Reviewerly/exHarmony huggingface dataset:
Enhancing English Reading Proficiency in Agriculture Vocational High Schools: Developing ESP-Based Supplementary Materials for Grade X Students

This research study aims to develop suitable supplementary English reading materials for tenth-grade students in the agriculture vocational high school program. The study utilizes the Research and Development (R&amp;D) approach, focusing on the Analysis, Design, and Development stages of the ADDIE model. Data is collected through document analysis, questionnaires, and interviews from a class of 16 X-grade agriculture students and their English teachers at SMK Gotong Royong. Two types of questionnaires are employed: one for the needs analysis and another for expert judgment. The data is analyzed quantitatively and qualitatively, with mean central tendency measurement used to categorize the developed materials. Suharto's theory is utilized to determine the intervals and categories based on the scores obtained. The data analysis process involves organizing and preparing the data, reading and reviewing all the data, coding the data, generating descriptions and themes, and representing the findings. The research findings indicate the following target needs of the students: (1) understanding agriculture science texts and types of agriculture tools, (2) improving reading skills for agricultural work and acquiring relevant vocabulary, and (3) expanding their vocabulary related to the study program. The findings serve as the basis for the development of appropriate supplementary English reading materials. English teachers are encouraged to understand students' needs and add relevant sources or develop materials in collaboration with other teachers. Other materials developers are suggested to consider the learners' needs and the practical context of their field to create valuable materials for vocational high school students. Keywords: English reading proficiency, agriculture vocational high schools, ESP-Based supplementary materials, grade X students
The pragmatic profile of ChatGPT: assessing the communicative skills of a conversational agent

This study examines the pragmatic abilities of OpenAI’s ChatGPT, a conversational agent based on the multi-layer Transformer network GPT-3.5. To do so, we administered a language battery to assess expressive and receptive pragmatic skills and compared the results with human performance. ChatGPT results were mostly human-like but revealed weaknesses in the domains of the Gricean maxim of quantity, text-based inferences, physical metaphors, and humor comprehension. On the one hand, these findings suggest that at least part of the linguistic pragmatic competence, as evaluated via current assessment tools, might be distributionally encoded in language. On the other hand, situated and meta-representational aspects of pragmatic inferencing appear to be not yet fully accounted for in LLMs.
3D Pose Estimation of Porous Workpiece Basedon Binocular Vision System

Abstract The workpiece with multi-hole is common in the field of machine production and manufacturing. Robot-oriented automated docking is an important means to improve product assembly efficiency. How to accurately estimate the position and the pose of porous workpiece is drawing more and more attention. In this paper, we propose a new binocular vision method for 3D pose estimation. First, an ellipse detection method is used to extract the hole feature. It uses three geometry constraints to ensure the accuracy of the ellipse detection. Second, the vanishing point and vanishing line of the workpiece plane are calculated using the ellipse information. Then the real projection point of the circle center can be accurately obtained through the vanishing line. The position of the circle center is reconstructed by the triangulation technology in our method. Last, the structural characteristics of the multi-hole workpiece are fully employed in the step of pose estimation. The posture is calculated by fitting the plane of those 3D points of circle center in order to reduce the error. Experiments are performed. The precision error of position estimation could be within 0.15mm and the precision error of pose estimation could be within 0.5°, meeting the operation requirements of machine production and manufacturing. These results verified the high accuracy and strong robustness of our proposed method.
Review of Image Classification Algorithms Based on Graph Convolutional Networks

In recent years, graph convolutional networks (GCNs) have gained widespread attention and applications in image classification tasks. While traditional convolutional neural networks (CNNs) usually represent images as a two-dimensional grid of pixels when processing image data, the classical model of graph neural networks (GNNs), GCNs, can effectively handle data with graph structure, such as social networks, recommender systems, and molecular structures. In this paper, we will introduce the problems that graph convolutional networks have had, such as over-smoothing, and the methods to solve them, and suggest some possible future directions.
The Implementation of Piano Proficiency Course in Music Education Program under Online Environment

Piano proficiency course includes the basic skills of piano, it has the posture, hand position, finger strengthening and note-reading.The model of this course shift face-to-face to online class since the outbreak of COVID-19.The aim of this research is to investigate the strategies of piano playing applied in online class of piano proficiency course.This study used the qualitative research method and lecturers were interviewed by semi-structured interviews for collecting data at Universiti Pendidikan Sultan Idris (UPSI) in Perak, Malaysia.Furthermore, it applied content analysis method and the thematic analysis method to analyze the collected data.The findings of this study indicated that lecturers should provide the variety of teaching platforms and develop the pluralistic teaching tools for online classes.The significance of this study is to improve the online model of piano proficiency courses in music education program.
Generation Alpha Numeracy Learning Assistance with Anti-Gadget Media at SDN Tongas Wetan IV Probolinggo

