Reviewerly/exHarmony huggingface dataset:
Dimensionality Reduction Algorithms for Scalable Monitoring of Virtual Machines

In the past the statistical technique of Principal Component Analysis has been used for finding relevant information to be used for monitoring virtual machine clustering. We intend to use other dimensionality reduction algorithms for the same namely Latent Dirchlet Allocation,Autoencoders, and backward elimination to find a better alternative for the same. We propose a method wherein we try to incorporate the data obtained from these executions and then reduce its dimensionality or in Layman terms we try to minimize the monitor-able data to a minimum so as to efficiently monitor data. This will not only reduce the time taken for cloud monitoring but also help in scaling virtual machine clusters as the data obtained for monitoring is directly proportional to number of clusters and the application, hence if we are able to minimize the data to be monitored then we can increase the amount of clusters and still be able to maintain a proper monitor-able environment with lesser variables to be concerned about.
Evaluation of Multi-Sensor Gloves Development as an Assistive Interpreter for Indonesian Sign Language

Individuals who suffer in speaking and hearing are known as people with hearing and speech impairment. Sign language is used by them to communicate. Nevertheless, the general public does not fully comprehend sign language. In this study, assistive technology for the Indonesian Sign Language Interpretation System was developed to convert the alphabet sign into text. The system incorporates the usage of flex sensors, pressure sensor, and MPU6050 module. After the development, the evaluation for this interpreter is carried out. The classifier was able to identify 24 out of 26 alphabets with detection error frequently occurring in the letters N and R with an accuracy of 94% in the second experiment. By using a specific classification and including more test data, this study expected outcome can be heightened. The results show that the classification method, along with the tool, may be used effectively as a speech and hearing aid.
The Implementation of Hybrid Semantic Ontology-based Model on Movie Recommendation System

This paper adopts the Hybrid Semantic Ontology-based (HSO) model for a movie recommendation system. HSO consists of Collaborative Filtering (CF) and Content-based (CB) modules that respectively implement Matrix Factorization (MF) and ONTO Semantic Similarity algorithms. Since the feedback data type influences the MF algorithm choice, we individually implement the Non-Negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) algorithms for handling the movie rating data. Accordingly, our proposed methods are called HSO-NMF and HSO-SVD. Meanwhile, since the domain determines the ontology, we build and use a new movie ontology on the CB module. The experiments show that HSO performs the best when implemented using the SVD algorithm. On average, the increased percentages of HSO-SVD to HSO-NMF are 1.18% and 1.62% in Precision and NDCG metrics, respectively. The experiments also show that implementing the Hybrid model yields more accurate results than the CB or CF model.
Overcomplete Deep Low-Rank Subspace Clustering

Aiming at the fact that when the input data in the deep subspace clustering networks (DSC) has noise, its robustness is poor, the performance is significantly degraded, and the method has too many learnable parameters, we suggest an overcomplete deep low-rank subspace clustering (ODLRSC). The technique is easy to use, efficient, and has shown to be a great fit for subspace clustering. By inserting a fully connected linear layer and its transposition between the encoder and decoder in our suggested technique, we may automatically put rank restrictions on the learnt representations. Additionally, in order to obtain a more reliable representation of the input data for clustering, the characteristics of the under- and over-complete auto-encoder networks are fused in the encoder. Our technique beats DSC and other clustering algorithms in the field of clustering error, and can sustain high level of quality throughout a broad range of LRRs, according to experimental findings on benchmark datasets.
Customized Point Generation for Text Summarization

As the access to the internet is increasing amongst people, the number of articles and text write-ups is increasing day by day. This has made it difficult for a person to get information quickly as he/she will have to go through each paragraph line by line. Hence, there is a need for text summarization. In this paper, we have proposed a method to generate the customized summary of text entered by the user. Customization is done by replacing the words according to the user’s needs. The user needs to enter the words he/she wants to replace along with the short forms he/she wants to see in the result. This can also help people make the presentation with ease as it will provide a paragraph summary in points according to the user’s needs. It will also help in note-making and resume-making. The proposed methods were evaluated by generating customized summary of 10 paragraphs of distinct length. Experimental result shows that the paragraphs with a greater number of words have presented good scores of recall, precision and f1-score for all the metrics ROUGE-1, ROUGE-2 and ROUGE-U.
Computer Teaching Software Based on Artificial Intelligence

