Reviewerly/exHarmony huggingface dataset:
Formative Assessment of Students’ Extracurricular Activities in the Context of Implementation of A Posteriori Model of Professional Training

The paper discusses the experience of introducing formative assessment of future teachers’ extracurricular activities in the process of professional training at university. The study aims to substantiate and carry out a practical evaluation of the methodology for formative assessment of teacher trainees’ extracurricular activities in the practice of higher education. The work provides detailed insight into the content, organisational and practical components of the developed methodology for formative assessment of higher education students’ extracurricular activities. Experimental work on a practical evaluation of the methodology took place at Togliatti State University and made it possible to assess the level of personal and professional development of students of a pedagogical brigade, to give recommendations for further improvement of teacher trainees’ extracurricular activities. Scientific novelty of the study lies in using the case method as a tool for formative assessment of the results of higher education students’ extracurricular activities. As a result of the study, it has been proved that the introduction of the formative assessment procedure into the process of students’ professional training has a positive impact on the development of self-assessment of personal achievements, helps to find “educational deficits”, to outline ways to eliminate them, which ensures students’ professional formation.
POSSIBILITIES OF SOCIOLOGICAL METHODOLOGY IN DESIGNING A MODEL OF EFFECTIVE INTERACTION OF FAMILY AND SCHOOL

The article examines the problems of using sociological methods in the social design of organizational structures that ensure the implementation of the main directions of state youth policy, as well as civil and patriotic education in educational institutions.
THE EFFECT OF SELF -TALK STRATEGY ON STUDENTS SPEAKING ABILITY FOR ELEVENTH GRADE OF SMK SWASTA SWAKARYA SALAPIAN

&lt;p&gt;The purpose of this research is to know the effect of Self Talk Strategy on students’ speaking ability in academic year 2021\2022. The type of this research is quasi experimental group. It is a research to know that the effect between two variables, there are dependent variable is using Self Talk Strategy and independent variable is students’ speaking ability. This research was done at Smk Swasta Swakarya Salapian in academic year 2021\2022. The population of this research was three classes the total of the students was 88 students. Sample of the research was taken by two classes, for experimental class and control class. Based on the data, the result of in significance 5%, result was 1,029 &amp;gt; 0, 3365 it means that there was a significance effect between Self Talk Strategy to the students’ speaking ability in eleventh grade of Smk Swasta Swakarya Salapian in academic year 2021\2022.&lt;/p&gt;
Design and Optimization of Aerial-Aided Multi-Access Edge Computing towards 6G

Ubiquity in network coverage is one of the main features of 5G and is expected to be extended to the computing domain in 6G. In order to provide this holistic approach of ubiquity in communication and computation, an integration of satellite, aerial and terrestrial networks is foreseen. In particular, the rising amount of applications such as In-Flight Entertainment and Connectivity Services (IFECS) and SDN-enabled satellites renders network management more challenging. Moreover, due to the stringent Quality of Service (QoS) requirements edge computing gains in importance for these applications. Here, network performance can be boosted by considering components of the aerial network, like aircrafts, as potential Multi-Access Edge Computing (MEC) nodes. Thus, we propose an Aerial-Aided Multi-Access Edge Computing (AA-MEC) architecture that provides a framework for optimal management of computing resources and internet-based services in the sky. Furthermore, we formulate optimization problems to minimize the network latency for the two use cases of providing IFECS to other aircrafts in the sky and providing services for offloading AI/ML-tasks from satellites. Due to the dynamic nature of the satellite and aerial networks, we propose a re-configurable optimization. For the transforming network we continuously identify the optimal MEC node for each application and the optimal path to the destination MEC node. In summary, our results demonstrate that using AA-MEC improves network latency performance by 10.43% compared to the traditional approach of using only terrestrial MEC nodes for latency-critical applications such as online gaming. Furthermore, while comparing our proposed dynamic approach with a static one, we record a benefit of at least 6.7% decrease in flow latency for IFECS and 56.03% decrease for computation offloading.
FPGA-Based Cosimulation of S-Box Constitution from Fractional Order Liu System

Abstract Substitution box(S-box) has an important duty in encryption. In recent years plenty of S-box designs by chaotic systems have been studied. In this paper a high speed S-box constitution from fractional order Liu system on FPGA is proposed. The design was implemented by using Xilinx System Generator(XSG) toolbox in MATLAB/Simulink software in single precision floating point numbers format and a low cost Basys 3 FPGA trainer card was used. In addition, numerical fractional order equations was solved by using Grünwald-Letnikov(GL) method with 1024 elements length. The S-box table was constructed about 0.137s. After all performance analyzes as nonlinearity, strict avalanche criterion, differential probability, bits independent criterion, and equiprobable input/output XOR distribution, were performed. The performance analyzes shown that the design was satisfactory.
PROFICIENCY AND APPROACHES IN TEACHING CHILDREN WITH LEARNING DISABILITIES AMONG FILIPINO TEACHERS