The numeracy skills of SDN Tongas Wetan IV students are at a low level. The school is located in the remote Tongas Wetan village, surrounded by rice fields, with limited road access. Unfortunately, the absence of a cable or Wi-Fi internet network hinders technological adaptation and forces the numeracy learning process in the classroom to rely primarily on classical methods, with minimal use of learning media. Therefore, it is essential to assist students in learning numeracy using media that do not require the use of gadgets. To address this issue, a service-learning approach is employed to assist SDN Tongas Wetan IV students in three stages: the preparation stage, mentoring stage, and reflection stage. This assistance aims to create various simple learning media that can be utilized in the numeracy learning process without the need for gadgets. As a result, students become more enthusiastic and active participants in the learning process of numeracy. The positive response from the teacher indicates that the learning interactions have become more interesting. Additionally, students' numeracy skills have demonstrated an average increase of 30.47%.&#x0D; The numeracy skills of SDN Tongas Wetan IV students are at a low level. The school is located in the remote Tongas Wetan village, surrounded by rice fields, with limited road access. Unfortunately, the absence of a cable or Wi-Fi internet network hinders technological adaptation and forces the numeracy learning process in the classroom to rely primarily on classical methods, with minimal use of learning media. Therefore, it is essential to assist students in learning numeracy using media that do not require the use of gadgets. To address this issue, a service-learning approach is employed to assist SDN Tongas Wetan IV students in three stages: the preparation stage, mentoring stage, and reflection stage. This assistance aims to create various simple learning media that can be utilized in the numeracy learning process without the need for gadgets. As a result, students become more enthusiastic and active participants in the learning process of numeracy. The positive response from the teacher indicates that the learning interactions have become more interesting. Additionally, students' numeracy skills have demonstrated an average increase of 30.47%.
Video Compression By Eliminating Irrelevant Frames

Regarding the security of the ATM systems situated at various locations throughout the world, constant monitoring via surveillance at the ATM machine is required. Even though ATMs are equipped with manual protection, several theft events have made headlines over the past few decades. Since continuous surveillance provides an alternative to manual security, it is not feasible for the banks to do so at every one of these ATMs. With so much data generated by the monitoring system inside and outside the ATM, it becomes expensive for the banks to manage in terms of memory. As a result, the goal of this job is to reduce the number of frames in the video that was collected by removing all of the pointless frames. The technique employs transfer learning on a Mask RCNN Network to identify frames containing people before removing any such frames. Mask RCNN network, which is typically trained on the COCO dataset, will be used to detect humans in the so-called relevant frames and subsequently to filter out the frames with no humans. The pertinent frames are after that turned into video and kept in the backup. The plan decreases the amount of storage needed for continuous monitoring. Keywords- Region-Based Convolutional Neural Network (RCNN), Convolutional Neural Networks (CNN), Closed Circuit Television(CCTV), Discrete cosine transform (DCT), GPU (Graphics Processing Unit), Deep Learning techniques, area classification,
HETERO-FLEX MULTI-CLOUD STORAGE MANAGEMENT SCHEME FOR CLOUD DATA DE-DUPLICATION REDUCTION

The rise of cloud situations has made doable the conveyance of Internet-scale benefits by tending to various difficulties, for example, security support, fault tolerance and nature of administration. Data de-duplication has pulled in numerous cloud specialist organizations (CSPs) as an approach to lessen capacity costs. Despite the fact that the general deduplication approach has been progressively acknowledged, it accompanies numerous security and protection issues due to the outsourced data conveyance models of cloud stockpiling. To manage particular security and protection issues, secure deduplication systems have been proposed for cloud data, prompting an assorted scope of arrangements and exchange offs. Conventional deduplication conspires dependably center around particular application situations, in which the deduplication is totally controlled by either data proprietors or cloud servers. They can't adaptably fulfill different requests of data proprietors as per the level of data affectability. We propose a Hetero-Flex Multi-cloud (HFMC) stockpiling administration plot, which adaptably offers deduplication administration and multi-server get to controls. The outcomes demonstrate its security, adequacy and effectiveness and protection support towards potential pragmatic use are for the most part enhanced than the current work. Key Words: Cloud Computing, Fog Computing, IoT, Big Data, Access Control, Cloud – Based Applications.
Flex Sensor and Machine Learning-Based Mute Communication System

Individuals facing speech impairments encounter significant communication challenges. To address this, we have developed a groundbreaking system that seamlessly translates custom hand gestures into written text and spoken language. Our system combines flex sensors and state-of-the-art machine learning techniques, empowering individuals with speech impairments to express themselves effectively and improve their overall communication experience. By connecting flex sensors to an Arduino board and utilizing MATLAB for robust data collection, analysis, and processing, we achieve a highly accurate gesture recognition system. Through meticulous steps of data labeling, rigorous training of a machine learning model, and thorough accuracy evaluations, our system recognizes a wide range of gestures, including numbers 0-9, letters A-Z, and 10 Kannada language gestures conveying meaningful phrases. The hand gesture recognition system achieved notable performance with an accuracy of 83.5%, precision of 88.6%, recall of 95.1%, and F1 Score of 91.7%. Key Words: speech impairments, hand gestures, gesture recognition, flex sensors,
An Approach for Detection of Entities in Dynamic Media Contents

