Reviewerly/exHarmony huggingface dataset:
3D Garment Design Model Based on Convolution Neural Network and Virtual Reality

The development of virtual reality technology has promoted the unceasing reform and development in the field of fashion design. Aiming at the key technologies and research difficulties in 3D clothing design, the structure of convolution neural network was designed, and a 3D clothing design model based on convolution neural network and virtual reality was constructed. By designing experiments, the average error, matching rate, prediction accuracy, pressure value, and other evaluation indexes are used to measure the performance of convolution neural network model in virtual reality three-dimensional clothing design. The results show that: first, the convolution neural network has the fastest training speed, with the maximum error of 1.63% and the average error of 0.48%. Second, with the increase of parameters, the matching degree of each part of the version and the corresponding data of the human body will gradually increase, the matching rate will slowly improve, and finally tends to 0.9 or so to achieve stability, Thirdly, the prediction accuracy of the convolution neural network is 92.689%, and the loss value is the least. Fourthly, in the virtual fitting and garment wearing experiments, the fit of the sample is within the allowed range, indicating that the automatic generation of fit sample has wearability and rationality.
Analysis of Traditional Cultural Acceptance Based on Deep Learning

Technological development has resulted in the utilisation of advanced technology search using deep learning technology in all industries. Artificial intelligence is set to be filled with machines that can perform tasks that need human intelligence. These machines are controlled by computer systems. In this study, people are going to analyse the traditional cultural acceptance based on deep learning. Deep learning technology is a subfield of machine learning that makes use of algorithms that are inspired by the human brain. Artificial neural network is one such network used in deep learning. Traditional cultural acceptance is an integration of traditional cultures and cultural acceptance. The traditional cultures are said to be the tribes that are never exposed to the technological radar. These communities live traditionally in remote areas where there is no access to the latest technologies. The traditional cultures were found in the upper Amazon regions. Because they have not been exposed to the modern world, they have retained their ancient traditional and cultural value. Cultural acceptance can be said in simple words: accepting people as they are, without shaming their culture, traditional habits, attire, and body. In this proposed system, deep learning techniques were used to analyse the traditional cultural acceptance. The results proved that the proposed model called Faster R-CNN performs well than the existing algorithms.
Film and Video Quality Optimization Using Attention Mechanism-Embedded Lightweight Neural Network Model

In filming, the collected video may be blurred due to camera shake and object movement, causing the target edge to be unclear or deforming the targets. In order to solve these problems and deeply optimize the quality of movie videos, this work proposes a video deblurring (VD) algorithm based on neural network (NN) model and attention mechanism (AM). Based on the scale recurrent network, Haar planar wavelet transform (WT) is introduced to preprocess the video image and to deblur the video image in the wavelet domain. Additionally, the spatial and channel AMs are fused into the overall network framework to improve the feature expression ability. Further, the residual inception spatial-channel attention (RISCA) mechanism is introduced to extract the multiscale feature information from video images. Meanwhile, skip spatial-channel attention (SSCA) accelerates the network training time to achieve a better VD effect. Finally, relevant experiments are designed, factoring in peak signal-to-noise ratio (PSNR) and structural similarity (SSI). The experimental findings corroborate that the proposed Haar and attention video deblurring (HAVD) outperforms multisize network Haar (MSNH) in PSNR and structural similarity (SSIM), improved by 0.10 dB and 0.005, respectively. Therefore, embedding the dual AMs can improve the model performance and optimize the video quality. This work provides technical support for solving the video distortion problems.
A low-light image enhancement method with brightness balance and detail preservation

This paper proposes a new method for low-light image enhancement with balancing image brightness and preserving image details, this method can improve the brightness and contrast of low-light images while maintaining image details. Traditional histogram equalization methods often lead to excessive enhancement and loss of details, thereby resulting in an unclear and unnatural appearance. In this method, the image is processed bidirectionally. On the one hand, the image is processed by double histogram equalization with double automatic platform method based on improved cuckoo search (CS) algorithm, where the image histogram is segmented firstly, and the platform limit is selected according to the histogram statistics and improved CS technology. Then, the sub-histograms are clipped by two platforms and carried out the histogram equalization respectively. Finally, an image with balanced brightness and good contrast can be obtained. On the other hand, the main structure of the image is extracted based on the total variation model, and the image mask with all the texture details is made by removing the main structure of the image. Eventually, the final enhanced image is obtained by adding the mask with texture details to the image with balanced brightness and good contrast. Compared with the existing methods, the proposed algorithm significantly enhances the visual effect of the low-light images, based on human subjective evaluation and objective evaluation indices. Experimental results show that the proposed method in this paper is better than the existing methods.
Formation of Psychological Qualities of Future Specialists in the Humanities in the Process of Professional Training

