Reviewerly/exHarmony huggingface dataset:
Enhancing accessibility: object detection and optical character recognition for empowering visually impaired individuals

Addressing the global challenge of visual impairment, where 285 million individuals, including 39 million who are completely blind, are affected according to the World Health Organization (WHO), this project aims to empower visually impaired and blind individuals through an innovative application. This application leverages advanced technology to detect objects in their surroundings and provide them with the ability to comprehend text from various documents. The input consists of images, and the output is delivered as an MP3 file, which can be played on the user&apos;s mobile device. Object detection within the images is accomplished through the utilization of the YOLO (You Only Look Once) algorithm, renowned for its speed and efficiency in object detection tasks. Furthermore, Optical Character Recognition (OCR) technology is employed to extract text from these images, utilizing Py-Tesseract, a powerful OCR tool developed by Google. The process begins when the user captures an image from any document. Subsequently, the extracted text is converted into audible speech using the gTTS (Google Text-to-Speech) API. The resulting sound or voice is saved as an MP3 file, which serves as an instructional guide for visually impaired individuals to perform their daily tasks with greater ease and independence.
Efficiency Enhanced Super Resolution Gener ative Adver sar ial Network via Advanced Knowledge Distillation

Super-resolution (SR) stands as a prominent challenge in computer vision with diverse applications. Generative adversarial networks (GANs) yield impressive SR outcomes by restoring high-quality images from low-resolution input. However, GAN-based SR (particularly generators) have high memory demands, leading to performance degradation and energy consumption, making them unsuitable for resource-limited devices. Addressing this concern, our paper introduces a novel and efficient SR-GAN (generator) model architecture by strategically leveraging knowledge distillation, which results in reducing storage demands by 58% while enhancing performance. Our approach involves extracting feature maps from a resourceintensive model to design a lightweight model with minimal computational and memory requirements. Experiments across several benchmarks demonstrate that the proposed compressed model outperforms existing knowledge distillation-based techniques, particularly in regard to SSIM, PSNR, and overall image quality in x4 super-resolution tasks. In the future, this compressed model will be implemented and benchmarked with existing models in resource-limited devices such as tablet and wearing devices.
The Effect of Early Childhood Teachers' Perceptions of Child Abuse and Play Beliefs on the Practice of Respect for Early Childhood Rights

The purpose of this study is to investigate the relationship between early childhood teachers' perception of child abuse, play beliefs, and practice of respect for early childhood rights, and to investigate the effect of early childhood teachers' perception of child abuse and play beliefs on the practice of respect for early childhood rights. The subjects of the study were 309 early childhood teachers working in kindergartens and daycare centers in G and S cities, and a survey was conducted among them. The collected data were used using the SPSS 24.0 program, and correlation analysis and stepwise regression analysis were performed. As a result of the study, first, it was found that there was a correlation between early childhood teachers' perception of child abuse, play beliefs, and practice of respect for early childhood rights. Second, as a result of examining the effect of early childhood teachers' perception of child abuse and play beliefs on the practice of respect for infant rights, it was found that the early childhood teachers' perception of child abuse and play beliefs were variables that predicted the practice of respect for infant rights. This study is expected to be used as basic data to find ways to promote early childhood teachers' awareness of child abuse and belief in play, which are important variables in the practice of respecting the rights of infants.
Leveraging Blockchain Technology for a secure IoT data sharing

We live in the age of smart technologies, and the popularity of the Internet of Things has skyrocketed. As a result, billions of smart devices are expected to connect over the internet in the next few years. Tools like this have been used in in a variety of fields which are reaping its benefits across the globe. However, with the rise of smart devices producing more data than ever, managing them is a considerable challenge that introduces privacy and security issues. This paper aims to offer suggestions and improvements by proposing a system that leverages Blockchain technology to address some of the problems associated with data sharing by the Internet of Things. The proposed solution consists of the Ethereum Blockchain platform, an Interplanetary File System, fog devices, AttributeBased Encryption, and Advanced Encryption Systems. The research concludes that the proposed system is fully decentralized, eliminates single points of failure, offers scalability, overcomes latency issues, and is secure and viable.
Indonesian Part-of-Speech Tagger: A Comparative Study

