Reviewerly/exHarmony huggingface dataset:
Who Filters the Filters

Ad and tracking blocking extensions are popular tools for improving web performance, privacy and aesthetics. Content blocking extensions generally rely on filter lists to decide whether a web request is associated with tracking or advertising, and so should be blocked. Millions of web users rely on filter lists to protect their privacy and improve their browsing experience. Despite their importance, the growth and health of filter lists are poorly understood. Filter lists are maintained by a small number of contributors who use undocumented heuristics and intuitions to determine what rules should be included. Lists quickly accumulate rules, and rules are rarely removed. As a result, users' browsing experiences are degraded as the number of stale, dead or otherwise not useful rules increasingly dwarf the number of useful rules, with no attenuating benefit. An accumulation of "dead weight" rules also makes it difficult to apply filter lists on resource-limited mobile devices.
Image GPT with Super Resolution

A Generative Pre-trained Transformer (GPT) model which can generate text by looking at previous text was trained to generate image pixels sequentially by making a correlation between the image classification accuracy and the image quality. This model uses the generative model for generating images. The Image Generative Pre-trained Transformer (IGPT) works on a low-resolution image which in turn produces a low-resolution output. In this paper, we have attempted to eliminate this limitation by enhancing the resolution of the output image produced by IGPT. The primary focus during this research work is to check different models and choose the simplest model for improving quality of the image generated because there are several models that support deep neural networks that have been successful in upscaling the image quality with great accuracy for achieving super resolution for a single image. The output image of low resolution is upscaled to high-resolution space employing a single filter and bicubic interpolation. We have also considered peak signal-to-noise ratio (PSNR) score and structural similarity (SSIM) value to analyze the standard of the image produced by the algorithm. The proposed approach has been evaluated using images from publicly available datasets. We have used leaky ReLU instead of ReLU as the activation function which produces better PSNR score and SSIM value, improving the overall result. Combining efficient sub-pixel convolutional neural network (ESPCNN) algorithm with IGPT, we have managed to get better output compared to the output generated by IGPT solely.
APPLICATION OF FIXED POINT THEOREM FOR DIGITAL IMAGES

In this research paper, we prove some fixed point theorems for digital images. The papers main goal is to present another generalisation of the well-known Banach contraction principle for digital images. The fundamental concepts of digital images are discussed, as is an application of the fixed point theorem to digital image compression and fractal image compression. The digital contractive type mappings in digital metric space are introduced, and the uniqueness of fixed points in digital metric space is proved.
SCIENTOMETRIC INDICATORS FOR LEADERS IN THE SCIENTIFIC RESEARCH OF THE INFORMATION SECURITY

Following the scientometric indicators of scientific activity (publication activity, citation index and Hirsch index) in the information security in the current decade the areas of research and also Russian scientistsleaders, whose results of scientific activities are in high demand have been identified. Those areas include the application of methodological analysis in security research, methods for assessing inconsistencies in the data protection tools, the ways to improve the LTE network stability against destructive cyber-attacks, early warning systems for computer attacks, the use of big data technologies in the information security field etc. The author notes some specifics in the dynamics of the Russian scientists’ publication activity in 2012 – 2019 as well as citation and relevance of their scientific activities results. The analysis was carried out using the RSCI database (Russian science citation index). Further development of the information flow research analysis in the field of information security using the RSCI database will allow identifying Russian organizations and leading scientists in the main modern information protection areas: Intrusion Detection Systems (IDS systems); identification and authentication of access subjects, including using modern biometric methods and security tools; confidential Data Leakage Prevention systems (DLP systems; cryptographic information protection (including in the field of quantum cryptography); information security management systems.
Application of Computer Virtual Technology in Physical Training Video

The physical training of virtual computer technology is now a new hot spot in the field of international sports science, and it has received more and more attention from domestic sports research institutions. This article studies the application of computer virtual technology in physical training videos, and understands the related theories of physical training and computer virtual technology on the basis of literature data, and then designs a physical training system based on computer virtual technology. Explains the application of computer virtual technology in physical training, and then tests the designed system. According to the test results, the average frame rate of the system designed in this paper has reached 66 or more, indicating that it meets the basic requirements of the system.
Research Activities of Students as a Strategy for the Development of Human Capital