The notion of learning underlies almost every evolution of Intelligent Agents. In this paper, we present an approach for searching and detecting a given entity in a video sequence. Specifically, we study how the deep learning technique by artificial neural networks allows us to detect a character in a video sequence. The technique of detecting a character in a video is a complex field of study, considering the multitude of objects present in the data under analysis. From the results obtained, we highlight the following, compared to state of the art: In our approach, within the field of Computer Vision, the structuring of supervised learning algorithms allowed us to achieve several successes from simple characteristics of the target character. Our results demonstrate that is new approach allows us to locate, in an efficient way, wanted individuals from a private or public image base. For the case of Angola, the classifier we propose opens the possibility of reinforcing the national security system based on the database of target individuals (disappeared, criminals, etc.) and the video sequences of the Integrated Public Security Centre (CISP).
ENHANCING NON-NATIVE ACCENT RECOGNITION THROUGH A COMBINATION OF SPEAKER EMBEDDINGS, PROSODIC AND VOCAL SPEECH FEATURES

The transcription accuracy of automatic speech recognition (ASR) system may suffer when recognizing accented speech. The resulting bias in ASR system towards a specific accent due to under representation of that accent in the training dataset. Accent recognition of existing speech samples can help with the preparation of the training datasets, which is an important step toward closing the accent gap and eliminating biases in ASR system. For that we built a system to recognize accent from spoken speech data. In this study, we have explored some prosodic and vocal speech features as well as speaker embeddings for accent recognition on our custom English speech data that covers speakers from around the world with varying accents. We demonstrate that our selected speech features are more effective in recognizing nonnative accents. Additionally, we experimented with a hierarchical classification model for multi-level accent classification. To establish an accent hierarchy, we employed a bottom-up approach, combining regional accents and categorizing them as either native or non-native at the top level. Furthermore, we conducted a comparative study between flat classification and hierarchical classification using the accent hierarchy structure.
Modified Backpropagation Algorithm with Multiplicative Calculus in Neural Networks

Backpropagation is one of the most widely used algorithms for training feedforward deep neural networks. The algorithm requires a differentiable activation function and it performs computations of the gradient proceeding backwards through the feedforward deep neural network from the last layer through to the first layer. In order to calculate the gradient at a specific layer, the gradients of all layers are combined via the chain rule of calculus. One of the biggest disadvantages of the backpropagation is that it requires a large amount of training time. To overcome this issue, this paper proposes a modified backpropagation algorithm with multiplicative calculus. Multiplicative calculus provides an alternative to the classical calculus and it defines new kinds of derivative and integral forms in multiplicative form rather than addition and subtraction forms. The performance analyzes are discussed in various case studies and the results are given comparatively with classical backpropagation algorithm. It is found that the proposed modified backpropagation algorithm converges in less time to the solution and thus provides fast training in the given case studies. It is also shown that the proposed algorithm avoids the local minima problem.
Peluang dan Tantangan Kecerdasan Buatan Bagi Generasi Muda

Artificial Intelligence is the ability of computers to mimic human activities and execute tasks that normally require human intelligence. This service activity is a collaboration between young lecturers in providing understanding that is accommodated by the Strategic Studies Division of the Regional Leadership Council of the Indonesian Christian Youth Movement in South Sulawesi Province (DPD GAMKI SULSEL). The method is by way of Focus Group Discussion (FGD) with young lecturers from five universities in Indonesia. Based on the results of the FGD, the use of Artificial Intelligence for each field of science (Information Systems, Health, Education and Industry) has different roles and contributions. The use of Artificial Intelligence will benefit human resources if it is used according to the context of their knowledge. However, Artificial Intelligence can be dangerous if not used according to proper science. In using Artificial Intelligence, humans must understand their role as learners
Counter-Narrative Cyber Security Model to Address the Issues of Cyber Terrorism

