experimental results indicate that in the comparison of performance using conventional methods, MNB outperformed SVM in rating prediction, whereas SVM exhibited better performance in the review classification task. Additionally, the results demonstrate that the BiLSTM method outperformed all other methods in both tasks. Furthermore, this study includes experiments conducted on balanced and unbalanced small-sized sample datasets. Conclusion: Analysis of the experimental results revealed that the deep learning-based method performed better only in the large dataset setting. Results from the small balanced dataset indicate that conventional machine learning methods exhibit competitive performance compared to deep learning approaches. Keywords: Indonesian review dataset, Large dataset, Rating prediction, Sentiment analysis


Cash Sales Information System Multiuser Based On Human Computer Facilities Semarang

Abstract: Sarana Insan Computer (SIK), a sales company operating in the computer sector in Central Java, has utilized Information Systems even though they are not yet well integrated between one system and another. Therefore, in this thesis the author will develop a cash sales system model by choosing SIK as the research object. The cash sales information system created includes a system for ordering goods, sending goods, sales returns and cash sales reports. This research aims to make it easier to present reports directly, accurately, quickly and to enable systems that do not yet exist to be integrated into an integrated system. So that the cash sales information system can be implemented well, a software development process is carried out which is based on correct software engineering. The software development model used in this research is a multiuser development model. Information technology which is developing rapidly today really provides support for the development of a cash sales information system for a computer sales company such as SIK, especially with the use of computer-based information technology or better known as a Computer Based Information System, because of the use of computer technology in a system. information will be able to process data more quickly with minimal error rates, save labor and save costs. The Cash Sales Information System is designed using direct data processing methods. The use of the direct data processing method is intended so that every incident of cash sales information can be processed directly. The procedures that will be processed consist of the administration section which handles administration, the purchasing section which handles incoming goods, the sales section which handles customer service and the computer goods sales section, the personnel section and the leadership itself.


Catch Production Data Information System and Fish Auction at UPT PPP Pondokdadap

Abstract: Sendang Biru beach is facing directly to the Indian Ocean, an area with abundant fishing potential. People working as fisherman make Sendang Biru is a fishing ground. Fishing ports play an important role in the development of every activity related to fisheries, especially capture fisheries. The fishing port is a place for government activities in the field of fisheries, such as loading and unloading of fish. Unit Pelaksana Teknis Pelabuhan Perikanan Pantai (UPT PPP) Pondokdadap is one of the coastal fishing ports with the largest landing of fish, especially tuna, tuna, and skipjack in Malang. Fishery production results are obtained from fishing by fishermen in the sea. The types of fish mostly found in Sendang Biru are dominated by large pelagic fish, namely tuna. Fishing production is obtained from fishermen's catches with fishing gear in the form of purse seines and handlines. A lifeboat with handline fishing gear is the fleet used to catch large pelagic fish. Total fishery production from production volume and production value on January 1 to June 30, 2022 obtained results on fishery production volume reaching 5,596,271 kg with fishery production value reaching Rp. 137,691,524,000. There are several obstacles during the fishing process regarding bad weather or rainfall with high waves, which can affect fishermen's income in fishing activities classified as low.


Challenges and computer-aided reading strategies in vocabulary enhancement

Abstract: Early literacy development depends significantly on developing a vocabulary, and computer-aided reading techniques have the potential to improve this process. To maximize their efficacy, grade 1 teachers might encounter particular difficulties when implementing these tactics into their lesson plans. In light of this, the goal of this study is to determine the difficulties that Grade 1 instructors encounter when instructing vocabulary and investigate the possibility of using computer-aided reading procedures to help with these difficulties. The primary goal of the qualitative research for this study was to interview grade 1 teachers about their experiences and anecdotes. Six professional teachers—four from private and four from public schools— participated in the study's interviews. According to the results, the most challenges that first-grade teachers encounter when trying to expand their students' vocabulary include the transition from their native tongue to English, word pronunciation and spelling mistakes, and students' reluctance to learn new vocabulary. The twenty experienced educators use a range of techniques to help students with their vocabulary. These include using the Marungko Method in a multimedia presentation to introduce letter names and sounds, watching phonics and phonemic movies, pronouncing words using YouTube videos and audio-visual presentations, interactive slides, digital flashcards, recordings of the alphabet, and rereading while playing interactive language games to help with retention. The study's outcomes will contribute to the existing literature on vocabulary instruction and computer-aided learning, highlighting the potential of technology in addressing the challenges faced by Grade 1 teachers.


Concepts of Money Laundering Resulting Destroy the Structure of Financial Policy

Abstract: Money laundering is the technique used to make significant sums of money earned illegally through drug trafficking, terrorism, or other major crimes that appear to have come from legitimate sources. Money laundering has a negative influence on the nation's economic and political stability, hence it must be strictly prohibited. As one of the most significant components of the financial system, it serves as the backbone of the money market and plays a vital role in raising funds for beneficial investments that advance economic growth in a nation. An effective and stable financial system is a requirement for the nation's overall growth.


A Framework for Mobile Forensic Readiness in Yemen

Abstract: One of the most important inventions of the 20th century is the mobile phone. In addition to making and receiving phone calls, a mobile phone can be used for texting, gaming, taking photos, recording movies, and accessing the internet, among many other things. This paper develops a framework for mobile forensic readiness in Yemen. In addition, mobile forensic readiness (MFR) aims to know the level of readiness for mobile forensic in Yemen, the level of mobile forensic factors in Yemen, and factors that affect the readiness for mobile forensic in Yemen. The main goal of forensic preparedness is to maximize an organization's capacity for establishing the viability of conducting forensic investigations and for accessing digital evidence at the lowest possible cost of an inquiry. Fundamentally, the primary goal of forensic preparation is to guarantee that the evidence is pertinent and comprehensive enough for the forensic investigation. The research is descriptive and collects indicators of MFR from literature review and 110 questionnaire that given to the Yemeni criminal investigation bureau employees, technology users, public prosecutor office employees. Furthermore, through the interviews were done with the police station managers and their employees. However, the returned questionnaires were 101. Statistical Package for the Social Sciences (SPSS) was used to analyze and test the data through mean, standard deviation, and one sample T- test. Moreover, SPSS AMOS was used to design the framework. All mentioned hypotheses were tested using regression, T- Value, Beta, P- value, and R-square. Therefore, the results show that all hypotheses were supported. This paper develops a framework for mobile forensic which reveals if we have a suitable infrastructure for mobile forensic and increase the awareness of mobile forensic which assists the experts to recover and analyze data. Furthermore, this research increases the potential use of digital evidence and increases the trust in the mobile forensic process.


Hubungan antara minat baca dengan kemampuan berpikir kritis dalam pembelajaran IPS di sekolah dasar

Abstract: The research purpose was determined the correlation of reading interest and critical thinking ability in social studies learning at elementary school. This research type is a quantitative approach with correlational research design. The research sample was fifth grade students at SDN Laban 01, SDN Wirun 04, SDN Laban 02, and SDN Plumbon 02, Mojolaban District, Sukoharjo Regency. The sampling technique applies the probability sampling technique of cluster random sampling type. Data collection techniques for reading interest and critical thinking ability are in the form of tests and questionnaires. Analysis prerequisite test using normality and linearity tests. Hypothesis testing using simple correlation analysis by applying Pearson Product Moment correlation and multiple correlation analysis with the help of the SPSS 21 program. The results showed that reading interest affect critical thinking ability in fifth grade students of SD in Mojolaban District.


