Reviewerly/exHarmony huggingface dataset:
Edge Detection Algorithm of MRI Medical Image Based on Artificial Neural Network

In order to be able to extract useful information from MRI medical images, we need to carry out effective image segmentation of MRI medical image and use appropriate operators to detect the edge of the image. Firstly, this paper adopts a research method based on the characteristics of horizontal, vertical and diagonal differences, and then carries out comparative analysis on different operators from different perspectives such as frequency domain and velocity domain. After analyzing the results, it is concluded that: Canny operator can detect complete, continuous and detailed edges at a faster speed. This paper uses edge detector based on Canny operator as the training output of artificial neural network. By training artificial neural network. The optimized parameters were obtained, and subjective analysis and computational analysis were performed on the quality of edge detection results of MRI medical images based on different methods. The results show that compared with the single Sobel, Canny and other traditional operators, the edge information of MRI medical image obtained by the neural network-based edge detection method is more complete and its processing time is nearly 3 times faster than the traditional edge detection method.
Boosting Luxury Sustainability Through Blockchain Technology

Global luxury brands have been investing significantly in “green” technologies and other measures to fight against climate change. While this is not limited to innovations in the supply chain, embracing new values and perspectives in response to the evolving needs of consumers and the environment can act as a game-changer. Sustainable luxury is a new concept that refers to a business/brand’s excellence in social and environmental dimensions and stresses not harming and doing good for the planet, workers, consumers, and society at large. Luxury and sustainability may be considered contradictory by some consumers as luxury is tied to pleasure and superficiality which oppose moderation and altruism. Luxury is also purchased for status signaling that creates ‘social unrest’. Whereas sustainability is linked to mutual social benefits. Nevertheless, luxury can be an exemplar of sustainability especially the luxury industry accentuates quality excellence, preserving heritage and craftsmanship, as well as defends local products against delocalization in low-wage countries. Thus, luxury might be viewed as possessing a less negative socio-environmental impact compared to mass products. For this reason, luxury brands need to increase visibility and transparency and encourage traceability throughout the supply chain. This chapter aims to discuss how luxury brands can custom blockchain technology to trace their supply chains, and how this can help in creating a real brand sustainable image and value expected to enhance consumers purchasing behavior toward sustainable luxury brands.
Visualized Analysis of Intention Recognition Research Based on CiteSpace

Intention recognition has improved the accuracy and speed of interactions between human and machine.However, due to the rapid development, the diversity of research content and methods, and the wide range of applications, researches in this field lack systematic integration.Based on 1234 selected papers, this paper applied visualization methods in CiteSpace to figure out significant country, institution, journal, reference and keywords to demonstrate the developments and research hotspots from 2011 to 2021.He Huang and Aaron J. Young are thought to be the most productive authors.According to the keywords analysis, "Hidden Markov Model", "networks", "feature extraction" are current hotspots.Furthermore, the deficiencies and future directions have been indicated by conducting this composite analysis.
Research on the Copyright Protection of Multimedia Works Based on Internet Mobile Terminal Technology

With the advent of the mobile Internet era and the popularization of mobile terminals, multimedia has been integrated into people’s daily life with its universality and diversity, and has become the main way for people to learn new things and obtain new information. People publish information and express personal opinions through multimedia platforms. China’s existing online media represented by WeChat and Weibo have become an important part of people’s lives, affecting and even changing people’s lives. Multimedia has enriched people’s lives, broadened information channels, and provided more effective means for disseminating works. At the same time, the rapid development of the Internet has also caused many copyright infringement issues. In the “digital” era, new technologies such as big data and cloud storage provide convenience and protection for the public’s creative behavior. At the same time, it can avoid the occurrence of infringement incidents caused by excessive information and thus affect social stability and harmonious development. This article summarizes the four major issues existing in China’s current copyright protection legal system, including provisional reproduction regulations, fair use systems, legal licensing systems, and collective management systems. On the four important issues, on the basis of this article, it discusses the legislation and judicial practice of many countries, summarizes the four important aspects of the legislation and judicial experience of copyright protection in different countries in China, and draws on foreign laws and judicial practices. Based on current specific conditions and requirements, four suggestions are put forward on how to improve my country’s copyright system in the media environment: Provisional regulations, based on my country’s national conditions, the fair use system must be legislated in a flexible manner, expand the scope of legal permission, and establish extensive copyright management system. It can use computer technology to rectify and improve its safety measures.
84 Birds Classification Using Transfer Learning and EfficientNetB2

