Reviewerly/exHarmony huggingface dataset:
Difficulties Developing a Children’s Speech Recognition System for Language with Limited Training Data

Automatic speech recognition is a rapidly developing area in the field of machine learning and is a necessary tool for controlling various devices and automated systems. However, such recognition systems are more aimed at adults than at the younger generation. The peculiarity of the development of a child’s voice leads to an increase in the error in the recognition of children’s speech in applications developed based on adult speech data. In addition, many applications do not consider the peculiarities of children’s speech and the data used when children communicate between other children and adults. Thus, there is currently a huge demand for systems that understand adult and child speech and can process them correctly. In addition, there is the problem of the lack of these languages, which are part of the agglutinative, i.e. Turkic languages, especially Kazakh language. The difficulty of assembling and developing a high-quality and large case is still an unsolved problem. This paper presents studies of children’s speech recognition based on modified data from adults and their impact on the quality of recognition for Kazakh language. Two models were built, namely the Transformer model and the insert-based model. The results obtained are satisfactory, but still require improvement and expansion of the corpus of children’s speech.
Improved ant colony-based algorithm for solving traveling salesmen problem

The purpose of this research is to examine how well the Improved Memetic Continuous Pheromone-based Ant Colony Optimization (ICMPACO) algorithm solves standard Traveling Salesman Problem (TSP) instances in comparison to the basic Ant Colony Optimization (ACO) algorithm and the Improved Ant Colony Optimization (IACO) algorithm. The findings of the computer simulation show that the ICMPACO algorithm has higher optimization capabilities, particularly for the berlin52, eil51, and dantzig42 examples. This superiority is further emphasized through better mean values in comparison to traditional ACO methods. The study also proposes a novel approach, Genetic Ant Colony Optimization (GACO), which integrates ACO with genetic algorithms. Pheromone data is efficiently employed to direct the choice of genetic operation points and maintain the foundational elements within the genetic algorithm. Performance results for various TSP instances reveal the potential of GACO in generating high-quality solutions. These findings suggest that both ICMPACO and GACO methods hold significant promise in addressing complex optimization challenges and could pave the way for further advancements in enhancing optimization algorithm performance.
Capuchin Search Particle Swarm Optimization (CS-PSO) based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment

This review introduces the methods for further enhancing resource assignment in distributed computing situations taking into account QoS restrictions. While resource distribution typically affects the quality of service (QoS) of cloud organizations, QoS constraints such as response time, throughput, hold-up time, and makespan are key factors to take into account. The approach makes use of a methodology from the Capuchin Search Particle Large Number Improvement (CS-PSO) apparatus to smooth out resource designation while taking QoS constraints into account. Throughput, reaction time, makespan, holding time, and resource use are just a few of the objectives the approach works on. The method divides the resources in an optimum way using the K-medoids batching scheme. During batching, projects are divided into two-pack assembles, and the resource segment method is enhanced to obtain the optimal configuration. The exploratory association makes use of the JAVA device and the GWA-T-12 Bitbrains dataset for replication. The outrageous worth advancement problem of the multivariable capacity is addressed using the superior calculation. The simulation findings demonstrate that the core (Cloud Molecule Multitude Improvement, CPSO) computation during 500 ages has not reached assembly repeatedly, repeatedly, repeatedly, and repeatedly, respectively.The connection analysis reveals that the developed model outperforms the state-of-the-art approaches. Generally speaking, this approach provides significant areas of strength for a successful procedure for improving resource designation in distributed processing conditions and can be applied to address a variety of resource segment challenges, such as virtual machine setup, work arranging, and resource allocation. Because of this, the capuchin search molecule enhancement algorithm (CSPSO) ensures the success of the improvement measures, such as minimal streamlined polynomial math, rapid consolidation speed, high productivity, and a wide variety of people.
Human Action Recognition Based on Hierarchical Multi-Scale Adaptive Conv-Long Short-Term Memory Network

Recently, human action recognition has gained widespread use in fields such as human–robot interaction, healthcare, and sports. With the popularity of wearable devices, we can easily access sensor data of human actions for human action recognition. However, extracting spatio-temporal motion patterns from sensor data and capturing fine-grained action processes remain a challenge. To address this problem, we proposed a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM. The spatial information of sensor signals is extracted by hierarchical multi-scale convolution with finer-grained features, and the multi-channel features are fused by adaptive channel feature fusion to retain important information and improve the efficiency of the model. The dynamic channel-selection-LSTM based on the attention mechanism captures the temporal context information and long-term dependence of the sensor signals. Experimental results show that the proposed model achieves Macro F1-scores of 0.68, 0.91, 0.53, and 0.96 on four public datasets: Opportunity, PAMAP2, USC-HAD, and Skoda, respectively. Our model demonstrates competitive performance when compared to several state-of-the-art approaches.
Effective Moving Object Tracking Using Adaptive Background Subtraction with Advanced Probability Evolutionary Algorithm

