Reviewerly/exHarmony huggingface dataset:
Container Security in Cloud Environments

A bstract: The widespread adoption of containers in modern software applications has introduced new challenges to security and integrity. Containers, known for their lightweight and portable nature, facilitate agile deployment across diverse environments. However, this popularity has led to security risks such as vulnerabilities in container images, misconfigurations, and insecure runtime environments. This paper addresses these challenges by proposing automated and robust security techniques integrated into continuous integration and continuous development pipelines. The work emphasizes the importance of a solid security policy, container image scanning, orchestration security, and runtime monitoring. The study also identifies specific issues faced by the DevSecOps community and proposes initial fixes to fortify container security. In the cloud environment, containers play a pivotal role in application deployment by sharing the same OS kernel, reducing resource requirements, and minimizing start-up times. Despite their advantages, weak container isolation poses security challenges, including privilege escalation and information leaks. To mitigate these concerns, the paper conducts an in-depth analysis of existing access control mechanisms for container security. It discusses challenges in architecture modeling and presents use cases for fulfilling security requirements, encompassing container, inter-container, and host protection. The work emphasizes the need for both software and hardware solutions to enhance container security. Containers have emerged as a lightweight alternative to virtual machines, supporting microservices architecture. The container market is growing rapidly, but security concerns remain a significant barrier to adoption. This paper surveys existing literature on container security, categorizing it into four use cases: protecting containers from internal applications, inter-container protection, safeguarding the host from containers, and defending containers froma malicious or semi-honest host. The analysis reveals that software-based solutions, leveraging Linux kernel features and security modules, address the first three use cases, while the last use case relies on hardware-based solutions. The paper concludes with highlighting open research problems and future directions to guide further exploration in container security.
Qualitative Content Analysis for Critical Thinking and Skill Development: A Case of Chemistry Curriculum

In the era of globalization and knowledge, critical thinking development is a necessary skill. The research focuses on analyzing the Chemistry curriculum for grades IX-X to explore its emphasis on fostering critical thinking skills among students being an essential skill for the twenty-first century. It aims to develop problem-solvers, independent thinkers, and decision makers capable of applying their knowledge to real-life challenges. The curriculum advocates for interactive, learner-centered, participative, inquiry-based, practical, problem-solving, and analytical skills. It prioritizes the measurement of students' capacity for critical judgment, focusing on analysis and synthesis. NVivo 12 software was used with a qualitative content analysis approach to analyze the document. The findings of the study revealed a significant focus on the development of critical thinking to make them problem solvers, independent thinkers, and decision-makers to apply to the challenges of real life. As regards the standards and benchmarks, the curriculum is designed accordingly for the promotion of in-depth knowledge and higher-order thinking skills. Furthermore, the curriculum advocates for interactive, learner-centered, participative, inquiry-based, practical, problem-solving, and analytical skills. The assessment guidelines prioritize the measurement of student's capacity for critical judgment, focusing on analysis and synthesis. The study concludes by providing recommendations for teacher training, resource allocation, collaboration among stakeholders, regular curriculum updates, further research, and fostering a culture of critical thinking within educational institutions.
Probabilistic prediction of “irrational” decisions from semantic composition of contexts

Difficulties in prognosis of human behavior are due to the complexity of our cognition, routinely breaking the boundaries of classical rationality. The paper solves this problem for the simplest kind of such “irrational” behavior in which a single binary decision is made in three related contexts. Subjective meanings of these contexts relative to the basis decision alternative are represented by three qubit states, borrowed from quantum theory. These states are bound together by linear superpositions, which encode semantic composition of the contexts in the subject’s mind. The resulting theory supplements classical probabilistic model with nonlinear interference factor, accounting for the “irrational”, emotionally-semantic side of intelligence. This model is built for different realizations of two classic experiments used to study behavioral irrationality: the prisoner’s dilemma and the two-stage gambling task. In 24 such realizations, the interference phase is shown to fall in a narrow range of values, encoding regularities of semantic composition of contexts. Extrapolation of this regularity to novel experiments allows using the model in prognostic mode. This possibility is tested on the task of probabilistic prediction of target decision based on the same probability in two other contexts. For the prisoner’s dilemma and the two-stage gambling task the such prognosis has relative errors of 9 and 11% respectively. The proposed approach allows for putting other quantum models of cognition and decision to predictive and interpretable use, whereas its principles also apply to modeling of decisions with larger sets of contexts and behavioral options. By formalizing a novel type of semantic regularities behind “irrational” thinking, models of the present type open prospects for empowering the existing means for socio-economical analytics and prediction.
Directions for modifying the artificial bee colony algorithm to optimize control parameters for complex systems

