Reviewerly/exHarmony huggingface dataset:
The Use of Flash Cards Media in Reducing Speech Delay Symptoms in Children (A Case Study in BA Aisyiyah Prembun, Tambak District, Banyumas Regency)

The background of this study is to describe the process of using flashcard media to reduce the symptoms of speech delay in children, implemented by class teachers in BA Aisyiyah Prembun. It was found to have a significant impact on children's speech development. This study adopted a qualitative method with a case study approach. The qualitative method is used because this study aims to discuss the factors influencing children's speech delay and how the flashcards enhance the speaking skills of children with speech delays in BA Aisyiyah Prembun. Using the qualitative research method for this is more straightforward and more effective. Data collection methods in this research included observation, interviews, and documentation. The findings indicated improved speech development in the subjects after the class teacher continuously used flash cards five times a week. Initially, the subjects had difficulty speaking. However, by the 6th month, there was a significant improvement in the child's speech. They can speak fluently and clearly after the intervention using flash cards.
Viability of Convolutional Variational Autoencoders for Lifelong Class Incremental Similarity Learning

Incremental similarity learning in neural networks poses a challenge due to catastrophic forgetting. To address this, previous research suggests that retaining “image exemplars” can proxy for past learned features. Additionally, it is widely accepted that the output layers acquire task-specific features during later training stages, while the input layers develop general features earlier on. We lock the input layers of a neural network and then explore the feasibility of producing “embedding” models from a VAE that can safeguard the essential knowledge in the intermediate to output layers of the neural network. The VAEs eliminate the necessity of preserving “exemplars”. In an incremental similarity learning setup, we tested three metric learning loss functions on CUB-200 (Caltech-UCSD Birds-200-2011) and CARS-196 datasets. Our approach involved training VAEs to produce exemplars from intermediate convolutional and linear output layers to represent the base knowledge. Our study compared our method with a previous technique and evaluated the baseline knowledge ( $$\varOmega _{base}$$ ), new knowledge ( $$\varOmega _{new}$$ ), and average knowledge ( $$\varOmega _{all}$$ ) preservation metrics. The results show that generating exemplars from the linear and convolutional layers is the most effective way to retain base knowledge. It should be noted that embeddings from the linear layers result in better performance when it comes to new knowledge compared to convolutional embeddings. Overall, our methods have shown better average knowledge performance ( $$\varOmega _{all}=[0.7879, 0.7805]$$ ) compared to iCaRL ( $$\varOmega _{all}=[0.7476, 0.7683]$$ ) in the CUB-200 and CARS-196 experiments, respectively. Based on the results, it appears that it is important to focus on embedding exemplars for the intermediate to output layers to prevent catastrophic forgetting during incremental similarity learning in classes. Additionally, our findings suggest that the later linear layers play a greater role in incremental similarity learning for new knowledge than convolutions. Further research is needed to explore the connection between transfer learning and similarity learning and investigate ways to protect the intermediate layer embedding space from catastrophic forgetting.
Background and Shadows in CSS

This chapter covers the shadow effect using CSS. The first topic covered is text shadows. We’ll talk about how to add shadows to text to make it more attractive, what color and how color palletes can be applied, and what the properties of the shadows are. It also explains how to prevent text shadows using CSS. Another amazing feature that will be covered is the embossed text shadow effect. We’ll also cover box shadows, background gradients, and more.
Parent’s Involvement in Children’s English Literacy Skills

The current study aimed at investigating parental involvement in developing children’ English literacy skill. This study was a descriptive qualitative conducted in Aura Sukma Insani school Bali. To conduct the study, questionnaire, interview and observation were employed. The results showed that the parental involvement programs in Aura Sukma Insani Bilingual Kindergarten were parents conference, parents’ interview, final report, and eco-enzyme. The specific programs which involved parents applied four out of six parental involvement frameworks proposed by Epstein et al., (2002). Parenting was applied through giving parents’ guidance book about the learning process. Then, decision making was applied through asking parents’ decision about learning which will be applied. During the implementation of parental involvement programs, A number of problematic challenges exist in the parents' involvement implementation at Aura Sukma Insani Kindergarten. According to the interpretation level of the teacher's perspective, decision-making and collaborating with the community had the most problematic Challenges. Overall, the teachers faced at least one to two challenges in every type of involvement. The problems mostly came from fewer responses obtained due to the less effective activities. This imperfection could be fixed if these two pasties, the parents and the school, recognized their interchangeable roles were important.
[Exploration and practice of innovative teaching of molecular biology experiment course under the context of new agricultural science].

