Reviewerly/exHarmony huggingface dataset:
Development of a Security Audit Management System

Due to increasing applicability of information technology to various aspects of life as well as rapid and the technological advancement witnessed in our societies today, there is rapid increase in the use of information systems in various organizations. Information shared across these systems is usually sensitive information that unauthorized access to may result in crisis. […]
A Lightweight NTRU Lattice Cryptography based Cloud Data Security System

The safety and security of various cryptosystems are related to the hardness of the integer factorization problem and discrete logarithm problem, most cryptosystems will be rendered vulnerable to attack and outdated as quantum computers become more advanced. This study examines lattice-based cryptography (the NTRU cryptosystem), compares the suggested variation to other cryptosystems by simulation and […]
The Problem of Typology of Scientific Cognition in the Context of Cultural-Historical Epistemology

The existing variants of the classification of sciences differentiate and correlate the types of cognitive practices on various grounds. At the same time, the attention of epistemologists is usually concentrated on the instrumental logical and methodological functions of the proposed classifications, which guide scientists in the holistic cognitive space of rational cognition (by subject, by epistemological preferences, by methods, etc.). As for the sociocultural dimensions of scientific and cognitive activity, they (as long as they are taken into account) mostly correlate with the typological features of research practices only slightly. Meanwhile, science as a whole is undergoing significant changes today, affecting, among other things, the status and methodological functions of its socio-cultural parameters, which significantly change the configuration of the cognitive space of scientific and cognitive activity. The article attempts to demonstrate the features of the problematization of contemporary science’s philosophical and methodological foundations. When discussing these issues, it is fundamentally important to consider how a scientist realizes his methodological tools and the socio-cultural status of scientific and cognitive activity. According to the author, these problematics are stepping into the center of today’s philosophical and methodological reflection on science, forcing us to take a fresh look at the meaning (target bases) of science as a holistic cognitive phenomenon, accordingly, the typological features of its cognitive practices.
P73 Unrecorded alcohol consumption during first months of COVID-19 pandemic among online social media users in Russia

<h3>Background</h3> Unrecorded alcohol consumption in Russia is a significant public health problem and is associated with increased morbidity and mortality from various causes of death. The substantial share of the Russian population are active online social media users and can be reached by screening and brief interventions, aimed at preventing alcohol-related problems, through social media. The purpose of this analysis was to assess changes in unrecorded alcohol consumption and factors associated with the increase in unrecorded alcohol use among online social media users during the first months of the COVID-19 pandemic in Russia. <h3>Methods</h3> Between June and September 2020, 1518 users of popular in Russia online social networking services participated in online survey investigating changes in alcohol, tobacco, and substance use during the COVID-19 pandemic. Questions assessing unrecorded alcohol use were adopted from the WHO STEPS-survey instrument and investigated changes in consumption of homemade, falsified, counterfeited, surrogate alcohol, and alcohol brought from abroad during the first months of COVID-19 pandemic. Binary logistic regression was used to identify factors associated with increase in consumption of unrecorded alcohol. The analysis was adjusted for age, sex, change in income, restrictions in everyday personal life due to spread of COVID-19, typical (pre-pandemic) one-time volume of alcohol use and typical (pre-pandemic) frequency of heavy episodic drinking (6 or more portions of alcohol per occasion). <h3>Results</h3> 42.2% of the surveyed population consumed unrecorded alcohol in the past year. During the first months of COVID-19 pandemic, 22.5%, 25.9% and 51.6% of all users of unrecorded alcohol increased, decreased and didn’t change their consumption of unrecorded alcohol, respectively. Relative to those who reduced or didn’t change their use of unrecorded alcohol, those who increased consumption of unrecorded alcohol were more likely to: have been residents of rural settlements/small cities (OR = 2.492, 95% CI = 1.246 – 4.985), medium size cities (2.310, 1.314 – 4.062); typically (before pandemic) consumed alcohol more frequently (2–4 times a month: 2.449, 1.131 – 5.302; 2–3 times a week: 2.930, 1.190 – 7.217; and more often: 2.920, 1.128 – 7.555); experienced negative work/financial consequences due to pandemic (2.228, 1.270 – 3.909); and increased frequency of heavy episodic drinking during first months of the COVID-19 pandemic (7.249, 4.194 – 12.530). <h3>Conclusion</h3> Online social media users at risk to increase their consumption of unrecorded alcohol during the COVID-19 pandemic can be considered for various alcohol control policy options, including targeted screening and brief interventions, which can be delivered via online social media.
Fisher Kernel Discriminant Analysis Under Indefinite Kernel Function

