Reviewerly/exHarmony huggingface dataset:
A Novel Classification Technique for Safety Measures on Covid-19 Using Featured-Based Sentimental Analysis

Covid-19 is a term that has frightened the globe because it has broken beyond socioeconomic barriers in which people literally forgot the word social help because of this deadliest virus.The main goal of this study is to create a model that forecasts Covid-19 reviews based on coronavirus ratings from Kaggle repository. The World Health Organization(WHO) declared a pandemic of the coronavirus infection when it first appeared in 2019. People are worrying and concerned about their health as the number of instances rises throughout the world. People’s physical and emotional health is inversely proportional to the pandemic scenario. As a result, in this case, a categorization model based on numerous metrics is required to rescue nations by analyzing facts and information about the outbreak. In this article to organise the reviews or opinions provided by people worldwide, we performed emotional or opinion classification using a Novel classifier. Then, the accuracy of the proposed model is compared with existing base classifiers like NB(Naive-Bayes) and Support Vector Machine(SVM), where Novel classifier gave the best accuracy compared to the other two classifiers, i.e., 95
Smart Contract Implementation to Improve Security on Solid Waste Management Application *

This work proposes a secure architecture, based on smart contracts and permissioned blockchain, to enhance security and availability in LoRaWAN networks. To demon-strate the feasibility of the proposed architecture, a solid waste management application has been created using open-source tools and commodity hardware. Additionally, the performance of the blockchain network was analyzed in a cloud environment under multiple workloads and tests were performed to evaluate the impact of network failures. The results show a trade-off between availability and performance when choosing the number of blockchain peers in small scenarios. However, this behavior is reversed in large scenarios where the performance of multiple peers is best suited.
Wavelets and DCT Watermarking for Medical Image

In recent years, the transition to the digital world offers users several conveniences, to use, process, store, and transmit their data. In addition, the rapid and continuous evolution of image acquisition systems has allowed a tremendous growth in the use of digital images. Indeed, digitization is a double-edged sword; it offers several benefits on the one hand and poses security problems on the other hand. For this reason, it is mandatory to design digital image protection systems against illegal manipulation. In recent decades, digital tattooing, a brilliant technique proposed to address several aspects of security. The principle of this technique is to implement a digital signal called watermark in another digital signal called host signal (text, image, audio, video…). In this context, our work aims to propose novel digital image watermarking approaches based on wavelets and DCT for the copyright protection of medical images. The proposed methods will allow optimizing the two contradictory demands of watermarking: imperceptibility and robustness.
Anchor-free 3D point cloud target detection network with attention mechanism

The existing anchor-free three-dimensional object detection methods exhibit low accuracy for small objects. Therefore, an object detection algorithm based on CenterNet is proposed. High-dimensional features are extracted from meshed point clouds by three-dimensional convolution. Low-dimensional spatial information and high-dimensional abstract semantic information are integrated by the two-dimensional backbone network with attention mechanism. After obtaining target coarse regression by the CenterNet head, the detection accuracy is improved by the Voxel R-CNN ROI Pooling module as a two-stage regression. The pending point cloud may be imperfect, caused by occlusions and sensor malfunction. During the network training phase, the shape sensing data enhancement scheme from SE-SSD is introduced to strengthen the detection capability of small targets.Using the KITTI dataset to evaluate the designed model, the detection accuracy of cars car (medium difficulty) is 80.60%, while that of cyclists and pedestrians is 69.71% and 59.20%, respectively. The model inference speed is 21.3 FPS. The experimental results demonstrate that the proposed method can effectively improve the capability of detecting small targets while it also has good performance with large targets similar to vehicles.
Research on Pedestrian Tracking And Location in Dynamic Backgrounds

To examine the effects of dynamic background interference in pedestrian tracking and positioning technology in sports, a dynamic background model is built, followed by a dynamic background compensation model. Using dynamic background compensation, interference caused by changes in the background can be reduced. After the pretreatment, the centroid method is used to extract regions of interest from an image frame. Using the centroid position, the search area for adjacent frames can be predicted to improve algorithm performance and robustness.
SCA:Segmentation based adversarial Color Attack

