Reviewerly/exHarmony huggingface dataset:
A Mathematical Approach for Generating a Highly Non-Linear Substitution Box Using Quadratic Fractional Transformation

Nowadays, one of the most important difficulties is the protection and privacy of confidential data. To address these problems, numerous organizations rely on the use of cryptographic techniques to secure data from illegal activities and assaults. Modern cryptographic ciphers use the non-linear component of block cipher to ensure the robust encryption process and lawful decoding of plain data during the decryption phase. For the designing of a secure substitution box (S-box), non-linearity (NL) which is an algebraic property of the S-box has great importance. Consequently, the main focus of cryptographers is to achieve the S-box with a high value of non-linearity. In this suggested study, an algebraic approach for the construction of 16 × 16 S-boxes is provided which is based on the fractional transformation and finite field. This technique is only applicable for the even number exponent in the range (2-254) that are not multiples of 4. Firstly, we choose a quadratic fractional transformation, swap each missing element with repeating elements, and acquire the initial S-box. In the second stage, a special permutation of the symmetric group is utilized to construct the final S-box, which has a higher NL score of 112.75 than the Advanced Encryption Standard (AES) S-box and a lower linear probability score of 0.1328. In addition, a tabular and graphical comparison of various algebraic features of the created S-box with many other S-boxes from the literature is provided which verifies that the created S-box has the ability and is good enough to withstand linear and differential attacks. From different analyses, it is ensured that the proposed S-boxes are better than as compared to the existing S-boxes. Further these S-boxes can be utilized in the security of the image data and the text data.
A Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification. These limitations can result in the misjudgment of models, leading to a degradation in overall detection performance. This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block (CLME) to overcome the above limitations. The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations. The memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration mechanisms. These two modules together effectively alleviate the problem of generalization. Furthermore, this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature spaces. Such a strategy can enlarge the discrepancies between normal and abnormal data, which is more conducive to anomaly identification. The proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly detection. To validate the efficacy of the proposed approach, extensive experiments are conducted on well-established benchmark datasets, including SWaT, PSM, WADI, and MSL. The results demonstrate outstanding performance, with F1 scores of 90.58%, 94.83%, 91.58%, and 91.75%, respectively. These findings affirm the superiority of the CLME model over existing state-of-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.
A Bibliometric Analysis on Chatbot Application in Education

A bibliometric analysis study investigating chatbots' current state and developments in education research has not been adequately addressed in literature. Thus, this study highlights the current state, emerging research trends and directions of chatbots in education using bibliometric analysis. The significance of the study is to provide insights into the most recent developments of chatbots application in education and future research directions for academics and practitioners. A bibliometric analysis of publications on chatbots in education published between 2012 and 2022 was conducted. A total of 759 publications were collected from the Scopus database for the bibliometric analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adopted to identify and screen the dataset. VOSviewer (version 1.6.18) was used to perform the following network analysis: co-authorship and co-occurrence. Also, Bibliometrix was used for the descriptive statistics of the bibliometric data and the publication trend. The study’s findings showed that fewer studies have been conducted from the African region on chatbot education research. Researchers from the United Kingdom, United States, Australia, China, India, Greece, Japan, Vietnam, Malaysia, and United Arab Emirates have collaborated significantly in chatbot education research. Also, the main keyword occurrences in research on chatbots in education are chatbots, students, artificial intelligence, natural language processing, education, and learning. The trends indicate a steady increase in research on chatbots over the past decade.
Comparative analysis of collaborative filtering recommender system algorithms for e-commerce

