Reviewerly/exHarmony huggingface dataset:
EFL STUDENTS’ PERCEPTIONS ON THE USE OF LIVEWORKSHEETS AS TEACHING AND LEARNING PLATFORM

This study aims to provide comprehensive insights into EFL students' perceptions of using the live worksheets as a teaching and learning platform. The study involved 8-grade students at Al-Ikhlas Excellent Junior High School Rembang, consisting of 9 female and five male students. This study conducted the case study design by using the descriptive statistics method. Surveys and interviews were conducted to gather students' opinions and feedback regarding their experiences with live worksheets. The survey explored aspects such as ease of use, usefulness, satisfaction, and benefits of the live worksheets’ platform in EFL learning. The interviews provided more profound insights into students' perceptions and experiences. The data will be statistically analyzed using descriptive techniques. The result indicated that 78,6% of students had utilized live worksheets for their assignments. From the frequency of use, it was found that 57.1% of students formed their first impressions of Live worksheets through this research, while 50% found it to be well-suited to their needs. Impressively, 85.7% of students reported enjoying the platform. Furthermore, the Likert scale shows that 71,4% of students feel this platform is easy to access. 78,5% of students say this platform has a fascinating display. Then, 78,5% of students convey that this platform has various tasks. In conclusion, Live worksheets emerged as an efficient teaching and learning platform. It was found to be easily accessible, visually appealing, and diverse in its range of tasks, making it a valuable tool for EFL education. Hopefully, this study contributes to the existing literature on technology integration in language education and provides practical insights for educators, curriculum developers, and platform designers.
LANGUAGE LEARNING STRATEGIES OF ESP LEARNERS: A COMPARISON BETWEEN DURING AND POST-PANDEMIC SITUATION

Several studies have highlighted the importance of language learning strategies (LLS) for second and foreign-language learners. These strategies have played a significant role in distinguishing between more successful and less successful language learners. However, the outbreak of the COVID-19 pandemic in 2020 brought about unprecedented changes in the teaching-learning process, with a shift towards online platforms. This study aimed to determine what language learning strategies were applied by the 85 learners of the Mathematics and Biology Departments, ESP (English for Specific Purposes) learners taking English courses during and after the pandemic. In addition, this study also investigated whether there are any similarities or differences in the language learning strategies employed by using comparative design. To collect data, the researcher utilized the Strategy Inventory of Language Learning from Oxford (1990), translated into Indonesian, to ensure clear comprehension among the learners. Descriptive statistics for means and standard deviations were then used to analyze the data. The analysis revealed that, regardless of the circumstances, the students applied all strategies in their learning. Then, they consistently applied the metacognitive and memory strategies more frequently than others.Additionally, the findings indicated that the students utilized the social strategy during the COVID-19 pandemic, while the affective strategy was employed somewhat in the post-pandemic period. The outcomes of this study have significant implications as they raise awareness among learners and ESP instructors about the benefits of employing language learning strategies. Recognizing and utilizing these strategies can significantly enhance language learning outcomes. By understanding the preferred strategies employed by students during different periods, instructors can tailor their teaching approaches accordingly to maximize the effectiveness of language instruction.
PENGEMBANGAN MEDIA AUDIO VISUAL UNTUK MENINGKATKAN HASIL BELAJAR IPS PADA MATERI IPS KELAS V SEKOLAH DASAR

This study aims to produce a learning product in the form of kinemaster-based audio-visual media as teaching material for fifth grade students in social studies on the subject of struggle against invaders to improve student learning outcomes. The method used is the development research (R&amp;D) method, up to the testing stage of the effectiveness of the products that have been developed. To determine the feasibility of material and media, validity tests have been carried out by material and media experts. The results of product validity by two experts obtained very feasible category. Material experts obtained a value of 88.6% and media experts obtained a value of 87.5%. To find out the effectiveness of the media on subject matter, a field trial was carried out at SD Negeri Montor 1. The results of the field trial for student responses to the media obtained 95.6% in the very feasible category and student learning outcomes obtained an average score of 83.3%, the test results of the teacher's response to the media obtained 89.2%. Based on the results of the analysis of two experts and trials on students and teachers, the audio-visual media meets the criteria to be used as a learning medium for Social Sciences (IPS).
Geochemical Parameters for Evaluating the Aptian-Albian Kaolin Deposits at Abu Darag Region, Gulf of Suez: Implications for the Paleoclimatic Conditions in the Depositional Environments