Faktor kesulitan membaca pemahaman teks cerita bahasa jawa peserta didik kelas V Sekolah Dasar

Abstract: This research is motivated by observation and interview data which indicate there are problems at school regarding the low reading comprehension skills of fifth grade students at SDN Tegalayu Surakarta in the 2022/2023 academic year. To overcome these problems, the researcher conducted a qualitative study with the aims of: (1) To describe the difficulties in understanding Javanese texts (2)to explain the factors that cause difficulties in understanding Javanese texts.This research was conducted on teachers and students of class V SDN Tegalayu Surakarta. In this research, a qualitative descriptive approach was used, and observation, interviews, and documentation studies were used to collect data.Source triangulation and triangulation techniques are used to test the validity of the data.From the research, data were obtained in the form of difficulties experienced by students, namely difficulties in interpreting words, difficulties in making new sentences using words in the text of the story and difficulty answering questions according to the contents of the story. While the factors that affect the difficulty of reading comprehension skills are intellectual factors, psychological factors, environmental factors and physiological factors.


Kendala implementasi P5 dengan tema bhinneka tunggal ika di sekolah dasar

Abstract: This research was conducted to examine the obstacles to implementing P5 with the topic Bhinneka Tunggal Ika at SDN Bumi 1 No. 67 Surakarta starting from the planning stage, implementation, to assessment and efforts made to overcome it. This research uses a qualitative descriptive method approach. The obstacles encountered in the implementation of P5 are due to internal factors and external factors. The obstacles caused by internal factors are the absence of training regarding P5 for teachers, P5 is not well documented due to lack of teacher understanding, and differences in perception between teachers and school principals. Meanwhile, obstacles are caused by external factors such as limited learning media, the lack of role of teachers and school principals in establishing collaboration, and the lack of involvement of committees and supervisors. The solution to overcome these obstacles are to encourage the strengthening of teacher capacity through training to be able to implement P5 in accordance with the guidelines, to equalize perceptions and build communication between teachers and the school principals, to work with the parents of pupils in providing adequate learning media and to optimize the role of the head, teacher, committee, and supervisor to support a collaborative school ecosystem.


Pengaruh media audiobook cerita keberagaman sosial budaya di indonesia terhadap keterampilan menyimak peserta didik kelas V SD se-kecamatan banjarsari

Abstract: The aim of this research is to examine how audiobook media stories about social diversity in Indonesia influence the listening skills of grade V students in elementary schools in Surakarta's Banjarsari District. The design of this research is experimental. Methods of data collection include tests and observations. According to the research's findings, the use of audiobook media stories of socio-cultural diversity in Indonesia impacts the listening skills of students in Banjarsari District, Surakarta's grade V elementary schools. The paired sample t-test on the hypothesis test, which examines the post-test values of the experimental and control groups. The hypothesis test findings show significant values of 0.009 &lt; 0.05. The average post-test score in the experimental group was 81.3 &gt; 72.2, which was higher than the pre-test score. It was also discovered that the experimental group's average post-test score was higher than the control group's, with 81.3 &gt; 70.7. Students with this attitude are those who quickly focus on listening to the teacher's explanation and are able to reply to the story.


Total Factor Productivity and Financial Development

Abstract: The main objective of this study is to explore the relationship between total factor productivity and financial development for a group of Latin American countries consisting of Brazil, Colombia, Costa Rica and Mexico covering the period from 1996 to 2019. Total factor productivity, which is considered as one of the main drivers of sustainable economic growth, especially for emerging economies, is the measure of productive efficiency given by the ratio of aggregate output to aggregate inputs. Financial development refers to the development of financial institutions and financial markets. Latin American countries exhibit different levels of both total factor productivity and financial development mainly due to the emerging nature of their economies, which explains the motivation of choosing four of these countries as the sample to analyze the association between total factor productivity and financial development. Data for total factor productivity is taken from the Penn World Table while the Financial Development Index Database provided by the IMF is used in order to examine financial development. The results of this study indicate that countries with higher levels of total factor productivity also have higher levels of financial development. This finding can be interpreted as the outcome of the fact that there are macroeconomic and financial factors determining or similarly influencing both total factor productivity and financial development. Significant policy implications for emerging countries can be inferred from this close connection between total factor productivity and financial development, which constitutes the major contribution of this study to the literature.


Comparison of Naive Bayes and Support Vector Machine (SVM) Algorithms Regarding The Popularity of Presidential Candidates In The Upcoming 2024 Presidential Election

Abstract: This study aims to compare the effectiveness of two classification algorithms, Naive Bayes and Support Vector Machine (SVM), in analyzing the popularity of presidential candidates for the 2024 Presidential Election (Pilpres). The popularity of presidential candidates plays a crucial role in campaign strategies and political decision-making in the modern political era. This research utilizes data from social media, encompassing public sentiment towards presidential candidates and related political issues. The research results indicate that SVM achieves an accuracy rate of 97%, while Naive Bayes achieves 95%, demonstrating the superiority of SVM in predicting the popularity of presidential candidates. In conclusion, the selection of the appropriate algorithm for analyzing complex political data has a significant impact, and the high accuracy rates of both algorithms provide valuable guidance for political decision-makers and campaign teams in preparation for the upcoming 2024 Pilpres.


GUARANTEES OF FREEDOM OF SCIENTIFIC CREATIVITY IN THE NORMS OF INTERNATIONAL LAW

Abstract: The article examines the consolidation and development in the law "On Science and Scientific activity" of the internationally recognized human right to freedom of scientific research. The article analyzes the normative content of freedom of science and its reflection in legislation of Uzbekistan. Much attention is paid to the issue of the correlation of freedom of science with a number of civil, social and economic human rights, the beneficiaries of which are also scientists and scientific and technical workers. It is concluded that it is necessary to establish a balance between freedom of scientific research and human rights due to the increasing risky nature of modern scientific and technological progress.


METHODS OF DOCUMENT FORGERY AND THEIR DETECTION PROBLEMS

Abstract: In this article, the types of forgery of documents, the methods and tools used in forgery are analyzed, and the mechanism of appearance of traces and signs of forgery of documents is researched. Also, the elements that can be changed in the text of the document are divided into separate categories and analyzed. The second part of the article is directly devoted to the problems of identifying the elements of forgery, in which the methods and tools used to identify the signs of forgery are analyzed from a criminalistic and technical point of view.


Inaugural Editorial: Editorial about the Founding of Medinformatics

Abstract: The convergence of biology and artificial intelligence represents a paradigm shift in scientific exploration. The integration of AI into the study of omics—genomics, proteomics, transcriptomics, metabolomics, and beyond—has revolutionized our ability to decipher the complexities of biological systems. From predictive modeling of gene structures to the elucidation of protein interactions, from the analysis of transcriptomic profiles to the exploration of epigenetic landscapes, AI has become an indispensable tool in the biologist's toolkit. In this journal that focuses on the integration of information and life medicine, we embark on countless journeys of artificial intelligence applications in biomedical fields, focusing on the application of omics analysis in analyzing biological processes or disease occurrence and development. In addition, this journal also focuses on the forefront of interdisciplinary research, with a particular focus on the knowledge and methods of artificial intelligence, mathematics, physics, and other disciplines to solve biomedical problems, enhancing our rational understanding of life and disease. As we founded this magazine, we invited researchers, scholars, and practitioners from different backgrounds to publish cutting-edge biomedical informatics research or join our editorial board. Let us explore the frontiers of knowledge, push the boundaries of innovation, and together, forge a brighter future for biomedical science.


