A Proposed Framework of Vulnerability Assessment and Penetration Testing (VAPT) in Cloud Computing Environments from Penetration Tester Perspective

Abstract: Penetration testing is a process that focuses on finding security vulnerabilities in a target environment that could let an attacker penetrate the network or computer system or steal information. Due to the COVID-19 endemic, most employees still implement working from home or a hybrid approach, even though the number of new cases of COVID-19 is decreasing. However, working from home depends mainly on cloud computing applications that help employees efficiently accomplish their daily work. This situation also increased the number of data generated from various sources, so they may be exposed to different security risks. This research will propose a framework to conduct vulnerability assessment and penetration testing (VAPT) in cloud service models such as SaaS, PaaS, and IaaS from the perspective of penetration testers. This proposed framework is developed through the integration and mapping of existing frameworks and guidelines to conduct VAPT on testing components such as web applications, APIs, network testing, etc. In this proposed framework, the method of conducting VAPT for each cloud service model will be discussed in detail, from the planning and reconnaissance stage until the report is delivered to the cloud subscriber or cloud provider. An advantage of this proposed framework for the penetration tester is that there is still a lack of methods or guidelines for conducting VAPT that cover all the cloud service models in one comprehensive document


Blockchain Technology in Overcoming Security Threats for Smart Manufacturing System - A Systematic Literature Review

Abstract: Blockchain is a new emerging technology that is mainly used to add security layers to many applications. Studies showed that businesses and manufacturing industries suffer from cyber-attacks, especially after the emergence of Industry 4.0 and IoT devices connected to the cloud for data sharing purposes, these interactions between IoT devices and the information involved in the transactions between companies and customers produce a massive amount of data daily. This phenomenon becomes an easy target for attackers due to the security limitations, data corruption and compromised reliability of the traditional manufacturing systems during data transferring. Many have focused on the methods to address these security limitations of the manufacturing businesses. This paper shed the light on the major smart manufacturing sectors that can potentially benefit from blockchain technologies, including cloud manufacturing, collaborative manufacturing, digital twin manufacturing, robonomics based smart manufacturing and the supply chain industry. The blockchain can enhance the security by creating a decentralized environment for the data to be stored, which will make the data stored immutable for improved security. These immutable data are nearly impossible to manipulate, replace, or falsify the data stored on the blockchain secured network.


Coverage of Artificial Intelligence in Media

Abstract: Artificial intelligence (AI) is an important catalyst that enables media associations to optimize and ameliorate their operations. The aim of the paper is to emphasize the significance and eventuality of AI during high- performance data analytics and media content generation. The focus is on responsible and ethically respectable use of AI, which imposes the need to continuously make trust, through multitudinous challenges, during the operation of implicit pitfalls. Standardization and obligatory publication of AI product data can pave the way for increased translucency and trust structure. Else, the power of AI, which has a huge eventuality to contribute to a better quality of life, could be converted into a suicide device.


Bitcoin Price Prediction Using Hybrid LSTM-GRU Models

Abstract: Cryptocurrency price prediction is a challenging task due to the inherent volatility and complexity of the market. In this research, we propose a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model for predicting Bitcoin prices. The model is implemented using the TensorFlow and Keras libraries and is evaluated on historical Bitcoin price data obtained from Yahoo Finance. Our approach aims to leverage the strengths of both LSTM and GRU architectures to enhance the accuracy of price predictions. The results suggest that the proposed hybrid LSTM-GRU model holds promise for effectively capturing the complex dynamics of cryptocurrency markets, addressing the challenges associated with traditional time-series analysis techniques.


An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Abstract: Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.


A Comparative Study of Implicit knowledge of Creative Teachers among Middle School Teachers in South Korea and China

Abstract:
Objectives This research was to investigated the implicit knowledge of creative teachers possessed by middle school teachers in Korea and China. the study aimed to analyzed the cross-country variances and contribute to comprehension of the educational systems and cultural characteristics of both nations.
 Methods A survey was administered to a total of 174 middle school teachers in Korea and 187 middle school teachers in China. Participants were requested to freely crticulate the characteristics of creative teachers. The col-lected responses were subsequently analyzed from both cognitive and affective perspectives.
 Results The results revealed that Korean teachers ranked fluency, communication skills, originality, expertise, flexibility, respect, openness, consideration, breaking stereotypes, and encouragement as the foremost charac-teristics of creative teachers, in that order. Conversely, Chinese teachers mentioned originality, communication skills, expertise, applicability, flexibility, progressiveness, passion for education, fluency, respect, and humor as the key traits. While certain cognitive characteristics of creative teachers displayed similarities across both coun-tries, disparities were observed in their affective characteristics. Additionally, both countries placed greater em-phasis on the cognitive aspect of identifying a creative teacher, rather than the affective aspect. Chinese teachers exhibited a higher emphasis on the cognitive aspect than their Korean counterparts.
 Conclusions This study has effectively demonstrated dissimilarties in implicit perceptions regarding creative teachers between Korean and Chinese educators. The findings of this research are anticipates to enhance the un-derstanding of the educational systems and cultural characteristics prevalent in both countries.


Development of ethnoscience-oriented multimedia learning salt manufacturing process from conductivity materials

Abstract: The purpose of this study was to obtain an ethnoscience study of conductivity material and to determine student responses to media. This research uses a descriptive qualitative method with the ADDIE development model. The research location is in one of the salt ponds in Bangkalan Regency and SMP Negeri 2 Kamal. The sampling method was a simple random sampling of as many as 10 students of class VII. Data collection techniques using interviews, questionnaires, and documentation. The percentage of student responses on the display aspect is 97.33% and the material aspect is 94.25% which is included in the very positive criteria.


Credit Card Fraud Detection using KNN, Random Forest and Logistic Regression Algorithms : A Comparative Analysis

Abstract: Because credit cards are utilized so frequently, fraud appears to be a significant concern in the credit card industry. It is challenging to quantify the effects of misrepresentation. Globally, credit card fraud has cost institutions and consumers billions of dollars. Despite the existence of numerous anti-fraud mechanisms, fraudsters continue to seek out novel methods and strategies to commit fraud. An additional challenge in the estimation of credit card fraud loss is that the magnitude of unreported or undetected forgeries cannot be determined, only losses associated with those frauds that have been detected can be measured. Implementing effective fraud detection algorithms through the utilization of machine-learning techniques is crucial in order to mitigate these losses and provide support to fraud investigators. This paper presents a machine learning-based method for the detection of credit card fraud. Three methodologies are implemented on the raw and pre-processed data. Python is used to implement the work. By comparing the accuracy-based performance evaluations of k-nearest neighbor and logistic regression with Random Forest, it is determined that the former exhibits superior performance.


Comparative Analysis of Machine Learning and Deep Learning Models for Bitcoin Price Prediction

Abstract: This research endeavors to forecast Bitcoin prices by employing a suite of machine learning and deep learning models. Five distinct models were employed: Random Forest, Linear Regression, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), each evaluated based on their R-squared scores. Notably, the models showcased diverse performances, with the ensemble learning approach of Random Forest exhibiting near-perfect accuracy, closely followed by GRU and SVM. The deep learning architectures, LSTM and GRU, demonstrated remarkable predictive capabilities, showcasing their adeptness in capturing intricate temporal patterns within the cryptocurrency price data. This study sheds light on the comparative performance of these models, emphasizing their strengths and limitations in predicting Bitcoin prices.


Exploration of Problems and Key Points in Database Design in Software Development

Abstract: Starting from the necessity and principles of database design, this article explores the optimization issues. Firstly, analyze the necessity of database design, elaborating on effective management, maintainability, resource utilization, and running speed; Then, a series of issues in database management were discussed, such as user management, data object design specifications, and overall design ideas; Finally, the optimization issues such as normalization rules, inter table redundancy handling, query optimization, indexing, and transactions were elaborated in detail. In the software development lifecycle, database design is indispensable. Its role is not only to ensure the safety and reliability of data, but also to ensure the overall stability and speed of the system. Strengthening the rationality and optimization of design is the key to improving software quality.


Online Gaming Addiction Among UiTM Puncak Alam Pharmacy Students Amid COVID-19 Pandemic

Abstract: Stress among the university students had been triggered by the long-haul social distancing during the pandemic. Many had turned to playing video games more than usual as a social lifeline that could be potentially addictive. This study aims to evaluate the level of video gaming addiction during COVID-19 pandemic particularly among the UiTM Puncak Alam students. A cross-sectional study design with self-administered online survey was used to achieve the targeted objectives. The survey included several sections such as frequency of playing video games and 7-Item Game Addiction Scale (GAS). More than half of the cohort were females (60%) with average age of 23.46 ± 3.64 years. The findings showed that on average, these video gamers played approximately 2.33 ± 1.83 hours daily or 3.82 ± 2.04 days weekly. This study revealed that the prevalence of pathological and excessive gamers was low among the pharmacy students even during the pandemic restriction.


