Reviewerly/exHarmony huggingface dataset:
فعّاليّة القصص المصوّرة لترقيّة مهارة القراءة لدى طلاّب مدرسة المتوسّطة الإسلاميّة المتكاملة "موتيارى تشندكيا" لوبك لينجاو

The problem of this research is that many students still stutter in reading Arabic texts because the methods used by the teacher have not been able to attract the students interest. Therefore, there is a need for appropriate educational means to train students Arabic text reading skills, like a comic. This research aims to find out the effectiveness of comics to improve the reading skill of the students of the Integrated Islamic Middle School "Mutiara Cendekia" Lubuklinggau. In this research, the authors used the quantitative research method by designing true experimental research. As for the research community, it consists of the eighth class students. The research tools used in this research are observation, test and documentation. From the results of the research, the researcher decided that the different grades of the two classes, so that t-statistics is 2,155 is greater than that of t-table 5% is 2,024. And sig-2 tailed it is 0,38 less than 0,05. The researcher concluded that the null hypothesis is rejected and the alternate hypothesis is acceptable, meaning that there is the effectiveness of comic to improve the reading skill of the students.
في فهم المفردات لدى الطلاب الصف السابع في المدرسة الثانوية محمدية سانيخ باكر KOKAMI تأثير استعمال بطاقة

The main question in this study was "How does the use of the KOKAMI card affect the vocabulary understanding of class VII students of Madrasah Tsanawiyah Muhammadiyah Saning Bakar?". This study aims to identify the results of vocabulary learning for class VII students of Madrasah Tsanawiyah Muhammadiyah Saning Bakar and to find out the use of KOKAMI cards in teaching vocabulary to class VII students of Madrasah Tsanawiyah Muhammadiyah Saning Bakar and to determine the effect of using KOKAMI cards on vocabulary comprehension among class VII students of Madrasah Tsanawiyah Muhammadiyah Saning Bakar. The approach in this research is a quantitative approach. The type of research used by researchers is the field. The population in this study were students of class VII Madrasah Tsanawiyah Muhammadiyah Saning Bakar. Therefore, the researcher chose the sample by taking students of class VII (experimental class) and class VII B (control class). If you look closely at the post-test, it is known that the learning outcomes of the experimental class students using Kokami cards are superior to those of the control class students using traditional learning methods. This can be seen from the mean value of the experimental class is higher than the control class Experimental class 83.75 and the control class is 77.75 with a difference between the experimental class and the control class of 6. Based on the previous statement it can be concluded that learning using the KOKAMI card affects the learning outcomes of class VII students of Madrasah Tsanawiyah Muhammadiyah Saning Bakar.
IMPLEMENTASI POHON PINTAR DALAM PEMBELAJARAN CALISTUNG

At the elementary school level, the ability to read, write and count are basic capabilities that must be trained at every level of education. In Law Number 20 of 2003 concerning the National Education System, article 4 paragraph 5, it is stated that a principle in implementing education is to create a culture of reading, writing and arithmetic everywhere for every member of society. The aim of this activity is that there is a change in students' activeness and understanding in learning Calistung using smart tree media. The result of this Community Service activity is that there is an increase in students' understanding and activeness in learning Calistung as well as the creation of a balance between learning and playing, so that without students realizing it, playing is included in learning activities.
FedLoop: A P2P Personalized Federated Learning Method on Heterogeneous Data

In federated learning scenarios, data heterogeneity can significantly impact performance. Personalized federated learning seeks to provide individualized models for each client to enhance convergence on heterogeneous data. We discovered that initially training the personalized layers, also known as the head, of the model first can alleviate the effects of data heterogeneity. As a result, we propose a simple method named FedLoop. This method uses a loop topology structure, eliminating the need for a central server or data exchanges between participants, thereby safeguarding privacy. Within FedLoop, clients act as nodes in a loop. The training process for each node consists of two phases: an initial phase solely for the personalized layers and a subsequent phase dedicated to the training of all layers. This looping process continues until a set round limit is achieved. Experimental findings reveal that FedLoop outperforms the existing state-of-the-art algorithm, FedALA. FedLoop effectively addresses challenges posed by data heterogeneity and its rapid convergence significantly cuts down communication overheads in federated learning.
Protecting Voice-Controlled Devices Against LASER Injection Attacks