The main purpose of the study is to determine the approaches and proficiency of teachers in teaching children with learning disabilities in public elementary schools in San Mateo North and South District, Division of Isabela for the School Year 2019-2020. Descriptive survey was employed. The school heads and SpEd teachers served as respondents. Mean, t-test and Spearman rank were used to analyze data. The results showed that the teachers teaching students with learning disabilities are proficient and often used different approaches. The teachers are knowledgeable about the applicable teaching approaches to be employed in teaching students with learning disabilities. Likewise, as to the extent of employing of approaches, these were evident as practiced by the teachers and as confirmed and observed by the school heads. School Heads and teachers have the same level of assessment in terms of the extent of approaches in teaching students with disabilities. The teachers are proficient in handling students with learning difficulties. In terms of teachers’ proficiency, both teachers and school heads assessed the teachers as proficient. Therefore, they have the same level of assessment. Teachers frequently used the approaches as manifested by their level of proficiency except for content knowledge. Thus, proficiency of teacher in content knowledge do not depend on approaches being used. The teachers and school administrators shall attend trainings, workshops and conferences related to the varied teaching approaches applicable to the students with learning disabilities. The administrators and focal person of SPED in the district shall engage more related activities and trainings about the nature of content knowledge to be included in the curriculum of the special education to update the content and competencies.
Load balancing technique based on hybrid resource utilization in cloud computing

Cloud computing uses the internet to supply dynamic services including memory, data, bandwidth and applications. Work schedules have an influence on cloud service reliability and performance. A proper provisioning method is required for a systematic resource allocation, which comprises of large virtual resources. Depending on the present state of the system, load balancing solutions can be distinguished as dynamic or static. Dynamic or static load balancing solutions can be employed to increase server response time or to raise load balancing factors for quicker and more efficient resource utilization. To decrease the load across resources and maximize CPU usage, a hybrid load balancing technique is developed. In the cloud, we have a finite quantity of resources that must be efficiently managed in order to fulfill tasks. Requests are transmitted to a cloud server, which assigns work via quadratic probing. During load balancing, the load is shifted from heavy-weighted servers to lighter-weighted servers, enhancing CPU usage. The suggested methodology's performance was assessed using average mean response time, make-span, average make-span, and average resource utilization. The Load Balancing Algorithm (LBA) is created with the primary purpose of reducing job completion time and increasing the average resource utilization ratio.
Automatic Generation of Large-Scale Assessment Questions

Assessment is the means used to diagnose learning difficulties. Assessment through questionnaires is one of the ways used to measure the level of content learning by students. Manually elaborating questions is time-consuming and makes the teacher uncomfortable thinking about different questions. Using an automatic question generator allows the teacher to access several questions and apply automatic correction, disseminating assessment results more quickly. If we analyze Massive Open Online Courses (MOOCs), it becomes humanly impossible to correct the large volume of questions manually. We present an institutional tool that recognizes chemical entities from free texts inserted by the teacher and automatically generates questions and their respective answers. We recognized the chemical entities through Local Grammar (LG), and then we elaborated free-text and fill-in-the-blank questions through triplets with spaCy in Python language. To validate the tool, we used two recognized books in Organic Chemistry. Chemistry specialists validated and indicated the level of difficulty of the questions. We generated 64 questions, 33 of which were free-text questions, and 31 were fill-in-the-blank questions. The average of the experts’ assessment indicated that 78,77% (average) and 87,5% (median) of the questions have quality and that most questions have an intermediate difficulty level. This research suggests as main contributions the recognition of named entities in the chemical area and the automatic generation of questions in Portuguese. These tasks can facilitate the human being’s job, reducing the time for generating and correcting questions and reducing institutional costs with such activities.
Pembelajaran Menulis Teks Prosedur Menggunakan Model Discovery Learning pada Siswa Kelas XI SMK Negeri 2 Kediri

The low skill of writing procedural texts in class XI students at SMK Negeri 2 Kediri is the problem behind this research. Students who tend to be passive result in educational goals in general being less achievable. One of the important things that causes this problem to arise is the lack of interest of students to take an active role in the learning process. From the problems that arise, the author intends to provide a solution that can be applied, namely using the discovery learning learning model in learning to write procedure texts. This study uses an experimental quantitative research model. The population in this study were students of class XI SMK Negeri 2 Kediri. The sample of this study consisted of an experimental class, namely class XI Multimedia 1, and a control class, class XI Accounting 1. The stages of this research were divided into three, namely initial test, test, and final test. The author hopes that this research can be a source of reference for educators in accordance with the results obtained in the study.
Students’ Perceptions on the Use of Group Discussion Method towards English Speaking Proficiency