Youtube Video as A Media to Improve English Vocabulary Mastery of Children Aged 4 – 6 Years at Bina Jaya Kindergarten

Abstract: This research is classroom action research (PTK). This research aims to improve English vocabulary mastery through video media at Bina Jaya Kindergarten to make it even better. The subjects of this research were 13 Bina Jaya Kindergarten children consisting of 8 boys and 5 girls. This research was conducted at Bina Jaya Kindergarten Jl.protokol, Sawit Seberang, Langkat, North Sumatra. The data collection technique is carried out directly and is adapted to the observation format. The instrument used is observation of children in the process of learning to introduce English vocabulary through the use of Youtube video media and documentation. Based on the research results, it shows that with action, there is an increase in the pre-cycle percentage of student's learning achievements reaching 30.76% with the MB criteria. The percentage of children's learning achievement increased in cycle 1 when the percentage reached 69.71%. This percentage increased by 38.95% compared to the percentage in the pre-cycle. An increase also occurred in cycle 2 where the percentage of children's achievements reached 79.81%, an increase of 10.1% compared to the percentage in cycle 1. From the research results, it can be concluded that Youtube video media can improve the ability to master English vocabulary by listening to English vocabulary. , pronounce English vocabulary, mention English vocabulary and write English vocabulary.


Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS

Abstract: Purpose: Fraud is rampant in the current era, especially in the era of technology where there is now easy access to a lot of information. Therefore, everyone needs to be able to sort out whether the information received is the right information or information that is fraudulent. In this research, the process of classifying messages including ham or spam has been carried out. The purpose of this research is to be able to build a model that can help classify messages. The purpose of this research is also to determine which machine learning method can accurately and efficiently perform the ham or spam classification process on messages.Methods: In this research, the ham or spam classification process has been using machine learning methods. The machine learning methods used are the classification process with Random Forest, Logistic Regression, Support Vector Classification, Gradient Boosting, and XGBoost Classifier algorithms. Results: The results obtained after testing in this study are the classification process using the Random Forest algorithm getting an accuracy of 97.28%, Logistic Regression getting an accuracy of 94.67%, with Support Vector Classification getting an accuracy of 97.93%, and using XGBoost Classifier getting an accuracy of 96.47%. The best precision value obtained in this study is 98% when using the random forest algorithm. The best recall value is 94% when using the SVC algorithm. While the best f1-score value is 95% when using the SVC algorithm.Novelty: This research has been compared with several algorithms. In previous research, it is still very rarely done using XGBoost to classify the ham or spam in messages. We focus on giving brief information based con comparison algorithm and show the best algorithm to classify classify the ham or spam in messages. And for the novelty that exists from this research, the machine learning model built gets better accuracy when compared to previous research.


Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies

Abstract: Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.


Indonesian News Text Summarization Using MBART Algorithm

Abstract: Purpose: Technology advancements have led to the production of a large amount of textual data. There are numerous locations where one can find textual information sources, including blogs, news portals, and websites. Kompas, BBC, Liputan 6, CNN, and other news portals are a few websites that offer news in Indonesian. The purpose of this study was to explore the effectiveness of using mBART in text summarization for Bahasa Indonesia.Methods: This study uses mBART, a transformer architecture, to perform fine-tuning to generate news article summaries in Bahasa Indonesia. Evaluation was conducted using the ROUGE method to assess the quality of the summaries produced.Results: Evaluation using the ROUGE metric showed better results, with ROUGE-1 of 35.94, ROUGE-2 of 16.43, and ROUGE-L of 29.91. However, the performance of the model is still not optimal compared to existing models in text summarization for another language.Novelty: The novelty of this research lies in the use of mBART for text summarization, specifically adapted for Bahasa Indonesia. In addition, the findings also contribute to understanding the challenges and opportunities of improving text summarization techniques in the Indonesian context.


YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions

Abstract: Purpose: The purpose of this research is to detect vehicle types in various image conditions using YOLOv8n, YOLOv8s, and YOLOv8m with augmentation.Methods: This research utilizes the YOLOv8 method on the DAWN dataset. The method involves using pre-trained Convolutional Neural Networks (CNN) to process the images and output the bounding boxes and classes of the detected objects. Additionally, data augmentation applied to improve the model's ability to recognize vehicles from different directions and viewpoints.Result: The mAP values for the test results are as follows: Without data augmentation, YOLOv8n achieved approximately 58%, YOLOv8s scored around 68.5%, and YOLOv8m achieved roughly 68.9%. However, after applying horizontal flip data augmentation, YOLOv8n's mAP increased to about 60.9%, YOLOv8s improved to about 62%, and YOLOv8m excelled with a mAP of about 71.2%. Using horizontal flip data augmentation improves the performance of all three YOLOv8 models. The YOLOv8m model achieves the highest mAP value of 71.2%, indicating its high effectiveness in detecting objects after applying horizontal flip augmentation. Novelty: This research introduces novelty by employing the latest version of YOLO, YOLOv8, and comparing its performance with YOLOv8n, YOLOv8s, and YOLOv8m. The use of data augmentation techniques, such as horizontal flip, to increase data variation is also novel in expanding the dataset and improving the model's ability to recognize objects.


Literasi Digital Bijak Bermedia Sosial Di Desa Kaliangkrik

Abstract: Wise literacy in social media is a critical aspect in dealing with the complexity of online interactions in the digital era. A deep understanding of social media-platform use and the ability to critically evaluate information is at the core of this literacy. Individuals who have wise social media literacy can filter out inaccurate information, understand the social, cultural and political impacts of the content they consume or share. The importance of wise social media literacy also involves responding to disinformation, where individuals can effectively avoid the spread of fake or manipulative news. Ethical aspects of communication are also a focus, with this literacy teaching individuals to promote healthy dialogue and respect diversity of opinion, forming a more positive and inclusive online environment. This research aims to find out how the pattern of community service is a manifestation of the tridharma of higher education related to aspects of community service. In implementing the community service program that has been prepared previously, the method used is increasing knowledge by means of counseling or socialization with the aim of providing understanding and wise skills in digital literacy from the extension team. Apart from that, wise social media literacy also considers online security and privacy risks. Users need to be able to manage and protect their personal information, avoid threats such as fraud, and carefully guard their digital identity. This is not only an individual skill, but also a contribution to the sustainability and security of the global digital environment. By mastering social media literacy, individuals can better navigate the complexities of social media, participate positively, and make informed and wise decisions in using these platforms. Overall, smart social media literacy is an important foundation for forming individuals who are able to interact wisely in an ever-growing digital world. By combining an understanding of content, communication skills, and awareness of security risks, wise social media literacy helps shape an intelligent, ethical, and empowered online society.


Understanding Analytical Exposition Text Writing with an SFL Approach

Abstract: This research aims to explore the ability of eleventh-grade students to create analytical exposition texts based on their generic structure and language features, using a descriptive qualitative research approach. The study focuses on comparing model texts with students' writing to describe the types of Systemic Functional Linguistics (SFL) approaches employed and analyze the transitivity in writing analytical exposition. The participants of this study are eleventh-grade students selected based on a teacher's recommendation. The researcher reports the natural phenomena without applying any treatments that could influence the results. The primary objective is to understand the student’s ability to produce analytical exposition texts without intervention. The data collection involves analyzing sentences in the form of students' analytical exposition text assignments provided by the teacher. The researcher examines the clarity of ideas and evaluates the grammatical and generic structure present in the students' writing. This research utilizes a descriptive qualitative approach to capture and describe the natural phenomenon of students' analytical exposition text writing. The findings contribute to understanding the students' proficiency in creating analytical exposition student texts, as well as identifying areas that require further attention in instructional practices.


Code Smell Detection and Software Refactoring Research: A Systematic Literature Review

Abstract: Today we are experiencing rapid enhancements in software systems and their development. The software industry's demand for tools and techniques for software development, especially automatic and less time-consuming, is increasing daily. Software refactoring and code smell detection are now expanded from code-level changes to the architecture, model, and requirements restructuring. We are moving from an object-oriented paradigm towards cloud computing, web development and mobile application development and so much more. Therefore, code smell and refactoring techniques are talk of the town in various research communities in their objectives. In this paper we will study the existing tools and techniques, research progress by doing a systematic literature review in the field of code smell detection and software refactoring’s. We will also classify the existing research techniques, identify the trends in code smell detection and refactoring and try to highlight the gaps in the area for researchers.