The mineralogical and geochemical constituents of the Aptian-Albian Malha Formation at the Abu Darag region, Gulf of Suez, Egypt, can be discussed here. These constituents are related to paleoweathering and paleoclimatic circumstances that managed the depositional settings. The present study aims to evaluate the geochemical conditions that have control over the depositional environments and characterizes them in terms of lithological, mineralogical, and chemical composition. To achieve this aim, selected kaolinitic clay samples were geochemically and mineralogically examined. Kaolinite and quartz are the main constituent minerals in the investigated samples, whereas anatase and hematite serve as auxiliary minerals. The presence of hematite minerals indicates precipitation in an oxidizing environment, whereas anatase is related to basaltic rock. Bivariate discrimination provenance diagrams, major oxides, and trace elements all identify felsic-intermediate igneous source rocks as the leading contenders. The examined samples were deposited in non-marine environments and underwent weak to moderate chemical weathering as well as severe physical induration in tropical climates.
Digital Security Self-Efficacy Inventory: Validity and Reliability Study

In this research, it is aimed to develop a “Digital Security Self-Efficacy Inventory” to determine the digital security self-efficacy levels of secondary school 7th students. First of all, a 52-item article pool was created by scanning the Social Studies Curriculum and Information Technologies and Software Course and related literature. As a result of expert opinions, necessary arrangements were made, and 7 items were excluded from the scope, and a 45-item inventory consisting of 5 subscales of the five-Likert type was developed. The sub-scales consisted of technical, psychosocial, online shopping, rights and responsibilities, and health sub-scales, respectively. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed for the validity and reliability studies of the inventory, and for these studies, data were collected from 1174 secondary school 7th grade students in total. As a result of the EFA, 5 items from the technical sub-scale were removed from the scale, and a structure consisting of 15 items and 2 factors explaining 32.86% of the total variance was revealed. Psychosocial sub-40,33 percent of the total variance in the scale that explains the 8-point one-factor structure, online shopping sub-5 percent of the total variance in the scale that explains 49,79-item, single-factor structure, rights and responsibilities of the sub-44,32 percent of the total variance in the scale that explains a 7-point single-factor structure and health sub-scale of the total variance in 49,81 5% that explained the single-point-factor structure was revealed. As a result of the CFA, the model fit indices obtained for the subscales are at an acceptable level, and the structure is theoretically and statistically confirmed. As a result of the reliability studies, Cronbach's Alpha reliability coefficients were calculated as 0.85 on the technical sub-scale, 0.77 on the psychosocial sub-scale, 0.78 on the online shopping sub-scale, 0.78 on the rights and responsibilities sub-scale, and 0.75 on the health sub-scale. According to the results of the analyzes, the digital security self-efficacy inventory, which consists of 40 items and 5 subscales developed for secondary school 7th grade students, has proven to be a valid and reliable measurement tool.
An Efficient Interval Type-2 Fuzzy Neural Network Based Classifier

In this paper, a simplified Karnik-Mendel (SKM) is proposed to realize type reduction for type-2 fuzzy models. To construct the fuzzy rule based neural network classifier, the clustering algorithm is employed to generate the nodes in the network, and we explain the interpretable construction process detailedly. In addition, to improve the classification performance, a long term memory based gradient optimization strategy is presented to optimize the structure parameters. Finally, the effectiveness of the proposed model EIT2FNN-C is verified by 2 benchmark classification data sets.
DBoT: Enhancing Scalability and Privacy in Intelligent Building Spaces through a Direct Acyclic Graph Blockchain

Intelligent Building spaces are critical in creating smarter living spaces, which are composed of interconnected Internet of Things (IoT) devices. The growing dependence on these advanced, connected environments has led to several operational challenges related to privacy, security, and scalability due to their distributed architectures and the complex interplay among numerous IoT systems. Recently, Blockchain Technology has emerged as a critical solution for addressing privacy and security challenges in intelligent buildings. However, the IoT devices within Intelligent Building spaces (IBDs) are resource-constrained and face notable challenges in performance and scalability. This indicates a need for a more scalable, energy-efficient blockchain solution without compromising security or privacy. To overcome these challenges within IBDs, we propose a novel approach using the Direct Acyclic Graph Blockchain of Things (DBoT), which utilizes IOTA Tangle Blockchains. We develop a lightweight bundle message model that enhances scalability with an advanced algorithm for energy-efficient proof-of-work computation across network nodes. The DBoT architecture significantly boosts scalability and performance in IBD IoT nodes, simultaneously improving user privacy and access control with varied granularity. Our findings show that the Blockchain-based DBoT method effectively ensures secure connectivity and information flow with growing IoT systems without sacrificing performance.
A Privacy Protection Method of Blockchain-Based E-Voting Using Homomorphic Encryption and Order-Preserving Encryption

