Reviewerly/exHarmony huggingface dataset:
Predicting Solar Radiation Using Optimized Generalized Regression Neural Network

One of the most important components of the hydrological cycle is solar radiation. Three stations in Iran were used to predict monthly solar radiation (SOR) using the optimized generalized regression neural network (GRNN). The Henry gas solubility optimization (HGSO), antlion optimization (ANO), and salp swarm algorithm (SSA) were used to adjust the parameters of the GRNN. Sunny hours had the highest correlation with SOR at all stations. Furthermore, the GRNN-HGSO model outperformed the other methods. At Mazandaran station, the median of observed data, GRNN-HGSO, GRNN-ANO, GRNN-SSA, and GRNN model was 19 MJ m−2, 19 MJ m−2, 19 MJ m−2, 21 MJ m−2, and 24 MJ m−2, respectively. In this study, soft computing models had a high ability to predict SOR in different climates. Using the models of the current study, decision-makers can identify the regions with the highest SRO. These regions are suitable for the construction of power plants.
Antlion Optimization Algorithm

Modelers may encounter multidimensional problems. Some of these problems may have constraints. Solving such problems requires robust models. This chapter explains the structure and mathematical model of the antlion optimization algorithm (ALO). Antlions dig holes in the sand. Their prey is trapped in holes. The ALO uses elitism to maintain the best solutions. The ALO can be applied to solve complex problems. The other optimization algorithms can be coupled with ALO to improve the quality of solutions. Also, the ALO can be used as a robust training algorithm for training soft computing models. The fast convergence and high accuracy are the advantages of ALO.
V.N. AVTOKRATOV AND THE FORMATION OF THE SCIENTIFIC PEDAGOGICAL SCHOOL OF SOURCE STUDIES

The literature about V.N. Avtokratov evaluates his contribution to the theory of archival studies. It is noted that Avtokratov’s approach to the fund formation process as to a particular manifestation of a cause-and-effect relationship is the backbone element of the “general theory of archival studies”. The aim of the present research is to show the enormous positive impact of V.N. Avtokratov’s works on the development of source studies and on the formation of the Scientific and Pedagogical School of Source Studies within the framework of the Department of Source Studies of the Institute for History and Archives at the turn of the 20th-21st centuries. The articles published by Avtocratov in the 1960s – 1970s at that time already were conceptualized and perceived by the scientific community. The appearance at that time of a number of studies based on the analysis of inventories proves to be its striking confirmation. In subsequent years, the staff of the Department of Source Studies of the Institute for History and Archives of the Russian State University for the Humanities proclaimed a thesis about the reflection in the sources system of the past culture projection. The thesis was confirmed in historical and source studies. The efficacy of such an approach to sources has also been demonstrated. The works of that school researchers working within this school deal with the document complexes of various scales.
ARCHIVAL INFORMATION ENVIRONMENT IN THE VIEWS OF V.N. AVTOKRATOV (1922-1992)

The article is about the 100th anniversary of V.N. Avtokratov (1922–1992), a prominent theorist in the field of history and archival studies. The paper reveals his views on the issue of retaining the integrity for the “archival information environment”, set forth in the monograph “Theoretical Issues of National Archival Studies” – the work that was published after his death. Researching the level of the archival information environment and also of the funding according to the principles of historical or formally logical classification, V.N. Avtokratov investigated stages in the archival studies development within the context of the scientific knowledge theory. The article touches upon discussions on the issue within the scholarly community. The analysis of V.N. Avtokratov’s theoretical provisions on the historical principle of classification in archival studies as a determining condition for ensuring the scientific use of the information resource of the Archival Fund of the Russian Federation is correlated with the scientific knowledge theory of the great Russian thinker N.Yа. Danilevsky (1822–1885). Noting the high theoretical level of V.N. Avtokratov’s creative heritage, the author draws attention to the relevance and importance of the methodological approaches by V.N. Avtokratov with regard to the development of the theory and practice of archival science in today’s changed conditions
International Law’s Perspecitve in The Legality of Bitcoin as A Currency

In 2008, an anonymous person calling himself Satoshi Nakamoto created Bitcoin. The basic idea for the creation of Bitcoin came after the 2008 global financial crisis caused by bad housing loans in the United States. Since then, Satoshi Nakamoto has designed a decentralized financial system that will later challenge the hegemony of the centralized financial system of conventional banks that has been used by humans for decades. Bitcoin showed very rapid development both in terms of users and in terms of price, and has been adopted as an official currency countries such as El Salvador and the Central African Republic as well as several large companies such as PayPal, Tesla, and Twitch. Nevertheless, there are no international regulations governing the use of Bitcoin or cryptocurrency as a means of payment in international trade. This paper aims to examine the extent of international regulation of the use of Bitcoin as a means of payment, and also to analyse the rules regarding the legality of Bitcoin in various countries in the world.
The Journey to Hyperscale Architecture in Azure SQL