Voice-Controllable Devices (VCDs) have seen an increasing trend towards their adoption due to the small form factor of the MEMS microphones and their easy integration into modern gadgets. Recent studies have revealed that MEMS microphones are vulnerable to audio-modulated laser injection attacks. This paper aims to develop countermeasures to detect and prevent laser injection attacks on MEMS microphones. A time-frequency decomposition based on discrete wavelet transform (DWT) is employed to decompose microphone output audio signal into n + 1 frequency subbands to capture photo-acoustic related artifacts. Higher-order statistical features consisting of the first four moments of subband audio signals, e.g., variance, skew, and kurtosis are used to distinguish between acoustic and photo-acoustic responses. An SVM classifier is used to learn the underlying model that differentiates between an acoustic-and laser-induced (photo-acoustic) response in the MEMS microphone. The proposed framework is evaluated on a data set of 190 audios, consisting of 19 speakers. The experimental results indicate that the proposed framework is able to correctly classify 98% of the acoustic- and laser-induced audio in a random data partition setting and 100% of the audio in speaker-independent and text-independent data partition settings.
A STUDY TO ASSESS THE EFFECTIVENESS OF VIDEO ASSISTED TEACHING (VAT) ON PREVENTION OF HYPERTHYROIDISM AND ITSASSOCIATED FACTORS AMONG THE STUDENTS OF SECOND YEAR GNM AT A SELECTED SCHOOL OF NURSING, BENGALURU, KARNATAKA, BY THE STUDENTS OF SMT. NAGARATHNAMMA COLLEGE OF NURSING

Background: HEALTH IS NOT VALUED TILL SICKNESS COMES. Hyperthyroidism occurs when the thyroid gland makes too much of thyroid hormone. This condition also is called objective thyroid.Hypothyroidism speeds up the bodys metabolism. It causes many symptoms such as a weight loss, hand tremors and rapid or irregular heartbeat. A 2018 study explain that genetic factors can make many thyroidissues including hyperthyroidism. There is good scientific evidence that hyperthyroidism has important genetic component in many cases because an individual has a mutation in one and more genes. These mutations alter typically thyroid function, causing it to procedure to much T4 and T3. It is not exactly Clear why these genetic mutation as Ise.A 2021 study lists some genes that may play a role in hyperthyroidism. These include: -HCA, CTLA4, FCRL3, RNASET2, TSHR. Study notes that genetic factors might account 79% of an individual predisposition to develop gravis disease. Prevalence of hyperthyroidism is 0.8% in Europe and 1.3% in USA. Hyperthyroidism has been also studied in Cochin, India, subclinical and over hyperthyroidism were present in 1.6% and 1.3% of subject participating in community survey. Hyperthyroidism increases with age and is more frequent in women. The prevalence of hyperthyroidism seems slightly more frequent in white people than in other rules. The medicine of mild hyperthyroidism is also reported to be binges in iodine - deficient areas than in iodine sufficient areas. Hyperthyroidism is very common disease condition nowadays, so we have chosen our sample as second year GNM because, as they are already studying it so to assess their knowledge and to provide more knowledge about hyperthyroidism to give proper information and treatment about hyperthyroidism. Prevention can be done by getting your hormone levels checked all through pregnancy and 6 months after giving birth, keeping diabetes under control, check your vitamin B12 levels. People with previous anemia are at high risk, check for thyroid levels regularly if your family history has it.
Solar Panel Roof Top Study and Optimization in Abu Dhabi, United Arab Emirates

In this work, we study the effect of angle and direction on the solar cell performance in Abu Dhabi, United Arab Emirates. Understanding the optimal angle and direction of solar panels in a desert climate can be key in mitigating some of the negative effects. These include very high temperature, humidity, and soiling. Our results show the optimal direction in Abu Dhabi, UAE is due south that provides the highest performance with an angle around 30 degrees. Moreover, a solar tracker can be used to keep the solar cell at the maximum efficiency.
Advancements in Binary Number Processing: Analyzing a CNTFET-Based Digital Circuit for 3-Bit Up and Down Counting with Challenges in Artificial Intelligence

Carbon Nanotube Field- Effect Transistors (CNTFETs) have emerged as a promising alternative to traditional silicon-based transistors, offering enhanced electrical properties and potential integration with existing silicon technology. This research study presents a design and performance analysis of a digital circuit based on CNTFET technology, specifically tailored for precise computation involving 3-bit up and down binary numbers. The circuit's ability to accurately compute additions and subtractions is pivotal for various computational applications. Additionally, this study explores the challenges and implications of integrating Artificial Intelligence (AI) algorithms within the context of this emerging semiconductor technology. The findings shed light on the prospects of utilizing CNTFETs in arithmetic operations, providing insights into the intersection of advanced semiconductor technologies and the field of AI. The comprehensive analysis presented herein establishes a foundation for future research and advancements at the convergence of CNTFET -based digital circuits and AI methodologies.
Outcome Based Predictive Analysis of Automatic Question Paper Generation Using Hybrid MLP-CNN Approach