The article describes the problem of the development of scientific activity in universities. Few students see research activities as opportunities for self-realization. Science is not a mandatory field, it is voluntary. Nevertheless, the acquired skills (critical thinking, flexibility, etc.) during participation in research activities are the key qualities of a modern specialist, and scientific publications give significant advantages in the professional field. Thus, the purpose of the study is to justify the strategy of human capital development through the research activities of students. In the course of the work, the significance of scientific works (publications, participation in scientific conferences), opportunities for students engaged in science (professional, personal, material), and storytelling of successful students in the field of science were described. It is concluded that research activity forms valuable skills in students as future specialists who can develop and develop the country, therefore, increases the human capital of the country.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind • Conference submission management system: EDAS • Number of submissions received: 95 • Number of submissions sent for review: 95 • Number of submissions accepted: 85 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 89.5 • Average number of reviews per paper: 2 • Total number of reviewers involved: 113 • Any additional info on review process : • Contact person for queries : Name : Haniza binti Yazid Affiliation: Universiti Malaysia Perlis Email : hanizayazid@unimap.edu.my
Chris Ofili

Research Article| May 01 2021 Chris Ofili Chris Ofili Chris Ofili Chris Ofili was born in the United Kingdom and is based in Trinidad, West Indies. Search for other works by this author on: This Site Google Nka (2021) 2021 (48): 141. https://doi.org/10.1215/10757163-8971440 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Chris Ofili; Chris Ofili. Nka 1 May 2021; 2021 (48): 141. doi: https://doi.org/10.1215/10757163-8971440 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter Books & JournalsAll JournalsNka Search Advanced Search The text of this article is only available as a PDF. © 2021 by Nka Publications2021 Article PDF first page preview Close Modal Issue Section: Tributes You do not currently have access to this content.
Comparative Study of Classical and Quantum Cryptographic Techniques Using QKD Simulator

Security is the crucial aspect of all applications and networks. Due to the increasing use of the Internet all over the world, the importance of security is also increasing. Cryptography is used for converting plaintext into ciphertext for securing it from hackers. Based on speed and efficiency, the best cryptographic algorithm is decided among various other algorithms. With the advancement in time, attackers are becoming smarter than in the past, e.g., quantum adversaries. So to secure the network from those attacks, quantum cryptography is used, which is based on the laws of quantum mechanics. In this paper, we have compared various classical and quantum cryptographic algorithms based on different parameters in different settings to determine one of the best algorithms in both cryptography techniques (classical and quantum).
Study of text classification Natural Language Processing algorithms for four European areas’ English dialects

In the era of big data, the text data on the network is growing day by day. It is very important to organize and manage the massive data scientifically by using text classification technology. As the most widely distributed and the largest data carrier, how to organize and manage these data effectively is a problem to be solved.Text classification is a basic work in the task of natural language processing. Its purpose is to organize and classify text resources, and it is also the key link to solve the problem of text information overload.This article will use data mining technology to evaluate several NLP algorithms for text classification and use weka to classify and predict English dialects in four different European countries. The research method used in this paper is the CRISP-DM method, including business understanding, data understanding, data preparation, modelling and evaluation for algorithms.Five different models were used for text classification: ZeroR, J48, NaiveBayes, RandomForest and MultilayerPerceptron. Among them, RandomForest performed best for text classification. It can process high dimensional data without feature selection (because feature subset is randomly selected). Also, during training, trees are independent of each other, and the training speed is fast, so it is easy to make parallel method.
Effectiveness of ultraviolet light for disinfection in the OR

AORN JournalVolume 115, Issue 3 p. P11-P11 Did You Know? Effectiveness of ultraviolet light for disinfection in the OR First published: 25 February 2022 https://doi.org/10.1002/aorn.13638Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume115, Issue3Special Focus Issue: RoboticsMarch 2022Pages P11-P11 RelatedInformation
Changing practice in the management of non-palpable breast lesion during pandemic; Early experience using Magseed markers.

