Reviewerly/exHarmony huggingface dataset:
Insulator Detection in Intelligent Monitoring Based on Yolo Family and Customizing Hyperparameters

Monitoring of power transmission lines plays an important task in high voltage transmission systems. The problem of damaged insulator causes bad effects on an electrical power grid. To make sure the power grid worked properly, electrical personnel usually need to be climbed on the electric post to inspect them which consists of risk latent in occupational safety. Therefore, constructing smart monitoring systems plays an important task to monitor and inspect insulator conditions without manual monitoring. Nowadays, UAV (unmanned aerial vehicles) systems have been become popular, which combines with computer vision supporting to implement an autonomous system for detecting and monitoring bad insulators. In this study, we present an approach which apply deep learning based well-known models to automatic detect insulators with hyperparameter optimization. In this experiment, some models are investigated to select the best one for the insulator detection task in the intelligent monitoring system. Image insulator data was collected by using drone system. The experimental image data consists of 972 samples taken under a variety condition such as clutter backgrounds, high contrast… This study has surveyed and experimented some well-known models such as the Yolov5 family and the newer Yolov7. The hyperparameter optimization and augmentation were applied for improving detected performance. Yolov5x with optimal hyperparameters achieves higher performance with the ratio of recall, precision, mAP:0.5, and mAP:0.5:0.95 are 98.5%, 99.3%, 99.0% and 68.6%, respectively. Meanwhile, the default hyperparameters Yolov5 achieved results with the ratio of recall, precision, mAP_0.5 and mAP_0.5:0.95 are 97.3%, 97.1%, 99.2%, 65.7% and Yolov7 are 95.7%, 97.6%, 98.6% and 63.3%.
A Study on the Composition of the Russian National Corpus and Its Educational Use: Focusing on the Main Corpus

The corpus, which is based on authentic texts produced by language users, is a practical resource in terms of language education and research. However, unlike in research and teaching of English corpus has not yet been actively utilized in the fields of research and teaching of second foreign languages including Russian. This is primarily due to the lack of researchers and teachers with relevant knowledge, which leads to the absence of teaching using corpora and the discontinuity in follow-up research.&#x0D; Therefore this paper examines in detail the annotation system and search tools of the Main Corpus ― one of the subcorpora of the Russian National Corpus (RNC) — and presents some methods for its educational use. Among several subcorpora constituting the RNC, the Main Corpus is the most representative and balanced one. It is equipped with rich morphological, semantic, syntactic annotation systems and metadata, enabling the sophisticated search and analysis of linguistic phenomena. Moreover, since it provides visualizations and simple statistical data, it can also be utilized in academic research.&#x0D; This study — on a trial basis — constructs simple activities involving the use of data from the Main Corpus in the fields of lexicology, morphology, word formation, and syntax. It will be up to future research tasks to explore how RNC's other subcorpora can be used in education; how useful concordancers such as AntConc and Sketch Engine are distinguished from the RNC; and how to use them in language teaching.
Neural networks: Explaining animal behavior with prior knowledge of the world

Animal behavior is both facilitated and constrained by innate knowledge and previous experience of the world. A new study, exploiting the power of recurrent neural networks, has revealed the existence of such structural priors and their impact on animal behavior.
Can synthetic soda ash survive?

The self-taught chemist Ernest Solvay turned the sodium carbonate industry on its head in 1861 when he discovered a one-step process using brine, limestone, coking coal, and ammonia to make the chemical, which is known in the industry as soda ash. Solvay’s approach supplanted the less efficient two-step process introduced in 1791 by the French chemist and surgeon Nicolas Leblanc. The white, powdery inorganic compound is used more than ever in the production of glass, textiles, detergents, paper, silicates, and, more recently, lithium carbonate for lithium-ion batteries. And Solvay’s process is still the dominant production technology. Around three-quarters of the 64 million metric tons (t) of soda ash produced worldwide in 2022 was made using some version of this synthetic method. The rest came from trona, a naturally occurring ore composed of sodium carbonate and sodium bicarbonate. The Solvay process has a problem, however: it is extremely energy intensive, releasing
Chemists enlist computers to analyze tricky visual data

