Reviewerly/exHarmony huggingface dataset:
A Face Recognition and Sentiment Analysis Activity System using Machine Learning Algorithm

As well known, there has always been a strong connection between the attendance of a student being linked to their performance which indeed refers to their success ultimately, and seminars play an important role when it comes to assisting the students to meet industries’ expectations. And the traditional way of collecting the attendance before seminars is time-consuming, thus making less time for the presenter, and this can be solved using face recognition technology. Whenever there is a seminar to be held, all eligible members should register themselves in the proposed activity system with all their credentials along with their facial image. As the seminar starts, the admin captures the images of the members who are attending the seminar, and these images are used for attendance by using face recognition technology. Here, the Histogram of Oriented Gradients is used for face detection and the K- Nearest Neighbors algorithm is used for face recognition which has shown the highest accuracy. The accuracy of the algorithm in this project has shown to be 97%. The proposed system along with providing the list of all attended members also provides the attended members with a feedback slot where the members can provide feedback in the form of text. Using Natural Language Processing, Sentiment Analysis when performed on the feedback gives a clear picture of the opinions of the members, represented in the form of a histogram showing the number of members who opted for positive or negative or neutral feedback.
Advanced Online Cheque Clearing System using Blockchain

Cheque Truncation System (CTS) is an automated cheque clearing system that clears the cheque based on the image. Generally, a manual process is a very time taking process and there are some disadvantages to this process. In the manual process, there are many issues are facing by the people. Signature forgery is one of the frauds that occur in the manual process. Many forgery cases are registered nowadays; some persons misuse the signature of the actual person and provide the self-cheque themselves. This paper mainly focused on providing security for the online cheque clearing process by using block-chain. Block-chain is one of the fast processes and clears the cheques in the early stages. In this paper, an automated cheque clearing process is implemented by analyzing the signature of the cheque holder. Block-chain is used to store the signature into blocks and store it with security. Results show that the proposed system is better than the existing approach.
Scanning the Issue

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I. Kotseruba and J. K. Tsotsos</i> Since first cars appeared on the streets, driver inattention has been a safety concern. Recently, advanced driver assistance systems (ADAS) and autonomous driving have been introduced to tackle this issue. In this article, the authors review a broad range of vision-based algorithms proposed in the past decade that model drivers’ attention for assistive and self-driving systems and detect drivers’ gaze for monitoring applications. Furthermore, they provide a brief theoretical background on visual attention in the driving domain, survey public datasets with attention-related annotations, and discuss remaining open problems in this research area.
An Adaptive Long-Short Term Prediction Model for Resource Management in Cloud

Real-time resource usage prediction is a critical component of cloud data centre resource provisioning. Since cloud workloads change continuously, efficient material provisioning necessitates expecting higher resource demand patterns. The situation is intricate because of the frequently fluctuating data stored in the cloud resource. It is not enough to train the future resource usage prediction approach with an observation sequence to reflect the cloud system unpredictability. This paper suggests an adaptive Long Short -Term Memory (LSTM) approach to overcome the issues of the existing resource usage prediction models. The proposed adaptive architecture for online adaption of multivariate resource consumption models has been tested in the Google cluster and PlanetLab for CPU usage prediction. When comparing with the other frameworks, the proposed method outperforms the existing frameworks for live multivariate resource usage prediction.
Voice Control and Eyeball Movement Operated Wheelchair

Physically disabled persons rely heavily on rehabilitative mobility aids. Huge efforts are being undertaken to construct Human Interfacing Machines (HMIs) that will interface with bio-signals to regulate electronic mobility aids. Bio-signals are conveyed to an HMI using precise instructions and movement of body parts, which is the real issue for persons with a high level of handicap. As a result, this study introduces a signal-driven system employing ocular movement and speech recognition to accomplish the task of staircase climbing mechanism implementation in a wheelchair for a physically disabled population. The system is separated into three parts: First, the optical signal is used to identify ocular movement. The second is radiofrequency voice recognition. Third, with the procedure of the rocker-bogie, all three mechanisms are integrated into the wheelchair. The Wheelchair Skill Test (WST) is used to evaluate the arrangement's performance. Basic movements on rough and smooth surfaces are based on regulating apparatuses, compatibility, design prototypes, and usefulness in a variety of situations. The scheme efficaciously runs with a regular reaction time of 3s for eye movement and 3.4s for speech governor manner.
Semantic similarity measure for topic modeling using latent Dirichlet allocation and collapsed Gibbs sampling