POS Tagging is one of the essential tasks in Natural Language Processing (NLP). Researchers are competing to find the best model for Indonesian POS Tagging cases. However, they still use one corpus as a reference for model development. This study investigates the comparison of three different corpora and three state-of-the-art models to explore the use of appropriate corpora and models for Indonesian POS Tagging. We divided the corpus into training, validation, and testing datasets. We use the training and validation datasets to tune the model. We use the test dataset to evaluate the performance of the model. The experimental results show that Yunshan and Dinakaramani corpora have outstanding performance in POS Tagging. While the Feedforward and BiLSTM models have equally superior performance, outperforming the other models with the highest value of 96.10%. This experiment proves that both models are stable when applied to different corpus. Further investigation is needed to improve the performance of the models by considering variations in word embedding usage, architecture, and evaluation methods.
Robustifying Spoken Language Identification on Noisy Condition Using Curriculum Learning

Curriculum learning shows promise in improving machine learning tasks like spoken language identification (LID) in challenging scenarios. We propose an end-to-end language identification framework, the x-vector Transformer-based End-to-End Language Identification system, leveraging curriculum learning. The x-vector Transformer (x-Trans) combines x-vector and transformer-based language modeling to capture rich speaker embeddings and extract high-level linguistic features from raw audio signals, enabling robust language identification. Evaluation on the MLS14 set of NIST LRE 2017 demonstrates significant improvements, achieving equal error rates of 8.91 %, 4.83%, and 3.89% on 3s, 10s, and 30s test speech, which is respectively 34.96%, 47.50% and 52.03% reduction compared to the referred work's performance using x-vector and BLSTM. To assess noise robustness, the clean LRE data undergoes full corruption with radio channel noises extracted from the RATS corpus at various Signal-to-Noise Ratio levels. The subse-quent phase employs curriculum learning, facilitating progressive learning from simple to complex data, resulting in improved performance in both clean and noisy conditions. Finally, we conduct multilingual Asia dataset experiments, achieving over 97.5 % accuracy for in-domain languages, even under highly noisy conditions.
AI Empowered Quantitative Evaluation Method for Handwritten Chinese Character

In order to solve the problems of untimely evaluation, unspecific and ineffective feedback for writers and to improve writing quality in the daily standardized Chinese character writing practice of primary and secondary school students, a quantitative evaluation method for paper-pen handwritten standardized Chinese character based on neural network is proposed in this paper. It takes handwritten Chinese character by paper-pen writing as the evaluation object, and obtains the quantitative features of Chinese character through feature extraction of handwritten Chinese character image samples. On this basis, CNN classifier is used to complete the classification of handwritten Chinese character images. Then, based on the Gaussian distribution to fit the writing feature values of excellent samples, strict and loose normative interval thresholds are obtained. Finally, the deviation between the quantitative features of handwritten Chinese characters and the threshold is calculated, to realize the general quality evaluation and the detailed quantitative evaluation of strokes.
A Deep Understanding Video Q&amp;A System for Film Education in Acting Department

Recently, advancements in artificial intelligence technology have greatly influenced the field of education, particularly in the area of intelligent homework assistance. However, current approaches are primarily designed for procedural and logical tasks and often lack comprehension abilities. This limitation is particularly evident when it comes to multi-hop and continuous tasks. To address this challenge, the integration of Large Language Model (LLM) has significantly enhanced the capability of AI systems to handle multi-hop and highly interconnected inputs. In this study, we focus on the learning needs of students in Acting Department, specifically their study of movies and the significance of classic movie videos in their learning process. However, assessing deep comprehension of classic movies poses its own challenges. To overcome these challenges, we develop a quiz system utilizing Knowledge Graphs (KG) and LLM to facilitate a deeper understanding of classic films. The generation of video quiz pairs is achieved through the use of Automatic Speech Recognition (ASR) technology, which leverages movie subtitles for question generation. For answering these questions, we employ techniques KG and LLM to process questions and retrieve corresponding answers. The proposed method achieves good performance in Deep Video Understanding (DVU) task of NIST TRECVID, demonstrating its effectiveness.
T<sup>2</sup>GR<sup>2</sup>: Textile Touch Gesture Recognition with Graph Representation of EMG

The fashion industry’s negative impact and overconsumption require urgent action to improve and reduce fashion consumption. Tactile gesture plays a vital role in understanding, selecting, and feeling attached to clothes. In this paper, we introduce the FabricTouch II dataset with multimodal infromation, which focuses on fabric assessment touch gestures and aims to support sustainable fashion consumption. By integrating gesture labels, we enhance the dataset’s comprehensiveness, improve recognition accuracy, and provide valuable information for consumers and intelligent systems, such as conversational agents in shop or home wardrobe. Additionally, this study has made preliminary explorations on recognizing fabric touch gestures using time-spectral representations of EMG combined with graph representations on this small batch dataset. The experiment found that the graph representation of EMG outperforms the regular neural network and that the representation capacity of bilateral EMG data is superior to that of unilateral data.
EVALUASI KEMAMPUAN MEMBACA DAN MENULIS RAPI PADA SISWA JENJANG KELAS II SDN 095259 MANDARO