Patient Admission Projection Using Facebook Prophet

Abstract: Healthcare facilities face the continual challenge of managing patient admissions efficiently while maintaining high standards of care. Accurate forecasting of patient admissions is crucial for optimizing resource allocation, ensuring adequate staffing, and enhancing overall operational efficiency. To address this challenge, this research paper explores the application of Facebook Prophet, an advanced time series forecasting tool, in predicting patient admissions within healthcare facilities. Facebook Prophet, a specialized time series forecasting model developed by Facebook's Core Data Science team, is then employed to model and predict patient admissions. Facebook Prophet is particularly well-suited for handling time series data characterized by seasonality, trends, and special events. The model's flexibility and ease of use make it an ideal choice for healthcare providers seeking to leverage advanced analytics techniques for predictive purposes. In the implementation of Facebook Prophet, the model is meticulously trained and validated using the pre-processed historical admission data. The model's ability to capture complex temporal patterns, including seasonality and special events such as holidays or outbreaks, is thoroughly evaluated. By incorporating these patterns into the forecasting process, the model can provide accurate predictions of future patient admissions. By accurately predicting patient admissions, healthcare facilities can optimize resource allocation, streamline operations, and ensure adequate staffing levels to meet patient demand. Additionally, the model enables healthcare providers to proactively plan for special events or seasonal fluctuations in admission rates, further enhancing operational efficiency. The application of Facebook Prophet holds significant promise for improving patient care delivery and operational outcomes in healthcare facilities.


Cyber Security from a Business Perspective

Abstract: In today’s era of Information Technology, we have encountered drastic changes in computing methodologies due to a tremendous increase in ONLINE communication traffic both in terms of the number of users and data communication. The COVID–19 pandemic has brought the entire world online. Cyber Security plays an important role in the field of information technology. In order to secure information, one can face many challenges. Nowadays, governments and other organisations are following various measures to prevent vivid cybercrimes. In this chapter, we have raised concern over the drastic increment in an ONLINE communication system, which urges the need for the development and deployment of Cyber Security in a business environment.


Region-based convolutional neural networks for occluded person re-identification

Abstract: In a variety of applications, including intelligent surveillance systems, targeted tracking, and assistive human-following robots, the ability to accurately identify individuals even when they are partially obscured is imperative. Such Continuous person tracking is complicated by the close similarity between the appearance of people and target occlusions. This study addresses this significant challenge by proposing a two-step, detection-first approach that uses a region-based convolutional neural network (R-CNN) as the re-identification (re-ID)solution. The model is specifically trained to detect occluded persons at different levels of occlusion before forwarding the image for the re-ID process. Three occluded-specific datasets are selected to evaluate the model's effectiveness in detecting occluded people. There are 379 distinct people in total, and each has five images obstructed from different angles. A sample of the data is taken to simulate various environment settings, and new data points are generated with different degrees of occlusion to assess how well the model performs under varying levels of obstruction. The findings demonstrate that the proposed person re-ID model is reliable in most circumstances, correctly re-identifying at 74% (Rank-1) and 90% (Rank-5). Although there is a decrease in accuracy as the number of distinctive people in the dataset increases, this does not significantly impact the tracking performance in various applications, which are expected to recognize a single person or a small group of individuals. Future works will explore refining similarity matching algorithms by delving into robust image comparison techniques, thereby addressing the challenges presented by occlusions. A critical aspect is to assess the model under diverse lighting conditions and investigate scenarios with multiple individuals in a frame. It is also beneficial to exploit high-resolution datasets, such as DukeMTMC-reID, and integrate finer contextual details, like clothing or carried objects. These collective efforts are essential for optimizing the model’s efficacy in practical applications and advancing person re-ID technologies.


Development of the support strategy for Videoconferencing Discussion Class Using Facial Expression Analysis Data

Abstract:
Objectives In order to develop the design principles for online discussion in videoconferencing instruction, this study develops the design principles by using learning analysis data analyzed with facial expression analysis tool and confirms the user's reaction.
 Methods Using the design and development research method, the initial design principles was developed by analyzing previous research and problems of videoconferencing classroom field. They were modified and developed through expert validation tests for three times. A prototype of facial expression analysis tool was developed to evaluate usability, and the final design principles were developed.
 Results The final support strategy consists of 9 support strategies and 26 detailed guidelines, tailored to the pre-debate, during the debate, and post-debate stages. In this regard, it has been confirmed by users that the strategy enables to simultaneously perceive non-verbal elements and to provide timely feedback. However, it was also observed that the intervention of instructors can function both as a strength and as a disruptive factor to discussions.
 Conclusions The above design principles are significant in that it suggested the prescription using learning analysis data what is facial expressions. It showed a new possibility to apply artificial intelligence technology at learning analysis by using facial expression analysis tool that automatically analyzes present a result value.


Exploring The Intention of Using Mobile Learning in Flipped Classroom Approach for Arts and Design Students

Abstract: The flipped classroom is a pedagogical approach that encourages active learning experience activities in the classroom. The students will receive information and materials before class learning using mobile technology both inside and outside of the classroom to allow for more dynamic, interactive, and advanced learning activities that involve the application of knowledge with higher-order thinking. Studies have shown, students who use the Mobile learning or M-learning platform in a flipped learning method can provide an interactive, engaging, and cost-effective way for graduates to broaden their employability skills. Although research on mobile learning in the flipped classroom approach has been extensive, research on M-learning has been limited. The study's objective is to examine the effectiveness of mobile learning adoption in relation to students' perceptions and responses under the UTAUT2 (unified theory of acceptance and use of technology) model in the undergraduate arts and design students and to review the pilot study model in development for larger-scale future research. Through a quantitative research approach, the pilot study used questionnaires to gather data from 50 undergraduate arts and design in multimedia design programme students of first-year from a local higher education institution. The findings showed that every composite reliability value in this study exceeded the 0.70 threshold value, indicating strong indicator reliability of the factors. Moreover, flipped classrooms increase student involvement, learning motivation, achievements and both at the same time improving educators' knowledge of how to use the flipped learning model in ways that are most advantageous for their own students.


Pengembangan Video Tutorial Dampak Penggunaan Zat Aditif Pada Makanan Dan Minuman Mata Pelajaran IPA Sekolah Menengah Pertama

Abstract: The purpose of this study was to evaluate the development of video tutorials for junior high school science classes about the impacts of additive use on food and beverages, as well as to gauge the effectiveness of the tutorials. The five steps of the ADDIE development model analysis, design, development, implementation, and evaluation are used in this work. This study discovered that junior high school science classes have gone through multiple phases of developing video lessons on the impacts of utilising chemicals on food and beverages. Among them are validation from material experts, which received a very decent rating of 95.4 percent, and validation from media experts, which received a decent assessment of 86.4 percent. Field trials were conducted to measure the effectiveness of this video tutorial media. This includes a 93% science teacher trial with very decent assessments; a small group trial with 9 students showed a result of 84.8% with a decent assessment; and a large group trial with 25 students from class IX C at SMP Negeri 1 Muaro Jambi showed a result of 90.8% with a decent assessment. This development shows that video tutorials on the effects of additive use on food and beverages, especially those related to science lessons in junior high school, can be considered a feasible, efficient, and high-quality learning method. This development shows that video tutorials on the effects of additive use on food and beverages, especially those related to science lessons in junior high school, can be considered as feasible, efficient, and high-quality learning methods. Based on this development, it is suggested that many people, especially science teachers in junior high schools, consider using this video tutorial as one of the most useful learning media. Therefore, it is expected to increase students' understanding and interest in learning about the impact of the use of additives on food and beverages in science studies.


Copywriting Sebagai Pendukung Promosi Berbasis WEB

Abstract: Copywriting is an important thing to do to increase the promotion carried out by Fiber to increase the sales of a product or service. The creativity produced in copywriting aims to invite readers to take action from a promotion. With the development of technology, copywriting is now not only presented in physical writing results but also becomes mandatory digitally to keep up with the development of online marketing communication media, such as social media and websites. The creativity of copywriting balanced with up-to-date communication media will be able to spearhead the advertising of a company or organization. The official website, which is generally an information medium, needs promotional writing like social media, in general, to make the official website an effective media utilization, an informative medium, and also a promotional communication medium between the website owner and stakeholders.