Automatically extracting topics from large amounts of text is one of the main uses of natural language processing (NLP). The latent Dirichlet allocation (LDA) technique is frequently used to extract topics from pre-processed materials based on word frequency. One of the main problems of LDA is that the topics extracted are of poor quality if the document does not coherently belong to a single topic. However, Gibbs sampling operates on a word-by-word basis, which allows it to be used on documents with a variety of topics and modifies the topic assignment of a single word. To improve the quality of topics extracted, this paper developed a hybrid-based semantic similarity measure for topic modeling combining LDA and Gibbs sampling to maximize the coherence score. To verify the effectiveness of the suggested model, an unstructured dataset was taken from a public repository. The evaluation carried out shows that the proposed LDA-Gibbs had a coherence score of 0.52650 as against the LDA coherence score of 0.46504. The proposed multi-level model provides better quality of topics extracted.
Internet Botcoder: Natural Language Processing With Automation Code Generation Using Commands

The fact that everything is driven by automation has shown that every part of every system can be automated, leading to the creation of techniques that can even generate code for automation. Automated code generation is a breakthrough technology that will change the way software is developed. It will enable developers to rapidly build complex and scalable applications using little or no coding. This technology will be game-changing for the industry. There are many competitive programming sites, and the online interviews paved the way for many learning programmers to compete in the competitive Sites. However, beginner programmers could not quickly develop an interest in those sites since they cannot understand the logical flow of the questions and how they are made. The mentioned problem can be overcome by a program to understand the question and produce the python code that is as closely logical to the question as possible. The proposed paper does the aforementioned method in real-time using Natural Language Processing and Machine Learning algorithms such as Neural Networks and Deep Learning, and a Graphical Interface for user-system interaction. The results are compared with other iterations and similar papers and is found to be ideal in many situations.
Identification of IT Tickets and Bugs using Text-Supervised Pedagogical Approaches

“The business sector addresses the issue of task management in a number of different methods, all of which include some kind of triaging mechanism to accurately allocate tickets to developers. For Web and SaaS organizations that manage enormous amounts of tickets in the form of exceptions, support requests, user-reported bugs, and crash reports, automation of this activity has proved difficult and has resulted in less accurate results. In this work, several Machine Learning and Deep Learning algorithms for predicting assignees for new tickets based on previous tickets are investigated and analyzed. Investigating the utility of different machine learning (ML) approaches as a means of correctly constructing mathematical models for forecasting bugs and tickets was the primary focus of the effort. The research emphasizes the significance of the variables Term Frequency Inverse Document Frequency (TF-IDF) and relevant data word rate in order to guarantee that high accuracy is achieved by the Text-Supervised models that are used. In this study, an investigation of Text Supervised Learning Methods, including random forest, decision tree, and additional tree, will be carried out. The goal is to classify IT tickets and bugs more accurately.”
Object Detection in Unmanned Aerial Vehicle (UAV) Images using YOLOv5 with Supervised Spatial Attention Module

There have been various object detection models developed recently that have enabled better results in object detection. Object detection has gradually increased in computer vision with further development of application areas. Unmanned aerial vehicle (UAV) images feature smaller and more fragmented objects, as opposed to landscape images where objects occupy more space. Furthermore, rotational and measuremental factors reduce object detection accuracy. In this paper, an improved object detection framework based on YOLOv5 is proposed in order to resolve these issues. As such, this study proposes SSAM-Darknet for the detection of UAV images based on objects. SSAM-Darknet and Bi-FPN are used to extract multiscale and multilevel features from the input images. Additionally, dilated convolution and the Ada-bound optimizer are employed to enhance the proposed model in detecting the objects from UAV images. This experiment evaluates the accuracy of object detection by using VisDrone-DET. AP (Average Precision) and AR (Average Recall) metrics are proposed as a quantitative way of evaluating detection performance. The proposed model achieves an average precision of 34.32 making an increase in detection accuracy by 10% compared to other detectors.
Intelligent Analysis Method of Meteorological Data Based on Artificial Intelligence Algorithm