In this research, Fishers Indefinite discriminant analysis method was used on data representing three groups, the first with size (135), the second with size (120), and the third with size (55) by people with hepatitis A type, B, and c, which is the most common type of hepatitis. The virus was common, and the infection of the three diseases was considered as a response variable, and the value was given (1) for the first type, (2) for the second type, and (3) for the third type, and the independent variables that were relied upon in agreement with specialized doctors are (7) independent variable, gender, age, blood sugar level, mothers infection with viral hepatitis, previous blood transfusions or surgeries, injections of medicines and drugs, and the percentage of white blood cells in the blood. It was concluded that Fishers unbounded discriminant analysis method has an efficient performance because it recorded the lowest rate of classification error, and it was half the probability of error.
Tolerance enhancement of inefficient detection and frequency detuning by non-perfect phase-sensitive amplification in broadband squeezing-based precision measurement

Phase-sensitive amplification (PSA) can significantly improve the degradation caused by inefficient detectors in squeezing-based precision measurements. However, broadband incident light will lead to non-perfect PSA. The present work focused on the enhancement of non-perfect PSA for squeezed states with broad bandwidth for the measurement of weak absorption detection. Numerical calculations of the quantum advantage show that non-perfect PSA can effectively improve the inefficiency of detection in slight drift frequency detuning.
The role of sentiment analysis in a recommender system: a systematic survey

Currently, fields like e-commerce, education, social media, tourism, and the entertainment industry rely on recommender systems to provide personalised services to their clients. The most common and widely accepted technique - collaborative filtering, creates recommendations by examining the users' past rating patterns. Collaborative filtering assumes that a users' past rating data accurately reflects their actual preferences. However, different study found that the ratings may not accurately reflect user preferences in the real-world circumstances. Therefore, to deal with this problem, sentiment analysis of user-generated text is started to be used. It helps to improve the performance of recommender systems, as it provides more specific and trustworthy user preferences than ratings. A sentiment-aware recommender system captures sentiment from the user-generated content and provides most suited personalised services to the user. We have classified sentiment enhanced recommender systems according to the level of sentiment analysis and presented technical aspects such as datasets, methodologies and results.
Bank growth, competition, and small business financing in Nigeria

This study used ex post-facto design to examine how competition among high-growth banks has affected the banks' provision of assets to small business borrowers in Nigeria following several regulations. The study used a bi-sample structure through a judgmental technique that examined 126 and 96 firm-years between 2001 and 2017, which enabled us to test both pre- and post-bank growth-competition Boone model effects. We found evidence that in both the pre- and post-bank-growth eras, competition significantly increased small business loans consistent with 'competition-stability' hypothesis. A unit increase in the Boone-competition indicator resulted in a 3.8% credit supply. However, when we used concentration ratio (CR6), we found that pre-mega structure competition yields negative effects but that the post-growth effect on credit supply remains positive. Controlled macroeconomic factors yield a very significant effect on credit supply. Thus, policymakers should not ignore the variables in competition-monetary transmission policies in Nigeria.
For a pedagogy of summaries values

The title of the article is inspired by the work of J.S. Bruner, “For a theory of instruction”, built on the psychological theories of learning approached from a formative perspective, through an “effort to help and shape the development” of the personality of the educated (student) [3, p. 11]. By analogy, the study aims to design a pedagogy of values based on the higher formative resources offered by the concept of value, determined philosophically, sociologically, anthropologically, politologically, psychologically, historically. The analysis promotes a “tripartite vision” on the subject investigated with the means of fundamental pedagogical research (historical and theoretical), oriented in epistemological, methodological and practical plan. Such a “tripartite vision” refers to: 1) Defining and analyzing the concept of pedagogical value, reconstructed and fixed from a historical, (evolutionary) and theoretical (epistemological) perspective. 2) Deepening the value meanings of education in the postmodern (contemporary) society, specifically reflected, at the level of general theory of education, through the “model of pedagogical values in private education”, built in conditions of fundamental research, on the axiological continuity line between: purpose-values, norm-values, means-values, valence-values, utility-values. 3) The implementation of the technology for the promotion of pedagogical values in private (primary) education, evaluated in conditions of operational research, which constitutes a distinct final subject, approachable in conditions of empirical, experimental investigation.
Professional development of the teachers: relevant aspects of the extracurricular artistic education institutions

The state policy in the field of education prioritizes the training of teachers and management, which should correspond to the current requirements oriented towards change, innovation, dynamism, towards a modern education. Achieving these goals in the field of extracurricular (non-formal) arts education requires professional training for current trends in educational policies. The article reflects on key issues in training and development the professional competencies of teachers in art education, viewed from the perspective of a certain model of ensuring pedagogical mastery, in a broad sense.
Peer Review Statement