Rancang Bangun Game 3D Edukasi Basic Web Development Menggunakan Unity 3D

Abstract: The development of games is a media that is favored by the public which can have good or bad effects, where a game is basically made as for entertainment but there are also those who use games as educational media. This research was conducted to create a basic web development educational game packaged in the form of RPG (Role Playing Games) games with the result that providing basic web development education that can be played by all audience, where learning about web development is currently quite popular with some people. The game system design process is made using UML diagrams which will be applied to the unity 3D application and the assessment obtained through a questionnaire of 82% of respondents agreed that this game can be provide basic web development education. This game is also implemented using a finite state machine algorithm that controls enemy behavior in each state and collision detection for interaction with other objects. In this process create several interface designs and also for the finite state machine algorithm implemented to the enemy animator to control the state of enemy behavior, and the collision detection algorithm is applied to areas that have a collider to display the interface and also play background music.


Use of Social Networking Sites for Citizen Journalism

Abstract: Social networking sites have become important platforms for obtaining political information. Nevertheless, a crucial inquiry in this context pertains to the reliability of social networking sites (SNS) as a political information source. This study investigated the extent to which politically engaged online users perceive social networking sites (SNS) as trustworthy. The positivist philosophical stance was adopted to explain the phenomena. Moreover, the deductive approach was intricated in this study to test the framed hypotheses. A digital survey strategy was used for individuals who frequently engage with social media platforms. The data was electronically collected from October 19 to November 1, 2023. The target population for this study was active social media users in Pakistan who were between the ages of 18 and 34 and data was gathered using the purposive sampling technique. The ultimate sample comprised 384 users via using the cross-sectional study design. This study found social networking sites (SNS) the least credible among the various traditional and online sources. The strongest indicator of social networking site (SNS) credibility was found to be the extent of reliance on these platforms. Moreover, it is concluded that utilizing social network sites (SNS) can be an as effective means to convince voters to support their campaigns.


Perancangan Sistem Informasi Buku Tamu Pada Website Kantor Pencarian Dan Pertolongan Kelas A Medan

Abstract:
Abstract
 The Medan Class A Search and Rescue Office is a government agency whose main task is to carry out rescue, search and rescue operations. This agency is also a bridge for coordination with other agencies to improve the evacuation process in certain circumstances. An agency visit often occurs to this agency. Visitors or guests are often recorded in a special agenda or book which is recorded conventionally. This condition is certainly less effective because recording is still carried out using physical books which are easily damaged and often cause queues when guests register themselves. After knowing this problem, the author designed a guest book information system which was intended to fill in the features on the Medan Class A Search and Relief Office website. The goal is to fill in guest data more neatly and not cause data loss or damage. The method used in this research is the waterfall method, this method has its own specifications, so that a system can be developed according to what is desired. The way this method works is systematically and gradually so that all information and results will be recorded well and distributed accurately and quickly. The system was built using the PHP programming language with MySQL as the database.


Countering Negative Social Engineering Techniques

Abstract: The article reveals the issues of counteraction, prevention and suppression of destructive methods of social engineering in the sphere of information-telecommunication interaction. The techniques and methods of destructive social engineering that are currently widespread are considered . The available ways of counteraction and prevention of the negative influence of social engineering are analyzed, reasonable conclusions about the effectiveness of these methods are presented . The ways of solving the problems identified in the course of the study, namely, the methods of suppression (eradication) of negative methods of social engineering are proposed . The conclusions presented in the article have practical significance, which consists in the fact that the provisions and recommendations formulated in them can be used to justify proposals to improve the legislative framework in the field of suppression, prevention and prophylaxis of crimes committed with the use of information and telecommunication technologies .


The Degree to which Physical Education Teachers employ Educational Competencies from their point of View

Abstract: This study aimed to find out the degree to which physical education teachers employ educational competencies from their point of view. The descriptive analytical method was used, and to answer the study questions, a questionnaire consisting of (20) items was developed to measure the degree of physical education teachers' employment of educational competencies, and its psychometric properties were verified. The study population consisted of (44) male and female teachers in the Directorate of Education of the Northern AL- Mazar District for the academic year 2021/2022 AD. The study sample consisted of (44) male and female teachers. The results showed that physical education teachers employ educational competencies (the competencies of the planning field, the competencies of the implementation field, the competencies of the teaching strategies field, the competencies of the evaluation field). 3.58) and the rest of the competencies were at an average degree, respectively, for the competencies of the evaluation field, and with an arithmetic mean (3.38). The competencies of the field of planning with an arithmetic average (3.35), and competencies of the implementation field with a mean of (3.32), and in light of the results of the study, the researcher recommended focusing on educational competencies in all areas and through the physical education curriculum that the teacher teaches and for all classes, holding training courses for teachers that would It is concerned with preparing them practically and possessing the necessary competencies to teach physical education curricula. Conducting studies similar to the current study dealing with the viewpoint of educational supervisors, school principals and principals, urging them to possess the necessary educational competencies and teaching skills and developing them, and conducting several studies on comparing the educational competencies of male and female teachers in public schools and other private schools.


The effect of educational units according to the strategy of the flipped classes in learning to perform the tapping by changing the direction of basketball for students

Abstract: The study aimed to identify the impact of the strategy of the flipped classes on learning the performance of the flip flop by changing the direction in basketball and to identify the effect of the educational units according to the Wheatley model. The researchers identified the community of students of the College of Physical Education and Sports Sciences / University of Diyala for the academic year 2022-2023, and the researchers deliberately chose the sample represented by the first stage students, whose number is (86), and they were divided into an experimental group and a control group represented by (43) students for each group and (10) Students of the exploratory experiment, and the study concluded that the use of the flipped classroom strategy had a direct and significant impact on students' learning, and it came out with a number of recommendations, including the necessity of using modern strategies and methods in the lesson of physical education and moving away from the traditional methods used.


PLANNING ANALYSIS OF SHEET PILE WHARF WITH PLAXIS SOFTWARE AT SPECIAL TERMINAL LAMPUNG

Abstract: Special terminals are those that are located outside the Working Environment Area and the Interest Environment of the main business. Private companies with business licenses in Lampung Province want the construction of a special port or terminal. The efforts made are to analyze the planning of a special terminal wharf to serve the loading of commodity materials in the form of cement factory materials. A steel sheet pile is used as a structure on the Lampung Wharf. The purpose of this study is to determine the stability and optimality in the selection of sheet pile dimensions used by adjusting the soil conditions around the wharf. The analysis and modeling method using calculations of the Plaxis 8.2 software assistance will get the results of the analysis in the form of safety factor numbers and deformations. From the calculation and modeling of the Plaxis 8.2 software, the results of the deformation analysis are 1,29 mm and a safety factor of 1,7011. It can be concluded that the sheet pile and the selection of sheet pile dimensions used are considered safe. Keywords: port, wharf, sheet pile, Plaxis


Attitudes of first class teachers towards the use of the electronic achievement file in the assessment

Abstract: The aim of the current research is to identify the attitudes of the first-cycle teachers of the importance of using the electronic achievement file in the calendar. The importance of the research comes from the role occupied by modern evaluation tools in education. The electronic achievement file is one of the important tools in evaluating both the teacher and the student. The sample consisted of (173) male and female teachers.To achieve the aim of the research, the researcher used the descriptive analytical approach, and designed a trend questionnaire. The results showed that there were no statistically significant differences at the level of significance of 0.05 between the arithmetic means of the degree of attitudes to the importance of using the electronic achievement file in the evaluation due to the gender variable, and that the teachers' attitudes towards the use of electronic achievement files in the evaluation were weak, in light of the results of the study. A number of suggestions can be made:1- The need to start holding training courses for new and old teachers at all stages to familiarize them with the electronic achievement file and how to use it and apply it well in the evaluation.2- Working to provide the necessary requirements in the educational environment to activate the teachers' use of the electronic achievement file in evaluating students, especially modern computers and related computer programs, electricity..