In recent years, bioinspired algorithms based on the use of a population approach and a probabilistic search strategy have become especially popular among researchers involved in multidimensional and multicriteria optimization. Such algorithms are based on the principles of cooperative behavior of a decentralized self-organizing colony of living organisms (bees, ants, birds, etc.) to achieve certain goals (for example, to meet nutritional needs). However, their practical application encounters a number of difficulties leading to a decrease in convergence. This article discusses the possibility of modifying the artificial bee colony algorithm by using a hybridization strategy with various data mining methods. One of these difficulties is the lack of a reasonable approach to determining initial search positions. As a solution, it is proposed to divide the population into clusters, the centers of which will be the initial positions. The need for interaction between individuals makes it advisable to use fuzzy clustering, which allows the formation of intersecting clusters. Another difficulty is associated with the choice of “free” parameters, for which the authors have not developed recommendations for choosing their optimal values. To solve this problem, it is proposed to use the idea of coevolution, which consists in the parallel launch of several interacting subpopulations, for each of which different “settings” are applied. The proposed algorithm is applicable to multidimensional optimization tasks, in which it is necessary to find such a combination of different types of elements belonging to some “large” population that will ensure the achievement of the maximum effect under given restrictions. Examples of such tasks are determining the species and quantitative composition of plants to form the terrestrial ecosystem of a carbon farm or mass recruiting, which consists of selecting a large number of personnel for the same positions.
Development of a web-based Multimedia Learning Management System (MlMS) in High School Physics Learning

Purpose: This research aims to investigate: (1) students' needs for a web-based Multimedia Learning Management System (MLMS) in high school physics learning, (2) designing a prototype model for developing a web-based Multimedia Learning Management System (MLMS) in high school physics learning, (3 ) the validity of the web-based Multimedia Learning Management System (MLMS) development model in high school physics learning, (4) measuring the practicality of the web-based Multimedia Learning Management System (MLMS) development model in high school physics learning, and (5) the effectiveness of the Multimedia Learning Management System development model (MLMS) web-based in high school physics learning.&#x0D; Research methodology: The research method applied was research and development using the one-group pre-test and post-test design (ADDIE) model. The object of this research was high school students, with samples taken from four schools (three private schools and one state school) in South Tangerang.&#x0D; Results: The assessment results for static electricity material at schools I and II were in the medium category, with gain value of 0.64 and 0.63, respectively, while at school III, they were in the high category with a gain value of 0.70. Statistical significance (school I, school II, and school III) in Static Electricity shows t &lt; 0.05, while in dynamic electricity material, schools I and II are in the high category, with the same gain value of 0.7 and a significant level t-test results t &lt; 0.05.&#x0D; Implementation: The material expert validation results showed a validity level of 90.91% (very good), whereas the multimedia expert validation results reached 83.75% (very good). Professional physics teachers gave positive responses, with a material and multimedia acceptance rate of 84.82% (very good), whereas student responses reached 79.98% (very good). Effectiveness based on improving student learning outcomes was measured by providing pre-tests, treatments, and post-tests (One Group Pre-test, Post-test Design) using the N-gain value.
PERANAN INTERAKSI EDUKATIF DALAM PROSES BELAJAR PESERTA DIDIK MATA PELAJARAN BAHASA INDONESIA KELAS X SMKN 2 BONE

This research aims to understand the role of educative interaction in the Indonesian language learning process at SMKN 2 Bone. The research method used in this study is qualitative research. The data obtained and processed are the results of interviews with the Curriculum Vice Principal, Indonesian language teachers, and students of SMKN 2 Bone. Data collection methods used in this research include interviews, observations, and documentation conducted directly at the research site.&#x0D; The results of the research conducted by the author show that educative interaction plays a significant role in the Indonesian language learning process. In the educative interaction during the teaching and learning process, teachers play a crucial role as managers and implementers of the teaching-learning interaction. Teachers act as planners, guides, and motivators to facilitate effective interaction in the learning process. Teachers/educators play a role in creating an environment that keeps students focused on receiving lessons. In this context, teachers/educators behave professionally in performing their duties, making every effort to prevent any disturbances that could affect the students' development and never demeaning the dignity of the students. Through educative interaction, teachers/educators can successfully achieve educational goals.
PENGARUH PEMBELAJARAN BLENDED LEARNING TERHADAP KEMANDIRIAN BELAJAR SISWA KELAS X SMK NEGERI 2 BONE