Comunidades criativas na Covid-19: Um olhar para a tecnologia como meio de articulação de soluções habilitantes

Abstract: This article reflects on the importance of design today, especially with regard to social aspects.In view of this, emphasis is placed on the impact of technological transformations -through Web 2.0 -as a mobilizer of economic, political and social transformations, enabling both positive and negative effects.Evidencing the beneficial aspects of the online universe, it is observed that technological advances can help in the development of creative solutions and drivers of change, even more so in crisis contexts.Taking these factors into account, it was decided to study the period of the Covid-19 pandemic, which was one of the main health crises of the century, and it was possible to conclude that online platforms were a motivating means in the maintenance and development of Creative Communities that sought innovative solutions to problems faced at that time.


Efektivitas Model Pembelajaran Quantum Teaching Terhadap Keterampilan Menulis Cerita Dongeng Siswa Kelas III SDN Kecil To’pae Kabupaten Enrekang

Abstract: This research aims to determine the effectiveness of using the Quantum Teaching model on the fairy tale writing skills of class III students at SDN Kecil Topae, Enrekang Regency. This research is pre-experimental researchin the form of One Group Pretest-Posttest research. The population of this research is class III students at SDN Kecil To’pae in Enrekang Regency which consists of one class, namely 9 students. The research instrument used was a fairy tale writing test. The data obtained was then analyzed using descriptive statistical data analysis and inferential statistical data analysis. The result of this research show that the pretest result show that the average student learning score is 58,3 with categories, very high 0%, high 33,33%, medium 33,33%, low 33,33% and very low 0%. And the average score for posttest learning outcomes after using Quantum Teaching is 84,44 with categories very high 66,66%, high 33,33%, medium 0%, low 0%, very low 0%. These variations show that there is a significant influence of using the effectiveness Quantum Teaching learning model on fairy tale writing skills. Based on the description above, it can be concluded that the Quantum Teaching learning model is effectively used in learning to write fairy tales in class III students at SDN Kecil To’pae, Enrekang Regency.


Sistem Informasi Akuntansi Penjualan Tunai Dan Penerimaan Kas Berbasis Multiuser Pada Toko Harnova Tanggungharjo

Abstract: In the modern and instant era of globalization, companies are required to be able to compete in improving performance and be able to produce appropriate reports. In a trading company, sales are the main activity to generate profit. Good sales and recording procedures and in accordance with company needs can be carried out with a sales accounting system. Sales themselves are divided into two, namely cash sales and credit sales. In this study, the intended sales accounting system is the cash sales accounting system.This research was conducted at the Harnova Tanggungharjo Store, where the Harnova Store is a company engaged in the sale of clothes. Cash sales and cash receipts are very important in an agency, because cash is an important asset. In recording sales and cash receipts, this agency still uses manuals. A computer is a tool that can store data, process data and provide useful information for companies to advance their companies.This study designs a system that can help companies calculate sales and cash receipts. With the cash sales information system and cash receipts, it is expected to be able to carry out procurement, planning and sales supervision very well. besides that it can also produce reports that are precise, accurate and efficient. The software that I use is Microsoft Visual Basic 6.0 with SQL Server 2000 database


Web-Based Tourism And Culture Information System Using Google Api At The Blora Regency Tourism And Culture Office

Abstract: With the increasingly rapid development of computer technology, internet media is a very effective means of conveying information. Through the internet, various kinds of information from various fields can be obtained quickly. The internet has become an important part as a forum or promotional medium. With the advantages of the internet, the author tries to provide information about the existence of tourism and culture in Blora so that the people of Blora city in particular and people outside the city of Blora in general know it easily and save time. The design of this system was made through several stages. The supporting software used is Apache, Macromedia Dreamweaver MX and PHP as server side-scripting and the final result of this research is a website that provides information about Tourism and Culture of Blora Regency.


ANALYSIS OF THE INFLUENCE OF PSYCHOSOCIAL FACTORS ON JOB SATISFACTION OF TEACHERS

Abstract: Labor protection and health of people is a state task, therefore, in accordance with Russian legislation, everyone has the right to work in conditions that meet safety and hygiene requirements. The Constitution of the Russian Federation guarantees not only care for the life and health of workers, but also establishes the right to time intended for rest. The dynamic pace of modern life dictates its own conditions and requires high levels of mental and physical strength from people in different professions. In our opinion, pedagogical activity is especially filled with tense situations. In particular, the work of educators, which is associated with daily communication with children, their parents, work colleagues and management. A wide range of communication, relationships in a team, a variety of tasks and the intensity of work have a significant impact on the emotional state of teachers. Constant stress at work can lead to depression, poor health, low satisfaction with the profession and a formal attitude towards work, which is unacceptable when working with children. The purpose of the work is to analyze the psychosocial risks of preschool education that influence job satisfaction.


The landscape of cyber security education and training programmes: The case of France

Abstract: This chapter delves into the provision of cyber security education and training programmes in France. It focuses on examining the characteristics of the education and training programmes that lead to entry-level jobs in the cyber security field. Particular attention is given to strategies and initiatives aimed at diversifying enrolment, especially in bringing more females into cyber security field. The chapter also highlights efforts to increase employers' involvement in the design and delivery of cyber security learning opportunities, and to give visibility to cyber security in education and training programmes.


Optimizing Treatment of Herbal Plant Using SOPHERBAL Android Application Fordward Chaining Method

Abstract: The utilization of traditional herbal medicine among the inhabitants of Lombok is notably prevalent yet frequently hindered by a lack of comprehension regarding the efficacy of herbal remedies for specific ailments. Addressing this challenge, this study proposes the development of an Android application called ”sopherbal,” aimed at delivering personalized herbal plant recommendations via easily accessible mobile devices. Employing forward chaining methodology, the application identifies optimal herbal remedies based on ailment type, processing techniques, usage instructions, and recommended dosage and treatment duration. Notably, while effective in this context, the forward chaining approach entails certain trade-offs and hurdles. Previous research indicates that forward chaining facilitatesaccurate recommendation generation, and it may be constrained by its reliance on predefined rules and limited adaptability to complex, evolving scenarios. Despite these challenges, the ”sopherbal” application, featuring 50 Sasak medicinal plants curated for 15 common ailments, achieved an 86% validation rate, affirming its efficacy in bridging the gap between traditional herbal knowledge and modern healthcare needs.


Exploratory Analysis of Different Types of Adjectives for Sentiment Classification

Abstract: Online websites available on the internet, Amazon, provide a platform for users to share their valuable opinions. Users’ reviews are available in thousands, and extracting useful information from these reviews manually is a crucial task for managers, companies, and users. Reviews are written in natural language, and to extract useful information from the reviews, there is a need for an automatic technique known as sentiment analysis. Researchers have used polarity features like nouns, adjectives, verbs, and adverbs to mine sentiment using one feature or combination. The studies showed that adjectives remain the most prominent feature. However, previous research has not evaluated many types on a comprehensive dataset. This research focuses on the identification of different kinds of adjectives in a given text, the identification of the best type and the classification of different kinds as positive, negative, and neutral. Understanding the nuanced impact of various adjectives in sentiment classification is crucial for developing more accurate and context-aware natural language processing models, essential for applications ranging from sentiment analysis to customer reviews. We comprehensively evaluate different types of adjectives on machine learning algorithms. The experimental results are performed on an annotated dataset of 58,258 office product reviews collected from Amazon. Out of these evaluated adjectives, opinion adjectives have the highest precision of 0.931 on the Naïve Bayes classifier and show the best sentiments.


Nexus Between Technology, Education, Income, and Social Capital in Top 10 Terrorism Affected Countries: The Leading Role of Corruption

Abstract: There is a strong correlation between the expansion of institutions in nations with advanced educational systems and an increase in the frequency and severity of terrorist attacks. This trend remains substantial even after accounting for factors like per capita income, political stability, unemployment, inequality, and urbanization. This is when the frequency and severity of terrorist attacks appear to be at their highest. Therefore, this study analyzes the impacts of terrorism on education in the top 10 terrorist countries between 2010 and 2020. This study considers the determinants of education (EDU): GDP per capita, terrorism, social capital, technology and physical capital. The study employs FMOLS estimation and the DOLS test to check the results of variables in this study. The effect of GDP per capita, social capital, and physical capital significantly positively impact education in this model. However, terrorism and technology have a negative impact on education. Moreover, the moderate effect of terrorism on social capital, technology and physical capital is negatively related to education. On behalf of the results, this study proposes how a country can maximize education.