The development of new agricultural science is a new idea and a new measure that aims to deepen the reform of higher education in agriculture and forestry for the development of new agriculture, new countryside, new farmers and new ecosystem. It is therefore essential for the current undergraduate experimental teaching reform to timely introduce new technologies and new methods used in the development of agriculture and forestry industry into the experimental course teaching, and promote the integration of professional education with innovation and entrepreneurship education, according to the requirements of the new agricultural science. In view of this, the exploration and practice of molecular biology experiment course was carried out from the perspective of experimental projects, teaching modes and evaluation methods, according to the teaching requirements and characteristics of molecular biology experiment and teachers' scientific research achievements. The results showed that this reform greatly improved the students' comprehensive quality and innovation ability and may facilitate the innovation experiment teaching of other courses.
Comparison of Online and Offline Learning During The COVID-19 Pandemic using Naïve Bayes Method and C4.5

Learning is a process of interaction between educators and students who meet the elements of learning carried out in an educational environment, so that learning can develop student’s abilities, interests and talents optimally. In today's era learning is done online and inversely with offline. The purpose of this study is to analyze the comparison of percentages and classification results as well as the results of learning evaluations using the Naïve Bayes method and C4.5. This test is carried out with 4 variables and a comparison of the two methods. The results showed that the accuracy of Naïve Bayes was 74.07% and C4.5. of 77.77% so that the comparison results show that the level of accuracy of the C4.5 method is better than Naïve Bayes. The resulting importance variables are time and effectiveness as well as the results of the classification of learning decisions, namely the offline category as many as 16 data on the Naïve Bayes method and 19 data on the Decision Tree algorithm C4.5 method from 27 input testing data.
Contrastive learning-based multimodal fusion model for Automatic Modulation Recognition

Multimodal fusion-based methods are a research hotspot for Automatic Modulation Recognition (AMR). But the existing methods primarily emphasize information integration and neglect the balance between the modalities. This paper proposes a novel Contrastive Learning-based Multimodal Fusion (CLMF) model, which integrates both signals and key features to enhance AMR. To obtain adequate signal representations, a contrastive learning architecture is proposed to learn the meaningful representations from the multimodal fusion data, and a Multi-Layer Perceptron (MLP) is incorporated for precise signal classification. Moreover, a threshold discrimination disturbance strategy is designed to balance the information conflicts arising from the two modalities. The experiments demonstrate the efficiency of the CLMF model for AMR on the public dataset.
Sentiment Analysis on Worldwide COVID-19 Outbreak

Sentiment analysis has proved to be an effective way to easily mine public opinions on issues, products, policies, etc. One of the ways this is achieved is by extracting social media content. Data extracted from the social media has proven time and again to be the most powerful source material for sentiment analysis tasks. Twitter, which is widely used by the general public to express their concerns over daily affairs, can be the strongest tool to provide data for such analysis. In this paper, we intend to use the tweets posted regarding the COVID-19 pandemic for a sentiment analysis study and sentiment classification using BERT model. Due to its transformer architecture and bidirectional approach, this deep learning model can be easily preferred as the best choice for our study. As expected, the model performed very well in all the considered classification metrics and achieved an overall accuracy of 92%.
Recent Advances in Text Analysis

Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. We review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, we review Topic-SCORE, a statistical approach to topic modeling, and discuss how to use it to analyze MADStat - a dataset on statistical publications that we collected and cleaned. The application of Topic-SCORE and other methods on MADStat leads to interesting findings. For example, $11$ representative topics in statistics are identified. For each journal, the evolution of topic weights over time can be visualized, and these results are used to analyze the trends in statistical research. In particular, we propose a new statistical model for ranking the citation impacts of $11$ topics, and we also build a cross-topic citation graph to illustrate how research results on different topics spread to one another. The results on MADStat provide a data-driven picture of the statistical research in $1975$--$2015$, from a text analysis perspective.
Gun Detection System for Surveillance Cameras Using HOG-Assisted KNN Classifier

Mass shootings have become a norm in public places claiming thousands of innocent civilian lives. Firearm-related violence has been rampant in the last few decades and therefore, needs to be addressed immediately. This research aims to propose a weapon detection system that combines image processing techniques with the most suitable machine learning classifier. The experimental study has been executed in twofold: by providing a comparative analysis with previous study and with other existing algorithms as well. Results of the trained model on the dataset provide an accuracy of 98.7% which is significantly better than others published recently.
Optimized Detection, Classification, and Tracking with YOLOV5, HSV Color Thresholding, and KCF Tracking