With the continuous acceleration of the meteorological industry process and the continuous improvement of measurement accuracy, combined with the accumulation of meteorological data throughout the year, finding valuable information from the massive meteorological data is the most serious problem facing meteorology. This article analyzes the intelligent analysis method of weather data based on artificial intelligence algorithm. In the intelligent analysis of weather data, the artificial intelligence algorithm is introduced to analyze the weather data to solve some problems. Experiments on meteorological data analysis and the establishment of artificial intelligence algorithm prediction models to predict precipitation. The experimental results show that the predicted precipitation of the proposed artificial intelligence algorithm is not much different from the actual one, which is controlled within 10%, but this article there are still some unstable factors in the algorithm, and the relevant parameters need to be adjusted.
Research and Practice on the construction of deep learning algorithm experimental platform

This paper studies the research and practice of the construction of deep learning algorithm experimental platform. The research and practice of the construction of deep learning algorithm experimental platform is a project aimed at developing a software platform for the research and practice in the field of deep learn-ing. The main purpose of this platform is to promote communication with researchers, developers, students and practitioners from all over the world. Through this platform, we hope to improve our understanding of alu-minum technology and its applications. Deep learning is machine learning based on artificial neural network. Machine learning methods can also be divided into su-pervised learning and unsupervised learning. For exam-ple, convolutional neural network is a machine learning model under supervised learning, and deep confidence network is a machine learning model under unsupervised learning.
Pediatric Brain CT Image Segmentation Methods for Effective Age Prediction Models

Brain imaging is used to diagnose pediatric brain diseases. However, there is no quantitative method to estimate developmental conditions such as underdevelopment or early growth, and qualitative diagnosis is based on the experience of skilled physicians. Therefore, we are developing a computer-aided diagnosis system to estimate brain age from pediatric brain CT images. This system segmented cranial regions from CT images and calibrated their posture and position. The system also extracts features from CT images using a 3D convolutional neural network (3D CNN) and predicts brain age using a fully connected layer. This paper focuses on the cranial region segmentation method, which is an essential analysis processing method for the system. We investigated two different methods of region segmentation, and a comparison experiment with 204 subjects aged 0 to 3 years (47 months) showed that we could improve 32% of the prediction accuracy of the 3D CNN model.
The Usability of Visual Design Tool Based on Html5

WeChat is increasingly integrated into our daily lives, and the Html5 page that is widely spread through WeChat has gradually become well-known. More and more design developers have entered the field of Internet product segmentation. In the past year, the Html5 visual design tool market has shown explosive growth, and a large number of products with poor user experience have emerged on the market. This paper studies the usability of visual design tools based on Html5. After understanding some theories of usability, we select some usability evaluation indicators to pave the way for the following investigations. Through the use of Html5 visual design tools ease of use research, the result is that the users of Html5 visual design tools are mainly design, accounting for more than 70% of the total number, and then the main factors affecting ease of use are functional ease of learning.
Research on color enhancement method of planar image in interactive interface of mobile intelligent device

The definition of image color is closely related to the plane visual effect of the interactive interface of mobile intelligent devices. In order to improve the visual effect of plane visual image color, the plane visual image color enhancement system of the interactive interface of mobile intelligent devices is deeply studied, and the image acquisition module is designed to extract image information and transmit it to the image color enhancement module. The image color enhancement module uses the color image enhancement algorithm based on visual characteristics to enhance the overall brightness of the plane visual image in the way of histogram nonlinear adaptive lifting; The local contrast of the plane visual image is enhanced according to the correlation between the current point and the regional pixel, and the overall clarity of the plane visual image of the interactive interface of the mobile intelligent device is enhanced.
Posture Recognition for Human-robot Interaction Based on High Speed Camera

Physical movements are an important part of people’s activities and communication. The movements of the hands and upper limbs are relatively fast, and the postures in the lower part of the ordinary camera will produce smears, and the images will be clearly obtained only when they are stopped, which seriously affects the recognition speed. This article takes the hands-guessing game in the communication robot as an example. Using a high-speed camera, the corresponding posture can be recognized when the posture is not completed during the hands-guessing game. It is expected that postures can be recognized in a more timely manner in human posture recognition, and the sense of delay in communication can be reduced. The corresponding data is collected and the model is generated after deep learning. In the fast-moving stage of postures, using a ordinary camera, the percentage of time that different postures are unrecognizable is between 50% and 60%. Compared with ordinary cameras, using high-speed cameras, the unrecognizable time percentage of different postures is reduced from 50%-60% to 0%, and the effect is obvious. In human-computer interaction, using ordinary cameras to infer postures, the people participating in the test have a significant sense of delay. With a high-speed camera, this feeling of delay is barely noticeable.
Privacy security protection based on data life cycle