The traditional computer teaching model does not recognize the individual differences of students, does not conform to the principle of teaching students in accordance with their aptitude, and cannot vividly display computer teaching videos and pictures, resulting in poor interest in the learning process of students, especially for abstract teaching of core computer courses Knowledge points are more difficult to understand. Therefore, this paper uses artificial intelligence (AI) technology to design computer teaching software, introduces Unity3D technology to realize visual computer teaching, and tests the basic performance of the software. The test results show that the stability of the computer teaching software (CTS) designed in this paper has reached 100%, and the compatibility performance has reached 94 %. The expandable performance has reached 97%, the simple operation performance has reached 98%, and the responsive performance has reached 96%. It can be seen that the performance of the teaching software designed in this paper has passed the test requirements and verified the feasibility of the teaching software.
Research on Two-stage Conveying Bar Counting and Dividing System Based on Vision

A vision-based automatic bar counting system for two-stage conveying bars is proposed. The system solves the counting problems of sticking and relative sliding of a large number of rebar stacks through image processing algorithms. Through the improvement of the splitting end splitting machine, the design of two-step splitting and visual verification is adopted to achieve accurate splitting of stacked rebars. The system obviously reduces the requirements for installation space, and can make more effective use of the space in the rebar workshop. The system has fast running speed, high accuracy, small changes to the production line, and easy maintenance. It can not only meet the needs of production, but also greatly improve the efficiency of the existing counting system. It is suitable for rebar workshops with limited installation space. The experimental results show that the system can accurately count the bars and can accurately realize the steel separation. For bars with a diameter of 8-12mm, the counting accuracy is over 99.85%, and for bars with a diameter greater than 12mm, the counting accuracy is over 99.96%.
Distributed filter state estimation of topological random switching in WSN

A distributed filter design method for WSN with sensor nonlinearity saturation and random switching of network topology are proposed in this paper. A mass of sensor nodes are deployed in the sensor network, the target device is sensed and measured, and transmitted to the distributed filter through the network. In the filtering network, the local estimator receives the measurement information from the sensor node. The estimation from the neighboring nodes via a random time-varying topology to complete the state estimation and trajectory tracking for the objective system. The Bernoulli binary distribution is used to describe the random saturation nonlinearity of the sensor network, and the inhomogeneous Markov chain is adopted to represent the random switching topologies. The sufficient conditions are given as distributed filters in the form of linear matrix inequalities method. In the end, the effectiveness of this design method is illustrated by a simulation example.
Breast Cancer Prediction using Ensemble Technique

Breast cancer is one the most frequent illness in females, and it is the primary reason why so many women lose their lives to cancer overall. Over the course of the past several years, it has developed into a widespread concern, and its prevalence has increased substantially in recent times. Early identification of breast cancer is the most efficient method for treating its side effects and managing the disease. Women's mortality rates from breast cancer may be lowered thanks to the widespread adoption of Computer-Aided Diagnostic (CAD) devices for finding the disease at an early stage. A large degree of variance is observed when a single model is used. We have proposed ensemble-based models which reduce the variance of the model and hence result in better accuracy. We conducted our experiment on Wisconsin Breast Cancer Database (WBCD) dataset. Experimental results show that the ensemble models are easily outperforming stand-alone models.
Control Design of Quadcopter using Linear Quadratic Gaussian (LQG)

A quadcopter is an unmanned control aircraft with four rotors and is mounted in a square formation. Quadcopter moves with six degrees of freedom that are divided into two types of movements, there are rotational motion and translational motion. The Linear Quadratic Gaussian control method is used in this research, which combines the Linear Quadratic Regulator with Kalman Filter. LQG control was chosen because it can estimate state variables that are not measured, and it can save costs and time. The variables controlled and analyzed in this study are changes in the angle of rotational motion including roll, pitch, and yaw with a fixed linear position. The quadcopter mathematical model has been linearized and shows stability, controllability, and observability. Simulation results show that the system produces an overshoot less than 0.1 rad with a time stable less than 1 second.
Application research of computer big data technology in cognitive linguistics second language acquisition system

The whole process of language acquisition from input to output is inseparable from the learner's internal cognitive mechanism. Therefore, our research on second language acquisition is inseparable from the attention to the learner's cognitive psychology. From the perspective of English cognitive linguistics, we analyze the psychological mechanism of the second language acquisition process, so that we can explore more effective acquisition methods, which is the trend and hotspot of linguistics research in recent years. This paper analyzes the current situation of the second language acquisition system of English cognitive linguistics. This paper combines the relevant theories of cognitive linguistics to analyze the cognitive mechanism and acquisition mechanism of the second language acquisition system of cognitive linguistics.
Secure Verifiable Outsourced Watermark Embedding Framework in Multi-User Settings