This paper shows a detection and tracking approach regarding position estimation of pots and angle of arrow on-ground. The pots are detected by HSV color thresholding, and then classified on the basis of local positional parameters like distance of pots from the robot and relative position of pots with the robot. This is a computationally efficient solution for simple regular objects like pots. The Kalman filter specifically works on providing better depth estimates and thereby the position of pots even when the pot tables are overlapping. The approach of the detection-tracking algorithm for small objects like arrows is to combine the accuracy of object detection provided by the custom-trained YOLOv5 model and the speed of the KCF tracker to outperform the results. Multithreading is used to concurrently detect and track the arrows in consecutive frames, producing a computationally efficient approach as compared to standalone detection with YOLOv5. This paper also describes an approach to effectively get depth information on an object using an Intel RealSense D435i depth camera.
Crawl Smart: A Domain-Specific Crawler

With billions of people using the internet, which consists of an estimate of one billion websites, is explored by an individual with a diverse need and intent. The search engines that present results to internet user queries evaluate the websites on numerous parameters to sort the links from most to least relevant. It has become a pick-and-shovel task to extract the most relevant information for a given concept or user query. Classical crawlers that use traditional crawling techniques pull irrelevant data with the relevant ones, resulting in ineffective CPU time usage, memory, and resources. This paper proposes a knowledge-aware crawling system, Crawl Smart, which learns from its own crawling experiences and improves the crawling process in future crawls. The project’s key focus is a methodology that deploys a unique data structure to overcome the challenges of maintaining visited pages and finding a relation between the crawled pages after having them in the knowledge base, which helps the crawler preserve focus. The data structure design, annotations, similarity measures, and knowledge base supporting the Smart Crawl are detailed in the paper. The paper presents the results that show the comparison between the knowledge-aware crawler and the traditional crawler, assuring better results when used on large-scale data.
Realtime Object Distance Measurement Using Stereo Vision Image Processing

In recent years, great progress has been made on 2D and 3D image understanding tasks, such as object detection and instance segmentation. The recent trends in technology driverless cars are making a difference in daily life. The basic principle in these driverless cars is object detection and localization using multiple video cameras and LIDAR and it is one of the current trends in research and development, so attempts to achieve the same on small scale using the available resources. In the proposed method, firstly the stereo images are captured in a dual-lens camera, and secondly, converting the RGB image into a grayscale image. The third step is to apply a global threshold to separate the background, to get the same size of the image using morphological operation. Blob detection is used to detect the points and regions in the image. The fourth step is to detect the object distance and size measurement using the pinhole camera formula. Further, in the proposed work, an effort is made to determine the linear space between the camera and the object from the pictures taken from the camera. Typically, stereo images are used for computation. Binocular stereopsis, or stereo vision, is the capability to derive information about how far left the objects are, grounded uniquely on the comparative places of the object in the two eyes. It depends on both sensory and motor capabilities, using the similar principle the human brain employs, taking two images of the same object taken from two different linearly separated distances. The frame rate of the system can go a maximum of up to 15 frames per second. 15 frames per second can be considered as acceptable for most autonomous systems, and it will work in realtime. Effective convolutional matching technique between embeddings are used for localization that leads LIDAR to increase centimeter level accuracy by about 97%.
Automated Machine Learning-Based Diagnosis and Molecular Characterization of Acute Leukemias using Flow Cytometry Data