This research aims to determine the influence of Blended Learning on student self-directed learning in Grade X of SMK Negeri 2 Bone. The research was conducted from July to August 2023. This study is a quantitative descriptive research. The research population consisted of 304 students in Grade X of DKV at SMK Negeri 2 Bone, with a sample of 55 students. Data collection in the research process was carried out using questionnaires and documentation. Based on the research results, it is found that the respondents' perceptions of Blended Learning in relation to student self-directed learning amounted to 75%. This indicates that the respondents' opinions fall within the "agreeable," "good," or "likable" category because 75% falls within the range of 60% - 79.99%. Furthermore, the additional results of the t-test show that the calculated t-value is 0.625, which is smaller than the tabulated t-value of 2.006, and the significance value is 0.535, which is greater than 0.05. This suggests that the null hypothesis (Ho) is accepted, and the alternative hypothesis (H1) is rejected. Therefore, it can be concluded that Blended Learning has a non-significant influence on student self-directed learning.
Development of a Cross-Asset Model for the Maintenance of Road and Water Pipe Assets using AHP Method

Roads and water pipe assets undergo various deterioration processes due to the high demand for their services. Maintenance of these assets is often planned as individual assets, and the interdependency among different assets is neglected. An integrated framework for cross-asset maintenance is required for optimum utilization of the available funds for asset maintenance. To date, there are very few studies focusing on the use of the analytical hierarchy process (AHP) for cross-asset maintenance of roads and water pipe assets. Therefore, this research aims to develop an integrated fund allocation model for the maintenance of road and water pipe assets. A model was developed using AHP analysis based on expert opinions captured through a questionnaire in order to obtain optimum maintenance fund allocation for the cross-assets, roads, and water pipes. Then, a case study corridor segment with the considered cross-assets was selected, and a trade-off analysis was conducted for the intervention alternatives considering different levels of service (LOS) of the asset elements. The results of the trade-off analysis can be used to identify the optimum intervention alternative that satisfies the budget requirement and results in the maximum benefit. Overall, asset managers can use the approach presented in the present study to develop a cross-asset fund allocation model when multiple assets are involved in maintenance. Doi: 10.28991/CEJ-2024-010-02-01 Full Text: PDF
Modelling of Flood Hazard Early Warning Group Decision Support System

Early warning of flood hazards needs to be carried out comprehensively to avoid a higher risk of disaster. Every decision on early warning of a flood hazard is carried out in part by one party, namely the government or water resource managers. This research aims to provide a collaborative decision-making model for early warning of flood hazards through a Group Decision Support System Model (GDSS), especially in Indonesia. The novelty of this research is that the GDSS model involves more than one decision-maker and multi-criteria decision-making for early warning of flood hazards in the downstream Kali Sadar River, Mojokerto Regency, East Java Province, Indonesia. The GDSS model was developed using a hybrid method, namely the Analytical Network Process (ANP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). There was more than one decision result; voting was carried out using the BORDA method to produce the decision. The test results of GDSS were obtained using a Spearman rank correlation coefficient of 0.8425 and matrix confusion, an accuracy value of 86.7%, a precision value of 86.7%, a recall value of 86.7%, and an f-measure of 86.7%. Based on the test results, good results were obtained from the GDSS model. Doi: 10.28991/CEJ-2024-010-02-018 Full Text: PDF
Anomaly Sewing Pattern Detection for AIoT System using Deep Learning and Decision Tree

Artificial Intelligence of Things (AIoT), which combines AI and the Internet of Things (IoT), has recently gained popularity. Deep neural networks (DNNs) have achieved great success in many applications. Deploying complex AI models on embedded boards, nevertheless, may be challenging due to computational limitations or intelligent model complexity. This paper focuses on an AIoT-based system for smart sewing automation using edge devices. Our technique included developing a detection model and a decision tree for a sufficient testing scenario. YOLOv5 set the stage for our defective sewing stitches detection model, to detect anomalies and classify the sewing patterns. According to the experimental testing, the proposed approach achieved a perfect score with accuracy and F1score of 1.0, False Positive Rate (FPR), False Negative Rate (FNR) of 0, and a speed of 0.07 seconds with file size 2.43MB.
An Improved MAP-Elites Algorithm via Rotational Invariant Operator in Differential Evolution for Continuous Optimization