Deep Neural Networks (DNNs) are widely used nowadays. Some researchers have found an effective way to do harm to the DNNs, which is called adversarial examples. Via the way of generating the adversarial examples can divide the attack into two directions. Part of the researchers is focusing on generating imperceptible images whose noise is restricted by L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> - Norm. However, these adversarial examples have poor transferability. Therefore, some researchers are focusing on generating images with large unrestricted corruption. However, existing unrestricted images are noticeable, arousing human suspicion. Among them, the attack that modifies the color of images may generate images with better image quality. In this work, we proposed an unrestricted adversarial attack that modifies the color of original images with different strengths in each region. The regions are segmented by a segmentation network. The experiment shows that our method has strong attack strength with a quite high image quality compared with existing unrestricted methods.
Construction of Knowledge Map of Power Marketing User Address Data Based on Information Extraction Model

With the rapid development of smart grid, in the power industry, artificial intelligence is rapidly combining with the power grid field, and information and communication technology is rapidly integrated into the power grid production and enterprise management. Information system is the "central nerve" of intelligent grid supporting production and management development. In order to solve the problem of extracting entity knowledge and question and answer system from unstructured data, this paper proposes a method of constructing knowledge map of power marketing user address data based on BERT information extraction model. After the system test, it is proved that the service in the system has high feasibility and stability, and it can face various industries and users with different knowledge levels.
Evaluation of Information System Mental Model Based on Eye Movement Data

As the process of mental model matching evaluation is highly subjective and difficult to quantify and compare, this paper adopts the eye tracking method to optimize it and compares the actual gaze order of the user when operating the interface with the ideal gaze order in the “representation model.” The eye-movement evaluation metrics of “mental model initial variance,” “mental model reconstruction,” and “mental model operational resistance” are proposed in this paper, and their effects are validated. Taking the electronic reconnaissance task interface as an example, the evaluation object indicates that the above indexes can effectively distinguish the interfaces with different mental model matching degrees. Moreover, it can be combined with user interview data to locate the specific locations and causes of a mental model mismatch in the interface, which can help provide reliable data support for interface evaluation and assist in interface optimization design.
A Genetic Algorithm with Memory Function

A genetic algorithm with memory function (GA_MF) is proposed in this paper to improve the performance of the algorithm by using the historical search information. In GA_MF, a new crossover operator that allows each offspring chromosome to obtain genes from its parent chromosome, its parent's historical optimal chromosome as well as the global optimal chromosome is constructed to enrich the gene source of the offspring chromosome. This would be of great help to the inheritance and development of good genes, and therefore the proposed GA_MF could be expected to achieve good optimization results. To verify the GA_MF performance, it is compared with other eight genetic algorithms (GAs) on eight popular benchmark functions. The simulation results show that compared with the other eight GAs, GA_MF has the higher optimization precision and faster convergence speed.
Multimodal Machine Translation for Sanskrit-Hindi: An Empirical Analysis

Due to its extensive use in ancient Indian religious scriptures, Sanskrit is among the oldest indigenous languages and is rightfully referred to as the language of the gods. However, it is losing favour in contemporary India. Sanskrit is not widely used in current times due in large part to the lack of resources for translation into and out of it. In recent years, machine translation (MT) has improved above and beyond the norm and is now typically performed utilising supervised learning approaches. Due to the paucity of comparable corpora for Sanskrit, new research in the unsupervised MT domain appears to have promise for Sanskrit. With the aid of manually created parallel corpora for the Sanskrit-Hindi language pair, an analysis is conducted between various modelling techniques of building a machine translation system, namely Statistical and Neural, in order to bridge the gap between Sanskrit and its contemporary successor Hindi. In order to provide a fresh viewpoint on the area as a whole, the primary benefits and drawbacks of statistical and neural machine translation has been examined in this work. Our results suggest that Neural machine translation modelling technique performs better than Statistical machine translation.
Vehicle Detection System using YOLOv4

In today’s world, a commonly used computer technology - object detection focuses on finding items or examples of a specific class such as humans, vehicles, and animals in digital photos. Most computer vision systems require the capacity to detect objects. Object detection is performed often in computer vision tasks such as face detection, face identification, and video object co-segmentation, as well as in tracking people, surveillance cameras, and driverless cars. It is the fundamental notion for tracking and identifying things, and it has an impact on object recognition efficiency and accuracy. The major contribution of this paper is i) Comparison of recent research works in the domain of vehicle detection and ii) Classification of vehicles into 2 specified classes – car and bus are performed on a dataset of 10,000 images. The YOLOv4 model has been deployed for detecting cars and buses.
Animation Video Production System Based on Three Dimensional Vision Sensor Network