Evaluating the leadership role of middle school principals in mentally stimulating physical education teachers according to the principles of transformational leadership

Abstract: Tuition in mathematics and mathematics tuition in mathematics and math lessons in mathematics and mathematics.In order to achieve the goal of the research, the researchers prepared a special form for the purpose of measuring the assessment of the leadership roles of middle school principals and their effectiveness in the mental motivation of teachers in order to perform their duties well. For this purpose, the form consisted of (16) The sample of managers (82) after excluding samples of honesty and consistency from the final experiment of the questionnaire.The current research has reached:1. The principals of middle schools in Baghdad have a high level of mental stimulation for teachers physical education.2. The degrees of motivation for middle school principals working in the middle schools in Baghdad depending on the direction of motivation, whether moral or material.3. Principals of middle schools working in middle schools in Iraq are highly motivated towards external competitions.4. Principals of middle schools working in middle schools in Iraq have little incentive to take into acc


Pengaruh Store Atmosphere dan Visual Merchandising terhadap Niat Beli pada Toko Kue Mamayudesserts

Abstract:
Abstract. This study aims to find out how the description of store atmosphere, visual merchandising and purchase intention at Mamayudesserts cake shop, as well as how much influence store atmosphere, visual merchandising has on purchase intention. This research is a type of descriptive and verification research using quantitative research methods, the sampling technique used in this study is non-probability sampling, namely purposive sampling, the population in this study are consumers who have purchased food products at Mamayudesserts cake shop with a sample of 100 respondents. The data analysis used in this research is descriptive analysis and verification using multiple linear regression analysis, f test, t test, and coefficient of determination. The results of this study indicate that store atmosphere partially has a significant effect on purchase intention, visual merchandising partially has a significant effect on purchase intention, and store atmosphere and visual merchandising simultaneously purchase intention.
 Abstrak. Penelitian ini bertujuan untuk mengetahui bagaimana gambaran store atmosphere, visual merchandising dan niat beli pada toko kue mamayudesserts, serta seberapa besar pengaruh store atmosphere, visual merchandising pada niat beli. Penelitian ini merupakan jenis penelitian deskriptif dan verifikatif dengan menggunakan metode penelitian kuantitatif, Teknik sampling yang digunakan dalam penelitian ini adalah nonprobability sampling yaitu purposive sampling, populasi dalam penelitian ini adalah konsumen yang pernah membeli produk makanan Mamayudesserts dengan sampel sebanyak 100 responden. Analisis data yang digunakan dalam penelitian ini adalah analisis deskriptif dan verifikatif menggunakan analisis regresi linier berganda, Uji f, Uji t, dan koefisien determinasi. Hasil penelitian ini menunjukkan bahwa store atmosphere secara parsial berpengaruh signifikan terhadap niat beli, visual merchandising secara parsial berpengaruh signifikan terhadap niat beli, serta store atmosphere dan visual merchandising secara simultan berpengaruh terhadap niat beli.


6-Parts Haematology Parameters Evaluation Using Abbott Alinity H

Abstract: Nowadays, there are variety of automated hematology analyzers available in the market. However, the new product of diagnostic equipment should be evaluated and validated to ensure their capability. Thorough process of testing on all new hematology analyzers must be performed to certify that reliable result will be released and for patient safety. This study was carried out to evaluate automated hematology analyzers; Abbott Alinity H, in the Hospital Universiti Sains Malaysia setting. Blood samples send for complete blood count were selected randomly (n = 50) from healthy subjects and those who have different blood disorders. Blood specimens and quality control materials were analysed on the Abbott Alinity H to evaluate the precision. For correlation, the Sysmex XN-1000 was used as the comparative method. The study showed very good correlation between Abbott Alinity H and Sysmex, XN-1000 in the parameters such as white blood cell (WBC), red blood cell (RBC), hemoglobin (HGB), mean cell volume (MCV), platelet (PLT), neutrophil (NEU), lymphocyte (LYM), monocyte (MONO), eosinophil (EOS), and basophil (BASO). For the precision, all the parameters are performed well within allowable limits of performance for Abbott Alinity H. In conclusion, the Abbott Alinity H demonstrated an acceptable performance in term of precision and the analyzer was equivalent with the Sysmex XN-1000.


An Analysis of Students’ Obstacles in Learning English for Grade VIII Students at SMP Negeri 1 Selesai

Abstract: This research explores the obstacles faced by students at SMP Negeri 1 SELESAI in learning English, particularly in reading. The study was conducted through preliminary analysis and a questionnaire, revealing a strong preference among students for topics related to the internet and entertainment, which might distract them from focusing on reading English materials. The research also revealed that students prefer to learn in groups rather than individually, suggesting a strong influence of peer interactions. Despite the high interest in English, students still face numerous obstacles in their learning process, including a lack of understanding about word distribution and pronoun usage in English, and difficulty in understanding and interpreting complex terms and phrases. The study suggests potential solutions to overcome these challenges, such as the use of more varied and interesting teaching methods, increasing students' understanding of English sentence structure, and enhancing students' interest and motivation in learning English, especially in reading. Keywords: Obstacle;, English learning; motivation


Information Security Management System Assessment Model by Integrating ISO 27002 and 27004

Abstract: The rapid development of information and communication technology has also led to a significant increase in cybercrime activities. According to the Annual Cybersecurity Monitoring Report by the National Cyber and Cryptography Agency, there were 495 million instances of traffic anomalies or attempted attacks in 2020, which rose to 1.6 billion in 2021 in Indonesia. Implementing the ISO 27001 standard for information security management system (ISMS) can help mitigate these cyber-attack attempts. However, with various levels of resources and organizational commitment, different levels of ISMS maturity can be achieved. Therefore, there is a need for an ISMS assessment model. This is crucial, considering cyber incidents such as data breaches in organizations that have implemented or are certified with ISO 27001. This research proposed a concept of ISMS assessment model by integrating ISO 27002 and 27004 to a case study (Directorate XYZ), where the guidance function of ISO 27002 is transformed into assessment parameters and ISO 27004 for measuring performance. Using this model, the score of the case study’s ISMS was found to be 53.925, which is still below the established standard of 80.


A Literature Review on Access Control in Networking Employing Blockchain

Abstract: Access Control (AC) in networking attempts to make sure that only authorized devices perform actions formed upon privileges defined for them with a view to prevent malicious users' entry and interaction in the communication grid. Blockchain solutions contain an arrangement of related blocks that naturally safeguards the trustworthiness, defending the incontestability, defend masked-identity of its transactions/transfers due to scattered consensus strategies and cryptographic solutions. Our survey comprehensively reviews BC-formed AC in broad scope of networking considering AC techniques while breaking down into 4 propositions and assessing them in terms of blockchain roles, AC technique and approach, network elements, and rest. We stockpiled a primary sample of 79 bibliographic references by weeding out them for screening criteria sought from scientific information reservoirs exploiting a qualitative and extensive strategy. Formed upon this survey, in blockchain-formed AC, blockchain can be exploited as an AC manager to administrate network devices and access information, implement automatic AC by means of smart contracts, secure storage of AC related data to reinforce overall AC security, and for safe data exchanging in the operation of AC. Minute assessment highlights that from blockchain-formed AC, 52.5% provide AC using blockchain itself or using smart contracts, 92.5% exploit sequential blockchain, 35% exploit PBFT consensus, provide 100% fine-grained and host-formed AC, 85% decentralized AC, 87.5% have single-factor authentication, 92.5% provide dynamic AC, and 45% have opted for IoT. Finally, we evaluate the chances and difficulties of the principle of blockchain-formed AC and then giving recommended actions to beat them.