&lt;p&gt;Collaborative recommender systems are information filtering systems that seek to predict a user’s rating or preference for an item. They play a vital role in various business use cases, such as personalized recommendations, item ranking and sorting, targeted marketing and promotions, content curation and catalog organization, and feedback analysis and quality control. When evaluating these systems, rating prediction metrics are commonly employed. Efficiency, including the prediction time, is another crucial aspect to consider. In this study, the performance of different algorithms was investigated. The study employed a dataset consisting of e-commerce product ratings and assessed the algorithms based on rating prediction metrics and efficiency. The results demonstrated that each algorithm had its own set of strengths and weaknesses. For the metric of Root Mean Squared Error (RMSE), the BaselineOnly algorithm achieved the lowest mean value. Regarding Mean Absolute Error (MAE), the Singular Value Decomposition with Positive Perturbations Singular Value Decomposition with Positive Perturbations (SVDPP) algorithm exhibited the lowest mean value; Mean Squared Error (MSE) also achieved the lowest mean value. Moreover, the BaselineOnly algorithm showcased superior performance with the lowest mean test times when considering efficiency. Researchers and practitioners can use the findings of this study to select the best algorithm for a particular application. Researchers can develop new algorithms that combine the strengths of different algorithms. Practitioners can also use the findings of this study to tune the parameters of existing algorithms.&lt;/p&gt;
Quantum Radar with Genuine Tripartite Entanglement

Quantum radar is a remote sensing technique for target detection that has extensively been explored in conjunction with entangled two-mode squeezed vacuum states. This work extends the existing protocol to a scheme that utilizes a three-mode squeezed state with genuine tripartite entanglement. The three entangled modes are divided into two signal modes and one idler mode. A numerical evaluation of the Chernoff bound shows that the proposed protocol has the potential to offer a higher quantum advantage in specific parameter regimes.
A Comparative Study between Extrovert and Introvert Students’ on Writing Achievement

In line with the objective of this study is to provide a descriptive account of the student's achievement. Students have better writing achievement. In this study, the researcher uses comparative research. The population of this research will be the students in SMP Negeri 1 Barru in the 2022/2023 academic year and the total population is 129 students. The research participants in this study were the students of SMP Negeri 1 Barru. A random class method is employed to select the 31 participants. This is evidenced by the t count t-test (-,0426) which is lower than the ttable (1,69913) at a significance level of 0.05. Thus, the hypothesis (H1) is accepted and null (H0) is rejected. In a comparative study between the personality of extrovert and introvert students, there is a significant difference between the personality of extrovert and introvert students in writing achievement. However, it can be seen from the average personality value of extrovert students (83.15) is higher than the personality of introvert students (78.83) students of SMP Negeri 1 Barru for the 2022/2023 academic year.
INFORMATION MANAGEMENT IN THE SCOPE OF EDUCATIONAL TECHNOLOGY: INTEROCEPTIVE PROFILE AS A TECHNIQUE TO PREPARE CONTINUING EDUCATION COURSES FOR TEACHERS

INFORMATION MANAGEMENT IN THE SCOPE OF EDUCATIONAL TECHNOLOGY: INTEROCEPTIVE PROFILE AS A TECHNIQUE TO PREPARE CONTINUING EDUCATION COURSES FOR TEACHERS
21ST CENTURY TEACHER COMPETENCES: GROUP CONCEPT MAPPING METHODOLOGY FOR CURRICULUM DESIGN

1 Sofia University (BULGARIA)2 Open University Netherlands (NETHERLANDS)3 South-West University Neofit Rilski (BULGARIA)4 Sofia University, St Kliment Ochridski (BULGARIA)
DIGITAL EDUCATION DURING THE PANDEMIC

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 2096-2109ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.0596Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
DESIGNING MODULAR TEACHING SCENARIOS FOR CONVEYING CONCEPTUAL APPROACHES OF ARTIFICIAL INTELLIGENCE USING THE EXAMPLE OF CLUSTERING AND ARTIFICIAL NEURAL NETWORKS

DESIGNING MODULAR TEACHING SCENARIOS FOR CONVEYING CONCEPTUAL APPROACHES OF ARTIFICIAL INTELLIGENCE USING THE EXAMPLE OF CLUSTERING AND ARTIFICIAL NEURAL NETWORKS
USING A ROLE-PLAY SIMULATION TO PROMOTE STUDENT REFLECTION ON ORAL COMMUNICATION IN FORMAL MEETINGS

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 1-10ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.0003Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
CREATION OF A DIGITAL MODULE AS LEARNING MEDIA FOR DEAF STUDENT'S