Every rock on Earth is a time capsule, holding traces of our planet from when the minerals first formed. Consider a smidge of 164-million-year-old clay, says M. Joseph Pasterski , an organic geochemist at the University of Illinois Chicago. A sample just about 2 cm long contains complex layers of mixed materials composed of myriad molecules from Earth’s distant past. Researchers like Pasterski want to use today’s state-of-the-art analytical tools to sort through the mélange of minerals to reveal chemical signs of ancient life. For example, using time-of-flight secondary ion mass spectrometry (TOF-SIMS), Pasterski can map elements in 500 by 500 µm patches within the ancient clay sample, generating around 62,000 spectra per patch. Pasterski then uses those data to look for organic molecules, such as steranes, that could be signs of life. The spectra capture not only these biosignatures but also all the other grit and debris that one
Voter Verification in an Election Using Merkle Tree

In this paper we propose a new way to verify eligible voters in a blockchain election. Usually verifying a voter in a list of eligible voters is done using linear search-based algorithms which runs with a worst-case time complexity of O(N) or O(kN). Our system shall use a Merkle tree to implement a search algorithm which runs with a worst-case time complexity of O(Log (N)). Under normal circumstances, when there are 1,000,000 eligible voters, the server would have to scan through 1,000,000 voter address hashes in order to verify a voter. In our system, only 20 hashes will be checked in order to verify a voter. The number of hashes required to calculate the number of hashes needed to process can be found out by getting the logarithm value of the number of hashes, to the base 2 since our Merkle tree has a branching factor of 2. A logarithmic algorithm will exponentially reduce the time and make the eligibility verification process more efficient. Since we are hashing the addresses of the voters before creating the tree, the privacy of the voters shall also be maintained. Instead of using a centralized database for handling the vote requests, we shall use a blockchain database in order to make this system decentralized. Making the system decentralized will make it almost impossible to tamper with the results of the election as once the election is deployed, even the owner of the election that is, the address that deploys the smart contract cannot tamper with the results or the functions in the deployed smart contract.
Crime Prediction Using Machine Learning Algorithms

In a recent survey, its observed that there is arise in crime rate in India, and due to this, many peoplefeel unsafe in this country. So to reduce it, predicting thecrime before it happens is very important. IndianGovernment uses a software called CCIS (Crime CriminalInformation System), this software is only used to storeinformation, but with the stored information, it doesn’t doanything else, the existing systems predict crime only on aday basis, but it doesn’t predict exactly at what hour is itgoing to occur, to predict it on a hour basis, accuracy isimportant, so to analyze and predict crime, the accuraciesof machine learning algorithms such as KNN, Decision treeand Random forest are compared in order to use the bestfor analysis and prediction.
Software Bugs Detection Using Supervised Machine Learning Techniques

A software bug is some sort of a fault in the source code or a computer program. These bugs work in unusual and unintended ways which is a serious problem for a programmer and the company. Detecting bugs in software has been tried and tested through multiple means, the most recent of which is Machine Learning algorithms. Using a revolutionary dataset consisting of real software code snippets of the C language into key values, we were able to train the algorithms based on numerical parameters. This in turn simplified our algorithmic process. Furthermore, we run multiple classification algorithms to gain precision, recall and Fn scores, and improve upon these scores using key hyperparameter tuning techniques. Our observations revealed an increase in accuracy and were able to create an end module which can directly take the source code as an input from which the metrics and features are extracted and give the output if the code has a software bug or not.
Sign Language Detection Using Action Recognition

Sign language detection technique is a part of technology which is of extreme importance to the society. Sign languages is used by deaf and dumb people who are unable to communicate directly using sound since they lack the ability to produce or recognize sound waves which enable us to communicate easily. The proposed project aims in decreasing the distance between the sign language detection techniques which only focuses on detecting the meaning of letters like ASL and not actions provided by the users. The project detects sign languages by using key holes as the position locator and then trains the system to detect accordingly. Keyholes are used to find the position of gesture to use LSTM throughout coaching of the information. Experimental results demonstrate the efficaciousness of the planned methodology in sign language detection task
Medical Diagnosis through Chatbots

Medical attention is critical to living a healthy life. If you have a health concern, however, it is quite difficult to seek medical help. The idea is to create a medical chatbot that can assess symptoms and provide a list of illnesses the user might have using AI and other biometric parameters. In medical diagnosis, artificial intelligence aids in medical decision making, management, automation, administration, and workflows. It can be used to diagnose cancer, triage critical findings in medical imaging, flag acute abnormalities, assist radiologists in prioritizing life-threatening cases, diagnose cardiac arrhythmias, predict stroke outcomes, and aid in chronic disease management. Medical chatbots were created with the goal of lowering medical costs and increasing access to medical information. Some chatbots act as medical guides, assisting patients in becoming more conscious of their ailment and improving their overall health. Users will undoubtedly profit from chatbots if they can identify a variety of illnesses and provide the necessary information that may help the user to understand the predicament, he/she might be facing. The main idea is to create a preliminary diagnosis chatbot that allows patients to participate in medical research and provide a customized analysis report based on their symptoms
Development of LSTM Model for Fall Prediction Using IMU