This research is a class action research (Classroom Action Research). The research was conducted in class II of SDN 095259 Mandaro in semester I of the 2022/2023 academic year starting on September 19 2022 to September 28 2022. The research data is in the form of primary data sources, namely: students, teachers, parents or related parties and secondary data sources, namely: observation sheets, documents, and notes. Data collection techniques were carried out by observing during the learning process to find out student activities, research documents by collecting written data, lists of formative values, and notes. This data collection is to find out about the student's personality. The data analysis technique used is an interactive qualitative data analysis technique. The results of this study were carried out by evaluation to measure students' ability to read and write neatly which was applied at SD095259 Mandaro N. In implementing this evaluation, it can measure students' ability to read and write neatly at the school level in class II SD. Recommendations that can be conveyed are that in choosing the method to be applied in the learning process in the classroom, the teacher should first know the limits of the abilities and conditions of the students. Both the conditions that exist in the classroom and outside the classroom by carrying out this evaluation method.&#x0D; &#x0D;
The influence of indicators of the level of development of stress factors on the psychological preparation of military servants to perform the tasks of military-professional activities

The experience of the participation of the Armed Forces of Ukraine in the conduct of hostilities against the aggressor of the Russian Federation showed that the result of the execution of combat missions by units depends, first of all, on the psychological readiness of personnel, which is ensured by professional selection, professional training of specialists for effective and safe activities, and direct professional activities. The purpose of the study is to investigate the influence of indicators of the level of development of qualities that characterize the degree of anxiety, worry, emotional stress due to the action of stress factors and factors on the psychological readiness of military personnel to perform the tasks of military professional activity in various conditions. Research material and methods. In order to identify the indicators and the level of development of qualities that characterize the degree of anxiety, worry, emotional tension due to the action of stress factors and that affect the psychological readiness of military personnel to perform assigned tasks, we organized and conducted testing based on the Spielberger Khanin questionnaire in accordance with the personal assessment scale anxiety with the servicemen of the formed groups at the point of permanent deployment and before the exercises. 78 military personnel participated in the study (n=39, I-II course of study; n=39, III-IV course of study). The age of the respondents is 17-24 years old. Results. According to the results of the survey, it was established that the majority of junior soldiers have a high level of personal anxiety in their everyday life (78.7 %). Among these servicemen, there is not a single one who has a low level of anxiety. The average indicator of personal anxiety corresponds to the indicator of a high level (48.92±1.15 um. unit). The analysis of personal anxiety of senior servicemen is 42.87±1.27 um. units, which fluctuates within the average level of anxiety. Conclusions. The conducted analysis showed that during the execution of a combat mission, there is a significant increase in the level of anxiety of the personnel. The average index of reactive anxiety of junior year cadets is significantly worse than the index of personal anxiety (p&lt;0.001; t=4.78). The same trend between reactive and personal anxiety indicators after the survey was also found in senior courses. This fact confirms that the level of psychological readiness and preparedness of personnel of infantry servicemen is at an insufficient level, which, in turn, leads to a decrease in the level of combat readiness of units and indicates the need to revise the content, forms, methods to ensure the readiness of servicemen to perform the tasks of professional activity.
Exploring Abandoned Alluvial Tin Mines Using Satellite Image and Mineralogical Data: A Case Study of Belitung Island, Indonesia

Tin in Indonesia is mainly mined from alluvial placer deposits located in the tin belt of SE Asia. The cassiterite concentration is in alluvial placer and was formed by erosion and weathering of cassiterite-bearing granites. Although most resistate economic minerals (such as cassiterite and monazite) have been liberated due to weathering processes, economic trace minerals have been overlooked for many decades. Furthermore, the cessation of alluvial tin mining has generated a large amount of mine tailings, most of which represent nonviable waste materials. These tailings and vegetations occasionally capped the undisturbed areas that still have a potential value of tin, heavy, and rare earth bearing minerals. The current investigations focused on the advancing exploration technology using multi-temporal satellite imagery data to assess land changes due to past tin mining activities from year 1994 to 2017. The algorithms employed to evaluate overburden, tailing deposits, and land change were spectral angle mapper (SAM) and supervised classification. Examination based on X-Ray Diffraction (XRD), grain size analyses, and spectrum data have jointly applied to determine the mineral composition of tailings, overburden, concentrate, alluvial placer allochthonous deposits (kaksa), and bedrock (kong). The results showed that multi-temporal satellite images and mineralogy can be implemented for identifying a change detection due to mining activities and to delineate the potential mineable heavy and rare earth mineral zones, which are being covered by historic tailings or vegetations.
Development of Edmodo-Based E-learning on Newton’s Laws