Corruption in Arab countries: a grease or sand in the wheel of economic growth? a panel smooth transition regression approach

Abstract: The relationship between corruption and economic growth has long been dealt with in the literature. Yet, no consensus has been drawn about the way how corruption might affect economic growth. This paper aims at revisiting the relationship between corruption and growth in the context of Arab countries using a panel threshold model with smooth transition (PSTR). The study covers 14 Arab countries over a period from 2007 to 2017. Our results show that the institutional quality variable divide the sample into two regimes. In the first regime (low institutional quality), reducing corruption risk hinders economic growth. The second regime (high institutional quality) stipulates that a lower risk of corruption will have a positive effect on economic growth. The results on a country basis reveal two heterogenous groups and one homogenous group in terms of hydrocarbons endowment: (1) Algeria, Egypt, Jordan, Lebanon Sudan ,Syria and UEA are in favor of “grease in the wheels” hypothesis, (2) Bahrain, Kuwait, Morocco and Tunisia demonstrate “the sand in the wheel” hypothesis and (3) Oman, Saudi Arabia and Qatar showed a strong support toward “the sand in the wheel” hypothesis.


DocFlow: A Visual Analytics System for Question-Based Document Retrieval and Categorization

Abstract: A systematic review (SR) is essential with up-to-date research evidence to support clinical decisions and practices. However, the growing literature volume makes it challenging for SR reviewers and clinicians to discover useful information efficiently. Many human-in-the-loop information retrieval approaches (HIR) have been proposed to rank documents semantically similar to users' queries and provide interactive visualizations to facilitate document retrieval. Given that the queries are mainly composed of keywords and keyphrases retrieving documents that are semantically similar to a query does not necessarily respond to the clinician's need. Clinicians still have to review many documents to find the solution. The problem motivates us to develop a visual analytics system, DocFlow, to facilitate information-seeking. One of the features of our DocFlow is accepting natural language questions. The detailed description enables retrieving documents that can answer users' questions. Additionally, clinicians often categorize documents based on their backgrounds and with different purposes (e.g., populations, treatments). Since the criteria are unknown and cannot be pre-defined in advance, existing methods can only achieve categorization by considering the entire information in documents. In contrast, by locating answers in each document, our DocFlow can intelligently categorize documents based on users' questions. The second feature of our DocFlow is a flexible interface where users can arrange a sequence of questions to customize their rules for document retrieval and categorization. The two features of this visual analytics system support a flexible information-seeking process. The case studies and the feedback from domain experts demonstrate the usefulness and effectiveness of our DocFlow.


WRIST: Wideband, Real-Time, Spectro-Temporal RF Identification System Using Deep Learning

Abstract: RF emissions' detection, classification, and spectro-temporal localization are essential not only for understanding, managing, and protecting the radio frequency resources, but also for countering today's security threats such as jammers. Achieving this goal for wideband, real-time operation remains challenging. In this paper, we present WRIST, a Wideband, Real-time, Spectro-Temporal RF Identification system. WRIST can detect, classify, and precisely locate RF emissions in time and frequency using RF samples of 100 MHz spectrum in real-time. The system leverages an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-stage object detection</i> Deep Learning framework, and transfer learning to a multi-channel visual-based spectral representation. Towards developing WRIST, we devised an iterative training approach which leverages synthesized and augmented RF data to efficiently build a large dataset with high-quality labels. WRIST achieves over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$99 \%$</tex-math></inline-formula> class detection accuracy, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$94 \%$</tex-math></inline-formula> emission precision and recall, with less than 0.08 bandwidth and time offset ratios in a large anechoic chamber over-the-air environment. In the extremely congested in-the-wild environment, WRIST still achieves over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$80 \%$</tex-math></inline-formula> precision and recall. WRIST currently supports five 2.4 GHz technologies (Bluetooth, Lightbridge, Wi-Fi, XPD, and ZigBee) and is easily extendable to others. We are making our curated dataset available to the whole community. It comprises over 10 million labelled RF emissions from off-the-shelf wireless radios spanning the five classes of technologies.


The importance Markov chain

Abstract: The Importance Markov chain is a novel algorithm bridging the gap between rejection sampling and importance sampling, moving from one to the other through a tuning parameter. Based on a modified sample of an instrumental Markov chain targeting an instrumental distribution (typically via a MCMC kernel), the Importance Markov chain produces an extended Markov chain where the marginal distribution of the first component converges to the target distribution. For example, when targeting a multimodal distribution, the instrumental distribution can be chosen as a tempered version of the target which allows the algorithm to explore its modes more efficiently. We obtain a Law of Large Numbers and a Central Limit Theorem as well as geometric ergodicity for this extended kernel under mild assumptions on the instrumental kernel. Computationally, the algorithm is easy to implement and preexisting librairies can be used to sample from the instrumental distribution.


A Scalable Method for Readable Tree Layouts

Abstract: Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or distances) that need to be communicated to the viewers. Yet, scalable, easy to read tree layouts are difficult to achieve. We consider tree layouts to be readable if they meet some basic requirements: node labels should not overlap, edges should not cross, edge lengths should be preserved, and the output should be compact. There are many algorithms for drawing trees, although very few take node labels or edge lengths into account, and none optimizes all requirements above. With this in mind, we propose a new scalable method for readable tree layouts. The algorithm guarantees that the layout has no edge crossings and no label overlaps, and optimizes one of the remaining aspects: desired edge lengths and compactness. We evaluate the performance of the new algorithm by comparison with related earlier approaches using several real-world datasets, ranging from a few thousand nodes to hundreds of thousands of nodes. Tree layout algorithms can be used to visualize large general graphs, by extracting a hierarchy of progressively larger trees. We illustrate this functionality by presenting several map-like visualizations generated by the new tree layout algorithm.


Collective Decision of One-vs-Rest Networks for Open-Set Recognition

Abstract: Unknown examples that are unseen during training often appear in real-world pattern recognition tasks, and an intelligent self-learning system should be able to distinguish between known examples and unknown examples. Accordingly, open-set recognition (OSR), which addresses the problem of classifying knowns and identifying unknowns, has recently been highlighted. However, conventional deep neural networks (DNNs) using a softmax layer are vulnerable to overgeneralization, producing high confidence scores for unknowns. In this article, we propose a simple OSR method that is based on the intuition that the OSR performance can be maximized by setting strict and sophisticated decision boundaries that reject unknowns while maintaining satisfactory classification performance for knowns. For this purpose, a novel network structure, in which multiple one-vs-rest networks (OVRNs) follow a convolutional neural network (CNN) feature extractor, is proposed. Here, an OVRN is a simple feedforward neural network that is designed to assign confidence scores that are lower than those in the softmax layer to unknown samples so that unknown samples can be more effectively separated from known classes. Furthermore, the collective decision score is modeled by combining the multiple decisions reached by the OVRNs to alleviate overgeneralization. Extensive experiments were conducted on various datasets, and the experimental results show that the proposed method performs significantly better than the state-of-the-art methods by effectively reducing overgeneralization. The code is available at https://github.com/JaeyeonJang/Openset-collective-decision.


ViTPose++: Vision Transformer for Generic Body Pose Estimation

Abstract: In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model dubbed ViTPose. ViTPose employs the plain and non-hierarchical vision transformer as an encoder to encode features and a lightweight decoder to decode body keypoints in either a top-down or a bottom-up manner. It can be scaled to 1B parameters by taking the advantage of the scalable model capacity and high parallelism, setting a new Pareto front for throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, and pre-training and fine-tuning strategy. Based on the flexibility, a novel ViTPose++ model is proposed to deal with heterogeneous body keypoint categories via knowledge factorization, i.e., adopting task-agnostic and task-specific feed-forward networks in the transformer. We also demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Our largest single model ViTPose-G sets a new record on the MS COCO test set without model ensemble. Furthermore, our ViTPose++ model achieves state-of-the-art performance simultaneously on a series of body pose estimation tasks, including MS COCO, AI Challenger, OCHuman, MPII for human keypoint detection, COCO-Wholebody for whole-body keypoint detection, as well as AP-10K and APT-36K for animal keypoint detection, without sacrificing inference speed.