Terrorism is the imminent threat that Malaysia and the entire world are facing nowadays. This has become a severe threat to Malaysia’s national security as extremist groups such as ISIS and DAESH are leveraging cyberspace to gain more supporters and sympathisers. These extremists used social media to disseminate their narratives among the Malaysian Armed Force (MAF) personnel to recruit them for their excellent military skills. Nevertheless, the current MAF counter-narrative is still using traditional media operations like roadshows, brochures dissemination, articles, and short videos in combating the terrorism agenda, which are less effective and time-consuming. Therefore, this study proposes an enhanced counter-terrorism approach to fight this terrorism narrative by utilising the MAF IT infrastructure to focus on the social media platform. The model was built based on ISO / IEC 27032:2012 standard, concerning three cyber-terrorism models. Six military IT experts then verified this new enhanced model. They agreed that it is crucial to establish such a model and emphasise that counter-terrorism effort needs full cooperation with other services and collegiality. This study’s final output is the Counter-Narrative Information Technology Model, which will later potentially be adopted to the MAF environment in line with the national inspiration in fighting terrorism.
The Effect of @englishhero TikTok Video on Students' Vocabulary Mastery at the 10th grade of MA Zainul Hasan Pare

This study aims to know the effect of @englishhero TikTok Video on Students' Vocabulary Mastery at the 10th Grade of MA Zainul Hasan Pare through pre experimental research design conducted with only pre-test post-test one experimental group. The researchers took 24 students as the sample of the study from 65 population by using simple random sampling. To collect the data, the researchers used vocabulary written test with 25 items in essay format. In analyzing the data, the researcherss used IBM SPSS statistic versions. 20 application of non-parametric t.test from Wilcoxon test, because data was not normally distributed. It aims to know whether there is or not a difference between before given the treatment and after given the treatment using @englishhero TikTok Video on Students' Vocabulary Mastery. After collecting and calculating data, the researchers found that mean score of post-test 95.33 was higher than pre-test 63.50. In testing the hypothesis, they used t-test formula. The researchers found that p value (Asymp. Sig. (2-tailed)) =0.000 &lt; 0.05, at the level significance of df = 23. So, the null hypothesis was rejected. It means that @englishhero TikTok Video affects Students' Vocabulary Mastery at the 10th Grade of MA Zainul Hasan Pare.
EFEKTIVITAS MEDIA PEMBELAJARAN DAKOTA (DAKON MATEMATIKA) DALAM MENINGKATKAN HASIL BELAJAR MATEMATIKA DI SEKOLAH DASAR

This study aims to find out whether the use of Dakota or Dakon Matematika learning media is effective in improving students' Mathematics learning outcomes in elementary schools, especially in learning materials for Least Common Multiple (KPK) and Greatest Common Factor (FPB). This study uses a library research methodology (library research). The study materials in this study used 8 main books, 13 main research articles and 7 previous studies, as materials or sources in this research. Analysis using description. The results of the study stated that Dakota or Dakon Matematika learning media is a visual learning media that is effective in improving student learning outcomes in elementary schools on KPK and FPB material. In addition, the use of Dakota learning media makes the learning process in class more effective, fun, and increases student activity in learning.
ANALYSIS THE INCREASE LEARNING OUTCOMES IN POETS SUBJECT THROUGH CTL APPROACH TO GRADE III OF STATE ELEMENTARY SCHOOL

This research is entitled Analysis the Increase Learning Outcomes in Poets Subject Through CTL Approach to Grade III of State Elementary School 060807 Medan. The reason for this research is to improve the learning outcomes and learning exercises of grade III of State Elementary School 060807 Medan in learning to write poetry. This study utilized classroom research activities conducted in two cycles with student research subjects as the research method. Research information collection methods in the form of test and non-test procedure methods. At that point, the analysis in this consider used the quantitative method. The results of this study show an increase in student learning results. In the preaction test, the average value was 59.57, while in cycle I the normal value was 72.50. In this way the increment in the normal value of writing poetry from the preaction to cycle I was 12.75 points. In cycle II the normal score came to 81, an increase of 8.5 points from cycle I and 21.25 points from the preaction test. Based on the research comes about, teachers are advised to apply the Contextual Teaching and Learning (CTL) approach since it is demonstrated to progress student learning results.
ANALISIS KEMAMPUAN MENULIS CERPEN MELALUI TEKNIK RUMPANG PADA KELAS IV SEKOLAH DASAR

The purpose of this study was to find out how to analyze the ability to write short stories through the gap technique in class IV of elementary school. This research was conducted in several elementary schools, namely: SDN Ciawi 2, SDN Sukadame 2, SDN Sentul 1 and SDN Baros 3 with a total population of 5 elementary schools for each student. The research used is included in the type of qualitative research. To explain the results of research using descriptive method. Data collection techniques used are observation and interviews. Based on the results and discussion previously described, it can be concluded that the analysis of the ability to write short stories through the gap technique in class IV elementary school students produces a score of 69.5% with the High criteria. This is evidenced by the learning outcomes of students in answering the gaps in the short story text on the LKPD given by their class teacher. The results of the analysis of the ability to write short stories of students from SDN Ciawi 2 were 72% included in the high criteria, 66% of SDN Sukadame 2 were included in the high criteria, SDN Sentul 1 was 68% included in the high criteria and SDN Baros 3 was 72% fall into the high criteria.