Reviewerly/exHarmony huggingface dataset:
Mineralogical and Geochemical Characteristics of Lead-zinc Ore Deposits, and Potential Accompanying Components in the Cho Don - Cho Dien Area, Bac Kan Province, Vietnam

The Pb-Zn mineralization in the Cho Don - Cho Dien ore districts often occurs in 2 types: (1)oxidized ore near to the surface and (2) sulfide ore at deeper section. Based on microscopic observations,sulfide ores can be divided into sphalerite-galena-pyrite and/or galena-sphalerite mineralization types. Toexamine the geochemical features of the Pb-Zn ores, SEM-EDX and ICP-MS analytical methods wereperformed in this study. Previous δ34S data of Pb-Zn concentrates, and sulfide minerals from variousdeposits suggest that the Pb-Zn ore-forming fluids might be related to the felsic-granitic magmaticactivities rather than a genesis of stratiform type. Geochemical data show that the major, minor, and traceelement compositions of lead-zinc ores have wide ranges of variation even in each deposit. The sulfideores are generally higher in economic components than those in the oxidized ores. The positivecorrelations between Pb-Ag can be found in the entire dataset, whereas excellent Zn-Cd correlation canonly be observed from Cho Don ore samples. Apart from the principal components (Pb and Zn), the oresalso contain other accompanying elements that supply high-technological manufacturing industries. Ofwhich As, Cu, Ag, Sb, and Cd could be potential by-products and can be extracted during smelting Pb/Znconcentrate processes, and need more detailed studies for every deposit.
Sharing of encrypted lock keys in the blockchain-based renting house system from time- and identity-based proxy reencryption

To design an efficient protocol for sharing the encrypted lock keys in the renting house system, we introduce a new notion called time- and identity-based proxy reencryption (TIPRE) and the blockchain platform. Our CPA secure TIPRE scheme is constructed from Green et al.'s identity-based proxy reencryption scheme by adding the time property. In every time period, a time stamp authority generates a public key embedded with the current time stamp for each user. In our protocol for the renting house system, the TIPRE scheme is the primary building block, and the blockchain platform serves instead of a trusted third party, such as a real estate agency between landlords and tenants. The TIPRE scheme allows the landlord to change the lock key at each time period for safety. The blockchain platform allows the landlords and tenants to directly interact, and all of the interactions are recorded in the blockchain database to provide the desired security requirements, such as non-repudiation and unforgeability. Finally, we provide the secure analysis of our protocol and test its performance by implementing it in the MacBook Pro and the Intel Edison development platforms.
An efficient signcryption scheme for vehicular satellite-based networks

With the widespread of the vehicular ad-hoc network (VANET), a huge number of vehicles are connecting to networks. To provide the position of these vehicles, the global position system (GPS) is required. Usually, the GPS is commoned with internal sensors mounted inside the vehicle. Thus, the communication with this sensor is needed when we need to specify the position of vehicle through the satellite. This communication is done by using a secure channels. However, the authentication and privacy are deemed as the main goal of the network communication. Therefore, an efficient signcryption scheme for vehicular satellite-based network (SVSN) is proposed in this paper. The proposed scheme meets the security demands for VANETs, for instance authentication, unforgeability, confidentiality, and integrity. Based on the Discrete Logarithm (DL) problem, the presented scheme is secure. Compared with the existing signcryption schemes, the performance analysis show that our proposed scheme is more suitable for vehicular satellite networks.
Actual Infinity: A Pseudo-problem or a Meta-Foundation of Western European Philosophy and Science?

The author of the article reveals the theological context of the origin of the concept of actual infinity and clarifies the problem of actual infinity. The author shows that this problem is not a paradoxical category of thinking, but a problem of the unity of two realities (eternal, unchanging and infinite, and temporary, changeable and finite), which has been misunderstood. The author raises the question of the relevance of the problem of actual infinity brought by Christianity for modern secularized science and philosophy. The author shows that the problem of the unity of the two realities was declared much earlier than Christianity. This problem was already dealt with by the ancient Eleans. They initiated the one-sided view and incorrect understanding of this problem, which opened the main path of development of the entire Western European philosophy. With the advent of Christianity, all the dangers identified by the Eleans (and above all by Zeno) and then still unclear on this path received a new sharpness and now real force. The author of the article shows that the regularity of the relation of the finite, the actually infinite, and the potentially infinite, revealed by Zeno, was the basis for changing the classical rationality to the non-classical one. In turn, the fact of the collapse of the classics has become evidence of modernity that the problem of actual infinity is not a mental paradox, but contains the real possibility of changing the finite nature. But this change is not carried out in the direction suggested by the recognition of actual infinity itself, but in another direction, opposite to it, but closely connected with it. The disclosure of the essence of this connection will be the disclosure of the problem of actual infinity.
Database Building, Recognition, and Categorization of Handwritten Kannada Words Using Convolution Neural Networks

