Reviewerly/exHarmony huggingface dataset:
THE IMPLEMENTATION OF MASLAHAH MURSALAH IN THE CIRCULAR ECONOMY OF WASTE OF UKM TEMPE BERKAH IN HAMPARAN PERAK VILLAGE

&#x0D; &#x0D; &#x0D; This study aims to implement maslahah mursalah in the Circular Economy of UKM Tempe Berkah waste in Hamparan Perak Village by focusing on 3 elements in it, namely hifdzu an-nafs (care of the soul), hifdzu al-mal (maintenance of assets) and hifdzu al-bi'ah (maintenance of environment). This study used a descriptive qualitative research method, with primary and secondary data sources used as data sources. Data collection techniques were used in the form of interviews, observation, and direct surveys. In addition, data collection techniques were also obtained from several selected sources as additional information in this study. The results of the study showed that the implementation of Maslahah Mursalah in the Circular Economy of UKM Tempe Berkah, Hamparan Perak in the element of hifdzu an-nafs (care of the soul) can provide comfort and maintain health for employees, the community, and the surrounding environment from the dangers of pollution. And able to help the community in meeting its basic needs. Then, the implementation of Maslahah Mursalah on the Circular Economy of waste of UKM Tempe Berkah in Hamparan Perak Village in the element of hifdzu al-mal (maintenance of assets), where UKM Tempe Berkah obtains their assets in a good way through buying and selling, using the assets following sharia and there is no element of appropriation other people's assets because they can provide appropriate salaries to their employees so that no one feels abused. And the implementation of Maslahah Mursalah in the Circular Economy of UKM Tempe Berkah's waste in Hamparan Perak Village in the element of hifdzu al-bi'ah (environmental care), namely Tempe Berkah UKM can minimize environmental pollution by managing and utilizing its waste, both solid waste and liquid waste, so as not to cause damage to society and the environment.&#x0D; &#x0D; &#x0D;
Optimizing Local History Learning with The Saloka Application

This study develops an android-based augmented reality catalog model application. The catalog form was chosen because of the large amount of cultural heritage information that is important to research and document through the application. The results of this cultural heritage research will be packaged in an interactive application that is expected to help history teachers in teaching the history of Lamongan in an interesting way. The virtual catalog application is designed to be a pioneer of historical and cultural information for the Lamongan community and its surroundings. Plus the COVID-19 outbreak that is still hitting Indonesia, this application offers free and safe roaming of virtual cultural heritage. This research method includes the historical method to examine all cultural heritage as teaching materials and the ADDIE-based development method to develop the SALOKA application. The targets of this research are students, teachers, academics, and the community in Lamongan. In particular, this application is expected to help history teachers to teach local cultural/historical heritage to students in Lamongan so that learning is more interesting.&#x0D; Keywords: virtual catalog, learning history, lamongan, heritage
A Chabot Supported Secured Portal for Affordable Artificial Limbs

In the last few years, there has been much demand for artificial limbs. Artificial Limbs Manufacturing Corporation of India has announced that 42 million people require artificial limbs. There are 12 million people who are suffering from limb loss and 40,000 amputations are performed in a year in India. The New Indian Express has published in 2022 that, approximately 25 leg amputations are performed each day in Kerala. The demand for artificial limbs has also raised issues such as, will the products be delivered fast, are they cost efficient, and whether customized products are available. Protecting the buyer’s credentials and answering to their queries is essential. So, in order to overcome these issues, this research aims to build a website which acts as an intermediate between the buyers and manufacturers. The website is a one-step solution for the buyers where they can identify different manufactures by securing their data. Buyer’s privacy can be achieved by safe-guarding all the passwords using SHA-256 algorithm. The chatbot can be used as a user-friendly mode to clarify the buyer’s doubts about the products. Review and rating about the products are also described to achieve customer’s belief.
Vision-Driven Dynamic Texture Recognition for Light Bar Intelligent Assembly Process

During the production and assembly process of televisions, the efficiency of assembly is limited by manual assembly methods. Conventional image matching algorithms such as SIFT and ORB fail to meet the real-time accuracy requirements of intelligent assembly processes. To address this issue, a novel algorithm called Adaptive Threshold oFAST and rBRIEF-BRISK (AFK) is proposed. This algorithm utilizes the Hamming distance as a similarity measure and integrates AToFAST and rBRIEF-BRISK fusion feature descriptors. Through a comparison of 12 sets of image matching scenarios, it is demonstrated that the AFK algorithm achieves a matching speed 4.5 times faster than the SIFT algorithm, with an image matching accuracy of up to 90%. Additionally, this paper presents the GLMLBP-TOP (Global-Local and Michelson Contrast LBP-Three Orthogonal Planes) dynamic texture recognition method. Through a comparison of 21 sets experiments, it is shown that the comprehensive classification accuracy of the GLMLBP-TOP method is approximately 94.5%, improving the performance of VLBP (87.71%) and LBP-TOP (91.1%) by 6.79% and 3.4% respectively. Real-world application results demonstrate that this method meets the requirements of intelligent assembly processes and has been successfully applied to the LB intelligent assembly process of a certain company’s televisions.
AUTOMATED ATTENDANCE SYSTEM WITH FACIAL RECOGNITION