Background: The Covid Pandemic has influenced how cancer surgeries are performed and their pre-operative management to allow appropriate social distancing and self-isolation prior to Surgery. With the requirement of wire localisation of non-palpable breast cancers/ lesions on the day of surgery and non-availability of mammogram and ultrasound machine in an isolated zone, we embarked on localisation of non-palpable breast lesions using MagseedR markers and SentimagR localisation system. We describe our initial experience in the management of these cancers and diagnostic excision biopsies using Magseed.
Consistency Driven Cross-Modal Twitter Sentiment Analysis and Opinion Mining

Nowadays, social media is being widely used by the public to convey their views and opinions on various topics. Among various social media platforms, Twitter is used to a greater extent for these purposes. Users of social media are increasingly utilising text and images to express their opinions and offer their insights. To overcome the challenges of effectively exploiting the content from both the textual and visual part of multi-modal posts, we propose a consistency based sentiment classification (CBSC) approach. The work presented here is a multi-modal sentiment analysis approach based on image-text consistency. The approach proposed here examines the image-text consistency before applying the adaptive multi-modal sentiment analysis algorithm. Finally, from the classified tweets, the overall public opinion in a particular domain is summarised.
Single Image Super Resolution Reconstruction Based on the Combination of Residual Encoding-Decoding Structure and GAN

In recent years, methods based on deep neural networks have achieved excellent performance in the field of image super-resolution. In order to generate more realistic images, based on the super-resolution generative adversarial network (SRGAN), this paper proposes a single-image super-resolution reconstruction model combining residual encoding-decoding structure and generative adversarial network. To further improve the visual perception effect, the encoding-decoding module with residual groups is used as a generator network. Compared with residual blocks, the encoding-decoding module uses a larger receptive field to obtain more effective features and enhance the ability to obtain contextual information of the input image. The residual group is compromised of multiple residual channel attention modules connected with the residual of a single convolutional layer. It can refine the eigenvalues of the network layer and improve the ability to obtain high-frequency detail information in an image. Experimental results have proved that our method is better than existing ones in both objective and subjective visual effects, and the texture details of the reconstructed high resolution image are more realistic.
Role of Blockchain-Oriented Smart Contract in Supply Chain

Supply chain visibility and optimisation were always a concern of utmost importance due to the increasing complexity of the network of trading parties in the chain which result in inefficient processes. This has led to a higher cost and poor customer satisfaction. A novel elucidation for this old-age problem of trust can be given by blockchain technology (BT). It is an evolving technology that comforts the supply chain by providing means to track and manage the goods movement in the chain using a decentralised network that is secured enough and provides the benefit of removing the intermediaries. With the introduction of the blockchain, the smart contract has gotten one of the most looked for after advancements on account of the high customisation they supplement to transactions. In this paper, we will focus on smart contracts as one of the probable future research areas of BT. This paper is a literature review-based research of smart contracts in supply chain management, which includes analyses of the technological principles, benefits and challenges and how it works. The paper also deals with the historical background of the smart contracts to make the readers familiar with the subject which eventually will help in a better understanding. This work may provide a platform for further research with a wider scope on this topic. Looking at the current scenario in the supply chain and logistics industry, this paper can also guide the business processes to implement blockchain-based applications in the industry.
KRB02 Method Used to Secure the Generalized Data

The current and future world is data world in a citizen’s life; because data only decides one’s existence in the world. Especially, data is more available in government, hospital, social media and, etc. This data does not have good security, due to which hackers can hack the data very easily. To address this problem, we are applying a new method namely KRB02. This method has four processes. The first process is applying for T-test technique and “0th” matrix value “start from end”, followed by swap of the values in matrix; the second process is applying for odd operations; the third process is applying for even operations, and final process is applying for column operations. The KRB02 method gives high security, while compared to ChaCha method.
An Approach for Building Content Recommendation System for Bilinguals