With the development of the mobile Internet, supporting multi-user has become a popular concern for Internet-based algorithms. In this paper, an efficient watermark embedding framework is proposed to achieve secure outsourced image watermarking in multi-user settings. In addition, an authentication mechanism for authorized users is designed to ensure the security and correctness of the information. The proposed method enables watermarking operations in complex outsourced environments while embedding watermarks in encrypted images of multiple users. The experimental results show that the framework is feasible and scalable.
Bharatnatyam Pose and Mudra Recognition Using MediaPipe and Deep Features

Bharatnatyam is a traditional dance form from Tamil Nadu. This ancient form of dance, originally known as Sadhir Attam, is well documented in paintings, stone and metal sculptures. There is a wealth of image data pertaining to this dance form and its history, but no general classification models or systems to recognise the dance form as variations in poses exist. The goal of this paper is to recognise and classify bharatnatyam dance forms and poses in real time. Using a customised bharatnataym dataset, we propose a solution that employs the Resnet architecture and Media Pipe to locate landmarks on input frames for hand gestures and poses in order to develop a real-time classification model. The proposed work compares the VGG-16, Resnet, and Mobilenet V2 architectures for real-time classification tasks and concludes that the Resnet model outperforms the others on testing and training data in terms of various evaluation metrics.
News Text Classification Algorithm Based on Machine Learning Technology

With the rapid development of Internet technology, there are more and more text data on the Internet. Traditional manual text sorting methods can no longer cope with the current data volume. Automatic text sorting technology has become a research hotspot. As the main branch of text mining technology, text classification technology can effectively solve the needs of automatic text classification under the development of big data. The purpose of this article is to study news text classification algorithms based on machine learning technology. This article solves the problem that traditional machine learning methods require time and effort to manually construct mechanical features. This paper designs two common deep learning models, which can automatically extract feature convergence neural networks and long- and short-term memory neural networks. Experimental research shows that the classification effect of the topic model used in this article is better than that of the SVM model by 20%, which shows that it is advisable for us to select the topic model as a model for processing short text. Secondly, the result after adding the decision tree classification is also a certain improvement over the LDA model.
The Design and Application of Smart Classroom Teaching Mode in Higher Vocational Education Based on Deep Learning

The impact of new technology on higher vocational education(HVE) has attracted the attention of many domestic scholars. The current direction of education informatization research is mostly smart classrooms. Therefore, schools will inevitably promote education informatization through the use of smart classrooms, and smart classrooms with classroom teaching(CT), teacher-student activities, and Internet + education will also become the core. This article uses experimental analysis and questionnaire survey methods to experiment on the design and application of the intelligent CT model of deep learning in HVE, compare and analyze the learning effects of the experimental and control classes of computer professional courses, and investigate and analyze their attitudes to the application of the intelligent classroom model. The experimental survey results show that the outstanding students in the experimental class has more students than that in the control class, and the learning effect is better than the traditional mode in the smart CT mode of deep learning, and most students in the experimental class prefer the use of the smart classroom mode. It can be concluded that it is necessary to study the design and application of the intelligent CT model of deep learning in HVE. In the intelligent classroom teaching system, data mining, K-means algorithm and MapReduce framework are comprehensively applied. By analyzing various behavioral data of students in school, the potential value behind these data is mined, so as to improve the construction of intelligent classroom and promote the improvement of higher vocational education.
Mobile Device Facial Beauty Prediction using Convolutional Neural Network as Makeup Reference

Beauty or good looks is one aspect of human attraction. Highly attractive people have an 8 to 12 % higher success in negotiations. One of the things that can be used to increase attractiveness is makeup or cosmetics. Makeup can be used alone or through the services of a Makeup Artist (MUA). MUA services are often used during important and once-in-a-lifetime events such as weddings. The difference in beauty before and after using this makeup cannot be measured but only seen and assessed manually. One application of machine learning in this field is Facial Beauty Predictions (FBP). FBP measures the value of a person's facial beauty or good looks. This paper investigated and implemented FBP on mobile devices, which makes it easier to assess anytime and anywhere. We investigated FBP models using five CNN architectures, including EfficientNetB0, VGG-16, ShuffleNet, MobileNet, and Inception. After evaluation, we choose MobileNetV2 CNN architecture as the primary model for mobile application FBP. Experiments on the FBP dataset show that MobileNetV2 achieves Pearson Correlation 0.7893 with only 10.5 MB for model file size. The model implemented for mobile devices works well in 13 different tests.
Deep Learning Methods for Super Resolution of Video