Deaf people have learning characteristics that rely more on visual aspects. This research aimed to produce learning media in the form of digital module that could accommodate the needs and learning characteristics of deaf students in the course of thesis guidance theory course. This research belonged to the type of development by applying the ADDIE procedure. The results showed that the digital module scored 85.28% in the aspect of the validity test by learning media experts and deafness experts. While in the test practicality by students, the digital module received a score of 85.63%. These two scores indicated that both digital modules were at a very high level of validity and practicality. Subsequently, they were worthy of being a learning media option that could be used for deaf students.
Simulasi Sistem Otomasi Sederhana Pada Light Emitting Diode (LED) Berjalan di Sekolah Menengah Atas Negeri 6 Kabupaten Kepahyang

Community service activities in the form of socialization and practice regarding simple automation system simulations on running Light Emitting Diode (LED) have the aim of providing knowledge and information to students at SMAN 6 Kepahyang Regency so that they gain knowledge about current technological advances. The subjects in this community service activity were students at SMAN 6 Kepahyang Regency. The socialization process is carried out by providing information and hands-on practice in assembling components of a simple automation system simulation on a running Light Emitting Diode (LED). Where the running LED simulation is an example of a simple implementation of a simple automation system from artificial intelligence (Artificial Intelligence). In practice, the system will use the Arduino Uno Microcontroller for data processing. Then the result of this community service activity is that students at SMAN 6 Kepahyang Regency will gain knowledge about current technological advances, besides that students at SMAN 6 Kepahyang Regency are also expected to be able to develop more creative and innovative ideas related to technological developments that exists. The conclusions obtained from carrying out community service activities, especially students at SMAN 6 Kepahyang Regency are in the form of obtaining knowledge and information regarding simple automation systems on Light Emitting Diode (LED) running on the implementation of artificial intelligence (Artificial Intelligence), knowing what electronic components can be used in a simple automation system simulation on a running Light Emitting Diode (LED), practice directly in making a simple automation system simulation circuit on a running Light Emitting Diode (LED). Then through socialization and hands-on practice students at SMAN 6 Kepahyang Regency can gain knowledge so that in the future it is hoped that they will be able to develop more creative and innovative ideas, especially in the field of technology and information advancement.
CURRENT STATE OF IMPLEMENTATION OF STREAM-EDUCATION IN GENERAL SECONDARY EDUCATION INSTITUTIONS OF UKRAINE

The rapid development of technological processes, the IT industry, nanotechnology, and robotics is driving the need for specialists capable of integrated scientific and engineering activities. Today, the priority is to provide quality education in science and mathematics and technological training for applicants. Over the past eight years, STEM, which combines the sciences in an “engineering” way and consists of the following components, has been a steady trend in Ukraine: Science, Technology, Engineering, Mathematics, in particular in its methodological extensions STEAM (Art) and STREAM (Reading + wRiting), and is popular among teachers, researchers and methodologists. The paper considers the urgent problem of introducing the innovative STEM direction in the educational process of general secondary education institutions in the STREAM variation, given its versatility and multicomponent nature. The purpose of the study is to analyze the state of implementation of STREAMeducation, to summarize the main theoretical data and methodological approaches of scientists and the experience of implementing STREAMeducation in the context of the implementation of the Concept of the New Ukrainian School. It is emphasized that since 2022, the implementation of this direction has begun at the level of basic secondary education, taking into account the Action Plan for the implementation of the Concept of Development of Science and Mathematics Education (STEM-education) until 2027 according to the developed model programs. It is also noted that in the system of general secondary education, there are 3 stages of STREAM implementation in primary, secondary and high school, which are carried out through the integration of traditional subjects: mathematics, physics, chemistry, biology, geography, astronomy, technology, art and disciplines of the language and literature field. The advantages of implementing this area are outlined, which primarily consist in the ability to implement a competency-based approach at an advanced level through high-quality cross-curricular integration and the use of powerful teaching tools. It is specified that the development of STREAM education is a priority and is carried out through all forms of education in Ukraine.
An Experience in the Evaluation of Fault Prediction