Tracking moving objects is a very difficult task for many computers. The task of tracking is a moving object. There are many virtual applications such as video surveillance and object recognition. Many ideas have been suggested for moving objects tracking and detection. One of the main problems solving tracking is finding identical objects in different frames. In this article, this application mainly focuses on the detection and tracking of moving objects using adaptive Background subtraction with adaptive probability evolutionary algorithm (AREA) method. Normal probability evolutionary algorithm will track only human motion but our algorithm tracks all the moving objects very accurately. Our proposed system shows the tracked objects with the red mark border.
Lightweight Weight Update for Convolutional Neural Networks

Convolutional neural networks are usually composed of convolutional layers and pooling layers. Pooling operations effectively control the weight update of convolutional neural networks. The existing pooling operations result in a large number of weight parameter updates of convolutional neural networks, causing large memory usage. In this paper, a pooling operation called ApproxM is proposed to address this problem. The proposed pooling operation is a simple and similar to median pooling. It takes the mean of multiple values near the median as the pooling result, and CNNs only update the weights of these values during the back propagation. Finally, extensive experiments on benchmark datasets demonstrate that the proposed pooling operation achieves top-1 results of 92.65% and 68.24% and top-5 results of 99.84% and 91.31% model test accuracy on Cifar-10 and Cifar-100 based on ResNet-20, respectively, and the corresponding number of weight updates in an 8 $$\,\times \,$$ 8 pool is 4, which is better than other pooling techniques in the experiments.
Semilayer-Wise Partial Quantization Without Accuracy Degradation or Back Propagation

In edge AI technologies, reducing memory bandwidth and computational complexity without reducing inference accuracy is a key challenge. To address this difficulty, partial quantization has been proposed to reduce the number of bits in weight parameters of neural network models. However, existing techniques monotonically degrade accuracy with the compression ratio without retraining. In this paper, we propose an algorithm for semilayer-wise partial quantization without accuracy degradation or back-propagation retraining. Each layer is divided into two channel groups (semilayers): one being positive for loss degradation and the other negative. Each channel is classified as positive or negative in terms of cross-entropy loss and assigned to a semilayer accordingly. The evaluation was performed with validation data as input. Then, the quantization priority for every semilayer is determined based on the magnitude in the Kullback-Leibler divergence of the softmax output before and after quantization. We observed that ResNet models achieved no degradation in accuracy at certain parameter compression ratios (i.e., 79.43%, 78.01%, and 81.13% for ResNet-18, ResNet-34, and ResNet-50, respectively) in partial 6-bit quantization on classification tasks using the ImageNet dataset.
Real-Time Pedestrian Detection

Real-time object detection has become increasingly important. In this exploration we will study the real-time pedestrian detection on raspberry pi 3B+. The Raspberry Pi, a popular imperative edge device for various applications, is utilized to examine the real-time inference of pedestrian detection models. We primarily pick models based on model size, computational time, model flexibility, and model reliability under various scenarios. EfficientDet was chosen for its fewer parameters, state-of-the-art precision, and scalability. Yolov7 is the present state in real-time object detection model, and yolov7-tiny is an optimised and tiny version of yolov7. Following that, we select TensorFlow-lite models that are mostly enhanced for Edge deployment. Optimizations such as quantizing and pruning significantly contribute to the increased efficiency of TensorFlow-Lite models, which achieve an FPS of 5-6 on the Raspberry Pi. While YOLOv7-tiny achieved higher accuracy than TensorFlow-Lite Models, it did so at a slower rate of inference. EfficientDet, which was originally trained in TensorFlow, is more computationally expensive than previous models but has higher accuracy.
A State of the Art of Non-fungible Tokens: A Literature Review

Non-fungible tokens, also known as NFTs, are based on the blockchain and are a type of proprietary digital asset registered on the blockchain, making them unique, irreplaceable, and indivisible assets. This main feature makes up the nascent and emerging phenomenon that has revolutionized the way digital assets are and will be handled in the future. Its success and importance has resonated particularly in the first and second quarter of 2022, and however, the full application and reach of NFTs through its technology in human life are still yet to be discovered. The main objective of this chapter is to inform the audience of developments in the research field, discuss the importance and significance of the issues investigated to analyze the relevance and suitability of the topic in order to inform the audience about its operations, and generate, through the NFTs, economical value and further knowledge for future applications. This is the reason why a systematic review of the literature on the subject of “non-fungible tokens” is carried out based on publications of Scopus indexed journals, with a collection of information from 2017 to 2022.
Application of Pure Birth to the Phenomenon of Covid-19 Infected Cases in Indonesia with A Time Interval (0, 24) Hours from June to September 2020