Estimation of monthly global solar radiation over twelve major cities of Libya

Abstract: • 7 empirical models are compared for estimation of solar radiation over Libya. • The models are established using measured data of global solar radiation. • Statistical indicators are calculated to evaluate the performance of the models. • The best model performance for each site is determined. • The results show that there is no unique model valid for all considered sites. This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.


PEMANFAATAN INTERNET OF THINGS PADA SMARTHOME DENGAN MODEL SIMULASI PROTOTYPE

Abstract: Internet of Things has been a recent focus in technology discussion. This tecnology enables every devices we own to connect to the internet, allowing remote control throught smartphones or even voice commands. The development of iot extends beyond large industries like power grids and factories, reaching into everyday household electronics. For instance, light can now be connected to the internet. The final project involves designing and creating an internet controlled relay system with comamands sent throught a telegram bot. This project aims to implement iot tecnology in a simple and affordable manner. The control system utilizes the nodeMCU microcontroller with a two -channel relay output to remote turn light on and off via telegram.


The Implementation of Bottom-Up in Reading Comprehension for Vocational School Students

Abstract: This research aims to investigate the improvement reading comprehension of SMK PGRI 2 Mojokerto by using Bottom-Up approach in reading comprehension of recount text. The research was conducted in two Cycles of Classroom Action Research (CAR) where each Cycle was conducted in 4 (four) phases, they were: planning, action, observation, reflection. Each Cycle was applied in (four) meetings. The subjects of the research were 35 students of tenth grade (X-C Mechatronics). In collecting data, the researcher applied three instruments: observation, questionnaire and reading test. There were 2 (two) observations in field note which conducted by the researcher: the first observation which was done before implementing Bottom-Up to find the problem during reading activity in English lesson. Meanwhile, the second observation was to find the improvement in having Bottom-Up that conducted by English teacher during the approach applied. The questionnaire was delivered to students in the fourth meeting of the second Cycle to find out the responses of the students in learning reading by using Bottom-Up. The data was analyzed through descriptive and statistical method. In this research, the researcher acted as a teacher and the English teacher as an observer. The research findings showed that the students reading comprehension improved from Cycle I to Cycle II. Furthermore, the data from the observation and questionnaire indicated that the majority of the students gave positive response toward the use of Bottom-Up approach in teaching reading. It emphasized that the approach could improve students’ ability in reading, effective, interesting, and grab the students’ enthusiasm in reading activity.


Detecting Shadows in Computer Vision: A MATLAB-Based Approach

Abstract: The abstract outlines a novel MATLAB-based technique for enhancing shadow detection accuracy in computer vision applications. This MATLAB-based method uses thresholding and color analysis to improve the accuracy of shadow identification. Luminance (L*) is distinguished from chromaticity (a* and b*) in digital photographs by converting them into the Lab color space. This makes it easier to identify possible shadowed areas. The luminance channel's thresholding makes it possible to distinguish between regions that are well-lit and those that are shadowed. The computation of chromaticity distance also helps in detecting modest color changes that are suggestive of shadows. The culmination of the suggested technique is the creation of a binary shadow mask, which isolates the image's shaded regions. The research addresses limitations of existing approaches by maintaining robustness in diverse lighting conditions and complex scenarios, thereby enhancing image comprehension and the precision of shadow detection. The proposed technique holds promise for improving object recognition and scene analysis in computer vision applications, offering a robust and reliable solution for identifying and isolating shadows in digital images.


CRIISL: Convert Rasterize Image Into SVG Layers

Abstract: Raster images in multiple layers or vector format can be challenging for digital artists. However, converting them into Scalable Vector Graphics (SVG) format, such as Adobe Illustrator, Inkscape, or CorelDRAW, offers advantages. SVG files allow for easy resizing without loss of quality, making them ideal for creating logos, icons, and other graphics that need to be scaled to different sizes. They are lightweight and can be easily edited using code, providing more control over design elements. This versatile and flexible tool streamlines the design process and provides greater flexibility for artists in their creative projects. SVG files are smaller in size compared to raster images, making them easier to work with and share. Developers can also use code-based approaches to further manipulate and customize SVG images as needed.


Review on green resources and AI for biogenic solar power

Abstract: The need for clean and renewable energy has grown dramatically during the past few years. As potential candidates for producing green energy in this region, photovoltaic and bio-solar energy technologies have arisen. This review presents a novel approach for designing and developing photovoltaics and bio-solar cells using eco-friendly materials and artificial intelligence (AI) techniques. An intriguing architecture is outlined for a bio-solar cell that fuses photovoltaic electronics with photosynthetic organisms. A recyclable thin-film solar cell serves as the basis of our photovoltaic system. To further maximize the effectiveness of the device, we use AI algorithms. According to statistical calculations, the proposed bio-solar cell can produce a sizable amount of electricity while being ecologically sound. This paper outlines significant advances in developing solar cells and photovoltaics using green nanomaterials and AI, which provide exciting potential for improving energy harvesting capacity. This review also presents an overview of the effects of the potential commercialization of our strategy, its social and environmental benefits, and its pitfalls.


Efforts to improve nutritional knowledge in adolescent girls through contemporary video-based education

Abstract: Adolescent girls belong to a vulnerable age group that experiences nutritional problems with various risks of health problems such as anemia, chronic energy deficiency, and overnutrition. Increasing the nutritional knowledge of adolescent girls can be an opportunity to improve their nutritional status throughout the initial 8000 days of life. This community service activity uses contemporary video media to expand the knowledge of adolescent girls related to nutrition. Community service is integrated with life-cycle nutrition courses. The target audience was 30 adolescent girls. This activity is located at SMA Negeri 4 Palembang for 1 month. The activity method was carried out by designing and making contemporary videos by 3rd-semester nutrition students, followed by distributing educational videos to participants. Feedback from participants as well as pre- and post-test results were used in the evaluation process. Results from community service projects show that 30% of targets have anemia based on Hb examination with the GC-Hb tool, and 30% of targets have malnutrition based on IMT/U assessment. The target's knowledge increased after providing education with contemporary videos (p0.0001). After completing this community service project, it was concluded that the knowledge of adolescent girls increases by providing contemporary video-based nutrition education.


Developing community technology: A profile website for SDI Miftahus Sholihin

Abstract: The Community Service Program titled "Developing Community Technology: A Profile Website for SDI Miftahus Sholihin" aims to address various challenges associated with school accreditation through the utilization of information and communication technology (ICT). The program's objectives are to enhance the quality of education and facilitate better access to information. It entails the design, development, and management of a profile website for SDI Miftahus Sholihin, alongside training sessions focused on website management and promotion strategies. These efforts are geared towards achieving improvement targets in school accreditation. The program timeline spans six months, encompassing phases of preparation, coordination, website development, training, and evaluation. Anticipated outcomes include heightened information quality and accessibility pertaining to school-related matters, as well as the enhancement of website management skills among school staff. Ultimately, the program aspires to elevate the educational quality of SDI Miftahus Sholihin, foster greater community engagement in educational initiatives, and facilitate the attainment of higher accreditation standards for the school.


Microcontroller based food dehydrator for Rengginang crackers micro businesses in Prambontergayang Village, Tuban District

Abstract: The micro-business of Rengginang crackers with the Urnika brand in Prambontergayang Village, Tuban District, is an industry in the food sector. The process of drying Rengginang crackers in partner’s still uses conventional methods or under sunlight to carry out the drying process. Hence, it takes quite a long time to dry Rengginang, namely around 6-7 days. This community service program aims to develop and train the use of alternative technology for drying Rengginang crackers to Rengginang crackers micro-entrepreneurs in Prambontergayang Village, Tuban District. The method used for implementing PKM is a participatory approach (participatory action research/PAR). The results of the PKM carried out have had a positive impact on partners, namely more optimal and efficient drying. Drying using a microcontroller-based food dehydrator can speed up drying up to 120 minutes with a capacity of 5000 pcs of wet Rengginang. With this technology, partners can increase their production capacity for Rengginang crackers so that they can increase the expected number of sales.


Online Learning Attitude, Engagement, and Academic Achievement of College Students in China

Abstract: With the rapid development of online education, understanding the performance and experiences of college students in this emerging learning mode is of paramount importance for improving educational quality. This study aims to delve into the close relationship between Chinese university students’ learning attitudes, engagement, and academic performance in the context of online learning environments. Employing a questionnaire survey and quantitative analysis, this study explores the intricate relationships between learning attitudes, engagement, and academic performance among Chinese university students engaged in online learning.