All papers published in this volume have been 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. 1. Type of peer review: Double Anonymous 2. Conference submission management system: Morressier 3. Number of submissions received: 88 4. Number of submissions sent for review: 55 5. Number of submissions accepted: 47 6. Acceptance Rate (Submissions Accepted /Submissions Received × 100): 53.4% 7. Average number of reviews per paper: 2.00 8. Total number of reviewers involved: 10 9. Contact person for queries: Name: Mochammad Tanzil Multazam Affiliation: Universitas Muhammadiyah Sidoarjo Email: tanzilmultazam@Umsida.ac.id
Cloud spot price prediction approach using adaptive neural fuzzy inference system with chaos theory

The dynamic pricing of cloud computing is a major challenge for cloud users all over the world. This challenge was first addressed by Amazon under the name of Amazon spot instance market. Cloud users can bid for a spot instance using this market and obtain the requested spot if their bids exceed a dynamically changing spot price. Amazon publicises the spot price but does not reveal how it is determined. In this paper, we perform chaotic time series analysis over the spot price trace. We also develop a chaos based adaptive neural fuzzy inference system (ANFIS) model based on phase-space vectors obtained during the phase of chaotic analysis. Next, we study the effect of chaos existence on the prediction accuracy of the spot price by comparing the proposed chaos-ANFIS model with the baseline ANFIS model (non-chaotic approach). Evaluation results show that the proposed chaos-ANFIS model yields better predictions of spot price compared to the baseline ANFIS model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).
Passenger Needs on Mobile Information Systems – Field Evaluation in Public Transport

Passenger information is an essential part of public transport and mobility in general. Enabling passengers to plan their journey, to check travel times and routes as well as prices and local information. Technological developments have made information available, e.g. real-time information of vehicles, which supports seamless information before and during the trip. These developments have lead to new information systems along the travel chain, e.g. real-time information displays at stop points, interactive public displays or mobile passenger applications. In this regard, mobile passenger information systems often serve as personal assistants along the journey, providing necessary information for the individual journey of each passenger. Designing these systems for different users and for different journeys, requires knowledge about when and where information and functions are needed along the journey. This knowledge enables developers to design these systems more in regard to the user’s needs. This paper shows a user and context depended evaluation of a mobile passenger information system and an assessment of twenty different functions, related to the fulfillment of information needs along the journey and the creation of added value.
Distributed Storage Infrastructure: Foundations, Analytics, Tools, and Applications

The rapidly shifting technology landscape has allowed organizations to acquire the benefits of streamlined processes and cost-efficient operations. However, the availability of data from sources such as social data, machine data, transactional data, and many more has become a game changer for businesses of all sizes. These large stores of data known as Big data is challenging for an organization to handle or process. To improve the IT operations and optimize the faster processing, enterprises have adopted cloud computing. The integrated model of Cloud and Big data is a powerful combination that can transform the IT operations of the organization. This chapter provides a systematic overview of distributed storage infrastructures and discusses the current state-of-the-art solutions for storage technologies using Big data and cloud models. This chapter investigates the foundations, tools, and open research challenges of storing data using distributed storage infrastructures.
Preface

Dynamic Adaptive Streaming over HTTP (DASH) is a representative scheme to improve Quality of Experience (QoE) of video streaming users. However, the QoE fairness is degraded when multiple users stream videos over the same network. The reasons of this problem are a lack of coordination among clients and bitrate adaptation with the fixed heuristics. In this paper, we propose an HTTP adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme generates a policy based on reinforcement learning to optimize QoE fairness and utilizes edge computing to assist policy generation. We evaluated the proposed scheme through simulations under various network conditions. The experimental results show that the proposed scheme improves QoE fairness of multiple clients compared with the existing scheme.
Analyzing the Structure and Dynamics of Control Relationships in the Case of “Illegal Aliens” in the Library of Congress Subject Headings