The researchers place a great deal of emphasis on the Group Discussion Method since it is necessary for students towards their communication proficiency. Communication is an important element to achieve better understanding among communicators. Therefore, this present study aims to discover students’ perception of using the Group Discussion Method towards their proficiency in English speaking. This research relays on a quantitative descriptive research design using a questionnaire to collect data. The questionnaire was distributed online to thirty-four respondents. The respondents are fifth-semester students of the English Language Education Department of Universitas Teknologi Yogyakarta who has learned English speaking using the Group Discussion Method. Data were analyzed using quantitative descriptive perceptions presentation. The research findings indicate that 80% of respondents state that Group Discussions Method is an interactive, friendly, innovative, and interaction-built approach between teacher and students. Besides, it also increases their English speaking proficiency since they are learning in a group where they are required to talk and discuss in English. This research is limited to small-scale respondents; thus, further researchers are recommended to conduct research with wide-scale respondents.
PERTUMBUHAN MISELIUM JAMUR TIRAM PUTIH (Pleurotus ostreatus) PADA MEDIA FORMULA JERAMI JAGUNG DAN LIMBAH BIJI KOPI

Corn straw and coffee bean waste as a medium and source of nutrients needed for the growth of mycelium white oyster mushroom (P. ostreatus). This research has been carried out at the Microbiology Laboratory, Department of Biology, Faculty of Mathematics and Natural Sciences, Tadulako University. The purpose of this study was to determine the growth of mycelium and the characteristics of white oyster mushroom (P. ostreatus) in the formulation of corn straw and coffee bean waste media. This study was designed in a completely randomized design (CRD), consisting of seven treatments and three replications, namely P1 (100% corn straw powder), P2 (95% corn straw powder + 5% coffee bean waste), P3 (90% corn straw powder + 10% coffee bean waste), P4 (85% corn straw powder + 15% coffee bean waste), P5 (80% corn straw powder + 20% coffee bean waste), P6 (75% corn straw powder + 25% coffee bean waste), P7 (70% sawdust + 20% rice bran + 10% corn flour as a positive control). The results showed that corn straw and coffee waste which were used for growth media of white oyster mushroom mycelium (P.ostreatus) could be used as basic media because mycelium could grow well in all treatments. The growth rate of white oyster mushroom mycelium in P3 treatment enjoyed very fast growth and P6 treatment showed very slow growth. The highest number of colonies was in treatment P3 6.40x1011CFU/grand the lowest number of colonies was in treatment P6 2.66x1011CFU/g. Treatment P3 takes a shorter time to fill the culture bottles, which is 30 days, while P6 takes a longer time to fill the culture bottles, which is 48 days.
Machine Failure Analysis Using Multinomial Logistic Regression

Purpose: The main purpose of this study is to carry out a failure analysis of a filling machine by applying multinomial logistic regression.Design/methodology/approach:For this purpose,data related to failure mode, product, scrap rate, and shift parameters were collected from the machine and analysis was conducted by establishing two multinomial logistic regression models.Findings: Statistical results suggest that a hydraulic failure must be expected while filling the mix product. Besides, it is highly probable that a final folder failure will occur while filling the cherry product. Paper failure stands out while filling the apple product compared to other products. In addition, it is likely that a final folder failure will occur while filling this product. Photocell failure is common while filling the peach product. Results of the study show that the odd for low-level scrap is high when there is any failure in the machine.Discussion: A more effective analysis can be performed by collecting parameters that may affect the position of machinery such as vibration, humidity, temperature and pressure through the sensors to be installedon various units of the filling machine and adding them into the models developed under the study.
An Analysis on Students’ Pronunciation of a Scientific Text

This research was conducted to find out what errors the student made when reading a scientific text and the sources of those errors. The subjects of this research were 14 fourth-year-students of English Department of Teacher Training and Education Faculty of Tanjungpura University. The research methodology used in this research is Error Analysis. There were three steps that were done to find the result of this research, distribute, quantify, and analyze. The result of this research was all 14 subjects made errors when reading the text. Based on the analysis that was done during the research, it was concluded that the sources of errors occurred were intralingual and interlingual errors. Intralingual errors are errors that were made during the transfer process from L1 to TL. Interlingual errors are errors that are made because of the influence of external factors, such as L1 characteristics, slip of tongue, and habits.&#x0D; Keyword: Error Analysis, Scientific Text, Fourth-Year Students
Privacy-Preserving Feature Selection with Fully Homomorphic Encryption