Scalar fields features of video sequences energy characteristics

Abstract: To improve the methods of spatio-temporal filtering, an approach based on the analysis of finitary images energy spectra can be used. The purpose of this work is to study the possibility of using energy spectra for motion identification in video sequence frames. Each frame of a video sequence is a spatially finite image, the energy spectrum of which contains information about the phase components. This information is of particular importance for determining the location of dynamic objects. The most important information is also contained in the harmonic amplitude increments of the frame energy spectra, i.e. the energy spectrum generates a scalar discrete field. The energy functions describe inter-frame changes in the amplitudes of harmonics of the energy spectrum. To apply energy spectra in motion identification algorithms, it is necessary to analyze the properties of these scalar fields. Scalar fields generated by energy spectra of frames of test video sequences are studied. Test video sequences with an object moving in an arbitrary direction are used. Scalar fields of energy functions are presented graphically. Characteristic regions of scalar fields are identified and their projections are built using isolines. The boundaries of the characteristic areas and the coordinates of extrema of energy functions are determined. From them, all coordinates of the dynamic object and its horizontal/vertical displacements can be found. The dependence of phase-energy function extrema on the position of the object before the motion and on the direction of motion is studied. When moving in an arbitrary direction, different types of extrema are observed: point and extended. At vertical or horizontal movements, the energy functions graphs have only two-dimensional pulses. Additional criteria for eliminating ambiguities are proposed. The ambiguities are due to the fact that the number and nature of extrema may coincide for different directions of motion and location of objects in different quadrants of the coordinate plane. The extrema of energy functions for object movements in all possible directions and locations in any quadrant of a video sequence frame are determined. The obtained results showed that energy functions for object motion in arbitrary direction have a large number of characteristic regions containing point and extended extrema. The configuration of extrema depends both on the direction of motion and on the quadrant of the image in which the object is located. Therefore, it is most advisable to begin to determine the spatial coordinates of an object and the magnitude of its horizontal/vertical movement by finding the maximum and minimum boundaries of energy functions, and then refine it using the coordinates of the extrema. Consideration of characteristic regions and extrema of scalar fields of energy functions can be used in the development of algorithms effective in terms of "accuracy/speed" ratio, and can increase the performance of image and video processing systems. Algorithms for determining the object position, velocity and direction of its movement by analyzing scalar fields of energy spectra components can be used as an additional or main tool for motion identification in technical vision systems.


A Study on the Priority Derivation of Implementation Strategies for Resource Management and Operation Technology Development in the Logistics Industry

Abstract: Globally, the industrial structure is changing to respond to digital transformation and climate change, and the logistics industry is also experiencing unprecedented upheaval, away from the existing labor-intensive industry. Currently, 10 high-value-added convergence logistics and delivery infrastructure innovation technology development-related R&D projects are underway under the leadership of the convergence logistics group of the Korea Agency For Infrastructure Technology Advancement to change the status of the domestic logistics industry’s technology development from follower to first mover. However, in this study, a basic study on the priority of implementation strategies for the development of detailed technologies (policy) was conducted based on a survey of logistics experts in relation to the “logistics industry resource management and operation technology development” research project to be additionally carried out. The Importance-Performance Analysis (IPA) results of the average of importance and preparedness were placed in four quadrants to analyze the meaning of each sector’s alternatives, and the need to improve items related to the basic foundation such as private data opening, infrastructure construction, legal system improvement, and standardization was derived in the areas of concentration where the importance was high but the preparation was insufficient. However, this study has limitations such as the composition and method of survey respondents due to limited time and budget, so it is inevitable that research will be advanced through follow-up studies in the future.


Empirical Study on the Impact of Smart Logistics Platform Utilization Capabilities in the Era of the Fourth Industrial Revolution on Data-Driven Demand Forecasting Performance

Abstract: The rapid emergence of Industry 4.0 technologies, such as big data, IoT, and AI, has significantly transformed the distribution and logistics landscape. Smart logistics platforms are increasingly adopted in this sector, leading to improvements in various aspects, including demand forecasting accuracy. This study aims to investigate the mediating effects of data compatibility, real-time risk response, and product history tracking on the relationship between a company’s smart logistics platform utilization capability and its data-driven demand forecasting performance. Drawing upon theoretical insights from existing literature, this study develops a research model and corresponding hypotheses. Operationalization of key constructs is achieved through the development of a structured questionnaire based on established measurement scales. To ensure the representativeness of our sample, the survey is distributed to professionals utilizing smart logistics platforms in domestic distribution and logistics companies. Following a rigorous data collection process, a total of 280 valid responses are retained for further analysis. This study aims to highlight the growing importance of smart logistics platform utilization capability as smart technologies advance. Based on the research results, it presents practical situations in the industrial field and provides policy implications for supporting small and medium-sized enterprises (SMEs) to government agencies and local governments.


Priority and Weight Analysis of Factors for Selection of 3rd Party Logistics Companies by Manufacturing Companies: Focusing on Comparison between Efficient and Responsive Supply Chains

Abstract:
Using AHP, it is to clearly analyze that there is a difference in the importance and weight of the selection criteria factors that should be applied first in the selection of a third-party logistics company depending on the characteristics of the product and supply chain from the viewpoint of Korean manufacturing companies. For this purpose, previous studies that presented factors for selecting third-party logistics companies were reviewed. As a method of integrating the presented selection criteria, 4 items of ‘logistics service’, 3 items of ‘logistics information (IT) system’, 4 items of ‘logistics price (cost)’, 5 selection criteria of 3 items of ‘Corporate Reputation’ and 6 items of ‘Capacity and Infrastructure’ and 20 application items were selected. And using these contents, a survey was conducted on manufacturing companies.
 Survey results As a questionnaire that can be used for AHP analysis, 51 companies in the efficient supply chain group and 35 companies in the reactive supply chain group were collected. The priority and weight of the selection criteria and application items were determined by performing pairwise comparisons on Tier II selection criteria and Tier III application factors, and calculating overlapping weights.
 The priority and importance of the Tier II selection criteria and the Tier III application factors were confirmed, and the weights for the selection criteria and application items that should be applied preferentially were calculated by synthesizing them. In the efficient supply chain group, the importance of stability, service price, image, cost reduction, accuracy, financial stability, and compatibility was high. In the reactive supply chain group, the importance of stability, accuracy, image, service price, information capability, compatibility, and incentives was high. Through these research results, by reflecting the characteristics of product manufacturing and supply chain, selection criteria and application items that should be applied first when manufacturers select third-party logistics companies are clearly presented.


Development of the method for fuzz testing mail services based on an intelligent input parameter mutation

Abstract: This article proposes a method of fuzz testing mail services based on an intelligent input parameter mutation, which is applicable not only to standard email protocols but also to their extended versions. The research examines the main extensions of POP, IMAP, and SMTP protocols. The described method was applied in laboratory conditions to evaluate its effectiveness compared to traditional fuzz testing methods. The results confirmed the possibility of using the method for fuzz testing not only email clients but also servers. Further research is planned to create intelligent methods for evaluating testing coverage in the absence of prior instrumentation of email client and server source code.


Choosing optimal distance between ground-based technical observation posts

Abstract: The article develops an approach to rational video monitoring construction of spatially distributed objects territories. Target. Justification for choice optimal distance between ground technical observation posts (TOP) based on electro-optical surveillance equipment in the visible and thermal imaging ranges. Results. The developed model makes it possible to determine maximum distance between adjacent TOPs, ensuring guaranteed surface targets detection and recognition in standard conditions. Practical significance. The optimal distance between TOPs ensures not only guaranteed quality of monitoring of territory boundaries, but also minimum linear cost of the security line.


"Clustering Algorithm for the Connected Model of Repeated Measurements and Survival Data: Application to HIV Study"

Abstract: Many clinical trials applications generate repeated measurements and time to event (survival data). In longitudinal studies the patients are followed over many occasions (repeated measurements) and their data indicates biomarkers. Sometimes these longitudinal data include time to event, for example time to death, Alzahrani [1].


Divisibility of spheres with measurable pieces

Abstract: For an $r$-tuple $(\gamma\_1,\ldots,\gamma\_r)$ of special orthogonal $d\times d$ matrices, we say that the Euclidean $(d-1)$-dimensional sphere $\mathbb S^{d-1}$ is $(\gamma\_1,\ldots,\gamma\_r)$-divisible if there is a subset $A\subseteq \mathbb S^{d-1}$ such that its translations by the rotations $\gamma\_1,\ldots,\gamma\_r$ partition the sphere. Motivated by some old open questions of Mycielski and Wagon, we investigate the version of this notion where the set $A$ has to be measurable with respect to the spherical measure. Our main result shows that measurable divisibility is impossible for a "generic" (in various meanings) $r$-tuple of rotations. This is in stark contrast to the recent result of Conley, Marks and Unger which implies that, for every "generic" $r$-tuple, divisibility is possible with parts that have the property of Baire.


Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment

Abstract: Fault detection or anomaly detection relies heavily on learning from datasets where only normal samples are available, resulting in the emergence of numerous one-class classification (OCC) methods. However, learning discriminative deep representatives with good generalization from cross-domain positive samples remains challenging. Therefore, this work proposes an end-to-end framework, deep transfer one-class classification (DTOCC) for unsupervised fault detection, which combines adversarial generative OCC and distribution alignment from the perspective of manifold learning. Specifically, pseudo-negative samples are generated outside the positive manifold, facilitating the model to learn discrimination with respect to normal and anomaly. Further, cross-domain positive samples are aligned in log-Euclidean manifold space to enhance representation learning. Then, we provide the specific implementations for fault detection and validate its superiority through case studies on multi-class and run-to-failure datasets, simulating both offline and online scenarios.


A Semisupervised Approach for Industrial Anomaly Detection via Self-Adaptive Clustering

Abstract: With the rapid development of the Industrial Internet of Things (IIoT), log-based anomaly detection has become vital for smart industrial construction that has prompted many researchers to contribute. To detect anomalies based on log data, semi-supervised approaches stand out from supervised and unsupervised approaches because they only require a portion of labeled data and are relatively stable. However, the state-of-the-art semi-supervised approaches still suffer from two main problems: manual parameter setting and unsatisfactory performance with high false positives. We propose AdaLog, an integrated semi-supervised approach based on self-adaptive clustering, for industrial anomaly detection. In particular, the clustering step performs automatic label probability estimation by distinguishing twelve situations so that the label probability of each unlabeled data can be carefully calculated, leading to high accuracy. In addition, AdaLog employs a pre-trained model to learn contextual information comprehensively and a transformer-based model to detect anomalies efficiently. To alleviate class imbalance, an undersampling method is incorporated. The results on three popular datasets demonstrate that AdaLog significantly outperforms three state-of-the-art semi-supervised approaches by 17.8%–2,489.8% on average in terms of F1-score, and is even superior to two supervised approaches in most cases with average improvements of 10.9%–23.8%.


Reconstruction and Preservation of Feature Curves in 3D Point Cloud Processing

Abstract: Given a 3D point cloud, we propose a method for suitably resampling the cloud while reconstructing and preserving the feature curves to which some points are identified to belong. The first phase of our strategy enriches the cloud by approximating the curvilinear profiles outlined by the feature points with piece-wise polynomial parametric space curves through the use of the Hough transform. The second phase describes how the removal of a point or its insertion can be performed without affecting the approximated profiles and preserving the enriched structure of the cloud. The combination of the two steps provides multiple possibilities for processing a point cloud by varying its size or improving its density homogeneity without affecting the retrieved feature curves. The various capabilities of our approach are investigated to produce simplification, refinement, and resampling techniques whose effectiveness is evaluated through experiments and comparisons.


To change or not to change? Modeling software system interactions using Temporal Graphs and Graph Neural Networks: A focus on change propagation

Abstract: The world is quickly adopting new technologies and evolving to rely on software systems for the simplest tasks. This prompts developers to expand their software systems by adding new product features. However, this expansion should be cautiously tackled to prevent the degradation of the quality of the software product. One challenge when modifying code - whether to patch a bug or add a feature- is knowing which components will be affected by the change and amending possible misbehavior. In this context, the study of change propagation or the impact of introducing a change is needed. By investigating how changing one component may impact the functionality of a dependency (another component), developers can prevent unexpected behavior and maintain the quality of their system. In this work, we tackle the change propagation problem by modeling a software system as a temporal graph where nodes represent system files and edges co-changeability, i.e., the tendency of two files to change together. The graph representation is temporal so that nodes and edges can change with time, reflecting the addition of files in the system and changes in dependencies. We then employ a Temporal Graph Network and a Long Short-Term Memory model to predict which other files will be impacted by a modification performed on a file. We test our model on software systems of different functionality, size, and nature. We compare our results to other published work, and our model shows a significantly higher ability to predict files impacted by a change. The proposed approach effectively predicts change propagation in software systems and can guide developers and software engineers in planning the change and estimating the cost in terms of time and money.


Commutative encryption and watermarking algorithm based on compound chaotic systems and zero-watermarking for vector map

Abstract: Commutative encryption and watermarking (CEW) is an efficient and safe protection technology combining cryptography and digital watermarking. It features the dual functions of secure transmission and copyright safeguarding. In traditional CEW algorithms for vector map, the watermark is embedded by modifying the host data, which cannot meet the needs of high precision vector map, especially for vector maps applied in military and navigation positioning fields. To fill this gap, a CEW algorithm for vector map based on zero-watermarking and permutation encryption is proposed. Firstly, compound chaotic systems are applied to permutation encryption to encrypt the storage order and position of the vertex coordinates respectively. The key is generated by the SHA-256 hash method, which has higher security. Secondly, the number of vertex features is regarded as the feature invariants to establish the watermark information and watermark index. After that, the zero-watermarking image is constructed through the XOR operation of the feature matrix and the copyright information matrix. Experiments show that the combination of permutation encryption and zero watermark can better meet the needs of secure transmission and copyright protection of high-precision vector graphics. The security, robustness and watermark capacity of the algorithm have been improved, making it more practical.


DyFusion: Cross-Attention 3D Object Detection with Dynamic Fusion

Abstract: In the realm of autonomous driving, LiDAR and camera sensors play an indispensable role, furnishing pivotal observational data for the critical task of precise 3D object detection. Existing fusion algorithms effectively utilize the complementary data from both sensors. However, these methods typically concatenate the raw point cloud data and pixel-level image features, unfortunately, a process that introduces errors and results in the loss of critical information embedded in each modality. To mitigate the problem of lost feature information, this paper proposes a Cross-Attention Dynamic Fusion (CADF) strategy that dynamically fuses the two heterogeneous data sources. In addition, we acknowledge the issue of insufficient data augmentation for these two diverse modalities. To combat this, we propose a Synchronous Data Augmentation (SDA) strategy designed to enhance training efficiency. We have tested our method using the KITTI and nuScenes datasets, and the results have been promising. Remarkably, our top-performing model attained an 82.52% mAP on the KITTI test benchmark, outperforming other state-of-the-art methods.


Robust Localization-Guided Dual-Branch Network for Camouflaged Object Segmentation

Abstract: The existence of camouflage targets is widespread in the natural world, as they blend seamlessly or closely resemble their surrounding environment, making it difficult for the human eye to identify them accurately. In camouflage target segmentation, challenges often arise from the high similarity between the foreground and background, resulting in segmentation errors, imprecise edge detection, and overlooking of small targets. To address these issues, this paper presents a robust localization-guided dual-branch network for the recognition of camouflaged targets. Two crucial branches, i.e., a localization branch and an overall refinement branch are designed and incorporated. The localization branch achieves accurate preliminary localization of camouflaged targets by incorporating the robust localization module, which integrates different high-level feature maps in a partially decoded manner. The overall refinement branch optimizes segmentation accuracy based on the output predictions of the localization branch. Within this branch, the edge refinement module is devised to effectively reduce false negative and false positive interference. By conducting context exploration on each feature layer from top to bottom, this module further enhances the precision of target edge segmentation. Additionally, our network employs five jointly trained output prediction maps and introduces attention-guided heads for diverse prediction maps in the overall refinement branch. This design adjusts the spatial positions and channel weights of different prediction maps, generating output prediction maps based on the emphasis of each output, thereby further strengthening the perception and feature representation capabilities of the model. To improve its ability to generate highly confident and accurate prediction candidate regions, tailored loss functions are designed to cater to the objectives of different prediction maps. We conducted experiments on three publicly available datasets for camouflaged object detection to assess our methodology and compared it with state-of-the-art network models. On the largest dataset COD10K, our method achieved a Structure-measure of 0.827 and demonstrated superior performance in other evaluation metrics, outperforming recent network models.