The blockchain based e-voting system has realized decentralized storage and eliminated the need for third-party trusted institutions to count votes. However, due to the openness of the blockchain, it still has some security issues such as privacy leakage in the voting process. In this paper, we proposes a privacy protection method for blockchain-based e-voting system by using homomorphic encryption and order-preserving encryption to avoid privacy leakage. The method transmits the transaction data encrypted by homomorphic encryption through a secure channel and records it on the blockchain, and after the voting is over, the results are encrypted in order and published in ciphertext, which realizes the data confidentiality of the entire voting process and avoids the tracking of malicious users. Experimental results show that the proposed method is feasible in terms of performance and safety.
The Influence of Premenstrual Stress on Auditiory and Visual Reaction Time in I Year MBBS Students

Background &amp; Objectives&#x0D; Present study was aimed to assess abnormalities of ulnar nerve in stable COPD patients.&#x0D; Method&#x0D; Study comprised of 60 healthy adults and 60 stable COPD patients (40–50 years) with no clinical neuropathy. Duration of illness and spirometric indices (FEV1%, FEV1/FVC, PEFR %) were assessed. Nerve conduction study of motor and sensory component of ulnar nerve was recorded bilaterally using RMS EMG MKII. Distal latency, nerve conduction velocity and compound motor action potential (CMAP) and sensory nerve action potential (SNAP) were analysed. Significant abnormality was defined as variations beyond mean ± 2SD from healthy adults.&#x0D; Results&#x0D; Observations revealed significantly prolonged distal latency and decreased conduction velocity (demyelination), decreased CMAP (axonal loss) bilaterally of both sensory and motor components of ulnar nerve in COPD patients compared with controls.&#x0D; Interpretation &amp; conclusion&#x0D; Observation suggests that hypoxemia of COPD, by inducing direct action on nerve fibres or pontomedullary portion of brain or by enhancing effect of other neurotoxic substances causes nerve impairment
Research on Imbalanced Data Classification and Computing Methods Using Support Vector Machine

In the current era of big data, all walks of life are transforming towards data-based operations. In this process, many scenarios often produce unevenly distributed data, which is often determined by the nature of the actual problem. Aiming at the problem of imbalanced data set classification, this paper proposes an improved Support Vector Machine model. After random sampling of most samples without replacement and sampling of minority samples with replacement to generate balanced training subsets, Support Vector Machine is trained to solve the problem of low accuracy of imbalanced data set prediction. Then we select the imbalanced data set on the public data set, and conduct an empirical analysis of our proposed scheme to verify the effectiveness of the improved Support Vector Machine model in solving the imbalanced data set classification problem from a relatively comprehensive perspective. The results show that the improved method proposed in this paper can significantly improve the classification performance of imbalanced data.
Chinese Medical Named Entity Recognition based on Expert Knowledge and Fine-tuning Bert

Medical Named Entity Recognition (MNER) plays a pivotal role in Natural Language Processing (NLP), particularly within the medical domain. This research presents an innovative methodology for MNER, adeptly tackling the challenges presented by the diversity of terminologies, privacy issues, and the significant costs associated with annotation in Chinese medical records. In this study, we begin by assembling a meticulously curated medical terminology database, drawing upon the expertise of domain specialists. This comprehensive database enhances the precision in comprehending medical semantic structures, enabling more accurate recognition of named entities in medical texts. To further improve the performance of MNER, we leverage the power of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) language model. By fine-tuning BERT on Chinese medical diagnostic and treatment texts, we achieve proficient recognition and categorization of entities, along with their respective types. Through rigorous experimentation on a large-scale, real-world Chinese medical record datasets, we showcase the superior performance of our proposed framework. Our comprehensive and novel approach is instrumental in accurately extracting named entities from medical texts, thereby enhancing clinical decision support, organizing medical knowledge, facilitating drug discovery, and contributing to a broad spectrum of other medical applications. The results of this study demonstrate the effectiveness and potential impact of our methodology in the field of medical named entity recognition. By addressing the challenges specific to Chinese medical records, we provide valuable insights and advancements that can benefit various healthcare applications and ultimately improve patient care. This research holds significant value and relevance in the field.
GrMC: Towards Interpretable Classification Models That Are Also Accurate

