Reviewerly/exHarmony huggingface dataset:
Pedagogical Technologies for Competent Training of Teachers in Ukrainian Professional Education

In recent decades, technological advances have revolutionized all areas of society, including the teaching resources and methodologies used in education. The relevance of pedagogical technologies in vocational education cannot be overemphasized, since technologies are constantly changing in today's environment, thus creating new challenges for modern education in globalization. The research aims are to establish patterns for promoting the implementation of pedagogical technologies in educational institutions for providing quality competent training of specialists in professional education. Research methods: comparative analysis; survey; systematization, generalization. As a result of the survey was established that pedagogical technologies positively influence the provision of competent training of specialists in Ukrainian professional education (89.7%), a smaller number of students (7.5%) were not sure about the quality of competent training of specialists in Ukrainian professional education with modern pedagogical technologies. It was determined that the students of educational institutions are most familiar with such pedagogical technologies as mobile learning (100%), e-learning (98%), independent learning (90%), virtual classroom (85%), and massive open online courses (MOOC) (71%). The study found that teachers need to improve digital competency from technologies such as Gamification and Next Generation Digital Learning Environment (NGDLE). Based on statistical data, it was found that the need for competent specialists in vocational education in the labor market is increasing every year, while those willing to study at vocational education institutions are decreasing every year. According to the negative trends, it has been determined that vocational education institutions should improve the educational process to increase the number of future competent specialists for the labor market.
Classification of Single- and Multi-carrier Signals Using CNN Based Deep Learning

In a non-cooperative context, to recover data from the received signal, the receiver must estimate the communication parameters used in the transmitter. In this paper, we propose an algorithm for classifying single-carrier and multi-carrier signals by using convolutional neural network based deep learning and analyze classification performance. Simulation results show that the proposed algorithm outperforms the conventional methods in an additive white Gaussian noise channel and Rician fading channel.
Offensive-Language Detection on Multi-Semantic Fusion Based on Data Augmentation

The rapid identification of offensive language in social media is of great significance for preventing viral spread and reducing the spread of malicious information, such as cyberbullying and content related to self-harm. In existing research, the public datasets of offensive language are small; the label quality is uneven; and the performance of the pre-trained models is not satisfactory. To overcome these problems, we proposed a multi-semantic fusion model based on data augmentation (MSF). Data augmentation was carried out by back translation so that it reduced the impact of too-small datasets on performance. At the same time, we used a novel fusion mechanism that combines word-level semantic features and n-grams character features. The experimental results on the two datasets showed that the model proposed in this study can effectively extract the semantic information of offensive language and achieve state-of-the-art performance on both datasets.
Single-Rail Adiabatic Logic for Energy-Efficient and CPA-Resistant Cryptographic Circuit in Low-Frequency Medical Devices

Designing energy-efficient and secure cryptographic circuits in low-frequency medical devices are challenging due to low-energy requirements. Also, the conventional CMOS logic-based cryptographic circuits solutions in medical devices can be vulnerable to side-channel attacks (e.g. correlation power analysis (CPA)). In this article, we explored single-rail Clocked CMOS Adiabatic Logic (CCAL) to design an energy-efficient and secure cryptographic circuit for low-frequency medical devices. The performance of the CCAL logic-based circuits was checked with a power clock generator (2N2P-PCG) integrated into the design for the frequency range of 50 kHz to 250 kHz. The CCAL logic gates show an average of approximately 48&#x0025; energy-saving and more than 95&#x0025; improvement in security metrics performance compared to its CMOS logic gate counterparts. Further, the CCAL based circuits are also compared for energy-saving performance against dual-rail adiabatic logic, 2-EE-SPFAL, and 2-SPGAL. The adiabatic CCAL gates save on an average of 55&#x0025; energy saving compared to 2-EE-SPFAL and 2-SPGAL over the frequency range of 50 kHz to 250 kHz. To check the efficacy of CCAL to design a larger cryptographic circuit, we implemented a case-study design of a Substitution-box (S-box) of popular lightweight PRESENT-80 encryption. The case-study implementation (2N2P-PCG integrated into the design) using CCAL shows more than 95&#x0025; energy saving compared to CMOS for the frequency 50 kHz to 125 kHz and around 60&#x0025; energy saving at frequency 250 kHz. At 250 kHz, compared to the dual-rail adiabatic designs of S-box based on 2-EE-SPFAL and 2-SPGAL, the CCAL based S-box shows 32.67&#x0025; and 11.21&#x0025; of energy savings, respectively. Additionally, the CCAL logic gate structure requires a lesser number of transistors compared to dual-rail adiabatic logic. The case-study implementation using CCAL saves 45.74&#x0025; and 34.88&#x0025; transistor counts compared to 2-EE-SPFAL and 2-SPGAL. The article also presents the effect of varying tank capacitance in 2N2P-PCG over energy efficiency and security performance. The CCAL based case-study was also subjected against CPA. The CCAL-based S-box case study successfully protects the revelation of the encryption key against the CPA attack, However, the key was revealed in CMOS-based case-study implementation.
Rotational analysis of ChaCha permutation