Super Resolution of video is a type of processing technique dealing with multimedia such as video to improve the resolution of videos. The theory is that given enough training data, one can learn a set of filters that, when applied to an image that isn’t in the training set, will produce a higher resolution version of it, with a low-complexity learning algorithm. One can build the image frame by frame to get the video in a higher resolution so Image Quality Assessment for reconstruction is done. This helps us as an evaluation criterion for the Super Resolution learning model. It has been proposed to use Progressive up sampling which uses a Laplacian pyramid Super Resolution Network. It reduces difficult tasks into simple tasks and reduces the learning difficulty. It is also planned to interpolate the image using Bicubic interpolation since it helps us give smoother results. The planned model i.e., the Convolutional Neural Network (CNN) framework effectively brings in the previously mentioned layer and combines in multiple frames to produce a high-quality image and it is also scalable. These are then passed through a discriminator to check the validity of the super resolution images and then combined as frames to reproduce the video frame by frame.
Unmanned aerial vehicle trajectory planning based on enhanced sparrow search algorithm

In order to solve the problems of the original sparrow search algorithm (SSA) in the unmanned aerial vehicle (UAV) trajectory planning, such as low optimization accuracy and slow convergence speed, an enhanced sparrow search algorithm (ESSA) was proposed. Firstly, Halton sequence was used to initialize the population to increase the diversity of the population and improve the subsequent search accuracy of the algorithm. Secondly, the quasi-reflection learning mechanism is introduced to improve the individual quality of the algorithm after each iteration, and improve the optimization accuracy and convergence speed of the algorithm. The improved algorithm is applied to the trajectory planning of the UAV, the results show that the flight cost of the UAV trajectory found by ESSA is lower and the convergence speed is faster.
Design of Intelligent Autonomous Learning Management System Based on Artificial Intelligence Technology

In recent years, with the rapid development of the Internet, artificial intelligence technology is constantly leading the reform and development of traditional education. At the same time, the concept of guiding students to learn independently and advocating diversified teaching methods is becoming more and more mature. How to realize the design of intelligent autonomous learning management system is a major problem to be solved urgently in the development of autonomous learning. Based on the related applications of artificial intelligence technology, this paper researches and designs the intelligent autonomous learning management system to meet the needs of the current education field for the intelligent autonomous learning management system. This paper takes the development of modern Internet with big data, cloud computing and artificial intelligence as the main characteristics, and builds a complete intelligent autonomous learning management system, which can give full play to the subjectivity of students, help students to strengthen their learning motivation, formulate learning plans; strengthen learning initiative, Explore learning methods; stabilize one's own emotions and relieve learning pressure. The final results of the study show that the average learning attitude of the new class using the intelligent autonomous learning management system is 45.8, the average learning responsibility is 46.1, the average planning arrangement is 47.4, the average monitoring reflection is 45.5, and the average management resource is 46.9. The new class using the intelligent autonomous learning management system is superior to the traditional class in five dimensions: learning attitude, learning responsibility, planning, monitoring and reflection, and management resources.
Analyzing fashion trends using CNN and Cloth classification with respect to season and state

Machine learning now does have a broad aspect of apps in a variety of fields. Deep learning methods, particularly convolution neural networks (CNN), are frequently used to analyse visual imagery. CNN main applications include face recognition, image recognition, object recognition, and so on. As the use of machine learning in many industries grows rapidly around the world, the fashion industry is following suit. The proposed model describes the creation of a computer vision system for clothing detection and classification in e-commerce images. The architectures used in this work for detection and classification are YOLO v3 and Residual Networks, respectively. We are training and testing the clothes detection and classification networks using a portion of the Fashion image dataset, which contains box annotations for clothing location, as well as manually collected data. The proposed models detect clothing using bounding boxes. The dataset consists of 8 different classes with 500 images per class. Investigational outcomes show that, as anticipated, CNN algorithms are the most effective and efficient network configurations for clothing detection and classification. Following testing, it was discovered to have 90% accuracy. In addition, Indian, Western, and Indo-Western cuisines are classified by state and season.