The article reveals the theoretical and experimental principles of formation of psychological qualities of future specialists in the humanities in the process of professional training. It is established that the most significant psychological qualities that a future humanitarian specialist should possess are tolerance (tolerance to other opinions, manifestations of subjects of different qualities), communicativeness (ability to establish contacts with different people, exchange information, form strategies and tactics of cooperation with people and various social institutions), reflexivity (the ability of the individual to direct their own activities to themselves, to carry out self-assessment of thoughts, actions, behavior). The content and technologies (programs of trainings and classes, games, creative tasks, exercises, etc.) of formation of psychological qualities of future specialists of humanitarian profile in the process of professional training are developed. Their effectiveness has been proven experimentally. The conclusion about the necessity of their introduction into the educational process of higher education institutions of Ukraine is made.
Need for Speed, but How Much Does It Cost? Unpacking the Fee-Speed Relationship in Cryptocurrency Transactions

The growing popularity of blockchain-based cryptocurrencies as a payment mechanism is driven by low fee requirements, among other factors. Different from conventional payment systems with fixed transaction fees, many cryptocurrencies allow users to “name your own price”, giving rise to a large variation in fee offerings and hence, variation in confirmation times. Yet, the time it takes a cryptocurrency transaction to be confirmed in the blockchain is not only affected by the fee offered, but also by the contemporaneous congestion level and the inherent randomness in the verification process. Although it is generally expected that higher fees lead to quicker confirmation, the uniqueness of the cryptocurrency setting adds important nuances to the fee-speed relationship. Using Bitcoin – the original and most heavily used cryptocurrency by far – as our empirical context, we stylize the transaction confirmation processes, propose a theoretical framework that maps the causal path from fee to speed, and estimate this framework using Bitcoin transaction data between November 2017 and February 2018 – a period of high volatility. Our results show strong evidence for two characteristics of fee’s impact: congestion dependence and tail shrinkage. Our finding that the speed acceleration effect of fee is particularly strong on the tail of the confirmation time distribution motivates a target service level approach to fee recommendation. Accordingly, we develop an efficient computational procedure that helps Bitcoin users answer the following question: “how much should I pay if I want my transaction to be confirmed in t minutes with x% likelihood?”. We discuss the implications of our analysis on future cryptocurrency development and the long-term adoption of this revolutionary technology.
Generative adversarial exploration for reinforcement learning

Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses on designing heuristic rules or distance metrics to check whether a state is novel without considering such a discrimination process that can be learned. In this paper, we propose a novel method called generative adversarial exploration (GAEX) to encourage exploration in RL via introducing an intrinsic reward output from a generative adversarial network, where the generator provides fake samples of states that help discriminator identify those less frequently visited states. Thus the agent is encouraged to visit those states which the discriminator is less confident to judge as visited. GAEX is easy to implement and of high training efficiency. In our experiments, we apply GAEX into DQN and the DQN-GAEX algorithm achieves convincing performance on challenging exploration problems, including the game Venture, Montezuma's Revenge and Super Mario Bros, without further fine-tuning on complicate learning algorithms. To our knowledge, this is the first work to employ GAN in RL exploration problems.
TRAINING AND EDUCATION THE POPULATION FOR SELF-PROTECTION AND MUTUAL ASSISTANCE

The aim of the journal is to publish quality scientific articles that would present original results from theory and practice of crisis management, security management, rescue services, crisis planning, enterprise risk management and other general and specific areas of crisis management at the national and international level.
Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality

Augmented Reality (AR) tries to seamlessly integrate virtual content into the real world of the user.Ideally, the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user's viewpoint (or that of a camera) with regard to the virtual content's coordinate system.Therefore, the real-time establishment of 3-dimension (3D) maps in real scenes is particularly important for augmented reality technology.So in this paper, we integrate Simultaneous Localization and Mapping (SLAM) technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF (ORB) feature extraction and optimized key frame selection, as well as the use of the Progressive Sample Consensus (PROSAC) algorithm for planar estimation of augmented reality implementations, thus solving the problem of increased system runtime because of the loss of large amounts of texture information in images.In this paper, we get better results by comparing experiments and data analysis.However, there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points.
Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background, several authentication and accessibility issues emerge with an intention to protect the sensitive details of the patients over getting published in open domain.To solve this problem, Multi Attribute Case based Privacy Preservation (MACPP) technique is proposed in this study to enhance the security of privacypreserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient's records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this, crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further, linear integrity verification provides authentication rights to verify the data, improves the performance of privacy preserving technique against intruders and assures high security in healthcare setting.
Realtime Object Detection Through M-ResNet in Video Surveillance System

Object detection plays a vital role in the video surveillance systems.To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks.However, monitoring the video continually at a quicker pace is a challenging job.As a consequence, security cameras are useless and need human monitoring.The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions.The anomalous action does not occur at a higher rate than usual occurrences.To detect the object in a video, first we analyze the images pixel by pixel.In digital image processing, segmentation is the process of segregating the individual image parts into pixels.The performance of segmentation is affected by irregular illumination and/or low illumination.These factors highly affect the real-time object detection process in the video surveillance system.In this paper, a modified ResNet model (M-Resnet) is proposed to enhance the image which is affected by insufficient light.Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream.The proposed model shows better results in the metrics like precision, recall, pixel accuracy, etc., and finds a reasonable improvement in the object detection.
An Optimized Method for Information System Transactions Based on Blockchain

Accounting Information System (AIS), which is the foundation of any enterprise resource planning (ERP) system, is often built as centralized system.The technologies that allow the Internet-of-Value, which is built on five aspects that are network, algorithms, distributed ledger, transfers, and assets, are based on blockchain.Cryptography and consensus protocols boost the blockchain platform implementation, acting as a deterrent to cyber-attacks and hacks.Blockchain platforms foster innovation among supply chain participants, resulting in ecosystem development.Traditional business processes have been severely disrupted by blockchains since apps and transactions that previously required centralized structures or trusted third-parties to authenticate them may now function in a decentralized manner with the same level of assurance.Because a blockchain split in AIS may easily lead to double-spending attacks, reducing the likelihood of a split has become a very important and difficult research subject.Reduced block relay time between the nodes can minimize the block propagation time of all nodes, resulting in better Bitcoin performance.In this paper, three problems were addressed on transaction and block propagation mechanisms in order to reduce the likelihood of a split.A novel algorithm for blockchain is proposed to reduce the total propagation delay in AIS transactions.Numerical results reveal that, the proposed algorithm performs better and reduce the transaction delay in AIS as compared with existing methods.
<i>Demiurge</i>: A Music of Babel

Abstract The authors explore the unfinished, attempting a fragmentary and blurred snapshot of an ongoing research project on the musical application of generative adversarial networks, entitled Demiurge. The project was initiated in July 2020 as a collaboration between an international team of interdisciplinary artists and researchers led by Marek Poliks (instrument design) and Roberto Alonso Trillo (violin). In keeping with its namesake, Demiurge asks questions about creation, specifically in the era of machine learning, and works to formalize a new creative process endemic to and natural within a world of machine collaborators.
A Strata-Based Approach to Discussing Artistic Data Sonification

Abstract Much discussion surrounding data sonification for musical and artistic purposes focuses on seeming contradictions that arise from the ways in which this practice differs from that of data sonification as a scientific tool. Over the past 30 years, this debate has become a rabbit hole of questions and arguments regarding the nature of music/sound-art and data sonification, and of their relationships with one another. In the following article, the authors identify three areas/classifications of artistic sonification, using a “strata” metaphor with the hope of bringing clarity to this discussion, as well as enabling reflection on the nature of science-art collaborations using this approach.
Sentimental Analysis (SA) of Employee Job Satisfaction from Twitter Message Using Flair Pytorch (FP) Method