<p style='text-indent:20px;'>We show that the underlying permutation of ChaCha20 stream cipher does not behave as a random permutation for up to 17 rounds with respect to rotational cryptanalysis. In particular, we derive a lower and an upper bound for the rotational probability through ChaCha quarter round, we show how to extend the bound to a full round and then to the full permutation. The obtained bounds show that the probability to find what we call a parallel rotational collision is, for example, less than <inline-formula><tex-math id="M1">\begin{document}$ 2^{-505} $\end{document}</tex-math></inline-formula> for 17 rounds of ChaCha permutation, while for a random permutation of the same input size, this probability is <inline-formula><tex-math id="M2">\begin{document}$ 2^{-511} $\end{document}</tex-math></inline-formula>. We remark that our distinguisher is not an attack against the ChaCha20 stream cipher, but rather a theoretical analysis of its internal permutation from the point of view of rotational cryptanalysis. Whenever possible, our claims are supported by experiments.
Gender Equality and Taxation from the Rwandan Perspective

Gender equality has attracted the attention of scholars and politicians around the world. The interest in writing about gender equality and taxation in Rwanda stems from the fact that Rwanda is seen as a country that is progressive in protecting and promoting women's rights based on the principle of gender equality. Although it is at the centre of debates worldwide, there are few academic analyses that link the principle of gender equality to taxation. This shortcoming is particularly true for developing countries, of which Rwanda is one. This paper therefore identifies the explicit and implicit gender biases in the Rwandan tax system. In doing so, it shows that Rwanda, like other modern and democratic states, has taken an important step towards eliminating explicit tax gender discrimination. However, it also highlights some grey areas that need to be addressed in order for Rwanda to remain free from implicit gender-based tax biases.
Bimbingan Kemampuan Berbicara Melalui Penerapan Pembelajaran Media Gambar Di Raudhatul Athfal Al-Huda Desa Sungai Tonang

The research aims to improve children's speech skills through the implementation of image media learning at Raudhatul Athfal Al-Huda in Tonang River Village. The research method used is class action research (PTK) with Kemmis and McTaggart models. Data collection techniques using observation and documentation. The results of the study show that the implementation of the learning method of image media has a positive impact on children's speaking skills. There was a significant improvement in children's ability to describe objects in detail and specifically as well as in describing images. More than 89% of students achieved a significant improvement in speech skills after following this guidance. In conclusion, the implementation of image media learning is effective in improving speaking skills in Raudhatul Athfal Al-Huda, Tonang River Village. The suggestion of this study is to continue the application of this method by considering the needs and development of children in developing speech skills to improve language teaching approaches at an early age.
An Analysis Of Students’ Interest In Learning English Through Pop Up Book Media

This researcher aims to determine the students' interest through pop up books towards the success of English learning and the students' ability to use this technique in the learning process. For qualitative data, a questionnaire was used by distributing questionnaires to students and then conducting several interviews about the application of the technique. The test results increased to 85%. Meanwhile, as a result of analyzing students' opinions, we found that pop up book media is effective in improving students' English interest. Applying pop up technique in English learning, especially interest, is an appropriate strategy to increase students' English interest in the classroom.
Applications of linear algebra in data science

Linear algebra serves as a foundational pillar in the realm of data science, underpinning various analytical techniques and methodologies. This research paper explores the multifaceted applications of linear algebra in the field of data science, elucidating its pivotal role in data manipulation, analysis, and interpretation. By leveraging concepts such as matrix operations, eigenvectors, and singular value decomposition, data scientists can effectively model complex systems, extract meaningful insights, and solve intricate problems encountered in diverse domains. Through a comprehensive examination of real-world case studies and applications, this paper highlights the indispensability of linear algebra in driving innovation and advancement in data-driven decision-making processes. Furthermore, it discusses the significance of understanding linear algebraic principles for aspiring data scientists to proficiently navigate the intricacies of modern data analysis tasks.
Aplikasi Media Pembelajaran Praktik Sholat Idul Fitri Menggunakan Augmented Reality Berbasis Android