Improving the overall caliber of the student body is the overarching goal of any educational area. In order to save teachers time and effort, we implement cutting-edge technology in the form of a system that can automatically produce questions for exams. This was due to the fact that each writer's knowledge, experience, and individual tastes contributed uniquely to the final product. This approach eliminates the need for human work by automatically processing unique paper collections. In a second, the computer system picks up all the paperwork and tedious responsibilities. Database of questions categorized by category, course, chapter, and number of points awarded. Exam papers can only be reliably produced by the automated paper generator. This system automates the entire paper-making process. For instance, picking a question bank to work from could be the first step in writing a paper. This approach works for organizations of all sizes. The proposed approach employs the procedures of preprocessing, feature selection, and model training. Preprocessing methods often make use of missing values. With its roots in subsampling and selection algorithms, stability-based selection is an innovative approach to deciding on characteristics. Once features have been decided upon, Hybrid CNN-MLP is utilized to train the models. In contrast to the two most used substitutes, the proposed technique outperforms both CNN and MLP.
Entanglement Assisted Quantum Radar Demonstration over Turbulent Free-Space Optical Channels

In entanglement assisted communications and sensing the optical phase-conjugation (OPC), required before homodyne detection takes place, is typically performed on signal photons on receive side. Unfortunately, in turbulent free-space optical channels, the signal photons can be scattered or absorbed by the channel, and if they reach the receive side they are very weak. Here we propose an entanglement assisted radar technique in which the OPC is performed on bright idler photons instead. By performing the OPC on idler photons on transmit side, while employing adaptive optics and classical homodyne detection on receive side, we experimentally demonstrate that the target detection probability of the proposed entanglement assisted radar over turbulent free-space optical channel is significantly better than that of corresponding classical detection scheme.
Training on Statistical Data Processing Using SPSS Application

The Industry 4.0 era requires resources equipped with critical thinking, innovative abilities, and the capacity to address complex problems using the latest technology. Education emerges as a crucial factor in meeting Human Resources (HR) needs across various industrial sectors. Therefore, this community service activity focuses on students to enhance insights and competencies that support lectures and research projects. The training places emphasis on utilizing the Statistical Package for the Social Sciences (SPSS) as a statistical testing tool, enabling students to grasp accurate data processing. Activities encompass the introduction of basic statistical concepts, practice in data processing, statistical testing, and both descriptive and comparative analysis. The training offers additional experience, skills, and competencies, fostering participants' interaction with SPSS technology as a statistical analysis tool. Evaluation of the activities indicates a significant improvement in participants' ability to process statistical data using SPSS.&#x0D; AbstrakEra Industri 4.0 menuntut sumber daya yang memiliki kemampuan berpikir kritis, inovatif, dan mampu menyelesaikan masalah kompleks dengan memanfaatkan teknologi terkini. Pendidikan menjadi kunci penting dalam memenuhi kebutuhan Sumber Daya Manusia (SDM) di berbagai sektor industri. Oleh karena itu, kegiatan pengabdian ini difokuskan pada mahasiswa untuk meningkatkan wawasan dan kompetensi yang mendukung perkuliahan serta proyek penelitian. Pelatihan menitikberatkan pada penggunaan Statistical Package for the Social Sciences (SPSS) sebagai alat uji statistik, memungkinkan mahasiswa memahami pengolahan data secara tepat. Kegiatan ini mencakup pengenalan konsep dasar statistika, praktik pengolahan data, uji statistik, analisis deskriptif, asosiatif, dan komparatif. Pelatihan memberikan pengalaman, keterampilan, dan kompetensi tambahan, mendukung interaksi peserta dengan teknologi SPSS sebagai alat analisis statistik. Evaluasi kegiatan menunjukkan peningkatan signifikan dalam kemampuan peserta dalam mengolah data statistik dengan SPSS.
A Robust Approach for Real Time Activity Classification Using Wearable Sensors and Ensemble Learning

There are many ways to observe people's behaviour, but wearable sensors are a reliable way to keep track of their function. The paper adopts an ensemble learning approach for modelling and recognising body sensor-based activities. Once the data is collected, activities are modelled with robust time-sequential features using an ensemble learning approach combining weights from multiple machine learning models. Finally, those weights are used to train a final model for activity classification. The proposed system achieved a 99% mean recognition rate on a public dataset, indicating the robustness and superiority of the approach compared to traditional methods, yielding a maximum recall rate of 95%. The approach was also tried in real-time as well for a few primitive activity classifications in the home environment and obtained satisfactory performance. The system may be deployed in different practical applications to determine how people behave daily, such as at homes, clinics, and offices.
Detecting Hate Speech Utilizing Deep Convolutional Network and Transformer Models