Large capacity, fast-paced, diversified and high-value data are becoming a hotbed of data processing and research. Privacy security protection based on data life cycle is a method to protect privacy. It is used to protect the confidentiality, integrity and availability of personal data and prevent unauthorized access or use. The main advantage of using this method is that it can fully control all aspects related to the information system and its users. With the opening of the cloud, attackers use the cloud to recalculate and analyze big data that may infringe on others' privacy. Privacy protection based on data life cycle is a means of privacy protection based on the whole process of data production, collection, storage and use. This approach involves all stages from the creation of personal information by individuals (e.g. by filling out forms online or at work) to destruction after use for the intended purpose (e.g. deleting records). Privacy security based on the data life cycle ensures that any personal information collected is used only for the purpose of initial collection and destroyed as soon as possible.
Navigation System of Coal Mine Rescue Robot Based on Virtual Reality Technology

In the coal mining environment, robots have great application potential. Our country is a big coal mining country, and the demand for coal resources is huge. However, our country's coal mining is generally difficult and frequent accidents. Therefore, there is an urgent need for a robot to help rescue after disasters. This article aims to study the coal mine rescue robot navigation system based on virtual reality technology. Based on the analysis of X-3D virtual reality technology characteristics, system design, Hector-SLAM algorithm and Cartographer-SLAM algorithm, the test scenes are constructed through virtual reality technology and compared Hector-SLAM algorithm and Cartographer-SLAM algorithm composition effect and calculation pressure and other indicators. The test results show that the Cartographer-SLAM algorithm does not depend on the odometer, the CPU load is low, the calculation speed is fast, the calculation pressure is small, and the comprehensive comparison is more adaptable to the coal mine environment.
Applying Face Recognition in Video Surveillance for Security Systems

In order to meet the security needs that are becoming more and more important with the economic advances, the development of physical or biometric access control systems is constantly growing. Several biometric modalities can be used and each one presents a particular interest, according to the targeted application. Within the framework of our study paper, we have realized a facial recognition system based on the EfficientDet model following the architecture of a deep neural network. The facial recognition process is divided into several steps, namely: face detection in each image, face normalization, facial feature extraction, classification and decision. The training and evaluation of the system were done on the database: Casia-web face. As Casia-web Face is unlabelled, we have developed an algorithm using the open source deep learning framework Mxnet to convert the images into binary format, reduce their size and give each image an identifier. Finally, the optimization of the system has been done using Root Mean Squared Propagation (RMSProp) and the Shard shuffling optimizers.
Key technologies of physical layer of radio and television wireless network based on 5g wireless communication network

The physical layer design of 5g wireless communication network also plays an important role in the application of 5g wireless communication technology and the optimization of communication effect of wireless communication technology. In the process of designing the 5g network, it is necessary to integrate the 5g network technology and ensure the application of the 5g network technology. In the actual design of the 5g network, it is more necessary to integrate the 5g network technology. Radio and television wireless network 5g the physical layer of wireless communication network is the part that manages the transmission, reception, modulation and demodulation of radio waves. It is also called RF (radio frequency) or microwave link. This layer controls all data transmission between two nodes in the wireless network. Radio and television wireless network 5g the physical layer of wireless communication network uses electromagnetic wave to transmit signals from one point to another through antenna.
Design of Online Education Big Data Platform Based on Data Mining and Data Collection Technology

With the continuous development of modern information technology, my country has gradually entered the era of big data. The salient features of the big data era are rich data resources, convenient data processing and information exchange, and smoother learning and communication between people. The impact of big data on education is also very significant. This paper studies the online education big data platform based on data mining and data collection technology, uses data mining technology and data collection technology to design the online education big data platform, and tests the designed platform. The test results show that this paper improves the algorithm the accuracy of clustering analysis is good, and the number of errors is controlled within 5, and then the query time of the platform is tested. The time for the platform from data query to acceptance is within 30 minutes, which meets the requirements of platform design.