Since the emergence of the 2019 novel coronavirus (2019-nCoV) in Wuhan City, China, in 2019, many countries in the world have been infected with the virus with very serious cases. This has caused a slump, especially for Indonesia, with a lower-middle-class society of 115 million people or 45% of the total population. The virus's rapid spread through droplets resulted in the Indonesian government implementing a lockdown called Large-scale Social Restrictions within months. This system is an effort to break the chain of virus spread. A pure birth process can be called a stochastic process with a continuous parameter space and a discrete state space. This research will focus on the application of the Pure Birth Process, which is expected to provide scientific studies on Covid-19 in Indonesia, become a new reference for the development of mathematics, and provide information on research results so that they can be used in policymaking to overcome and overcome the spread of Covid-19. 19. At the end of this study, it was known that the spread of Covid-19 in Indonesia was very drastic, which had a rate of increase in cases as big as a person/hour or person/day, which is a very significant increase. Based on calculations using Pure Birth, it is hoped that it can be used in policymaking for the government and the community to overcome and overcome the spread of Covid-19.&#x0D;
Spatial confidence regions for combinations of excursion sets in image analysis

The analysis of excursion sets in imaging data is essential to a wide range of scientific disciplines such as neuroimaging, climatology, and cosmology. Despite growing literature, there is little published concerning the comparison of processes that have been sampled across the same spatial region but which reflect different study conditions. Given a set of asymptotically Gaussian random fields, each corresponding to a sample acquired for a different study condition, this work aims to provide confidence statements about the intersection, or union, of the excursion sets across all fields. Such spatial regions are of natural interest as they directly correspond to the questions 'Where do all random fields exceed a predetermined threshold?', or 'Where does at least one random field exceed a predetermined threshold?'. To assess the degree of spatial variability present, our method provides, with a desired confidence, subsets and supersets of spatial regions defined by logical conjunctions (i.e. set intersections) or disjunctions (i.e. set unions), without any assumption on the dependence between the different fields. The method is verified by extensive simulations and demonstrated using task-fMRI data to identify brain regions with activation common to four variants of a working memory task.
Towards smart glasses for facial expression recognition using OMG and machine learning

Abstract This study aimed to evaluate the use of novel optomyography (OMG) based smart glasses, OCOsense, for the monitoring and recognition of facial expressions. Experiments were conducted on data gathered from 27 young adult participants, who performed facial expressions varying in intensity, duration, and head movement. The facial expressions included smiling, frowning, raising the eyebrows, and squeezing the eyes. The statistical analysis demonstrated that: (i) OCO sensors based on the principles of OMG can capture distinct variations in cheek and brow movements with a high degree of accuracy and specificity; (ii) Head movement does not have a significant impact on how well these facial expressions are detected. The collected data were also used to train a machine learning model to recognise the four facial expressions and when the face enters a neutral state. We evaluated this model in conditions intended to simulate real-world use, including variations in expression intensity, head movement and glasses position relative to the face. The model demonstrated an overall accuracy of 93% (0.90 f1-score)—evaluated using a leave-one-subject-out cross-validation technique.
Analysis of effective integration of information platform courses and daily teaching in universities based on adaptive network

Abstract This paper first analyzes the effective application of web platforms and information technology in college teaching and constructs a web-based teaching aid platform for college courses based on the open Web. Secondly, the A3C algorithm is used to implement adaptive coding decisions to improve the performance of the platform by adaptive network coding. Finally, the performance test analysis is conducted for the web teaching aid platform built in this paper. The results show that the average response time of the system using the communication function with 2000 concurrent users is 1.807s, and the CPU and memory usage are 1146MHz and 1486MB, respectively, after running for 25s. This shows that adaptive network coding can effectively improve the performance of the supplementary teaching platform.
An infrared image detection method of abnormal hot spot of distribution equipment based on YOLOv4 algorithm