The problem of this research is the lack of interactive and interesting resources to help individuals understand and perform Eid prayers correctly, as well as the lack of learning time given to students related to learning prayer practices. To overcome this problem, this research develops a learning media application for Eid prayer practices that uses augmented reality with the Android-based Marker Based Tracking Method. The development model used is the Research and Development (R&amp;D) development model. The results of this study indicate that based on the benefit aspect questionnaire data, the calculation of the percentage score of the application benefit aspect shows a result of 83.75%. Meanwhile, the visual communication aspect received a percentage score of 82.19%. Both aspects fall into the good category, indicating that this application is feasible to use as learning media.
Study on the Influencing Factors of Fitness Behavior of Young Female Teachers in Hubei Province

Hubei Province; College female teachers; Fitness behavior; Influencing factor
LOGIC OF DIDACTIC DISCOURSE. AN EPISTEMOLOGICAL

The need to assume a teaching discourse implies a specific approach that has as determinant core the subjectivity of the educational actor who transmits/receives certain messages. Such messages are decoded and reformulated, we could say, even repeated at the level of curricular contents, most often of a scientific nature. Thus, the understanding, or the misunderstanding of such contents lies in the fact that a discursive algorithm is not always observed, precisely in order not to confine the teaching activity to the level of a convergent specific attitude. In other words, the understanding and misunderstanding of a message, the curricular content, can result from the very mutual attitude existing among the participants in the teaching communication process. Such an attitude is especially obvious when using a divergent specific attitude, which has the effect of using strategies based upon creativity. Beyond the existing conceptual paradox (the term "strategy" itself implies, or at least should imply the use of a clear, coherent, well-structured and organized algorithm) at the level of teaching, our reference is mainly focused on the ways in which one can build a teaching discourse taking into account a specific substantiation of a logical nature, i.e. an educational logic. Therefore, the purpose of this approach that we initiated is to analyze how a logic can be achieved at the level of teaching discourse and to what extent it can validate an educational reality.
Domain Specific Transformers-Based Prioritization of Re-admission for Patients in Healthcare

Hospital readmissions, indicative of healthcare quality and resource-intensive, require improved efficiency through accurately identifying higher-risk patients; while numerous prediction models exist for re-admissions, none have prioritized them specifically. In this research, we focused on creating a predictive model designed to prioritize patient readmissions based on their discharge summaries from MIMIC III. To improve the model's performance and address class imbalance problems in this domain, we applied data augmentation. BioBERT was employed for feature extraction during model training, and the Optuna framework was utilized for hyperparameter optimization. The predictive model demonstrated remarkable performance when evaluated on the testing dataset, achieving AUROC score of 0.69. The experimental results highlight the model's potential to effectively prioritize patient re-admissions, thereby contributing to more informed and efficient healthcare decision-making processes.
The transformation of the training of preschool educators in the digital age

Nowadays, the informatization of education, the purpose of which is the use of new information technologies in the global rationalization of intellectual activity, is considered as a mandatory and absolute condition for the creation of a new information society. Therefore, we have singled out the main ways of high-quality professional training of future teachers of preschool education institutions in the modern information society. The professional training of future teachers of preschool education institutions is considered as a multifactorial structure in the modern information society and its tasks and educational components are distinguished. Four stages are distinguished and two methods of integration of professional training of future educators are characterized (horizontal and vertical). The application of the personally oriented, competence-based, integrative, activity approaches discussed in the article will provide, by the requirements of employers, a high level of training of a creative, competitive personality of the future specialist. The main technologies and factors that are effective in the modern information society are highlighted for high-quality professional training of future specialists. The importance of distance education, cloud technologies, and educational blogs is proven and the advantages of using them for the professional training of specialists are shown.
Cataract Disease Diagnostic Expert System Using Forward Chaining Method

Cataracts are a disease when the lens of the eye becomes cloudy and cloudy. In general, cataracts develop slowly and are not bothersome at first. However, over time, cataracts will interfere with vision and make the sufferer feel like seeing a foggy window, making it difficult to drive, read, and carry out daily activities. The expert system for diagnosing cataracts at Rafflesia Hospital Bengkulu was created to assist in diagnosing cataract patients by entering the symptoms experienced by the patient. In the process of diagnosing the expert system, a method has been applied as an inference engine that makes it easier to find out the results of the diagnosis, this method is Forward Chaining. Based on the results of the tests that have been carried out, the functionality of the expert system for diagnosing cataracts at Rafflesia Bengkulu Hospital has been running well as expected and can provide information on the results of consultations to determine cataracts.
Decentralized Identity Management and Blockchains: Design Patterns and Architectures