Every day, many billions or even trillions of transactions are processed by the ever-growing number of Azure SQL databases deployed globally in the Microsoft Azure public and government clouds. The number of transactions and the size of these databases are continuously increasing. The accelerating adoption of public cloud technology has enabled new use cases for Azure SQL databases as well as the migration of larger line-of-business applications from on-premises SQL servers.
Configuring Transparent Data Encryption to Bring Your Own Key

In the previous two chapters, we deployed a Hyperscale database into a virtual network using a private endpoint and learned how to manage it using a management VM and Azure Bastion. In this chapter, we will continue to satisfy the nonfunctional requirements laid out in Chapter 4, by configuring transparent data encryption to use a key managed by our organization.
Cryptocurrency and the Washington Nationals: Once Bitten, Twice Shy?

In May 2022, Jennifer Chen, vice president of strategic partnerships for the Washington Nationals, faced a dilemma. Just months before, the Nationals had embarked on a high-profile sponsorship partnership with Terraform Labs, a decentralized autonomous organization that had created two cryptocurrencies, TerraUSD (UST) and Luna. The Nationals had agreed to accept the stablecoin UST as payment and had emblazoned its ballpark with Terraform Labs' logo, even naming its luxury indoor seating space the Terra Club. But now both coins' value was tanking, and Chen had to scramble to figure out what to do now that the team she represented was tied to a company with a failing offering. Cryptocurrency was a known risk. It was an unpredictable asset for many reasons. Accepting it as payment required businesses to stay on top of fluctuating technology and regulatory policies. Even collateralized cryptocurrency stablecoins backed by "safe assets" were often criticized for a lack of transparency about what exactly those assets were and how safe they could really be. But like a lot of risky investments, it offered potentially very lucrative rewards, especially to sports teams, whose fan profiles often aligned with those of cryptocurrency holders. Many teams other than the Nationals were excited about partnering with cryptocurrency companies—but would the fallout from Terraform Labs' coins change the landscape? And, crucially for Chen, what would the Nationals' relationship with cryptocurrency be going forward?This case is taught at Darden in the Leadership Residency for Darden Executive MBAs; it would also be suitable in a course on stakeholder engagement strategies or on digital strategies.
Reverse Migrating Away from Hyperscale

In the previous chapter, we talked about common migration methods from different database deployments to the Azure SQL Hyperscale service tier. We covered several migration scenarios, various methods, and available migration tools.
Navigating Data Subject Rights in the UK and EU: Understanding the Right to Erasure on Blockchain and the Impact of AI on the Right to Non-Automated Decision-Making

In the digital age, the collection and processing of vast amounts of personal data have made it necessary to establish legal frameworks that protect the rights of individuals. This legal essay provides an overview of the existing data subject rights in the UK and EU, with a focus on the right to erasure on blockchain and the right not to be subject to automated decision-making in respect of artificial intelligence. The paper examines the legal framework surrounding these rights as well as any potential challenges or limitations they may face in practice. The right to erasure on the blockchain is explained, including its benefits and drawbacks, and its interaction with data protection laws in the UK and EU. The right not to be subject to automated decision-making is also discussed, including the legal framework surrounding this right, as well as its relationship with artificial intelligence and data protection laws. The implementation of these rights is essential in safeguarding individuals' privacy and promoting trust in the use of technology. Although there may be barriers preventing individuals from fully exercising these rights, legal remedies such as increased awareness and transparency, as well as changes to the law and harsher consequences for noncompliance, can help to mitigate these obstacles.
The Exemplification Modality for the 'Public Humiliation Type Education' in the Growth Experience of Children

This research explores how Public Humiliation Type Education (PHT-Edu) specifically shapes childhood trauma and its consequences for the trauma representation. In educational future imagination and development, this research integrates the conceptual analysis of heterogeneous narratives, which may be able to provide possibility thinking of guidance to prevent PHT education from happening in educational practice for children.
A Novel Approach for Automatic Avalanche Detection System