Interpretability of classification models is an important issue. However, there is a lack of intrinsically interpretable models that are also highly accurate. To fill the gap, we introduce a new classification model type, namely Group Model Committee (GrMC), and an associated learning algorithm. Our key ideas are: (1) Divide a classification task's data space into several groups such that each group is defined by a simple condition and it has a unique, interpretable group model; (2) A data instance belongs to a group if it satisfies the group's defining condition and it is classified by utilizing the group models of the groups it belongs to. Experiments show that small interpretable GrMC models are often more accurate than existing intrinsically interpretable models, and also more accurate than Random Forests models. GrMC also has other strengths.
One-Shot Grading: Design and Development of an Automatic Answer Sheet Checker

Technological advancements have transformed the landscape of examination grading, necessitating efficient and reliable solutions. This paper introduces “One-Shot Grading, “ an Automatic Answer Checker that utilizes machine vision and image processing to evaluate multiple-choice and true-false questions. The system offers adaptability to diverse exam formats, allowing for modifications to the answer sheet's choices, parts, and size. Extensive validation tests demonstrate consistent performance with 100.0% accuracy in recognizing student IDs and grading sheets marked with a 2B pencil. The system effectively identifies and rejects erroneous sheets, maintaining a robust grading process. Comparison with the ZIP GRADE app confirms the reliability and accuracy of the Automatic Answer Checker. The system displays flexibility and accuracy in grading papers of varying weights, showcasing its adaptability to real-world conditions. Future research should explore extending the system to different question formats and investigating environmental factors' impact on performance.
Modeling Machine Learning to Solve Distribution Problems and the Number of Backlogs in Maintenance

The purpose of this research is the need to reduce backlogs and distribution for technician problems in the maintenance process. The distribution of work to technicians for maintenance will be effective in meeting the goals of the maintenance system of the University of the Thai Chamber of Commerce to be more efficient while reducing delays in maintenance by implementing Machine Learning (ML) using the form Support Vector Machine (SVM), while research is developed on the application UTCC-CMMS is able to work more comprehensively by allowing the application to act as a staff. The work related to maintenance can receive maintenance requests on the algorithm VSM to suitable technicians. and evaluate the performance as well. Also, contact purchasing or support external technicians in maintenance, resulting in faster maintenance there is a lot of backlog work reduced than before, allowing the technician and everyone to work together as well. The evaluation of operations in the past found that it received a score of 2.44. After the adoption of ML, it was found that the score was 4.44, representing an increase of 81.96%.
Substitute and Complementary Open Source Software in Blockchain

Recently, blockchain technologies seem to have emerged from a period of disillusionment named in the hype cycle, and development has become active again. In this research, substitute and complementary repositories were identified from GitHub records in order to grasp the state of representative blockchain platforms: Bitcoin, Ethereum, Hyperledger, Ripple, and Corda. Within many blockchain platforms, it is common to have complementary relationships. Ethereum and Hyperledger also have a complementary relationship across platforms. The results showed that Ethereum is reactivating Hyperledger, whose development is stabilizing. This research proposes a methodology to find next-step software development from the network based on developers' skills via their movements between repositories.
Design and Development of Operation and Maintenance Platform for Material Service Performance Test Equipment

Material testing equipment is a significant piece of equipment in the process of material development. Its working condition is extreme and complex. However, there is no mature mode for the operation and maintenance of this kind of equipment. The research designed and developed a platform for the operation and maintenance of material service performance test equipment, the platform was designed for the whole process of material testing, and functional design was carried out from three scenarios: experimental preparation, test process, and data analysis. Based on the whole process, the platform developed operation and maintenance modules: Test Data Monitoring Modules, and Business Function Modules. To ensure the usability of the platform, the interface design was carried out according to the economic principle of information processing during the development process, and the developed platform has been practically applied in a laboratory.
Method for Determining Material Demands by Combing Deterministic and Probabilistic Information in Flexible and Changeable Production Systems

In today's dynamic manufacturing environment, flexible and resilient production systems are crucial for coping with constantly changing internal product and production requirements coupled with external market and customer demands. Conventional production systems often lack the capability to adapt to changing requirements due to their fixed structures and technical limitations. To address these challenges, various flexible and changeable production system approaches have been developed in the last years. However, material provision becomes challenging due to increasing degrees of freedom and uncertainty due to arising turbulences, making it difficult to match demand and material provision in terms of time, location, and quantity. This paper presents a method to determine material demands that considers both deterministic and probabilistic information regarding material demand location, quantity, and time. An experimental research approach based on a minimal system was pursued, incorporating simulation experiments covering parameter variation using the Monte Carlo method. The results demonstrate that the developed method successfully determines material demands, enabling flexible and target-size-optimized material provision with potentially arising turbulences.