This book explores the latest advances in how knowledge organisation can both draw upon and inform diverse disciplines and technological developments. It explores how best to combine theory and practice, with its contents considering practical solutions as well as the theory behind the design, development and implementation of knowledge organising systems, ranging from controlled vocabularies, classification systems and metadata schemas to ontologies and taxonomies. With contributions by Carmen Agustín-Lacruz, Mayukh Bagchi, Mario Barité, Maria Teresa Biagetti, Briony Birdi, Lynne Bowker, Thiago Henrique Bragato Barros, Ana Inés Brozia, Gema Bueno-de-la-Fuente, Julia Bullard, Rafael Cacciolari Dalessandro, José Augusto Chaves Guimarães, Christian Cole, Stephanie Colombo, Luciana Corts Mendes, Luis Corujo, Niamh Nic Daeid, Patrícia de Almeida, Hadassa De Zen Itepan, Anthony Dunbar, Jonathan Furner, Francisco-Javier García-Marco, Fausto Giunchiglia, Claudio Gnoli, Míriam Gontijo de Moraes, Hazlem Granados-Amaya, Hugo Alberto Guadarrama-Sánchez, Carlos Guardado da Silva, David Haynes, Ania Hernández Quintana, Philip Hider, Chris Holstrom, Eva Hourihan Jansen, Fidelia Ibekwe, Mat Kelly, Michael Kleineberg, Nair Yumiko Kobashi, Deborah Lee, Maël Le Gall, Joaquín López del Ramo, Luís Miguel Oliveira Machado, Hugo Moquet, Catalina Naumis-Peña, Mirtha Rauch, Mariana Ramos Crivelente, Raissa Resende de Moraes, Jorge Revez, Riccardo Ridi, Somayeh Roshan, Clarissa Schmidt, Negin Shokrzadeh Hashtroudi, Richard P. Smiraglia, Mariângela Spotti Lopes Fujita, Adriana Suárez-Sánchez, Erik Stubkjwær, Rick Szostak, Seyed Mahdi Taheri, Marc Tanti, Ana Lúcia Terra, Amanda Terrero Trinquete, Natalia Bolfarini Tognoli, Jéssica Beatriz Tolare, Raquel Torrão Valentim, Hyung Wook Choi and Mohsen Haji Zeinolabedini.
A new fuzzy and Gaussian distribution induced two-directional inverse FDA for feature extraction and face recognition

In the area of face recognition research, the high dimensionality of the data is indeed a crucial problem. This paper proposes a new fuzzy and Gaussian distribution induced two-directional inverse Fisher's discriminant analysis (FGD-2DIFDA) which computes the fuzzy and Gaussian distribution membership values and thereby combined the values with the training samples to obtain the class-wise mean and the global mean. These fuzzy and Gaussian function induced membership values are incorporated in inter- and intra-class scatter matrices for diminishing the effect of uncertainty formed by variation of light facial expression and pose. Finally, the eigenvalue problems of the inverse Fisher's criteria are solved to finds the optimal inverse projection vectors. The uncertainty associated in the image region is efficiently managed by the FGD-2DIFDA method than fuzzy generalised 2DFLD (FG-2DFLD) method as two membership values are employed in the former case while the later one uses only one membership value to handle such situation. The FGD-2DIFDA method has been evaluated on the AT&T (formally known as ORL), UMIST and FERET face databases using support vector machine (SVM) as classifier. Simulation results demonstrate that the proposed FGD-2DIFDA method can obtain higher recognition rate than some state-of-the-art face recognition methods.
International Security Standards

Organizations must increasingly demonstrate to their customers and regulatory authorities that they have sufficient protection, security, resilience, and privacy of their information, assets, and systems, based on best practices. International information security standards applicable for all organizations such as ISO 27000 series or industry-specific information security standards such as PCI DSS and SWIFT were created for that reason. When organizations show their compliance to these standards, their customers acknowledge that they understand their risks, perform risk mitigation actions, create baseline security, and manage the risk on their systems. This does not mean that compliant organizations are free of risks or vulnerabilities, but they certainly have a better security posture than non-compliant organizations.
The Cybersecurity Challenge

More than two decades ago, computer users were terrified that a destructive and undetected virus called CIH might be present in the memory of their computers and become active on April 26 and delete programs in hard drives, flash the BIOS, and brick the motherboard. The date was chosen as it is the anniversary of the Chernobyl nuclear meltdown. Back then, IT support staff informed users not to open their PCs on that date so that it would not be activated. All the leading antivirus companies at the time developed fixes for that virus, and it was estimated that the virus caused damage equivalent to $250 million to $1 billion. Not long after that, an email arrived at users’ mailboxes with the subject “ILOVEYOU” and containing a Visual Basic Script attachment (Figure 1-1). This email used social engineering to trick users into opening the attachment. When opened, it exploited a Microsoft Outlook vulnerability, changed the file name extensions, and spread via email using the infected computer contacts. The ILOVEYOU worm infected 50 million computing systems with some impact on many government bodies, intelligence agencies, and military institutions.
CNN-Based Optimal Image Restoration and Comparative Approaches

The paper proposed a comparative study for various image restoration techniques using deep learning models. While we capture an image, there is very high chances that the image might get disturb due to some reasons like shaking of hand makes the image blurry and light intensity makes the image very bright or very dark, the device used to capture the image is not up to the mark to take clear and perfect picture, etc. So, due to these reasons, the quality of image goes degraded, unsatisfactory, and not able to take that moment with accuracy. This problem of capturing or accepting noisy images leads to an increasing requirement for a quick solution. So, to overcome with all these problems and get the perfect and clear image, image restoration is the solution. The paper provides detailed insight regarding restoration techniques, majorly focusing on convolution neural network (CNN). At the end, the paper provides an analysis based on some deep learning techniques to get an optimal choice for restoration of image.