Nowadays, global warming, wildfires, and air pollution have caused significant effects and seriously threatened the life and diversity of animals in common and birds in particular. Therefore, the conservation of birds is very urgent. Identify and classify them for statistics on the number of species, and distribution, and as a way to come up with reasonable conservation measures from scientists who study the environment and animals. In this research, we propose a new approach to classify birds using the EfficientNetB2 model, transfer learning techniques, and customizing the model’s hyperparameters. Model trained on the original dataset with the highest accuracy of 93% on both the validation and the testing set.
Multi-task super resolution method for vector field critical points enhancement

&lt;p&gt;It is a challenging task to handle the vector field visualization &lt;span style="font-family: 'Times New Roman';"&gt;at&lt;/span&gt; local critical points. Generally, topological based methods firstly divide critical regions into different categories, and then process the different types of critical regions to improve the effect, which pipeline is complex. In the paper, a learning based multi-task super resolution (SR) method is proposed to improve the refinement of vector field, and enhance the visualization effect, especially at the critical region. In detail, the multi-task model consists of two important &lt;span style="font-family: 'Times New Roman';"&gt;des&lt;/span&gt;igns on task branches: one task is to simulate the interpolation of discrete vector fields based on an improved super-resolution network; and the other is a classification task to identify the types of critical vector fields. It is an efficient end-to-end architecture for both training and inferencing stages, which simplifies the pipeline of critical vector field visualization and improves the visualization effect. In experiment, we compare our method with both traditional interpolation and pure SR network on both simulation data and real data, and the reported results indicate our method lower the error and improve PSNR significantly.&lt;/p&gt;
Did a Robot Write This Title? Creativity, Ownership, Justice, and Copyright Law

This essay evaluates philosophical issues underlying legal rights in the training set data used by AI models.
Presentation of Self in Machine Life : A human-machine performance

The world has been driven apart by recent events, making long distance performative mingling difficult to achieve, especially those employing in-person collaboration between humans and machines. How shall we reclaim a tangible exchange with other parts of the world that has presence and meaning, as opposed to impersonal virtual interactions? We created and choreographed and an art technology performance that allows viewers in Oklahoma City to immerse themselves in a collaborative narrative space between a dancer in the US and a robot arm in City University of Hong Kong’s Studio for Narrative Spaces. The performance is shown in either online or offline form to audiences, who witness the narrative of a dancer and a robot who communicate with each other through movement, sometimes leading one another, sometimes frustrating each other, as if each are present to the other across a 12 hour divide. Without viewing the machine directly, the dancer relies on limited perspectives and sounds to enable bi-directional communication.
Sentiment Analysis of Facebook and YouTube Bengali Comments Using LSTM and Bi-LSTM

Recently, sentiment analysis has been performed on various information on social media to derive market intelligence. As we know social media is filled with different content as a result audiences are interacting there making it a huge opportunity to perform sentiment analysis on this information. In terms of Bengali content, many audiences on social media interact with the Bengali language which makes social media a treasure trove to perform sentiment analysis in Bengali Natural Language Processing (NLP) field. Here in this study sentiment analysis has been performed on the audience’s Bengali comments expressing different views towards social media’s Bengali contents. The dataset contains 4000 Bengali comments collected from Facebook and YouTube Bengali Contents. Here positive, negative, and neutral classes are used to categorize the Bengali data, and a tokenizer from the Keras library is used to tokenize the Bengali text. Deep learning algorithm Long Short-Term Memory (LSTM) and BiDirectional Long Short-Term Memory (Bi-LSTM) are performed and Bi-LSTM has the highest accuracy of 97.25% than LSTM.
Systematic Investigation of Continuous E-Learning as a Complex Information System