Abstract Current flow cytometric analysis of blood and bone marrow samples for the diagnosis of acute leukemias relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into the resulting diagnosis and increasing diagnostic turn-around time. Additionally, concurrent molecular characterization of these samples via cytogenetics and targeted sequencing panels can take multiple days, thereby delaying patient diagnosis and treatment. Attention-based multi-instance learning models are machine learning models that can make accurate predictions and generate interpretable insights regarding the classification of a sample from multiple events/cells; while these models have been developed for anatomic pathology applications, they have yet to be applied to flow cytometry data. By utilizing 1,820 flow cytometry samples from 2019-2022 at Brigham and Women’s Hospital, we developed attention-based multi-instance machine learning models for automated diagnosis of acute leukemia, including differentiation of acute myeloid leukemia (AML) from B-lymphoblastic leukemia/lymphoma (B-ALL). Additionally, using concurrent cytogenetic and targeted sequencing data from 674 acute leukemia samples, machine learning models for prediction of molecular aberrancies from flow cytometry data were developed. Machine learning models were created using the TabNet deep learning architecture and VIME self-supervised training algorithm, which are state-of-the-art approaches towards machine learning from tabular data including flow cytometry data. Attention-based multi-instance models demonstrated strong performance for the automated diagnosis of acute leukemia versus non-leukemia samples (AUROC 0.869), as well as the separation of AML from B-ALL samples (AUROC 0.971). These models also accurately predicted cytogenetic aberrancies among AML samples, including t(15;17);PML::RARA (AUROC 1.00), as well as mutations including NPM1 (AUROC 0.725). These models do not require any manual intervention including compensation or gating, and additionally provide quantitative scores for the relative importance of different flow cytometry events and markers for the diagnosis of a particular sample. These importance scores can be integrated into flow cytometry analysis software for visualization and interpretation by hematopathologists. In this study, we have demonstrated the capability of machine learning models to provide automated diagnoses of acute leukemia, as well as accurately predict cytogenetic and molecular aberrancies in blood and bone marrow samples using flow cytometry data. This automated workflow can significantly decrease diagnostic turn-around time and ultimately improve patient outcomes.
ANÁLISE DO FATOR DE DIMENSIONAMENTO DO INVERSOR PARA SISTEMAS FOTOVOLTAICOS CONECTADOS À REDE COM USO DE MEDIÇÕES CLIMÁTICAS LOCAIS EM GOIÁS

The costs reduction of grid-connected photovoltaic systems is a permanent theme in the area of renewable energy to enable the insertion of these systems in the energy grid. The inverter sizing ratio analysis has a potential of payback time reduction of these systems, increasing the generation capacity by increasing the generator power in relation to the inverter power, without resulting in significant losses to the system. This article presents an inverter sizing ratio analysis for building integrated grid-connected phovotoltaic system with the use of mathematical modeling of the main components of a real system with twelve years hourly measurements of global horizontal irradiation and ambient temperature in Goiania, Goias. The analysis resulted in a band of 0.54 to 0.77 sizing ratios considering variations from 0 to 90° tilt angle and -90° to +90° azimuth angle. The financial analysis resulted in a potential payback time reduction over 7.8% for different system configurations of inclination and orientation of the photovoltaic panels.
Children, care and crime: trauma and transformation <b>Children, care and crime: trauma and transformation</b> , by Alison Gerard, Andrew McGrath, Emma Colvin, and Annette Gainsford, London, Routledge, 2023, 230 pages, $160 (hardcover), ISBN 9780367554033

"Children, care and crime: trauma and transformation." Education 3-13, ahead-of-print(ahead-of-print), pp. 1–2 AcknowledgementsThe authors thank Lembaga Pengelola Dana Pendidikan (LPDP) for supporting this review and Indonesian Researchers for Language Learning and Teaching (iRecall) for supporting the publication.Disclosure statementNo potential conflict of interest was reported by the authors.
Improved particle swarm optimization for feature selection combining simplex method and niche

In order to solve the problems of local optimal solution stagnation and slow convergence speed in particle swarm optimization algorithms, this paper proposes an improved particle swarm feature selection (ISNPSO) that combines simplex method and niche. The initial solution is generated using the Relief algorithm, and the particle swarm is divided into multiple subpopulations using niche technology, allowing for information exchange between subpopulations. Local optimal particles of all subpopulations are extracted for simplex optimization, which accelerates convergence speed while maintaining diversity of the population. The performance of this method was compared with 5 algorithms on 6 datasets. The experimental results show that the algorithm effectively improves classification performance while reducing the number of features.
Machine learning platform design and application based on spark

This paper proposes a design solution for a distributed machine learning platform based on Apache Spark and expounds on its advantages in specific application scenarios. Through Spark's distributed computing framework, the platform realizes efficient machine learning model training and prediction. The paper details the platform's overall architecture, core components, and model implementation methods. We also validate the platform's efficiency and scalability in processing large-scale data through a product recommendation system example. Research indicates that compared to standalone systems, this Spark-based machine learning platform can significantly shorten model training time and support stream data processing. This study offers a reliable and scalable machine learning implementation solution in a big data environment.
The Application of Intelligent Speech Recognition Technology in the Comparison between Japanese and Manchu-Tungus Language

In order to understand the application of comparison between Japanese and Manchu-Tungus language, the application research of an intelligent speech recognition technology in comparison between Japanese and Manchu-Tungus language is put forward. In this paper, the development of intelligent speech re