Decision Support System for Selection of Internet Services Providers using the ROC and WASPAS Approach

Abstract: Along with the growth of the internet service provider industry, selecting an Internet Service Provider (ISP) has become an important decision to ensure optimal internet access. However, with so many ISP options available, consumers often face difficulties in choosing the service that best suits their needs. The aim of this research is to produce a decision support system that can help users choose the ISP that best suits their needs and preferences using the ROC (Rank Order Centroid) approach as a weighting technique and the WASPAS (Weighted Aggregated Sum Product Assessment) approach to determine the best alternative. The ROC approach is used to obtain criteria weights based on the ranking order of the importance of the criteria. On the other hand, the WASPAS method is used to determine the best alternative through weighted addition and multiplication, producing a final value that reflects the extent to which each alternative meets the specified criteria. The outcomes of the case study reveal a ranking of alternatives from highest to lowest scores, as follows: First Media (A2) achieving 0.8629, Indihome (A3) at 0.8416, MyRepublic (A5) with 0.7954, Biznet (A1) scoring 0.7844, and Oxygen (A4) at 0.7469. The usability testing yields an average score of 89%, suggesting that the system is apt for utilization, as it aligns with the functionalities users are seeking.


A Study on the Development of Digital Literacy Competency for College Students in Metaverse-based Extracurricular Program

Abstract: Thrning.


A Time-Varying Fuzzy Parameter Zeroing Neural Network for the Synchronization of Chaotic Systems

Abstract: Zeroing neural network (ZNN) has been applied to various time-varying problems solving, and numerous ZNN models have been developed in recent years, such as power-type varying-parameter ZNN (PT-VR-ZNN) for solving time-varying quadratic minimization problems, adaptive fuzzy-type ZNN (AFT-ZNN) for solving time-variant matrix inversion and fuzzy power ZNN (FPZNN) for solving time-varying quadratic programming problems. As a time-varying problem and imperative research hot spot in science and engineering, the synchronization of chaotic systems has developed for decades. However, the research on chaos synchronization using ZNN method is rarely reported. Therefore, this paper proposes a time-varying fuzzy parameter ZNN (TVFP-ZNN) model to realize chaotic systems synchronization against the external noises. The most prominent feature of the TVFP- ZNN model is that the time-varying fuzzy parameter generated by the fuzzy logic system is applied in this model. Moreover, the above mentioned three models are also applied to realize the same chaotic systems synchronization for comparison. Compared with above three models, the proposed TVFP-ZNN model not only possesses the fastest convergence speed, but also maintains strongest robustness to noises. Besides, the excellent performances of the TVFP-ZNN model are verified by rigorous mathematical validation. Furthermore, the effectiveness and robustness of the proposed TVFP-ZNN model for chaotic systems synchronization are verified by comparative numerical simulation results. Finally, the process of the proposed TVFP-ZNN model for chaotic system synchronization is displayed on the oscilloscope based on the field programmable gate array (FPGA) to further illustrate its practical application ability.


Audio–Visual Fission Illusion and Individual Alpha Frequency: Perspective on Buergers and Noppeney (2022)

Abstract: Integrating visual and auditory information is an important ability in various cognitive processes, although its neural mechanisms remain unclear. Several studies indicated a close relationship between one's temporal binding window (TBW) for audio-visual interaction and their alpha rhythm in the brain (individual alpha frequency or IAF). A recent study by Buergers and Noppeney [Buergers, S., & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732-742, 2022], however, challenged this view using a new approach to analyze behavioral data. Conforming to the same procedures by Buergers and Noppeney, here, I analyzed the data of my previous study and examined a relationship between TBW and IAF. In contrast to Buergers and Noppeney, a significant correlation was found between occipital IAF and a new behavioral measure of TBW. Some possibilities that caused these opposing results, such as a variability of "alpha band" across studies and a large inter-individual difference in magnitude of the fission illusion, are discussed.


FURTHER INVESTIGATION INTO THE LOUDNESS OF RUNNING REVERBERATION

Abstract: Many acousticians associate and with early reflected energy. (See Barron, Ando, Beranek, etc.) While such association is certainly correct in some cases, recent work (Morimoto and Plosselt, Gold, Soulodre, Gardner, Kahle, Griesinger) shows that spaciousness and envelopment are determined (mostly or in part) by sound energy arriving later than 100ms. (For example, Gold finds that early lateral reflections in a time range from 25-40ms added to a 2.0 second RT reverberant field REDUCE spatial impression.) In (8) we show that lateral energy causes source broadening for frequencies below 200 300Hz, and a feeling of being surrounded by an acoustic field for higher frequencies. This second sensation can be easily called spatial impression, spaciousness, or envelopment, but the arrival time is not critical for many types of music. When the music has sharp attacks the first 50ms can be separately distinguished, and the acoustic impression of this segment alone can be heard. However, in a concert hall the majority of the spaciousness and envelopment perceived comes from energy arriving later than this. For a soloist on stage, Gade has shown that any first order reflection arriving before about 75ms is inaudible, at least if it arrives in the medial plane. Thus musician self support must involve times later than this. In (2) we introduce the concept of reverberant loudness as a way of understanding these conflicting observations. reverberant loudness depends on the ratio of the loudness of an event stream which contains the musical notes the direct stream and the stream which represents the background noise and reverberation. To understand reverberant loudness in halls, we must understand how the ear separates incoming sound into these two streams, and this is particularly difficult when the majority of the sound is reflected. The results of a series of experiments into these issues have, as usual, been surprising.


Strategic Coalition for Data Pricing in IoT Data Markets

Abstract: This paper establishes a market for trading Internet of Things (IoT) data that is used to train machine learning models. The data, either raw or processed, is supplied to the market platform through a network, and the price of such data is controlled based on the value it brings to the machine learning model under the adversity of the correlation property of data. Eventually, a simplified distributed solution for a data trading mechanism is derived that improves the mutual benefit of devices and the market. Our key proposal is an efficient algorithm for data markets that jointly addresses the challenges of availability and heterogeneity in participation, as well as the transfer of trust and the economic value of data exchange in IoT networks. The proposed approach establishes the data market by reinforcing collaboration opportunities between devices with correlated data to limit information leakage. Therein, we develop a network-wide optimization problem that maximizes the social value of coalition among the IoT devices of similar data types; at the same time, it minimizes the cost due to network externalities, i.e., the impact of information leakage due to data correlation, as well as the opportunity costs. Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm. Simulation results show the efficacy of our proposed mechanism design toward a trusted IoT data market, with up to 32.72% gain in the average payoff for each seller.


Two new infinite families of APN functions in trivariate form

Abstract: We present two infinite families of APN functions in trivariate form over finite fields of the form F 23m . We show that the functions from both families are permutations when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m is odd, and are 3-to-1 functions when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m is even. In particular, our functions are AB permutations for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m odd. Furthermore, we observe that for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m = 3, i.e. for F 29 , the functions from our families are CCZ-equivalent to the two bijective sporadic APN instances discovered by Beierle and Leander. We thus generalize these sporadic instances into an infinite family of APN functions. We also perform an exhaustive computational search for quadratic APN functions with binary coefficients in trivariate form over F 23m with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m ≤ 5 and report on the results.