For the feature selection problem, we propose an efficient privacy-preserving algorithm. Let D, F, and C be data, feature, and class sets, respectively, where the feature value x(Fi) and the class label x(C) are given for each x∈D and Fi∈F. For a triple (D,F,C), the feature selection problem is to find a consistent and minimal subset F′⊆F, where ‘consistent’ means that, for any x,y∈D, x(C)=y(C) if x(Fi)=y(Fi) for Fi∈F′, and ‘minimal’ means that any proper subset of F′ is no longer consistent. On distributed datasets, we consider feature selection as a privacy-preserving problem: assume that semi-honest parties A and B have their own personal DA and DB. The goal is to solve the feature selection problem for DA∪DB without sacrificing their privacy. In this paper, we propose a secure and efficient algorithm based on fully homomorphic encryption, and we implement our algorithm to show its effectiveness for various practical data. The proposed algorithm is the first one that can directly simulate the CWC (Combination of Weakest Components) algorithm on ciphertext, which is one of the best performers for the feature selection problem on the plaintext.
The Rate of Reading Poverty After the COVID-19 Pandemic School Shutdown and Specific Intervention Strategies for Lower Primary School Pupils in the Southern Province and Western Area of Sierra Leone

The study investigates the rate of reading poverty after the COVID-19 pandemic school shutdown and specific intervention strategies for lower primary school pupils in the southern province and western area of Sierra Leone. Randomised experiments of mixed-methods reading interventions for 100 struggling readers in class 5 and 20 English teachers selected from 10 primary schools (5 in the south and 5 in the Western Area) were carried out. The rate of reading poverty and proficiency was first determined in the selected classes using the Access Center method of reading assessment. Two intervention strategies were administered, and the scores for each intervention strategy were measured and compared. The findings reveal that over 70% of the pupils tested in reading in the south mispronounced or skipped five or more words when reading. The overall scores for pupils in the Western Area were relatively lower (a majority scoring less than 30%). The READ 180 reading intervention strategy is recommended to be introduced as the preferred teaching method at the pre-primary and primary school levels in Sierra Leone because of its proven potentiality to increase reading proficiency more than the popular Direct-teaching Model.
Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual appearance while entangled with different objects, CZSL is still a challenging task. Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state; the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets. In this paper, we propose a novel Siamese Contrastive Embedding Network (SCEN) (Code: https://github.com/XDUxyLi/SCEN-master) for unseen composition recognition. Considering the entanglement between state and object, we embed the visual feature into a Siamese Contrastive Space to capture prototypes of them separately, alleviating the interaction between state and object. In addition, we design a State Transition Module (STM) to increase the diversity of training compositions, improving the robustness of the recognition model. Extensive experiments indicate that our method significantly outperforms the state-of-the-art approaches on three challenging benchmark datasets, including the recent proposed C-QGA dataset.
Smart Profitable Solutions with Recommendation Framework

Abstract: Discovering the frequent patterns in transactional databases is one of the crucial functionalities of apriori algorithm. Apriori algorithm is an algorithm which works on the principle of association rule mining. It is a dynamic and skillful algorithm used for discovering frequent patterns in a database, hence proving out to be efficient and important in data mining. Apriori algorithm finds associations between different sets of data. Every different set of data has a collective number of items and is called a transaction. The accomplishment of apriori is the set of rules that expose us how often any particular item or a set of items is contained in a set of data. In our proposed system, to provide efficiency, our basic aim is to implement apriori algorithm by setting up a threshold value and a varying support count which will act as a filter for our recommendation data. We can adjust the threshold value in order to increase or decrease the accuracy of the system. We have used apriori algorithm keeping in mind, its application in retailing industry and its capability of computing and handling large datasets and especially for the purpose of market basket analysis. The use of apriori algorithm along with analytical tools can provide insights into data and help the user in management and decision making provided that the user feeds the system in a correct way. Our aim is to provide user with recommendations which would ultimately help them in improving their business operations.
The “Funnel of Causality” as a Tool for Analysing Factors Affecting the Implementation of the Corporate Training Model for Teachers of an Educational Organisation

The study aims to adapt the “funnel of causality” model to analyse the factors influencing the implementation of an andragogical model of corporate teacher training by stages of its development. The paper describes the operational scheme of implementation of the said learning model and the factors (causes) that directly or indirectly affect the effectiveness of its adoption. Scientific novelty of the study lies in the fact that the “funnel of causality” model, designed for the analysis of socio-political processes, is used to describe and analyse the corporate training model for teachers of an educational organisation. As a result, it has been proved that the “funnel of causality” model, created to analyse the causes of a certain vote outcome taking into account various factors, can be interpreted for an andragogical corporate training model for teachers of a particular educational organisation.