Cities worldwide are reaching operational excellence and growing smarter with the introduction of innovative technologies. These technologies, if utilized efficiently, can become assets of tomorrow. Many weather-related natural disasters have increased in frequency and severity in recent times. These natural disasters can devastate the environment, the economy, and life as we know it. Technology, if used as an instrumental asset can enhance our preparedness for emergencies. Utilizing vision-based strategies to prevent natural disasters like avalanches have been one of the significant advancements. A valuable tool for avalanche forecasting and evaluating the efficacy of avalanche prevention measures in low visibility conditions is automated snow avalanche prediction. Automation offers an easy escape from the hassle of continuous human monitoring of avalanche activity and increases detection speed and accuracy. Our solution offers an economic and precise alternative to traditional prediction systems. It takes into account different climatological factors related to snow stability to predict avalanche danger. The system analyses past and present activity to predict if avalanches can be developed. It consists of a thermal imaging camera that collects infrared images of cracked snow. These images are then subsequently examined with the help of a deep-learning image processing model to investigate the likelihood of an avalanche occurring in the crack. The prediction, if positive the authorities can be alerted and further necessary actions can be taken.
Contrast Enhancement of Satellite Image Using Optimization Algorithm

Image enhancement is mostly necessary for satellite or medical pictures. In this paper, we will look at numerous methods for improving satellite images. We may detect minute features in a picture by employing Image Enhancement methods. The photos collected by the satellite are distorted as a result of atmospheric obstacles. To solve these issues, several digital image modification methods such as Edge Enhancement, Contrast Enhancement, Resolution Enhancement, and Brightness Preservation of a picture can be applied. The purpose of satellite image enhancement is to process a picture in such a way that the resultant image has more information than the original image and may be utilized for future study.
The smart body - from cyborgs to the quantified self

This chapter we engages in an exploration of the emergence of the smart body and its associated potential. Through a form of auto-technological ethnography, we draw attention to the lived experience of trying to create a smarter body. While we highlight the darker side of smart-body technology, we take seriously the benefits it can bring and draw attention to how these benefits can be more reliably achieved. This chapter begins by considering the history and nature of smart body technologies. We then move on to consider existing interpretations of these technologies and their consequences for human health and social systems more generally. The main section of this chapter introduces an auto-technology ethnographic. This ethnography recounts the experiences of one of the authors of this volume (Whitehead) as he utilised various smart body technologies over a three-week period. This auto-ethnography offers insights into the positive and negative aspects of smart body technologies from the perspective of the everyday experience of a user.
Lifeskill-Based Interactive Multimedia Training and Assistance to Improve Education Services at SDN Gondang Wetan 1 Pasuruan

Community service is carried out to the teachers of SDN Gondangwetan 1 Pasuruan in the form of lifeskill-based interactive multimedia training and mentoring. The purpose of this service is to improve the understanding and skills of teachers in creating interactive multimedia based on life skills to improve educational services. The implementation method is in the form of activity planning, activity implementation, implementation techniques, monitoring and evaluation and recognition. The results and discussion of this service are an enthusiastic increase in the understanding process and interactive multimedia products based on life skills so as to improve educational services at SDN Gondangwetan 1 Pasuruan.
Adaptation of Learning Technology for Teachers of SD Negeri 2 Pakistaji During the Covid-19 Period

The covid-19 pandemic has had a major impact on various sectors in Indonesia, one of them being the education sector. When online learning begins, teachers must adapt to various strategies and new media to ensure teaching and learning activities continue even when students are at home. But now after learning can be carried out in offline learning, educators need to re-adapt to this learning mode. The Teaching Campus Program is one solution to the problems faced by teachers during offline learning in the New Normal Era. This program aims to assist the adaptation of technology by teachers. This service is held at SD Negeri Pakistaji, Banyuwangi Regency. The activity was carried out for three months and was attended by teachers at SD Negeri Pakistani. The results of this activity examine the teacher's ability to operate digital devices as support.
Under the Radar: Analyzing Recent Twitter Information Operations to Improve Detection and Removal of Malicious Actors, Part 1

Russian information operations (IOs) targeting the U.S. political system have generated a significant amount of attention in recent years. Yet a recently published report that I co-authored argues that this focus on a single threat actor leaves the West open to being blindsided by other, emerging threats. While analysts in the West focused on Russia, other actors like Iran, North Korea, and China were actively conducting their own IOs on social media platforms such as Twitter. In fact, Twitter regularly makes announcements about IO takedowns. The company also makes datasets of tweets posted by accounts tied to these IOs available for researchers. This report analyzes some of the key similarities and differences across three tweet datasets collected from recent IOs in the hope of shedding some light on the possible reach and return on investment gained by malign actors conducting these IOs. Each of the three Twitter datasets focus on a separate country, Russia, China, and Iran, all disrupted by Twitter in 2021. This analysis found that Iran’s IO had the widest reach. This was contrary to, or perhaps because of, the growing attention placed on Russian IOs and, to a lesser extent, Chinese IOs. This analysis also revealed that more accounts do not necessarily yield a greater overall reach for the IO and that a more generalized response mechanism for countering IOs, regardless of country of origin, remains imperative. Lastly, these datasets lend themselves well to further analysis, which I am hopeful will prompt a series of follow-on reports aimed at improving detection and disruption of these operations.
Academic libraries and collaborative research services