Power equipment is the cornerstone of power system. Due to the advantages of infrared detection technology such as non-contact detection, power grid companies often use infrared detection technology to conduct regular inspection of power equipment. However, at present, the efficiency of manual inspection is low, and it is easy to be affected by the subjective experience and working time of inspection personnel, resulting in the misjudgment of the status of power equipment, which will be difficult to meet the requirements of real-time processing of infrared data of a large number of power equipment in the future. In addition, there are still many problems in the infrared image detection and recognition process of power equipment, such as low contrast of infrared image, mutual occlusion of power equipment and imbalance of positive and negative samples in the image. Based on the above difficulties, this paper develops the research of infrared image detection of power equipment based on deep convolutional neural network. This paper builds a power equipment infrared image detection network based on YOLOv4 algorithm in the Pytorch deep learning framework. Through the verification and effect testing of the proposed algorithm model, the results show that the proposed detection algorithm has realized real-time detection and achieved high detection accuracy. This lays a foundation for the realization of automatic and intelligent infrared image detection and recognition of power equipment, and lays a foundation for the subsequent state diagnosis of power equipment.
How to Handle Fuzzy Aspects in African Sociocultural Ontology

In this article, we propose an innovative methodology for creating a fuzzy sociocultural ontology based on the CriSO ontology, aiming to comprehensively represent the rich and diverse aspects of African culture. We address the limitations of the CriSO ontology and emphasize the necessity of incorporating fuzzy logic in order to effectively capture the nuances of African sociocultural knowledge. Our methodology begins with the extension of the CriSO ontology by integrating fuzzy concepts and properties. We subsequently present the formalization process of the fuzzy ontology using advanced fuzzy logic techniques and showcase its practical application in a restaurant recommendation scenario. In conclusion, our research demonstrates that employing a fuzzy ontology offers a more flexible and accurate approach to representing sociocultural knowledge. This methodology holds the potential to serve as a solid foundation for the development of fuzzy ontologies in various other knowledge domains, contributing to a better understanding and preservation of cultural heritage.
Legal Regime of Preparatory Materials Obtained During the Development of Computer Programmes

The paper is focused on the analysis of the legal regime of preparatory materials created during the development of computer programme. The article outlines existing doctrinal positions on this matter. Special attention is paid to the definition of materials, which fall under the notion of “preparatory materials”, as well as to possible purpose of inclusion of such object in Article 1261 of the Civil Code as separate copyrightable element of the computer programme. It is concluded that unlawful use of preparatory materials for development of a competing programme can be used as a basis for infringement claim (plagiarism), even if source codes of both programmes are not the same.
SMART TILES FOR ELDER TRACKING AND FALL DETECTION

The population of elderly people is increasing rapidly, and falls are a significant cause of injury and death among them. To address this issue, we propose a smart tile system that uses IoT technology to track the location and detect falls of elderly individuals. The system consists of small, wireless sensors (smart tiles) that can be placed on the floor of a room or a specific area, such as a bathroom or kitchen, to monitor the movements of the elderly person. The smart tiles communicate with a central hub, which collects and analyses the data using machine- learning algorithms to detect falls and abnormal movements. The proposed system has several advantages over traditional fall detection systems. It is non-intrusive and does not require any wearable devices or cameras, which can be uncomfortable or intrusive for the elderly person. The smart tiles can also be placed in multiple locations to provide more comprehensive coverage and can be easily installed or removed without the need for professional help. Additionally, the system can provide real-time alerts to caregivers or emergency services, enabling timely intervention and potentially reducing the severity of injuries. In conclusion, the smart tiles system proposed in this project provides an innovative solution to the problem of fall detection and elder tracking using IoT technology. It has the potential to improve the safety and well-being of elderly individuals and can be a valuable tool for caregivers and healthcare providers. Key Words: Smart Tiles, Fall Detection, IoT, Arduino IDE, Proteus ISIS7.
Analysis on the construction of horse cultural information resources under the background of artificial intelligence

With growing cultural self-confidence in China, horse culture is increasingly popular among the young. In the era of artificial intelligence, horse culture should use artificial intelligence to output diverse content that you ng people love, so that more young people can understand and love horse culture, use all available resources to increase the cultivation of young people's cultural self-confidence, and constantly adjust and improve the cultivation program according to the development and changes of The Times and young people themselves, so as to strengthen the foundation for the realization of the dream of cultural power . This not only contributes to the dissemination and activation of contemporary urban horse culture, but also contributes to the inheritance and innovation of Chinese traditional culture. Based on the above reasons, it is of importance that in this paper, information resources construction of horse culture is explored under the background of artificial intelligence.
Airborne Infection Control as Right to Life in the Indian Constitution: How Indian Polity is Paving the Way for the Future

How to cite this article:Singh R. Airborne Infection Control as Right to Life in the Indian Constitution: How Indian Polity is Paving the Way for the Future. Epidem Int. 2023;8(1):11-13. DOI: https://doi.org/10.24321/2455.7048.202304