V4D: Voxel for 4D Novel View Synthesis

Abstract: Neural radiance fields have made a remarkable breakthrough in the novel view synthesis task at the 3D static scene. However, for the 4D circumstance (e.g., dynamic scene), the performance of the existing method is still limited by the capacity of the neural network, typically in a multilayer perceptron network (MLP). In this article, we utilize 3D Voxel to model the 4D neural radiance field, short as V4D, where the 3D voxel has two formats. The first one is to regularly model the 3D space and then use the sampled local 3D feature with the time index to model the density field and the texture field by a tiny MLP. The second one is in look-up tables (LUTs) format that is for the pixel-level refinement, where the pseudo-surface produced by the volume rendering is utilized as the guidance information to learn a 2D pixel-level refinement mapping. The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task. Moreover, we propose a more effective conditional positional encoding toward the 4D data that achieves performance gain with negligible computational burdens. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance at a low computational cost.


A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification

Abstract: According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the disease's fatality rate. Early cancer detection raises the chance of survival by up to 8%. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy


Update on the use of gamified educational resources in the development of cognitive skills

Abstract: Introduction: the use of game elements and game design techniques in content unrelated to the same is known as gamification. It is likely that gamified environments generate a fruitful scenario to evoke complex processes within higher nervous activity and thus develop cognitive skills. This facilitates its application for educational purposes. The objective was to characterize the use of gamified educational resources in the development of cognitive skills. Method: a total of 27 articles in Spanish and English were reviewed, from Pubmed, Scielo and Scopus; using as keywords: gamification, game, cognition, being more than 50% of the last five years.Result: gamification improves the services in which it is applied through playful experiences, it can positively influence aspects of the student experience such as level of interest, intellectual intensity and intrinsic motivation, by providing opportunities for autonomy, relationship and competition. There is a strong need for further exploration and experimentation to provide a gamified design that meets user preferences, tailored to individual characteristics. Conclusions: Gamified educational environments facilitate learning like a game. Through gamification it seems to increase motivation, commitment, active interaction, level of interest, critical thinking, intellectual intensity and intrinsic motivation. The effectiveness of gamification largely depends on the use of different game features and how they are implemented


Constrained Nonnegative Matrix Factorization Based on Label Propagation for Data Representation

Abstract: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nonnegative matrix factorization (NMF) algorithms are a series of dimensional reduction techniques widely used in data preprocessing. To improve the performance of clustering and the discrimination of the low-dimensional representation in NMF, we proposed a novel semi-supervised <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constrained nonnegative matrix factorization based on label propagation (LpCNMF). Specifically, the proposed LpCNMF adopts graph and label propagation as regularization terms, then makes use of a small amount of labeled data to predict the label information of the unlabeled data and finally obtains a predictive membership matrix with more label information. At the same time, we introduce an efficient alternating iterative algorithm to solve the optimization problem of the objective function in the LpCNMF. Unlike other NMF algorithms that only update the basis and coefficient matrices, the LpCNMF algorithm increases the update of the predictive membership matrix obtained by label propagation. Experimental results on various benchmark datasets demonstrate the superiority of our algorithm over existing state-of-the-art NMF algorithms. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ycwang-libra/LpCNMF_github.git.</uri>


Business-to-business digitalization, artificial intelligence, and social action

Abstract: Digitalization, artificial intelligence (AI), and social action are critical concepts in business-to-business (B2B) marketing. In this article, we provide a conceptual overview of these subjects and delineate research questions for scholarly consideration. These subjects are changing the marketing concept both as concept and function, which entail the transformation of the role of marketers. Integrating digitalization, AI, and social action is expected to pave the road for significant B2B marketing advancement in the uncertain future ahead.


A new class of generalized almost perfect nonlinear monomial functions

Abstract: In this short note, we present a new class of GAPN power functions of the type xk2p2i+k1pi+k0 over finite fields Fpn with p odd and gcd(n,i)=1 (up to EA-equivalence).


A Multi-vocal Literature Review on challenges and critical success factors of phishing education, training and awareness

Abstract: Phishing is a malicious attempt by cyber attackers to steal personal information through deception. Phishing attacks are often aided by carefully crafted phishing emails, which can go undetected by automated anti-phishing tools due to their limited accuracy. Studies found that user education, training, and awareness can thwart phishing attacks. Understanding diverse interconnected challenges and critical success factors of phishing education, training, and awareness (PETA) approaches can help improve organizations’ defense against phishing. This study presents a comprehensive, structured view of the challenges and critical success factors of the design, implementation, and evaluation stages of PETA. We have conducted a Multi-vocal Literature Review (MLR) by systematically collecting 53 academic studies and 16 grey studies from popular databases by following a well-known MLR guideline. We identified 20 challenges and 23 critical success factors, some of which involve human-centric and socio-technical factors in PETA. Our findings point out the need for designing explainable anti-phishing systems and developing automated tools and platforms to conduct real-world phishing studies. Our systematic analysis of 69 studies has enabled us to highlight the need for addressing human-centric issues, incorporating users’ knowledge gaps, and adopting personalized approaches in PETA.


Probabilistic Programming with Exact Conditions

Abstract: We spell out the paradigm of exact conditioning as an intuitive and powerful way of conditioning on observations in probabilistic programs. This is contrasted with likelihood-based scoring known from languages such as Stan . We study exact conditioning in the cases of discrete and Gaussian probability, presenting prototypical languages for each case and giving semantics to them. We make use of categorical probability (namely Markov and CD categories) to give a general account of exact conditioning, which avoids limits and measure theory, instead focusing on restructuring dataflow and program equations. The correspondence between such categories and a class of programming languages is made precise by defining the internal language of a CD category.


MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks

Abstract: Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into HGNNs to associate nodes closely related in semantics but far apart in the graph. However, existing metapath-based models suffer from either information loss or high computation costs. To address these problems, we present a novel Metapath Context Convolution-based Heterogeneous Graph Neural Network (MECCH). MECCH leverages metapath contexts, a new kind of graph structure that facilitates lossless node information aggregation while avoiding any redundancy. Specifically, MECCH applies three novel components after feature preprocessing to extract comprehensive information from the input graph efficiently: (1) metapath context construction, (2) metapath context encoder, and (3) convolutional metapath fusion. Experiments on five real-world heterogeneous graph datasets for node classification and link prediction show that MECCH achieves superior prediction accuracy compared with state-of-the-art baselines with improved computational efficiency. The code is available at https://github.com/cynricfu/MECCH.


An intelligent auto-response short message service categorization model using semantic index

Abstract: Short message service (SMS) is one of the quickest and easiest ways used for communication, used by businesses, government organizations, and banks to send short messages to large groups of people. Categorization of SMS under different message types in their inboxes will provide a concise view for receivers. Former studies on the said problem are at the binary level as ham or spam which triggered the masking of specific messages that were useful to the end user but were treated as spam. Further, it is extended with multi labels such as ham, spam, and others which is not sufficient to meet all the necessities of end users. Hence, a multi-class SMS categorization is needed based on the semantics (information) embedded in it. This paper introduces an intelligent auto-response model using a semantic index for categorizing SMS messages into 5 categories: ham, spam, info, transactions, and one time password’s, using the multi-layer perceptron (MLP) algorithm. In this approach, each SMS is classified into one of the predefined categories. This experiment was conducted on the “multi-class SMS dataset” with 7,398 messages, which are differentiated into 5 classes. The accuracy obtained from the experiment was 97%.


Convolutional Transformer network for fine-grained action recognition

Abstract: Fine-grained action recognition is one of the critical problems in video processing, which aims to recognize similar actions of subtle interactions between humans and objects. Inspired by the remarkable performance of the Transformer in natural language processing, Transformer has been applied to the fine-grained action recognition task. However, Transformer needs abundant training data and extra supervision to achieve comparable results with convolutional neural networks (CNNs). To address these issues, we propose a Convolutional Transformer Network (CTN), which integrates the merits of CNN (e.g., sharing weights, capturing low-level features in videos and locality) and the benefits of Transformer (e.g., dynamic attention and learning long-range dependencies). In this paper, we propose two modifications to the original Transformer: (i) we propose a video-to-tokens module that can extract tokens from extracted spatial-temporal features in videos by 3D convolutions instead of the direct token embedding from raw input video clips; (ii) we completely replace the linear mapping in multi-head self-attention layer with depth-wise convolutional mapping, which applies a depth-wise separable convolution operation on embedded token maps. With these two modifications, our approach can extract the effective spatial-temporal features from videos and process the long sequences of tokens encountered in videos. Experimental results demonstrate that our proposed CTN can achieve state-of-the-art accuracy on two fine-grained action recognition datasets (i.e., Epic-Kitchens and Diving 48) with a small computational increase.