"Academic libraries and collaborative research services." Journal of Electronic Resources Librarianship, 35(1), pp. 81–82
A Research on 'The Paradigm Shifts of Marketing from 1.0 to 5.0': with Special Reference to Marketing Shifts in The FMCG Sector

“Marketing management is the art and science of choosing target markets and getting, keeping and growing customers through creating, delivering and communicating superior customer values of management.” Marketing involves the creation and satisfaction of needs and desires. With the recent lifestyle change, the buying behavior of the consumer is also varying. Such variations bring shifts in the marketing paradigms to enhance the effectiveness of the product and its reach. From being commodity focused to Institutional focused to Functional focused to Managerial focused to finally being Socially focused, Marketing goes through a paradigm shift.Objective: The study aims to analyze the shifts in marketing in the field of Fast Moving Consumer Goods. There are many major changes in the strategies for marketing Fast Moving Consumer Goods in such a manner that helps the GDP growth besides the brand's growth. From being product oriented to being technological and socially responsible, the brands change their strategies to promote and sell themselves along with CSR activities.Research Methodology: The research is completely based on secondary data and is exploratory in nature. The method used to find out acceptable research gaps is Content Analysis.Practical Implementation: The findings of the study will help marketers in understanding the shifts in marketing and also identify the current trends that promote brands positively and effectively. It also clarifies the impact of CSR activities on the brand image.
Two-party Secure Computation for an Active Adversary

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Multiparty Secure Computation

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Two-party Secure Computation for a Passive Adversary

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Computationally Secure Encryption and Authentication

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Merging of Machine Learning and Physics

Three areas where machine learning (ML) and physics have been merging: (a) Physical models can have computationally expensive components replaced by inexpensive ML models, giving rise to hybrid models. (b) In physics-informed machine learning, ML models can be solved satisfying the laws of physics (e.g. conservation of energy, mass, etc.) either approximately or exactly. (c) In forecasting, ML models can be combined with numerical/dynamical models under data assimilation.
Probability Distributions

As probability distributions form the cornerstone of statistics, a survey is made of the common families of distributions, including the binomial distribution, Poisson distribution, multinomial distribution, Gaussian distribution, gamma distribution, beta distribution, von Mises distribution, extreme value distributions, t-distribution and chi-squared distribution. Other topics include maximum likelihood estimation, Gaussian mixtures and kernel density estimation.
Decision Trees, Random Forests and Boosting

A decision tree is a tree-like model of decisions and their consequences, with classification and regression tree (CART) being the most commonly used. Being simple models, decision trees are considered ’weak learners’ relative to more complex and more accurate models. By using a large ensemble of weak learners, methods such as random forest can compete well against strong learners such as neural networks. An alternative to random forest is boosting. While random forest constructs all the trees independently, boosting constructs one tree at a time. At each step, boosting tries to a build a weak learner that improves on the previous one.
Classification

Under supervised learning, when the output variable is discrete or categorical instead of continuous, one has a classification problem instead of a regression problem. Several classification methods are covered: linear discriminant analysis, logistic regression, naive Bayes classifier, K-nearest neighbours, extreme learning machine classifier and multi-layer perceptron classifier. In classification, the cross-entropy objective function is often used in place of the mean squared error function.
Neural Networks

Inspired by the human brain, neural network (NN) models have emerged as the dominant branch of machine learning, with the multi-layer perceptron (MLP) model being the most popular. Non-linear optimization and the presence of local minima during optimization led to interests in other NN architectures that only require linear least squares optimization, e.g. extreme learning machines (ELM) and radial basis functions (RBF). Such models readily adapt to online learning, where a model can be updated inexpensively as new data arrive continually. Applications of NN to predict conditional distributions (by the conditional density network and the mixture density network) and to perform quantile regression are also covered.
Basics

A review of basic probability theory – probability density, expectation, mean, variance/covariance, median, median absolute deviation, quantiles, skewness/kurtosis and correlation – is first given. Exploratory data analysis methods (histograms, quantile-quantile plots and boxplots) are then introduced. Finally, topics including Mahalanobis distance, Bayes theorem, classification, clustering and information theory are covered.