Online social networks exhibit a significant prevalence of hate speeches, which poses a potential threat to the society and fosters targeted animosity towards specific communities and authorities. Although online platforms are available to automate few mechanisms of hate speeches but classification w.r.t. to different domains and their accuracy are the big issues, and are challenging the researchers, media, and the academic world. The present study addresses the identifying of Hate Speeches through the comparative analysis of the classification efficacy and model intricacy of four distinct Deep Neural Network models; namely CNN (baseline), bidirectional LSTM with attention, pretrained BERT, and fine-tuned RoBERTa transformer models, and utilizing a ternary classification system (hate, offensive, non-hate). The performance of the subject under consideration was assessed through the application of Accuracy, F1-score, and Matthew's correlation coefficient (MCC) metrics on the test set.
Solar Tracker with Obstacle Detection Vehicle

The aim of this paper is to develop a system that combines solar tracking technology with obstacle detection. This improves the safety and energy efficiency of a solar-powered vehicle. Solar powered vehicles face several challenges such as limited range and the need for efficient energy harvesting. This is done by adjusting the solar panel position based on the location of the sun to maximize energy harvesting. This ensures that the vehicle can operate for longer distances and remain charged. The system is designed to be reliable and efficient, and it was able to perform well in various testing environments.
Breast Cancer Detection Using SEResNeXt and Swin Transformer

In this work, we intend to device a methodology to solve the issue of Medical Image Classification on cancer patients using Deep Learning techniques. According to the World World Health Organization, the most common occurring cancer globally is breast cancer. In the year 2020, 2.3 million new breast cancer cases have been diagnosed. Due to this nearly 685,000 individuals died. From the 1980s regular checkup in mammography screening had been proposed by the health authorities which resulted in a 40 % reduction in several countries. At the moment, beginning stages of breast cancer can be detected using mammography specialized radiologists. In the past few years it has been reported that, there is a shortage among radiologists in several nations. This process could be automated using Deep Learning techniques, which would reduce the number of human-made errors and amount of time taken to inspect the MRI scan. Henceforth, in the work using feature vectors from SEResNeXt and applying Attention mechanism over these vectors to efficiently and accurately classify the MRI scans and we have obtained a probabilistic F1 score of 0.61 and an accuracy of 0.96.
GE-Blender: Graph Based Knowledge Enhancement for Blender

In spite of the considerable accomplishments in open-domain dialogue generation, the presence of unseen entities can substantially impact the task, resulting in a performance decline in model-driven dialogue generation. While previous research has utilized retrieved knowledge of seen entities to augment model representation, a comprehensive exploration of unseen entities, including their potential co-occurrence and semantic associations within the entity category, remains largely uncharted. In this study, we propose an approach to address this challenge. We construct a graph by extracting entity nodes, enhancing the contextual representation of unseen entities by leveraging the 1-hop surrounding nodes of associated entities. Furthermore, we introduce a named entity tag prediction task to address the issue of unseen entities not present in the constructed graph. We conduct our experiments on an open dataset Wizard of Wikipedia and the empirical results indicate that our approach outperforms the state-of-the-art approaches on Wizard of Wikipedia.
Design and Analysis of Programmed Face Monitoring System

This study proposes a unique method for a programmed face monitoring system that makes use of a Proportional-Integral-Derivative (PID) controller and Mediapipe, a well-known computer vision framework. The system makes use of Mediapipe's real-time face detection technology and a PID controller algorithm to precisely monitor the identified face by dynamically adjusting the pan and tilt angles of a servo motor system. The usefulness of the proposed approach is demonstrated by the graphical representation depicting setting time, steady-state error and the oscillation type. The results were compared between Proportional-Integral-Derivative (PID), Proportional-Derivative (PD) and Proportional (P) controllers. The PID controller showed favorable results among all three of the controllers utilized in the face monitoring system. For PID controller-based faced monitoring system, the setting time of the system designed is obtained as 1 second, the steady-state error is low and the oscillation type observed is critically damped. The usage of this method for programmed face monitoring systems has a potential application in a number of fields, including robotics, human-computer interaction, and surveillance systems.
Innovative Research on Teaching Methods of Polymer Material Processing Technology

The present study primarily investigates pedagogical methods for innovative processing techniques of polymer materials. Initially, the article examines the advantages and limitations of traditional teaching methods, as well as the utilization of virtual technology in education. Subsequently, the article outlines the educational objectives and current status of polymer materials processing techniques. Thereafter, innovative teaching methods are proposed, including the incorporation of various classroom instructional approaches, the integration of lecture and discussion, and the utilization of multimedia technology, among others. Finally, the research findings are summarized, emphasizing the significance of deepening the innovation of teaching methods. Through this study, novel perspectives and approaches can be provided for the teaching of polymer materials processing techniques, thereby enhancing student learning outcomes and fostering their engagement.