How to produce deformable human characters with a strong sense of realism more efficiently has been one of the hot spots in the research of computer 3D animation technology. The confusion of staffing in the process of animation production, the weak association between production staff and tasks and the difficulty for system managers to keep track of the progress of the project and other information restrict the further development of 3D animation. This paper therefore investigates animation video production systems based on 3D visual sensing networks. The paper begins with a brief description of the evaluation of visual comfort in 3D animation stereoscopic effects and explains the user requirements of the system. The results show that the number of surface and solid voxels tends to increase as the voxel size decreases, but does not affect the operation of the system, so the animation production system designed in this paper is able to meet the demand for the operational efficiency of deformable human characters.
Storage I/O Acceleration Scheme Based on SPDK in OpenStack Cloud Platform

Aiming at the problems of long storage I/O path, low IOPS (Input/Output Per Second) and high latency of virtual machines in OpenStack cloud platform, a storage performance acceleration scheme based on SPDK (Storage Performance Development Kit) is proposed: offload the back-end driver of virtualized I/O to user state at virtualization layer by mounting SPDK type cloud hard disk, replace interrupt mechanism with polling mechanism, and use lock-free queue to achieve the purpose of shortening I/O path, reducing context switching frequency, and reducing memory copy. The experimental results show that compared with virtual machines mounted with regular cloud hard disks, virtual machines using this acceleration solution saw a 32.75% and 549.02% increase in read IOPS and write IOPS, a 40.73% and 35.18% increase in read and write bandwidth, and a 32% and 18.81% decrease in read latency and write latency, respectively.
Image defogging algorithm based on sky area recognition

Foggy weather severely reduces the image quality and increases the difficulty of obtaining aerial image information from UAVs. In this paper, an image defogging algorithm based on sky region recognition is proposed, which solves the problems such as inaccurate estimation of sky region transmittance, halo, and color distortion after restoration. Firstly, this paper uses the improved Otsu algorithm to divide the image into sky region and non-sky region, and obtains the atmospheric light value by identifying the sky region. Secondly, the light transmittance of non-sky region is obtained by using the adaptive dark channel constructed by local information entropy, and the light transmittance of sky is obtained by using the improved linear model. Then, the overall transmittance is fused and refined. Finally, Leigh obtained the initial recovery image in the atmospheric scattering model, and the final image was obtained by adaptive color adjustment. The experimental results show that the algorithm has a good defogging effect, and both subjective and objective evaluation is better than the related algorithms.
Intelligent Positioning Method of Paging Buttons Based on Machine Learning

Information aggregation applications obtain massive amounts of data from the Internet as data support, most of which come from several different websites and need to rely on crawlers to collect from web pages. However, in the process of constructing a crawler system, it is often necessary to manually analyze and locate the web page structure of the target website in order to obtain the most relevant links and traverse the website effectively. In order to improve the automation of the crawler system and reduce manual involvement in the information collection, this paper studies the most common "list page"-"detail page" structure of content display websites, proposes an intelligent positioning method of paging buttons based on machine learning, and conducts experiments on the effectiveness of the method. The experimental results show that the proposed method can intelligently locate the paging buttons in the list page and get the most relevant links, which can improve the automation of the crawler system.
Research and software development on auxiliary checking method of harmonic tolerance for substation main equipment

The working state of the main equipment directly determines whether the substation can work normally or not, even when the main equipment works in normal state, there are always accompanied by complex harmonics, therefore. In this paper, the harmonic tolerance capacity checking method for power transformer, on-load tap switch, and filter are proposed, the harmonics tolerance capacity checking can provide accurate evaluation to judge whether the main equipment is working in proper state, which can further ensure the safe operation of substation. Firstly, the basic theory for harmonic tolerance capacity checking is studied. Secondly, the auxiliary tools for harmonics tolerance capacity checking are developed. Finally, the test examples are conducted to verify the effectiveness of the developed auxiliary tools. The checking results shows that the proposed checking method has high accuracy and the developed checking software has well practicability.
Image Super-Resolution based on Feedback Non-Local and Second-Order Attention

Convolutional neural network has shown strong feature extraction capabilities in the field of image super resolution (SR). However, the existing image SR technology has the problem of not being able to make full use of the generated SR image features and making the effect poor. Therefore, a super-resolution reconstruction algorithm under the condition of limited network parameters is proposed. The suggested algorithm can enrich the generation of shallow features by using deep features through feedback transmission and improve the performance of image reconstruction without increasing computational complexity. Experimental results show that PSNR is 0.33dB higher than some existing methods, with better visual effects and richer texture details.
Improving the Accuracy of Recommendation Systems based on the Relations in Knowledge Graph