One of the major contributor leading to cause of unintentional injuries after motor vehicle crashes and poisoning, is Falls. The existing Fall Prediction Algorithms are used to predict falls in older or disabled people by analyzing their fall history, capturing their movements through visual sensors (cameras, thermal imaging etc.) in a fixed environment, using inertial sensors to identify the patterns of movements. These algorithms are monologues for each person as they learn from their history and predict falls specific only to that person. The algorithm proposed in this paper aims to predict falls using kinematic data such as accelerometer, magnetometer, and gyroscopic values, for any user. This work involves developing an algorithm capable of predicting falls and to achieve this, we use Long Short-Term Memory (LSTM). The benefit of this algorithm is to prevent trauma to the body or at least reduce the impact of fall and the fatality caused by it. In the future, this algorithm can be used to design a device to predict falls in real-time to scenario be used by everyone irrespective of gender, age, and health.
Underwater polarization defogging technology based on Stokes

This paper mainly discusses the underwater fog technology, and mainly uses some image evaluation algorithms. Compared with other image processing algorithms, it improves the running speed of the algorithm. In short, the underwater image denoising and contrast after processing are better than the traditional image polarization defogging algorithm. The experiment specifically uses the split focus plane polarization imaging system to collect polarization pictures from multiple angles in multiple underwater scenes, and calculate its polarization stokes vector, calculate the polarization information of the scene through the stokes vector, use the general light source and linear polarization light source to illuminate the diffuse reflection and specular reflection targets respectively, compare the polarization information of different scenes, and get that the polarization information distribution of the background and target of the scene using polarization light source is clearer than that of the general light source. Then, a stokes polarization defogging algorithm is proposed. The polarization degree information calculated by stokes vector is compared with the transmission information obtained by the dark channel prior algorithm. It is found that under the condition of using polarized light source and uniform illumination, the transmission of scene can be predicted by DOP (Degree of polarization) calculated by stokes vector and used for image defogging, then some image evaluation algorithms are used to compare it with other image processing algorithms, which improves the running speed of the algorithm. The denoising and contrast of the underwater image after processing are better than the traditional image polarization defogging algorithm.
Secret Sharing Scheme with Fingerprint Authentication

Medical imaging is very important in medical diagnosis. X-rays, ultrasound images, CT scans, brain pictures, and patient mri images are examples of images that contain sensitive information. However, poor communication channels and loopholes in hospital and medical centre storage systems risk accessing these images by unauthorised individuals who utilise them for nefarious purposes other than diagnostics. Image encryption is a common strategy for enhancing the integrity of communication and storage channels for protecting medical images from unauthorized access. This task proposes a biometrics-based method of secret sharing. Instead of sharing secrets between participants as in encryption, the biometric properties of participants create a single biometric configuration. If the biometric vaults are verified for the required number of actual during the authentication process, participants must provide a valid encryption key from the configuration is disclosed.
Sensitive Data Transaction Using RDS in AWS

Today's and tomorrow's secret data exchange relies heavily on cryptography. Cryptographic applications offer a secure communication channel for safely transferring data. It provides individuals, groups, and organizations with greater privacy and access to communication and other information, as well as the opportunity to restore personal privacy. Nowadays, online users desire to create an account in order to get access to certain websites, such as online tutorials, online purchases, online resource access, hosting services, social networking sites, and so on. However, unless the service provider or authority person ensures that the registration and login processes are genuine, there is a potential that the account might be hacked by a third party using the ordinary user access method. With the growing usage of cloud emails and frequent reports of large-scale leakage occurrences, a security attribute known as forward secrecy becomes desired and necessary for both users and cloud service providers to improve the security of cloud email systems.
The Effectiveness of Canva Application as a Media in Writing Greeting Card at the Eight Grade of SMP Negeri 12 Banjarmasin

The use of technology for education is good to support the teacher in transferring knowledge that they want to teach for the students to improve their skill or knowledge. One of the digital educational apps that can help in writing is Canva. According to Christiana &amp; Anwar (2021) canva is a website graphic design tool, a drag-and drop format of visual technology media, provides access to more than one million photos, graphics and fonts. This study aims to find the effect of using canva as a media in writing greeting card at the eight grade of SMP Negeri 12 Banjarmasin. The research subject was the eight grade students of SMP Negeri 12 Banjarmasin. This research is classified as a true experimental design. The population of this research is 59 students but only 48 students are choosen as the sample based on 5% mistake level. This study is a quantitative method that using Randomized-Groups Pre-test-Post-Test Control Group Design. The instrument that used was writing greeting card. The data were collected in several steps: pre-test, treatment, and post-test; the data were processed in several steps: collecting data, scoring and calculating; data analysis was conducted using SPSS application including test of data normality, homogeneity and tests of Hypothesis. The result of this study conclude that the use of canva as a media has significant effect for the students in writing greeting card.
Book Recommendation Based on Emotion in the Online Library Using CNN