Securely and efficiently managing digital identities is a pre-requisite to enabling prosperous digital service economy underpinned by trust. The concept of digital identity goes beyond just providing a credential for logins. A truly effective digital identity scheme should be able to model the observations that people, throughout their life, tend to carry many different senses of "identities" (or personas) and each identity is fused with different sets of personal artefacts overtime. Of course, digitizing all aspects of a person's identity must be supported by the highest level of security and privacy protection possible. A decentralized identity management aims to provide an individual with a platform to manage multiple identities and associated artefacts for different contexts. More importantly, the users will exert full control over their identities in terms of when and with whom the identity is formed and shared with others, without having to rely on a centralised identity provider. A distributed ledger technology like blockchain can play a vital role in the design and implementation of a decentralised identity management scheme. Indeed, a decentralised identity management scheme such as self-sovereign identity (SSI) has been acclaimed the "killer app of Blockchain". This chapter aims to introduce the foundational concept and motivation of decentralised identity management, examine the current state of the art and present a set of software design patterns that could help guide the designers and developers of decentralised identity management systems.
Leveraging High Resolution Remote Sensing Images for Vehicle Classification using Sea Lion Optimization with Deep Learning Model

Abstract High-resolution remote sensing data renders worldwide images faster than traditional data collection strategies. Hence small objects such as cars are easily detected. Vehicle recognition on aerial remote sensing images (RSIs) in the complicated background of urban zones has always gained a lot of interest in the remote sensing field. The automatic vehicles enumeration research domain plays a significant role in several applications, including traffic management and monitoring. Target detection technology will be a crucial part of computer vision (CV) technology, and target detection techniques were enforced in several domains. Therefore, this study develops a new Vehicle Recognition and Classification using Sea Lion Optimization with Deep Learning (VRC-SLODL) model on RSI. In the presented VRC-SLODL technique, the major intention lies in recognising and classifying vehicles present in the images. The bilateral filtering (BF) technique can initially improve the RSI quality. The VRC-SLODL technique employs a modified residual network (ResNet) model to produce a set of feature vectors. Finally, the SLO algorithm with long short-term memory (LSTM) model is employed for vehicle classification, where the SLO algorithm acts as a hyperparameter optimizer. The experiments were performed on a benchmark dataset to examine the better performance of the VRC-SLODL technique. The obtained values reported the improved classification performance of the VRC-SLODL technique over other models.
Penerapan Metode Kangguru pada Bayi BBLR

Kangaroo Mother Care (KMC) is one of the non-pharmacological measures in handling the incidence of LBW. The success of KMC is supported by several factors, namely knowledge, attitudes and family support. This study aims to determine the factors associated with Kangaroo Mother Care (KMC) in low birth weight (LBW) infants in Sorowako Village. The research design used is a quantitative research with a cross sectional approach. This research was conducted in Sorowako Village in April 2022. The sample was determined using a total sampling technique with a sample size of 30 respondents. The relationship test was carried out using the chi-square statistical test with a significance level of &lt; 0.05. The results showed that the majority of mothers had less knowledge as many as 17 people (56.7%), the majority of mothers had a good attitude as many as 17 people (56.7%), and the majority of mothers had good family support as many as 16 people (53.3%). ). There is a relationship between mother's knowledge and the application of the kangaroo method (ρ = 0.001), there is a relationship between mother's attitude and the application of the kangaroo method (ρ = 0.004) and there is a relationship between family support and the application of the kangaroo method (ρ = 0.002). The conclusion of this study is that there is a relationship between knowledge, mother's attitude and family support with the application of the kangaroo method. Therefore, it is hoped that mothers who give birth to LBW babies can learn about LBW and KMC and can apply them at home. It is hoped that there will be further research related to other factors that affect KMC.
Conversational Breakdown Detector for a Motivational Interviewing Conversational Agent

A conversational breakdown in human-chatbot interaction refers to a disruption or failure in the communicative flow between the human user and the chatbot. To recover a disrupted conversation, the first step is to detect the breakdown. Researchers have proposed methods using supervised learning and semi-supervised learning in dialogue systems to achieve the goal of detecting conversational breakdown. However, few studies have focused on detecting breakdowns in automated therapeutic conversations, especially conversations led by motivational interviewing chatbots. The presence of conversational breakdowns has negative impacts on the human-chatbot interaction, such as frustration, dissatisfaction, or loss of trust. This gap suggests a need to build a robust and efficient conversational breakdown detector that recognizes interruptions during the conversation. Conversational breakdown detection paves the way for further action to recover conversations. In this paper, I develop a novel, unifying framework called “CIMIC” for characterizing the conversational breakdowns of “MIBot,” a motivational interviewing conversational agent for smoking cessation. I collect 200 pieces of conversational data through Prolific and annotate them using the CIMIC framework with a group of four trained annotators. The annotated dataset is then applied as the training set to fine-tune GPT-3 models to build a conversational breakdown detector for the MIBot.