Background. ROC (Receiver Operating Characteristic) curves are widely used to represent the performance (i.e., degree of correctness) of fault proneness models. AUC, the Area Under the ROC Curve is a quite popular performance metric, which summarizes into a single number the goodness of the predictions represented by the ROC curve. Alternative techniques have been proposed for evaluating the performance represented by a ROC curve: among these are RRA (Ratio of Relevant Areas) and $$\phi $$ (alias Matthews Correlation Coefficient). Objectives. In this paper, we aim at evaluating AUC as a performance metric, also with respect to alternative proposals. Method. We carry out an empirical study by replicating a previously published fault prediction study and measuring the performance of the obtained faultiness models using AUC, RRA, and a recently proposed way of relating a specific kind of ROC curves to $$\phi $$ , based on iso- $$\phi $$ ROC curves, i.e., ROC curves with constant $$\phi $$ . We take into account prevalence, i.e., the proportion of faulty modules in the dataset that is the object of predictions. Results. AUC appears to provide indications that are concordant with $$\phi $$ for fairly balanced datasets, while it is much more optimistic than $$\phi $$ for quite imbalanced datasets. RRA’s indications appear to be moderately affected by the degree of balance in a dataset. In addition, RRA appears to agree with $$\phi $$ . Conclusions. Based on the collected evidence, AUC does not seem to be suitable for evaluating the performance of fault proneness models when used with imbalanced datasets. In these cases, using RRA can be a better choice. At any rate, more research is needed to generalize these conclusions.
A qualitative study on the implementation status of local government's child policy: The child policy impact assessment and the south korean child policy initiative

Objective: The purpose of this study is to examine the implementation status of child policies in local governments and to derive improvement strategies.&#x0D; Method: The researchers conducted interviews with 23 local government officials responsible for the ‘Child Policy Impact Assessment’ and the ‘South Korean Child Policy Initiative’. The data was analyzed using a generic qualitative research approach.&#x0D; Results: The research results were categorized into two main themes: ‘Issues in the process of child policy implementation’ and ‘Alternatives for strengthening child policy implementation’. These themes were further subcategorized into ‘Alternatives for individual level ’, ‘‘Alternatives for organizational level’, and ‘Alternatives for implementation system level’. Furthermore, 18 concepts were identified according to the subcategories.&#x0D; Conclusions: Based on the research findings, the study discusses key issues that are centered around the local government's child policy implementation structure, policy targets, evaluation indicators, and implementation procedures. Additionally, suggestions for improvement are presented, including standardization and guideline establishment for the implementation structure, and an implementation approach that integrates the ‘Child Policy Impact Assessment’ and the ‘South Korean Child Policy Initiative’.
A portable augmented/virtual reality auralisation tool for consumer-grade devices with companion desktop application

Auralisation is the technique of presenting realistic acoustic scenarios to interested parties, through simulation using appropriately processed audio signals, without the need for the scenario to be physically constructed. Current state-of-the-art auralisations typically require a dedicated listening room with very low reverberance, very low background noise, and a multi-channel loudspeaker setup. More recently, auralisations have been coupled with virtual reality (VR) visualisations to enhance the user experience, through use of VR head-sets. Recent computational power increases in portable consumer-grade devices, such as smart phones and tablets, have meant that rendering a 3D augmented reality (AR) or VR simulation is now a possibility on such devices. The AR/VR auralisation application, AiHear, has been developed which utilizes the portable device platform in conjunction with an off-the-shelf (calibrated) headphone setup to play back accurate auralisation audio. An Ai-Hear companion desktop application enables rapid creation and modification of auralisation scenarios, and allows the audio and visuals to be previewed before they are imported onto the portable device. This paper looks at some of the technical aspects of successfully implementing an AR/VR auralisation application for use on a low-cost consumer-grade platform.
Deep Facial Phenotyping with Mixup Augmentation

The classification of genetic disorders from face images has the potential to assist with early diagnosis and effective treatment. A key objective in this field is to enhance the accuracy and robustness of deep learning models in classifying genetic disorders from face images for disorders that are not represented in the training data. In this paper, we propose the use of input mixup augmentation to improve few-shot classification performance on this task. Furthermore, we present a specialised version of mixup that warps together face images using face keypoints, and show that this improves performance further. The motivation for using keypoint guided mixup is to align face structure and produce an intermediate image between two disorders. We compare the performance of our proposed method with the baseline model and demonstrate significant improvements in accuracy, with a classification accuracy of 31.3% on the GMDB-Rare benchmark dataset. Our results show that incorporating input mixup augmentation and face keypoint-based mixup can enhance the ability of deep learning models to identify genetic disorders from face images, providing a promising approach for future research in this area.