Recommendation System is a kind of optimal resource allocation using machine learning and other technologies. Recommendation System can help users to actively display items they may like or need in the process of browsing and interaction, so as to promote user consumption and improve user experience. Nowadays, due to the wide variety of items, the recommendation system is not accurate. Aiming at this problem, this paper proposes an accurate recommendation technology based on knowledge graph. This method accurately pushes the relationship between the user and the product. First, by collecting information about users and products; Then, by enhancing the relationship between them; Finally, push according to the enhanced data. Experimental results show that this recommendation method can effectively improve the accuracy of the existing recommendation system.
A Web-Based Audio-to-Sign Language Converter for Chinese National Sign Language

About 20.54 million people in China suffer from hearing impairment, accounting for a quarter of the disabled population. However, only hundreds of thousands of people in China have revived formal sign language training. Therefore, for hearing-impaired people, how to communicate with ordinary people becomes a problem. Some articles have looked at converting sign language into text or audio. However, very few articles have dealt with how to help hearing-impaired people understand what ordinary people say. Therefore, this paper proposes a new method to convert audio into sign language video and play it on the web page. The main idea is to build a database based on Chinese common sign language and collect audio through computer microphones. After that, this method uses the Chinese word segmentation method of HanLP to segment words, and the obtained phrases are processed and matched with the database. After obtaining the database output, the videos are sorted and spliced by matching the original phrase list. Eventually, the stitched video will be played on the web. Experimental results show that the accuracy of this method is 90%. The relative code can be found at https://github.com/MelanthaWang246/A2SL-CN.
Development of a Context based Conversation State Prediction System

The evolution of the concept of Speaker Diarization using LSTM facilitated the process of understanding the speaker identities for specific segments of input audio stream data without manually tagging the data. Our study focuses on the possibility of the emergence of the concept of Speaker State Prediction on the basis of the contexts in a conversation. In this study, the Markov Chains are used to identify and predict the Speaker State for the next dialogue among speakers on the basis of previous contexts. The application of distance metric on speaker-specific contextual similarity enables the possibility of prediction of the speaker states in the most natural and long conversations specifically. The system has a prediction accuracy of 61 percentage calculated using live audio conversations. The findings imply that the proposed method is effective to predict a speaker's state during a conversation.
Financial opinion map production system based on cross-domain opinion mining

There are more and more data on the Internet. These data cover many fields. For example, information in the financial field covers many majors. How to make good use of the massive data on the Internet is a very important topic. Opinion mining is an important task in natural language processing. Opinion mining is the task of extracting effective information from massive data centers. It takes entities as the center, mining the attribute information and related opinions of entities. It can summarize people's opinions on entities well. In the financial field, opinion mining can reflect the sentiment of market. Therefore, this paper studies how to improve the ability of cross-domain opinion mining and designs a whole process opinion mining and displaying system.
Fuzzy and Machine learning Classifiers for Hate Content Detection: A Comparative Analysis

Hate content on social media is currently one of the most significant risks, where the victim is either a single individual or a group of people. In the current scenario, online web platforms are one of the most prominent ways to contribute to an individual's opinions and thoughts. Free sharing of ideas on an event or situation also bulks on the web. Information sharing is sometimes a bane for society if primarily used platforms are utilized with some lousy intention to spread hatred for intentionally creating chaos/ confusion among the public. Users take this as an opportunity to spread hate to get some monetary benefits, the detection of which is of paramount importance. This article includes various fuzzy pattern classifiers, including both the top-down and bottom-up algorithms for identifying the hate contents on multiple datasets, compared to the baseline results obtained from diverse machine learning or deep learning classifiers. Moreover, the result shows that fuzzy logic classifiers give decent results when classification is done on hate speech datasets.
Single-phase-to-earth Fault Line Selection Method for Medium-voltage Distribution Network Based on Wavelet Packet Analysis

Using the dynamic simulation tool Simulink of MATLAB software, the single-phase-to-ground fault in the medium-voltage distribution network system is simulated. A method of judging fault time by wavelet transform of zero-sequence voltage is proposed, select a small amount of data before and after the fault time for wavelet packet decomposition, the characteristic frequency band is determined according to the maximum difference of wavelet packet decomposition coefficients. Obtain the wavelet packet coefficients of each output zero-sequence current and zero-sequence voltage in the characteristic band, and perform product processing on them, a new method for judging faulty lines according to the polarity of their products. A large number of simulation experiments show that the method has the characteristics of strong adaptability and feasibility, then improve the accuracy and effectiveness of subsequent fault detection and fault identification, it provides reference for the development of fault algorithm and detection equipment of medium voltage distribution network.