The present pandemic situation has made people not go out anywhere because it’s getting difficult to learn the concepts briefly for both lecturers and students. Concepts are learned through videos, but reading is also an important aspect of learning. This paper talks about providing books and notes in online mode to read and recommend books based on facial expressions captured from the user. This paper aims to extract faces from an image, extract the expression (eyes and lips) from it and also classify them into six types of emotions, which are Happy, Fear, Anger, Surprise, Neutral and Sad. The algorithm used for facial expression recognition is the Convolutional Neural Network algorithm, also known as CNN.
Movie Recommendation System Using Machine Learning

There are more number of movies has been released the user gets confusion, which movie is suit for them and difficult to choose, so to become easier the recommendation system comes into play if the user search a movie it gives a accurate result with similar various suggestion . The suggestion movies are given by the recommendation system by the user search e.g., if the movie is action, love, crime, drama or by the director the similar movies will be suggested. The recommendation systems are used to recommend movies using the user previews choice. The Sentiment Analysis which helps to analyse the users sentiments, which is based on their choice.In recent year, the sentimental analysis became one of most major things for many of the recommendation systems
A Step towards Blockchain Scalability Resolution

After mainframes, people computers, the internet, mobile communications, and social media, blockchain has been dubbed the sixth disruptive technology. For system decentralization, it is referred to as a distributed peer-to-peer (P2P) database enabler. There is a scalability issue that is a major roadblock to widespread Blockchain adoption. The goal of this study is to overcome the barriers that are preventing this technology from becoming as widely accepted as it was designed to be. This research provides the groundwork for an improved blockchain by identifying the right balance between block size and block interval, switching to a better consensus mechanism like PoS, and indicating sharding.
Face Mask Detection Using OpenCV

As a biosafety precaution, the World Health Organization (WHO) introduced the wearing of face masks after the COVID-19 epidemic. This posed challenges to existing facial recognition systems, so this study was born. In this publication, we describe how to create a system that allows you to identify people from images, even when they wear a mask. The face detector in OpenCV is used in conjunction with Based on the Mobile NetV2 architecture, a classification model in this way, it is possible to determine whether the face is wearing a mask and where it is situated. To conduct face recognition, A Face Net model is used as a feature extractor and a multilayer feedforward perceptron is used for training facial recognition models using a collection of about 4000+ photographs. Of the images, 52.9 percent came with a face mask and 47.1 percent were without mask. The outcomes of the tests demonstration that determining whether or not someone is wearing a mask is 99.65% accurate. Face recognition accuracy for ten people wearing masks is 99.52 percent, whereas face recognition accuracy without masks is 99.96 percent.
Armament Detection Using Deep Learning

In the recent past there has been an increase in the occurrence of violent incidents involving dangerous objects such as arms and knives. Being able to quickly identify and defuse such situations are of utmost importance in order to preserve peace and to avoid human casualties. One of the most important and commonly used methods to increase security is the usage of surveillance cameras almost everywhere. The benefit of object detection techniques can be used in this field in order to help improve security. Using object detection techniques in order to detect objects of interest in surveillance footage is one method to identify dangerous situations and take necessary steps in order to minimise any damages.This paper uses convolutional neural network (CNN) based YOLO algorithm in its implementation to detect weapons such as knives and pistols
Recognition of American Sign Language with Study of Facial Expression for Emotion Analysis

Sign Language is a medium of communication for many disabled people. This real-time Sign Language Recognition (SLR) system is developed to identify the words of American Sign Language (ASL) in English and translate them into 5 spoken languages (Mandarin, Spanish, French, Italian, and Indonesian). Combining the study of facial expression with the recognition of Sign Language is an attempt to understand the emotions of the signer. Mediapipe and LSTM with a Dense network are used to extract the features and classify the signs respectively. The FER2013 data set was used to train the Convolutional Neural Network (CNN) to identify emotions. The system was able to recognize 10 words of ASL with an accuracy of 86.33% and translate them into 5 different languages. 4 emotions were also recognized with an accuracy of 73.62%.