This project's major goal is to develop a facial recognition-based attendance monitoring system for educational institutions to improve and modernize the current attendance system and make it more effective and efficient than it was previously.It has numerous advantages and can be used for security, identification, and authentication.The goal of this system is to create a face recognition-based class attendance system.The manual attendance system is time-consuming and cumbersome to maintain.This system consists of four phases-database creation, face detection, face recognition, and attendance updation.The database is created by the images of the students in class.Face detection and recognition are performed using the Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively.Faces are detected and recognized from live streaming video of the classroom.
On Graphical Shapes of χ2 Statistics of Leading Digits of Irrational Rotations

We discuss the outline of the shapes of graphs of χ 2 statistics for distributions of leading digits of irrational rotations under some conditions on m th convergent. We give some estimates of important coefficients L k ’s, which determine the graphical shapes of χ 2 statistics. This means that the denominator q m of m th convergent and the large partial quotient a m +1 determine the outline of shapes of graphs, when we observe values of χ 2 statistics with step q m .
Implementing Website-Based School Information Systems in Public Elementary Schools Using Waterfall Model

This research focuses on implementing a web-based school information system at SDN 14 Pontianak City, a renowned public elementary school with a rich historical background. The study aims to address challenges related to ineffective internal information delivery within the school. Currently, reliance on group chats for school-related activities poses obstacles in efficiently disseminating crucial information, including new student registration, extracurricular activities, competition achievements, vision and mission statements, and important updates. To overcome these challenges, a website-based school information system is proposed. It aims to seamlessly deliver essential school-related information to a wider audience, including parents and prospective parents. The system ensures easy access to information that was previously confined to the school's internal environment. The research employs the structured development process of the waterfall model, utilizing context diagrams, data flow diagrams (DFD), and entity relation diagrams (ERD) for a well-defined system design. Rigorous testing using the black box approach ensures website functionality and reliability. Implementation of this web-based school information system is expected to substantially improve information dissemination, benefiting the entire school community by providing convenient access to vital information for parents and prospective parents. This improvement will foster enhanced communication and engagement within the school ecosystem.
Mobile-Assisted Language Learning (MALL) in Senior High School English Classes

As technology continues to offer promising results in teaching and learning, teachers also shift their methods to maximize the potential it can offer. One popular method is the integration of mobile devices in teaching and learning macro skills. Thus, this study describes the implementation of the Mobile-Assisted Language Learning (MALL) strategy in Senior High School English classes. With the use of validated MALL-based lesson exemplars, the researcher has described the integration of MALL in different oral communication lessons in six public senior high schools. This involved qualitative analysis of classroom observations and focus group discussions with students. The results of the implementation revealed that the utilization of mobile devices can support language learning in several ways. MALL in English classes can be seamlessly integrated and provide access to authentic language input, practice opportunities, and personalized learning experiences. Even with the technical limitations during the implementation, students find learning English fun, engaging, and worthwhile. Thus, English teachers may use this research as a guide in their integration and implementation of MALL-based English lessons. Further, given the limitations of the study, it is recommended that larger-scale studies be conducted to examine long-term effects of MALL to provide a more comprehensive understanding of its potential to improve students’ language performance.
Reinforcement Learning-based Aspect Term Extraction using Dilated Convolutions and Differential Equation Initialization

Aspect term extraction is a crucial subtask in aspect-based sentiment analysis that aims to discover the aspect terms presented in a text.In this paper, a method for ATE is proposed that employs dilated convolution layers to extract feature vectors in parallel, which are then concatenated for classification downstream.Reinforcement learning is used to save the ATE model from imbalance classification, in which the training procedure is posed as a sequential decision-making process.The samples are the states; the network, and the agent; and the agent gets a more significant reward/penalty for correct/incorrect classification of the minority class compared with the majority class.The training phase, which typically employs gradient-based approaches, including back-propagation for the learning process, is tackled.Thus, it suffers from some drawbacks, including sensitivity to initialization.A novel differential equation (DE) approach that uses a clustering-based mutation operator to initiate the BP process is presented.Here, a winning cluster is identified for the current DE population, and a new updating strategy is used to generate candidate solutions.The BERT model is employed as word embedding, which can be included in a downstream task and fine-tuned as a model, while BERT can capture various linguistic properties.The proposed method is evaluated on two English datasets (Restaurant and Laptop) and has achieved outstanding results, surpassing other deep models (Restaurant: Precision 85.44%, F1-score 87.35%; Laptop:
Online Voting System using Face Recognition and OTP