Introduction. For quite a long time, this problem was considered from a pedagogical point of view in various aspects. With the technological development of society, more and more attention was paid to the informational view of e-learning, its importance in the global processes of formation of new constellations of specialists.This was noted in world publications of all countries. It has been proven that e-learning is a complex information system, the study of which should begin with a system analysis, it is the system studies that create the basis for further consideration. Based on the analysis of the main processes of continuous learning from a technological perspective. One of the most important is the mathematization of the processes and phenomena of the surrounding world. This process provides a transition from verbal natural language definitions to abstract models. Purpose. The purpose of the article is a systematic study of continuous e-learning as a complex information system by formalizing the formalization of the elements of continuous education. Methods. The paper presents studies of the mechanisms of formation of values based on the phenomenological approach. Basic formalized structures are considered. In the phenomenological approach at the highest level of abstraction lifelong learning is defined as a system of the highest level of abstraction, if the objects of the system and the relationships between them are given. Results. The article considers a number of issues related to systematic studies of electronic continuous learning. The role of formalization of elements of continuous education is presented. It is necessary to note the special importance of the procedure of formalization of tasks. The key principles of continuous learning as a complex system are defined. The application of the phenomenological approach made it possible to significantly improve knowledge, practical work and research. Conclusion. The article provides an idea of analternative view of continuous learning as a complex information system, opens a large field of research for mathematicians, systemengineers, specialists in the field of data bases.
Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions

Abstract Idiomatic expressions (IEs), characterized by their non-compositionality, are an important part of natural language. They have been a classical challenge to NLP, including pre-trained language models that drive today’s state-of-the-art. Prior work has identified deficiencies in their contextualized representation stemming from the underlying compositional paradigm of representation. In this work, we take a first-principles approach to build idiomaticity into BART using an adapter as a lightweight non-compositional language expert trained on idiomatic sentences. The improved capability over baselines (e.g., BART) is seen via intrinsic and extrinsic methods, where idiom embeddings score 0.19 points higher in homogeneity score for embedding clustering, and up to 25% higher sequence accuracy on the idiom processing tasks of IE sense disambiguation and span detection.
METHODOLOGICAL ASPECTS OF ASSESSING THE COMPETENCE OF LABORATORY PERSONNEL DETERMINING THE QUALITY OF SEED POTATOES

Most of the quality indicators regulated by the standard for seed potato certification are determined by the subjective component, that is, the competence of the personnel. However, the regulatory framework does not establish criteria for assessing competence, which poses uncontrolled risks in the laboratory. The purpose of the study is to develop and investigate the methodology for assessing the competence of laboratory personnel involved in the testing of seed potatoes and determine the extent of application of the methodology. The methods of probability theory and mathematical statistics are used for the tasks of selection and research of statistical criteria that correspond to the law of distribution of the obtained data and provide the necessary reliability for the available sample volumes. The statistics and the method of determining the threshold value, which mathematically describes the decision-making on competence, are provided for the binomial distribution law. The properties of the proposed statistical method for assessing the competence of personnel in terms of finding the maximum differences in the results of the analysis, at which the personnel are recognised as competent, are investigated. The study demonstrates the dependence of the method capacity on the data volume. It is determined that to obtain a confidence probability of 0.95 for quality indicators, the content of which in the average sample according to the technical conditions is minimal, the sample size should be 600-1000 tubers. At the same time, up to 100 tubers are sufficient for indicators whose content is under 5%. The method proposed by the authors showed that the average sample of 250 tubers recommended by the standard is entirely justified in terms of attracting resources during certification but must be adjusted during competence monitoring in a testing laboratory that confirms its competence by the requirements of DSTU ISO IEC 17025:2019. The findings presented can be used by the personnel in the seed testing laboratories as a measure to minimise the risks caused by the impact of personnel incompetence
Self-supervised Learning of Visual Graph Matching

Despite the rapid progress made by existing graph matching methods, expensive or even unrealistic node-level correspondence labels are often required. Inspired by recent progress in self-supervised contrastive learning, we propose an end-to-end label-free self-supervised contrastive graph matching framework (SCGM). Unlike in vision tasks like classification and segmentation, where the backbone is often forced to extract object instance-level or pixel-level information, we design an extra objective function at node-level on graph data which also considers both the visual appearance and graph structure by node embedding. Further, we propose two-stage augmentation functions on both raw images and extracted graphs to increase the variance, which has been shown effective in self-supervised learning. We conduct experiments on standard graph matching benchmarks, where our method boosts previous state-of-the-arts under both label-free self-supervised and fine-tune settings. Without the ground truth labels for node matching nor the graph/image-level category information, our proposed framework SCGM outperforms several deep graph matching methods. By proper fine-tuning, SCGM can surpass the state-of-the-art supervised deep graph matching methods. Code is available at https://github.com/Thinklab-SJTU/ThinkMatch-SCGM .
Techniques for using VRChat to Replace On-site Experiments