Organizations in the contemporary period face a number of problems as a result of the changing nature of the environment. One of a company's numerous problems is to please its workers in order to manage with an ever-altering and dynamic environment, attain success and stay competitive. The firm must meet the demands of its employees by offering appropriate working circumstances for enhancing efficacy, proficiency, job dedication, and throughput. Twitter is an online social networking site where users may share their thoughts on a wide range of topics, debate current events, criticize and express a wide range of feelings. As a result, Twitter is one of the greatest sources of information for emotion analysis, sentiment analysis, and opinion mining. Owing to a huge volume of opinionated material now created by internet users, Sentiment Analysis (SA) has emerged highly popular in both industry and research. Thus, this paper examines the problem by examining sentiment text as well as emotion symbols such as emoji. Therefore, utilizing the Flair Pytorch (FP) technique, an embedding type Natural Language Programming (NLP) system, and unique strategy for Twitter SA with a focus on emoji is presented. It overtakes state-of-the-art algorithms when it comes to pulling out sentiment aware implanting of emoji and text. Furthermore, 3520 tweets from an organization are accumulated as a dataset, with each tweet containing an emoji. As a result, the recommended FP technique has utilized the “en-sentiment” model for text classification and tokenization to determine the divergence of a sentence established on sentiment words, such as negative or positive, in the sentimental status of tweet, which could be assessed using the respective method’s confidence score.
A Comparison of Similarity Measures in an Online Book Recommendation System

To assist users in identifying the right book, recommendation systems are crucial to e-commerce websites. Methodologies that recommend data can lead to the collection of irrelevant data, thus losing the ability to attract users and complete their work in a swift and consistent manner. Using the proposed method, information can be used to offer useful information to the user to help enable him or her to make informed decisions. Training, feedback, management, reporting, and configuration are all included. Our research evaluated user-based collaborative filtering (UBCF) and estimated the performance of similarity measures (distance) in recommending books, music, and goods. Several years have passed since recommendation systems were first developed. Many people struggle with figuring out what book to read next. When students do not have a solid understanding of a topic, it can be difficult determining which textbook or reference they should read.
Extraction and Summarization of Disease Details Using Text Summarization Techniques

The application of machine learning (ML) and natural language processing (NLP) is being extensively used for research in the area of healthcare and biomedicine. This pattern goes especially in accordance with the course, and the healthcare system is headed in the highly networked world which includes the World Wide Web where the regular users and health experts conduct discourses on health issues. To glean knowledge from medical texts and discourses which are mostly in text in natural language, many text analysis frameworks and techniques have been designed. Those techniques do not produce a comprehensive summary about the content related to a disease from content available online. So in our work, we propose text summarization based on natural language processing algorithms combined with machine learning algorithms for extracting all information pertaining to a disease from online healthcare forums.
Classification of Breast Cancer Using CNN and Its Variant

Deep learning comes under machine learning. It includes statistics and predictive modeling, which plays vital role in data science. It helps in acquiring and analyzing vast amount of data quick and easier. This technique is employed in image recognition tools and natural language processing. Carcinoma is one other frequently occurring cancer in women. Carcinoma can be identified in two variants: One is benign, and another one is malignant. Automatic detection in medical imaging has become the vital field in many medical diagnostic applications. Automated detection of breast cancer in magnetic resonance imaging (MRI), and mammography is very crucial as it provides information about breast lesions. Human inspection is the conventional method for defect detection in magnetic resonance images. This method is impractical for large amount of data. So, cancer detection methods are developed as it would save radiologist time and also the risk faced by woman. Various machine learning algorithms are used to identify breast cancer. Deep learning models have been widely used in the classification of medical images. To improvise the accuracy in the model various, deep learning approaches are to be used to detect the breast cancer. The proposed approach classifies the breast cancer not just as benign or malignant, but it will classify the subclasses of breast cancer. They are Benign, Lobular Carcinoma, Mucinous Carcinoma, Ductal Carcinoma, and Papillary Carcinoma. To classify the subclasses of tumor, we use DenseNet Architecture. Image preprocessing is done using histogram equalization method.