The application of learning technology in the digital era, for example, the use of learning, has potential advantages to help educators and students achieve learning goals. Learning activities are still conventional and using less attractive learning media affect students' interest and learning motivation, so learning media are needed to increase motivation and help students to understand the material optimally. This study aims to determine the validity of Edmodo-based e-learning media in Newton's Law courses. This type of research is Research and Development (R&amp;D) with a 4D research design (Define, Design, Development, and Disseminate). This research is limited to the development stage. The respondents of this study were two teachers who were subject experts and media experts. The study results show that Edmodo-based e-learning on Newton's Laws is due to the validity of the learning media. Subject expert validity is in the very good category, with a score of 4.77. Media expert validity is in the good category, with a score of 3.90. The results of this study have implications for the readiness of learning media to support the achievement of learning objectives in Newton's Laws subjects.
Analyses the Performance of Data Warehouse Architecture Types

The concept of storing historical data for retrieving them when needed has been conceived, and the idea was primitive to build repositories for historical data to store these data, despite the use of a specific technique for recovering these data from various storage modes. The data warehouse is the most reliable and widely used technology for scheduling, forecasting, and managing corporations. Also, it is concerned with the data storage facility that extensive collection of data. Data warehouses are called ancient modern techniques; since the early days of relational databases, the critical component of decision support, increasing focus for the database industry. Many commercial products and services are now available, and almost all of the primary database management system vendors provide them. When opposed to conventional online transaction processing applications, decision support puts slightly different demands on database technology. This paper analyzes the performance of the data warehouse architectures by studying and comparing many research works in this field. The study involves extracting, transforming, and loading the data from different recourses and the important characteristic of the architecture types. Furthermore, the tools and application service techniques used to build data warehouse architecture.
Optimizing Handwritten Numeral Recognition for English and Devanagari Using MNIST and CPAR Data

In this paper, a technique for the categorization of offline handwritten digits in the Devanagari script as well as the Roman script (English numbers) is presented. Finding out how to get the best possible recognition results while concurrently working with several scripts is the primary purpose of this study. The approach that has been described makes use of an easy profiling method for the extraction of features, together with an innovative implementation of Linear Discriminant Analysis (LDA), and a neural network architecture that has been adapted specifically for numeral classification. An exhaustive series of trials including 36,000 examples of handwritten numerals were carried out as part of the evaluation of the effectiveness of this approach. These samples were chosen at random from the CPAR datasets, with 22,000 being put to use in the construction of the training set and the remaining 14,000 being assigned to the role of the test set. In addition, the well-known MNIST database was used, which provided 60,000 samples for training purposes and 10,000 samples specifically for testing. It was determined via thorough testing that the LDA classifier produced variable results. These varying results are essential in emphasising the success of the method as well as possible areas for improvement for the method. According to the results, this strategy, which combines profile-based feature extraction with advanced classification algorithms, has the potential to lead to major breakthroughs in the area of handwritten number identification.
Methods and Applications of Target Tracking in Sports Broadcast Videos

In recent years, the attention of sports events has been rapidly improved. The method of multi-target tracking can better help fans analyze sports games. This paper studies and discusses the target tracking method and application of sports broadcast video. The target tracking method adopts the multi-target tracking method combining yolov5 and deepsort. This method is one of the most commonly used multi-target tracking methods. It realizes multi-target tracking through two stages. Firstly, yolov5 is used for target detection, and then deepsort is used for Hungarian matching and Kalman filtering to realize multi-target tracking. In this paper, the experiments of target detection and multi-target tracking are carried out respectively, and the best multi-target tracking model is obtained through statistical data and analysis of the final experimental results. In addition, this paper adds the functions of target trajectory drawing and video editing to video tracking to better help fans analyze sports broadcast video.
Research on Graph Neural Network Algorithm and Image Recognition Application

The emergence of graph neural networks makes up for the limitations of traditional neural networks in dealing with complex graph data, and they are widely used in the fields of social networks and recommender systems, but at the same time, there are some defects in themselves. This paper introduces the basics of graph neural networks, and also summarizes the advantages and disadvantages of GSN, GraphSAGE, DeepWalk and Node2Vec algorithms. Finally, it also discusses the application of graph neural networks in the field of image recognition, summarizes its deficiencies in practical applications, and puts forward the prospect of the future development of graph neural networks.