In this paper, we propose a new approach that enhances the continuous optimization performance of the MAP-Elites algorithm. The existing self-referencing MAP-Elites algorithm employed the “DE/rand/1/bin” operator from the differential evolution algorithm, which, due to its lack of rotational invariance, led to a degradation in optimization performance when there were high correlations among variables. The proposed algorithm replaces the “DE/rand/1/bin” operator with the “DE/current-to-rand/1” operator. This operator, possessing rotational invariance, ensures robust performance even in cases where there are high correlations among variables. Experimental results confirm that the proposed algorithm performs better than the comparison algorithms.
Development of Software Education Program using Self-driving

As the importance of software and artificial education is emphasized on the digital transformation era, various educational materials are being developed and distributed. To achieve the purpose of software education, various software education programs suitable for school settings need to be provided. In this paper, we developed a software education program using self-driving that can be applied to secondary school software education and applied it to secondary school students. The developed software education program is a physical computing program consisting of various motion control programs such as object detection, line tracing using various sensors, focusing on experience and practice. As a result of the survey, students' attitudes and career orientation toward software and artificial intelligence, and satisfaction with software education were over 90%, and satisfaction with the proposed program was over 95%.
The Effects of Case-Based Learning using Flipped Learning in Emergency Nursing Classes

Objectives The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them.&#x0D; Methods This study was a single-group before and after quasi-experimental study, and a total of 6 sessions were conducted by applying the PATRNER model to operate flipped learning case-based learning for 54 third-year nursing students at J University in J Province. The collection period was It was from September to November 2022, and a pre-survey was conducted during the first class of the second semester and a post-survey after the last session. Data were analyzed using descriptive statistics, paired t-test, and Pearson’s correlation coefficient using the SPSS WIN 22.0 program.&#x0D; Results This finding indicated that case-based learning to flipped learning in an emergency nursing class, learning immersion, problem-solving skills, and confidence in performing first aid all improved, and there was a significant difference. There was a significant positive correlation between learning immersion and problem-solving ability, and between problem-solving ability and confidence in performing first aid.&#x0D; Conclusions After applying flipped learning in the emergency nursing class, case-based learning was effective in improving learning immersion, problem-solving skills, and confidence in performing first aid, so it can be used to develop and operate effective and diverse classes in basic and major nursing courses in the future.&#x0D;
A Study on the Role of Memorial Museums as Forum for Dialogue and Strategies for Building Participatory Public Memory: Focusing on the case of the USHMM

This study aims to reexamine the role of memorial museums as forums for dialogue where diverse perspectives converge in the context of a globalized modern society, where conflicting views and beliefs are inevitable, and to explore strategies for civic participation in this context. It investigates the changing role of memorial museums within the context of expanding memory spaces and examines theories related to civic participation. The case of the United States Holocaust Memorial Museum (USHMM), one of today's most innovative memorial museums that fosters extensive public engagement, was analyzed from the perspectives of transcultural memory theory and civic participation. Museums are places where public discourse occurs, and memorial museums, in particular, move beyond housing memories of the past to actively creating public memory with citizens. It was found that individual multi-layered and multifaceted memories are not only in competition but can also interact, show solidarity, and create a combined voice. This process of active civic participation does not bury certain memories within universal values but, rather, helps these memories gain sensitivity and contributes to the creation of new global memory landscapes. The methods by which USHMM facilitates civic participation reveal several strategies: 1) the organic arrangement of permanent and special exhibitions for varied narrative variations, 2) ways of connecting with the present, and 3) co-creation through the use of digital technology. These features suggest practical ways in which museums, including today's participatory memorial museums, can engage visitors in exhibitions, education, and social dialogue. In conclusion, this study demonstrates that memorial museums can play a central role in forming new public memories and fostering cultural dialogue through the participation of diverse citizens. This suggests that memorial museums can contribute to promoting understanding and solidarity within communities as more inclusive spaces for social dialogue.