EGNN: Energy-efficient anomaly detection for IoT multivariate time series data using graph neural network

Abstract: Anomaly detection has been widely applied in Internet of Things (IoT) to guarantee the health of IoT applications. Current studies on anomaly detection focus mainly on measurement design and discovery methods on the cloud, which, however, are associated with issues of computational heaviness and capacity limitation when applied at the network edge. Thus, it becomes important to ensure that the detection is not only accurate but also energy-efficient. To fill this gap, this paper proposes an accurate and Energy-efficient Graph Neural Network based anomaly detection method (EGNN) for IoT multivariate time series data. Specifically, correlations between sensory data upon different IoT devices, which are rarely considered in the literature, are explored through a developed Subgraph Generation Algorithm (SGA) based on graph structure learning. As a result, a dependency graph with multiple subgraphs and their corresponding centers is generated. Thereafter, to reduce anomaly-irrelevant sensory data transmitted in the network, only sensory data upon subgraph centers are utilized for anomaly detection by a computational-light approach, i.e., a multi-layer perceptron based forecasting method. Once an anomaly is detected, sensory data of whole subgraph data are adopted for obtaining accurate anomaly results, by a graph attention based forecasting method. This GNN-based anomaly detection strategy with Mode Switching (GMS) can greatly reduce anomaly-irrelevant data transmission, especially when anomalies occur infrequently. To validate the effectiveness of our mechanism, extensive experiments are conducted upon real-world IoT multivariate time series datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in terms of accuracy and energy-efficiency.


Eliciting Multimodal and Collaborative Interactions for Data Exploration on Large Vertical Displays

Abstract: We examined user preferences to combine multiple interaction modalities for collaborative interaction with data shown on large vertical displays. Large vertical displays facilitate visual data exploration and allow the use of diverse interaction modalities by multiple users at different distances from the screen. Yet, how to offer multiple interaction modalities is a non-trivial problem. We conducted an elicitation study with 20 participants that generated 1015 interaction proposals combining touch, speech, pen, and mid-air gestures. Given the opportunity to interact using these four modalities, participants preferred speech interaction in 10 of 15 low-level tasks and direct manipulation for straightforward tasks such as showing a tooltip or selecting. In contrast to previous work, participants most favored unimodal and personal interactions. We identified what we call collaborative synonyms among their interaction proposals and found that pairs of users collaborated either unimodally and simultaneously or multimodally and sequentially. We provide insights into how end-users associate visual exploration tasks with certain modalities and how they collaborate at different interaction distances using specific interaction modalities. The supplemental material is available at https://osf.io/m8zuh/?view only = 34bfd907d2ed43bbbe37027fdf46a3fa.


Foreground segmentation network using transposed convolutional neural networks and up sampling for multiscale feature encoding

Abstract: Foreground segmentation algorithm aims to precisely separate moving objects from the background in various environments. However, the interference from darkness, dynamic background information, and camera jitter makes it still challenging to build a decent detection network. To solve these issues, a triplet CNN and Transposed Convolutional Neural Network (TCNN) are created by attaching a Features Pooling Module (FPM). TCNN process reduces the amount of multi-scale inputs to the network by fusing features into the Foreground Segmentation Network (FgSegNet) based FPM, which extracts multi-scale features from images and builds a strong feature pooling. Additionally, the up-sampling network is added to the proposed technique, which is used to up-sample the abstract image representation, so that its spatial dimensions match with the input image. The large context and long-range dependencies among pixels are acquired by TCNN and segmentation mask, in multiple scales using triplet CNN, to enhance the foreground segmentation of FgSegNet. The results, clearly show that FgSegNet surpasses other state-of-the-art algorithms on the CDnet2014 datasets, with an average F-Measure of 0.9804, precision of 0.9801, PWC as (0.0461), and recall as (0.9896). Moreover, the FgSegNet with up-sampling achieves the F-measure of 0.9804 which is higher when compared to the FgSegNet without up-sampling.


Unsupervised image enhancement under non-uniform illumination based on paired CNNs

Abstract: This paper presents two CNN-based systems for unsupervised image enhancement under non-uniform illumination. The core of the systems is constituted by the difference of a pair of CNNs. Each CNN is composed of two convolutional layers of neurons with exponential activation function and logarithmic activation function. A weighted sum of the non-reference loss functions is used to train the paired CNNs. It includes an entropy enhancement function and a Bézier loss function to ensure global and local enhancement complementarily. It also includes a white balance loss function to remove color cast in raw images, and a gradient improvement loss function to compensate for the high frequency degradation . In addition, it includes an SSIM (structural similarity index) loss functions to ensure image fidelity. In addition to the basic system, CNNOD, an augmented version called CNNOD+ is developed, which features an information fusion/combination module with a power-law network for gamma correction. The experimental results on two benchmark datasets are discussed to demonstrate that the proposed systems outperform the state-of-the-art methods in terms of enhancement quality, model complexity, and convergence efficiency.


Sentimental analysis of audio based customer reviews without textual conversion

Abstract: The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.


Deep learning based Arabic short answer grading in serious games

Abstract: Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research.


Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques

Abstract: Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.


Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

Abstract: This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.


Wireless channel-based ciphering key generation: effect of aging and treatment

Abstract: Key generation for data cryptography is vital in wireless communications security. This key must be generated in a random way so that can not be regenerated by a third party other than the intended receiver. The random nature of the wireless channel is utilized to generate the encryption key. However, the randomness of wireless channels deteriorated over time due to channel aging which casing security threats, particularly for spatially correlated channels. In this paper, the effect of channel aging on the ciphering key generations is addressed. A proposed method to randomize the encryption key each coherence time is developed which decreases the correlation between keys generated at consecutive coherence times. When compared to the conventional method, the randomness improvement is significant at each time interval. The simulation results show that the proposed method improves the randomness of the encrypting keys.


A framework for cloud cover prediction using machine learning with data imputation

Abstract: The climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction.


EksPy: a new Python framework for developing graphical user interface based PyQt5

Abstract: This study introduces EksPy Python framework, a novel framework designed for developing graphical user interface (GUI) applications in Python. EksPy framework is built on PyQt5, which is a collection of Python bindings for the Qt libraries, and it provides a user-friendly and intuitive interface. The comparative analysis of EksPy framework with existing frameworks such as Tkinter and PyQt highlights its notable features, including ease of use, rapid development, enhanced performance, effective database management, and the model-view-controller (MVC) concept. The experimental results illustrate that EksPy framework requires less code and enhances code readability, thereby facilitating better understanding and efficient development. Additionally, EksPy framework offers a modern and customizable appearance, surpassing Tkinter’s capabilities. Furthermore, it incorporates a built-in object-relational mapping (ORM) feature to simplify database interactions and adheres to the MVC architectural pattern. In conclusion, EksPy Python framework emerges as a powerful, user-friendly, and efficient framework for GUI application development in Python.


SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection

Abstract: Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strength of both SVD and autoencoder to fully capture complex normal patterns in multivariate time series. An asymmetric autoencoder architecture is proposed, where two encoders are used to capture features in time and variable domains and a shared decoder is used to generate reconstructions based on latent representations from both domains. A new regularization based on singular value decomposition theory is designed to force each encoder to learn features in the corresponding axis with mathematical support delivered. A specific loss component is further proposed to align Fourier coefficients of inputs and reconstructions. It can preserve details of original inputs, leading to enhanced feature learning capability of the model. Extensive experiments on three real world datasets demonstrate the proposed algorithm can achieve better performance on multivariate time series anomaly detection tasks under highly unbalanced scenarios compared with baseline algorithms.


Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training

Abstract: Safety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue. In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes. To this aim, a fully 3D encoder-decoder network integrating spatio-temporal layers is designed, and it adopts hierarchical prediction and progressive learning to enhance prediction accuracy and shorten training time. Our network gets the depth estimation accuracy of MAE 2.55±1.51 (mm) and RMSE 5.23±1.40 (mm) using 8 surgical videos with a resolution of 1280×1024, which performs better compared with six other state-of-the-art methods that were trained on the same data. Our network can implement a promising depth estimation performance in intra-operative scenes using stereo images, allowing the integration in robot-assisted surgery to enhance safety.


Combining external-latent attention for medical image segmentation

Abstract: The attention mechanism comes as a new entry point for improving the performance of medical image segmentation. How to reasonably assign weights is a key element of the attention mechanism, and the current popular schemes include the global squeezing and the non-local information interactions using self-attention (SA) operation. However, these approaches over-focus on external features and lack the exploitation of latent features. The global squeezing approach crudely represents the richness of contextual information by the global mean or maximum value, while non-local information interactions focus on the similarity of external features between different regions. Both ignore the fact that the contextual information is presented more in terms of the latent features like the frequency change within the data. To tackle above problems and make proper use of attention mechanisms in medical image segmentation, we propose an external-latent attention collaborative guided image segmentation network, named TransGuider. This network consists of three key components: 1) a latent attention module that uses an improved entropy quantification method to accurately explore and locate the distribution of latent contextual information. 2) an external self-attention module using sparse representation, which can preserve external global contextual information while reducing computational overhead by selecting representative feature description map for SA operation. 3) a multi-attention collaborative module to guide the network to continuously focus on the region of interest, refining the segmentation mask. Our experimental results on several benchmark medical image segmentation datasets show that TransGuider outperforms the state-of-the-art methods, and extensive ablation experiments demonstrate the effectiveness of the proposed components. Our code will be available at https://github.com/chasingone/TransGuider.


Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing

Abstract: Recent work has shown that machine learning (ML) models can skillfully forecast the dynamics of unknown chaotic systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (“climate”) can be produced by employing a feedback loop, whereby the model is trained to predict forward only one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this feedback can result in artificially rapid error growth (“instability”). One established mitigating technique is to add noise to the ML model training input. Based on this technique, we formulate a new penalty term in the loss function for ML models with memory of past inputs that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. We refer to this penalty and the resulting regularization as Linearized Multi-Noise Training (LMNT). We systematically examine the effect of LMNT, input noise, and other established regularization techniques in a case study using reservoir computing, a machine learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while the short-term forecasts are substantially more accurate than those trained with other regularization techniques. Finally, we show the deterministic aspect of our LMNT regularization facilitates fast reservoir computer regularization hyperparameter tuning.


CT Image Denoising and Deblurring With Deep Learning: Current Status and Perspectives

Abstract: This article reviews the deep learning methods for computed tomography image denoising and deblurring separately and simultaneously. Then, we discuss promising directions in this field, such as a combination with large-scale pretrained models and large language models. Currently, deep learning is revolutionizing medical imaging in a data-driven manner. With rapidly evolving learning paradigms, related algorithms and models are making rapid progress toward clinical applications.


A multimodal biometric database and case study for face recognition based deep learning

Abstract: Recently, multimodal biometric systems have garnered a lot of interest for the identification of human identity. The accessibility of the database is one of the contributing elements that impact biometric recognition systems. In their studies, the majority of researchers concentrate on unimodal databases. There was a need to compile a fresh, realistic multimodal biometric database, nonetheless, because there were so few comparable multimodal biometric databases that were publically accessible. This study introduces the MULBv1 multimodal biometric database, which contains homologous biometric traits. The MULBv1 database includes 20 images of each person's face in various poses, facial emotions, and accessories, 20 images of their right hand from various angles, and 20 images of their right iris from various lighting positions. The database contains real multimodal data from 174 people, and all biometrics were accurately collected using the micro camera of the iPhone 14 Pro Max. A face recognition technique is also suggested as a case study using the gathered facial features. In the case study, the deep convolutional neural network (CNN) was used, and the findings were positive. Through several trials, the accuracy was (97.41%).


Exploring Current Trends in Artificial Intelligence Technology An Extensive Review

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


Hardware-efficient autonomous error correction with linear couplers in superconducting circuits

Abstract: Large-scale quantum computers will inevitably need quantum error correction (QEC) to protect information against decoherence. Given that the overhead of such error correction is often formidable, autonomous quantum error correction (AQEC) proposals offer a promising near-term alternative. AQEC schemes work by transforming error states into excitations that can be efficiently removed through engineered dissipation. The recently proposed AQEC scheme by Li et al., called the Star code, can autonomously correct or suppress all single qubit error channels using two transmons as encoders with a tunable coupler and two lossy resonators as a cooling source. The Star code requires only two-photon interactions and can be realized with linear coupling elements, avoiding experimentally challenging higher-order terms needed in many other AQEC proposals, but needs carefully selected parameters to achieve quadratic improvements in logical states' lifetimes. Here, we theoretically and numerically demonstrate the optimal parameter choices in the Star code. We further discuss adapting the Star code to other planar superconducting circuits, which offers a scalable alternative to single qubits for incorporation in larger quantum computers or error correction codes.


Empowering Students Through Digital Literacy: A Case Study of Successful Integration in a Higher Education Curriculum

Abstract: This study investigates the integration of digital literacy into higher education curricula, focusing on its impact at Online Women University. The introduction delineates the importance of digital literacy in preparing students for success in the digital era. The purpose of the study is to explore the implications of digital literacy integration, encompassing student perceptions, experiences, and technology usage patterns. Methodologically, a cross-sectional research design was employed, with data collected from 150 participants, including students and lecturers, using convenience sampling. Surveys were administered to capture insights into digital literacy, technology usage, and educational experiences. Both descriptive and inferential analyses, including frequencies, percentages, ANOVA, chi-square tests, and regression analysis, were conducted to discern relationships and patterns. Results indicate varying levels of digital literacy integration across faculties, with notable differences in technology usage and perceived empowerment through digital literacy. Notably, the mean score for post-test digital literacy skills increased significantly compared to pre-test scores. The study also reveals high confidence levels in using digital tools for academic purposes among students, influenced by factors such as experience and training. In conclusion, the findings underscore the importance of integrating digital literacy into higher education curricula. They highlight the need for ongoing support and training to enhance digital literacy skills among students and educators. Despite limitations such as convenience sampling, the study contributes valuable insights into the role of digital literacy in academia, emphasizing its significance in preparing students for the digital challenges of the future.


Optical Mark Recognition using Modify Bi-directional Associative Memory

Abstract: Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from answer sheets by using analysis phase. This proposal will be able to detect no selection or select more than one choice, in addition, using MBAM make it timeless because it will deal with a complete answer sheet, it no need to extract the answer boxes from the answers sheet. The proposed OMR exhibits accuracy is 99.998% in the recognition of marked and non-marked.


Peningkatan Keterampilan Menulis Puisi Melalui Metode ARCS Dengan Menggunakan Media Gambar

Abstract: The purpose of this study is to describe the poetry writing ability of grade VIII MTs students of Sorong City. The low ability to write poetry is caused by learning to write poetry that has not been carried out properly so that grade VIII students have difficulty when asked to write poetry.Therefore, the necessary learning strategies and instructional media fun and appropriate. To overcome these problems ARCS Metode and media images of events contained in the newspaper in teaching poetry writing class VIII MTs Sorong Affairs. The research was conducted in two cycles. Each cycle consists of planning, action, observation and reflection. The results of this study indicate the existence of a process of learning, improvement, and change behavior in a positive direction.