Abstract: An online voting system is a proposed solution to the challenges faced by traditional voting systems. This project aims to develop an efficient and secure online voting system that uses face recognition technology and one-time password to authenticate the identity of voters and prevent fraudulent activities. The system's user-friendly interface and advanced security features make it accessible and trustworthy for all voters, including those who cannot physically attend polling stations. The project will use Python programming language and various libraries such as OpenCV and NumPy for image processing and Haar Cascade Algorithm for face detection and recognition. The system will be tested extensively to ensure its effectiveness and prevent potential issues. The proposed system has the potential to revolutionize the voting process and make it more efficient, accessible, and secure for all citizens.
Learning to Recognize Reachable States from Visual Domains

While planning models are symbolic and precise, the real world is noisy and unstructured.This work aims to bridge the gap between noise and structure by aligning visualizations of planning states to the underlying state space structure.Further, we do so in the presence of noise and augmentations that simulates a commonly overlooked property of real environments: several variations of semantically equivalent states.First, we create a dataset that visualizes states for several common planning domains; each state is generated in a way that introduces variability or noise.E.g., objects changing in location or appearance in a manner that preserves semantic meaning.First we train a contrastive learning model to predict the underlying states from the images.We then evaluate how we can align the predictions of a given sequence of visualized states with the problem's reachable state space, taking advantage of the known structure to improve predictions.We compare two methods for doing so: a greedy approach and Viterbi's algorithm, a well-established algorithm for observation decoding given a hidden Markov model.The results demonstrate that these alignment methods can correct errors in the model and significantly improve predictive accuracy.
PENGGUNAAN MEDIA KARTU GAMBAR UNTUK MENINGKATKAN KEMAMPUAN MEMBACA PERMULAAN DI KELAS II SDN 096 SARIJADI SELATAN KECAMATAN SUKAJADI KOTA BANDUNG

&#x0D; This research is undermined by the fact that the level of students' reading ability in class 2B of SDN 096 Sarijadi Selatan Bandung is still low, especially for beginning reading. This is due to various reasons, including the preliminary reading carried out by the class 2B teacher, which is less attractive to students and the result is only 10% of the number of students who are able to follow the lesson well. One way that can be used is to apply the use of picture card media. Picture cards can make it easier for students to read words which then become sentences. The purpose of this study was to improve the beginning reading skills of class 2B students at SDN 096 South Sarijadi, Sukajadi District, Bandung. This type of research is classroom action research. The design in this study uses the Kemmis and Tagart models which have four stages in conducting classroom action research, namely planning, action, observation and reflection and consisting of two cycles. Data collection is done by observation, interviews and tests. Based on the results of data analysis it is known that the use of picture card media can improve the beginning reading ability of class 2B students at SDN 096 Sarijadi Selatan Bandung. The class average value in the first cycle was 60.4 and increased to 75.6 in the second cycle. Based on the findings in this study, it was concluded that the application of picture card media in learning has a considerable impact on the beginning reading ability of grade 2B students at SDN 096 Sarijadi Selatan.
Exploring Preferential Label Smoothing for Neural Network-based Classifiers

Overfitting, a common problem in Machine Learning, occurs when a predictive model learns the noise in the training data instead of the true underlying patterns and converges to perform very well with the training data but poorly with unseen data.Models that overfit cannot be deployed in practice.Regularization is a technique typically used to help a model better generalize.This is usually achieved by adding a penalty term in the loss function to discourage the model from fitting noise, making it more robust to noise in the data and, therefore more generalizable.One method of regularization is to take some of the concentration (called Smoothing Ratio (SR)) from the data sample's ground truth label and distribute it uniformly among all the other labels during training.This method is called label smoothing and is a simple yet effective method to improve generalization.In this work, we explore what happens if we distribute the SR to the non-ground truth labels based on how closely they are related to the ground truth label, instead of uniformly.We call this approach of distributing the SR based on relation between labels as Preferential Label Smoothing (PLS).PLS represents a more unified approach of performing label smoothing.Ordinary uniform label smoothing becomes pointless as the number of labels becomes large since the SR proportion distributed per label becomes negligible.PLS is inconsequential in the case of binary classification, since there are only two labels.Therefore, we investigate the effects of PLS when the number of labels in the dataset is high.We also examine the effects of uniform and preferential label smoothing, as well as the absence of label smoothing, on the training dynamics.We demonstrate our study on image classification and text classification.
The essence and content of the concept “methods of the diagnostic competence development of the future Philosophy Doctors in the process of educational and scientific training” and the main principles of methods implementation