Language diversity in India is an extension of the vast cultural diversity that exists in Indian society. The globalization and presence of multinational companies made an inevitable need of English language, usage including in academics, though there are regional universities that use local languages as the medium of instruction. The emerging digital content market cannot elude the language diversity prevalent in the Indian population. The paper explores the possibility of finding common patterns among Hindi-speaking university-educated netizens regarding their first language preferences. These common patterns could be used to build a recommendation system for multilingual content-providing systems. The dataset that is used for building the system is collected as part of pilot work for creating a database in line with the Language Experience and Proficiency Questionnaire (LEAP-Q) for Indian languages. The questionnaire is formalized by the Language Technology Research Centre, Hyderabad, in collaboration with Pauranik Neuro Center, Indore. The questionnaire comprises questions related to language preferences and usage in different situations and reading habits. The questionnaire is devised to assess the Hindi language usage among bilingual Hindi native speakers. For this purpose, respondents are university-educated Native Hindi language users. The analysis shows that there is little relation to the language preferences between formal language usage and informal language usage. The reading habits of Hindi newspaper is observed as more of a personal choice, rather depends on any other language preferences. The analysis shows that three to four variables are enough to estimate preferences of the first language reading habit (Hindi). The proposed method could be used to recommend the right language content to bilingual readers at an average accuracy of around 50%.
BRUSH-STROKES FOR A PORTRAIT

This publication is a reprint of a study by the now deceased M.N. Sokolov in a collection of articles dedicated to the thirtieth anniversary of I.E.Danilova as Deputy Director for research of the A.S. Pushkin State Museum of Fine Arts [Sokolov 1997, pp. 22–23]. It is not a memoir about a late senior colleague, but rather a dithyramb addressed to the still living and active leader of the Museum’s academic life and its human community. M.N. Sokolov managed to outline in a short witty essay the main features of I.E. Danilova as a researcher and as a human being.
Domain Text Classification Using Machine Learning Models

Natural Language Processing (NLP) is used to classify text domains. In all applications where data is critical, such as News media, educational institutes, business organizations, research organizations, scientific, technology companies, and government organizations maintaining huge every day generated data is a serious challenge. Automated text classification has long been regarded as a critical way for managing and processing a large number of digital documents that are widely distributed and growing. In this paper to classify text domain four algorithms such as SVM, Naive Bayes, Decision Trees, and Random Forest evaluated and proved that SVM have achieved more accuracy than other algorithms.
Advocating for Patient Privacy

AORN JournalVolume 115, Issue 3 p. 261-263 Speaking Up: Voices From the OR Advocating for Patient Privacy First published: 25 February 2022 https://doi.org/10.1002/aorn.13630Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume115, Issue3Special Focus Issue: RoboticsMarch 2022Pages 261-263 RelatedInformation
The implementation of shadowing technique in English language learning and teaching

In teaching and learning English, learners have always been encouraged to fulfill and master four skills namely listening, speaking, reading, and writing. Undoubtedly, both listening and speaking skills are the most fundamental ones in the English learning process. There are numerical methods to facilitate English learning and teaching to be worldwide acknowledged for every single skill but the shadowing technique is one of the latest terms that helps develop the language learners listening, speaking, and potentially reading skills simultaneously. In this paper, an overview of the shadowing method will be described as a tool to assist English learning and teaching procedures, especially for listening and speaking skills. Also, this paper ends with a discussion on how to implement shadowing techniques in EFL classroom settings.
The third annual CSI Portsmouth event opens tomorrow

Doors open on CSI Portsmouth Saturday 3 November, the third year of what is fast becoming one of the most popular events in the crime fiction calendar
Causal Graphs and Rule Generation: Application to Fault Diagnosis of Dynamic Processes

Abstract This paper describes a systematic off-line algorithm for constructing a rule-based fault diagnostic system using the signed digraph (SDG) model. In order to overcome certain limitations related to the SDG (Iri and al. 1979), (Kramer and Palowitch 1987) and (Oyeleye and Kramer 1988), we have developped an alternative strategy for elimining spurious interpretations attributed to system compensations and inverse responses from backward loops and forward paths in the process when fault exist. Moreover, this method apply fuzzy logic to represent the rule base, in order to explain fault propagation and to ascertain fault origins. The algorithm was then successfully applied to a level controlled tank process to extract rules and these rules were evaluated using on-line data to discover the fault origins in an early stage and perform the diagnosis.