In this paper, we present a technique to conduct on-site laboratory experiments using VRChat, a social Virtual Reality platform. COVID-19 pandemic has created challenges and restrictions on re-searchers in running experiments for Human-Computer Interaction research. Hence, universities and institutions set guidelines for social distancing and encourage people to work from home. Therefore, participants are harder to recruit for experiments that run in a physical laboratory. Our technique uses VRChat as a substitution for running on-site experiments despite social restrictions. We exploit features in VRChat to enable researchers to run experiments with domestically recruited participants. Furthermore, we propose a hassle-free method to retrieve study data. This paper also discusses and offers design considerations to run an on-site experiment using VRChat over a homemade system.
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-Tailed Recognition

The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. While training with class-balanced sampling has been shown effective for this problem, it is known to over-fit to few-shot classes. It is hypothesized that this is due to the repeated sampling of examples and can be addressed by feature space augmentation. A new feature augmentation strategy, EMANATE, based on back-tracking of features across epochs during training, is proposed. It is shown that, unlike class-balanced sampling, this is an adversarial augmentation strategy. A new sampling procedure, Breadcrumb, is then introduced to implement adversarial class-balanced sampling without extra computation. Experiments on three popular long-tailed recognition datasets show that Breadcrumb training produces classifiers that outperform existing solutions to the problem. Code: https://github.com/BoLiu-SVCL/Breadcrumbs .
Secured Blockchain Technology for Agriculture Food Supply Chain

As we all know when we talk about marketing the thing is it needs a plan of action that relates peer to peer relationships of farmers as well as consumers, whereas this process was followed and there will be no loss for consumers or dealers, the problem comes to only the farmer in ways of both financially and work issues during selling.so this paper proposes the work of getting more gain for cultivators by including the blockchain technology in the food chain supply by using FERNET (AES encryption algorithm) whereas this one doesn't have any vulnerabilities we know, that did it not secure and this has not been broken when compared to another popular hashing algorithms.thereafter by using a software infrastructure that includes in pre-processing of data, making transactions secure between cultivators as well as consumers/buyers, whereas blockchain is less time taken as well as a way of security for all kinds of transactions by using smart contracts for transactions for valuation of goods by using this algorithm we can securely protect digital signatures.using this model or the application we develop, all farmers are good and it is designed in such a way that every farmer can easily log in and check the details of his product and sell their crop and make secure payments.
Demonstration of Multi-image Switchable Visual Displays Using Carpets

In this demo, we demonstrate the system which can present different images on the carpet depending on the viewpoint or the light source position by raising the fibers of existing carpets. There are some carpets in which shades of gray can be changed by changing the fiber's directions. By controlling the direction of the fibers of the carpet in different direction, we found that the shades of gray can be changed depending on the viewpoint position and the light source position. We demonstrate the images which switch between two images by changing the viewpoint or the light source position.
Impact of Organizational Culture on Work –Life - Balance

Cultural diversity is common nowadays as a result of globalization. There are differences in race,language, nationality socio economic status, gender etc. within a same group. Work-life balance is considered to be crucial for employee’s well- being in an organisation. Difference in cultural dimensions causes impact on work related variables.Previous studies have shown that culture has a moderating role on human resource practices regarding work-life balance have on employees’ well-being at work. Previous researches have indicated that all employees do not react similarly to the same incentive since they have differentvalues which affect their behaviour. Thus, in the current paper researcher has done review of impact of culture on work-life balance of employees. The study will be based on Hofstede framework of cultural dimensions.From the review the researcher attempts to identify key areas and research gap for future studies. This review of past findings potentially offers two main contributions to the literature. It can advance our understanding of the Work-life balance and cross cultural management. It will also help to apply suitable work-life balance practices in the organisations.
Application and effectiveness of weighted KNN in pattern recognition of communication modulated signals

Modulation pattern recognition of communication signals is a key technology in wireless communication, and the continuous complexity of the channel environment has put forward higher requirements on the communication signal modulation capability. This paper proposes a pattern recognition method of communication modulated signal based on weighted KNN. Firstly, signals of different modulation modes were generated in MATLAB. Then, the modulated signal features in the time and frequency domain were extracted, and the information entropy was included. Secondly, the extracted features were used to train and test the weighted KNN algorithm. By comparing the recognition effect of the modulated signals before and after signal feature extraction, it is concluded that feature extraction has a substantial improvement on the recognition accuracy and computing efficiency of weighted KNN, and different features have a significant effect on the recognition.