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
System of test quality control of students' knowledge

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Psychological personality types of student pianists of higher educational institutional of art and their influence on the learning process

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
PERENCANAAN PENGADAAN BAHAN PUSTAKA DI SMA NEGERI 4 KARAWANG

This study aims to describe the planning for procurement of library materials in the Karawang 4 Public High School library and what are the factors that influence it. Researchers used descriptive qualitative research. In this study, the information in this study was the head of the library, librarians and users. The data collection techniques in this study were through observation, interviews and documentation. The data analysis technique of this research uses Miles and Huberman analysis which divides the analysis into data collection, data reduction, data display and conclusion drawing, verification. The results of this study indicate that the planning process for procuring library materials begins with determining the budget, then selecting library materials up to the purchasing process, the methods used in procuring library materials at SMA Negeri 4 Karawang are, 1) Purchases, 2) Gifts, and 3) donations . The supporting factors for planning the procurement of library materials in the SMA Negeri 4 Karawang library are, 1) There is assistance from outside parties, both agencies or individuals who contribute in the form of books to add to the collection of library materials, and 2) The planning system for procuring library materials is already going well . While the inhibiting factors are 1) Lack of competence of managers in properly managing the procurement of library materials.&#x0D; Keywords: Planning, Procurement, Library Materials
Effective Breast Cancer Prediction Based on Feature Extraction, Fusion And Selection using Hybrid Methodologies

Women have been affected by many diseases for decades due to their low immune systems.Especially breast cancer is the second largest and most harmful disease for a woman that, leads to death.The earlier prediction of breast cancer can help for easy treatment and Save lives.Deep Learning methods are implemented in medical fields to attain an effective prediction and higher prediction accuracy.Therefore, in this work, the novel hybrid prediction topology is implemented two feature extractions, feature fusion, feature selection and classification with several algorithms such as Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM), Transit Search Optimization (TSO), Support Vector Machine (SVM) respectively.The LDA and CNN-LSTM method is used to execute a two-feature extraction, and the CCA model is used to merge the two extracted features, a feature fusion task.Next, the TSO-based feature selection is implemented to minimize the redundant features.At last, the classification is carried out using an SVM method to acquire an efficient breast cancer prediction.The proposed hybrid method has achieved more accurate decision-making and efficient training than the conventional methods.The experimental result of the novel hybrid method acquired a superior performance in terms of precision, Recall, Accuracy, F1 Score, RMSE and MAE as 98.81%, 99.02%, 98.18%, 98.85%, 1.001 and 1.016, respectively.
Efficient GEMM Implementation for Vision-Based Object Detection in Autonomous Driving Applications

Convolutional Neural Networks (CNNs) have been incredibly effective for object detection tasks. YOLOv4 is a state-of-the-art object detection algorithm designed for embedded systems. It is based on YOLOv3 and has improved accuracy, speed, and robustness. However, deploying CNNs on embedded systems such as Field Programmable Gate Arrays (FPGAs) is difficult due to their limited resources. To address this issue, FPGA-based CNN architectures have been developed to improve the resource utilization of CNNs, resulting in improved accuracy and speed. This paper examines the use of General Matrix Multiplication Operations (GEMM) to accelerate the execution of YOLOv4 on embedded systems. It reviews the most recent GEMM implementations and evaluates their accuracy and robustness. It also discusses the challenges of deploying YOLOv4 on autonomous vehicle datasets. Finally, the paper presents a case study demonstrating the successful implementation of YOLOv4 on an Intel Arria 10 embedded system using GEMM.
Language Report Italian

Abstract In the last few years, three important factors have influenced the Italian Language Technology (LT) community: 1. in 2015, the foundation of the Associazione Italiana di Linguistica Computazionale (Italian Association for Computational Linguistics, AILC); 2. the organisation of CLiC-it, the annual Italian Conference on Computational Linguistics; 3. the organisation of the EVALITA (Evaluation of NLP and Speech Tools for Italian) evaluation campaigns. This situation is producing a widespread expansion of interest in LT for Italian in academia and industry.