This work focuses on building the database for handwritten Kannada words and makes it open source as there are no publicly available datasets online. The dataset can be used as a benchmark dataset to evaluate the performance of various recognition, feature extraction methods, and the results of various researchers. The dataset is rich in variations in writing styles because the data is collected from 370 different people. The words are extracted by the projection profiles. Experimentation is done to recognize the words using convolution neural networks (CNN) with an accuracy of 93.20%, and it is followed by category recognition with an accuracy of 91.90. An exhaustive dataset of 65,378 words and 11 categories is built with experimentation
Pathology: the next chapter

It is with great pleasure that I ‘take the baton’ from Professor Brett Delahunt as Editor of Pathology from the next issue of the journal. Professor Delahunt (Fig. 1) has led the journal through a remarkable period in its history; that it is now recognised internationally as a leading journal in the field is testament to his leadership, creativity, innovation and hard work. After a period of such success, I look forward to the challenge of maintaining and building the momentum and working with the editorial team to continue the high standards of Pathology. There are challenges and opportunities ahead which will inevitably lead to changes in the journal as it continues to evolve in future years.
A key inequality for lower bound formulas for lattice event probabilities

We introduce and discuss some key inequalities that underlie the lower bound formula for the probability of lattice events in the Levin-Robbins-Leu family of sequential subset selection procedures for binary outcomes. The present work combines the notion of lattice events—as previously discussed for the nonadaptive member of the family—with the positive cumulative sum property for the adaptive members—as previously discussed for the special lattice event of correct selection, thereby extending the key inequality to its broadest scope.
BARRIERS IN DEVELOPMENT OF SMART EDUCATION: SOCIO-CULTURAL PECULIARITIES OF RUSSIA

Smart Education is the most advanced stage of the evolution in education associated with new information and communication technologies. The world is forming a new technical and technological reality. Digital media technologies (smartphones, wi-fi connections) penetrate into open spaces of cities and towns (parks, gardens, etc.). Modernity dictates the need for a person to acquire such knowledge and skills that should allow him not only working effectively, but also spending his free time productively. This situation is not entirely new, but it makes researchers find solutions for further implementation of smart technologies in the education system in order to search for the most appropriate answers to this challenge. Russia needs to accelerate the transition to education of the future, which involves the modernization of all educational processes. The presented research touches upon the issue of the development of Smart Education in Russia. The paper reveals the current state and identifies the key barriers in the development of smart education in the world. The empirical basis of the work is a sociological survey conducted in March-June 2018. The survey used the technique of semi-formalized interviews with representatives of five groups of stakeholders: government officials, heads of organizations, teachers, students and researchers. The educational system of Tomsk (Russia) affects the interests of all above mentioned stakeholders. The survey was aimed at the determination of the role of Smart technologies in education, their importance, barriers and prospects of implementation. The article presents the results of a sociological study that revealed the position of stakeholders in relation to various aspects of the socio-cultural environment that has an impact on the formation of smart education in Russia. The evaluation of the results of the implementation of smart city and smart education projects in Europe, America and Asia shows the presence of private success, which has not led to the formation of unambiguously positive examples of their implementation. Synergy, which unites the efforts of many participants of these projects, has not yet led to the realization of the ultimate goal – significant indicators in improving the quality of life of citizens. The comparative analysis of barriers to further development of smart education identified in the studies of foreign authors with the assessment of Russian stakeholders shows the existence of significant differences between them. The introduction of smart technologies in the life of Russian society is catching up. This leads to a rather superficial nature of acquaintance of Russian society of smart education and a lack of reflection on the barriers to its implementation.
Photonic schemes of distribution and reconstruction of an entangled state from hybrid entanglement between polarization and time-bin via quantum dot

Abstract We propose photonic schemes for the distribution and reconstruction of a two-qubit entangled state using a hybrid entangled state under a noisy quantum channel. First, to generate a hybrid entangled state correlated with polarizations and time-bins, we employ a quantum dot (QD)-cavity system (nonlinear optical gate) and linear optical devices to implement controlled operation. These schemes can achieve the distribution and reconstruction of a two-qubit entangled state from hybrid entanglement by utilizing only linear optical devices without a QD-cavity system (i.e., a nonlinear optical device) for users who want to share an entangled state under a noisy quantum channel. For a feasible realization of the proposed schemes, we analyze the interaction between the photons and QD-cavity system and demonstrate the experimental conditions under which the reliable performance of the QD-cavity system is achieved.
Recognition of Ancient Tamil Characters from Epigraphical inscriptions using Raspberry Pi based Tesseract OCR

Optical Character Recognition (OCR) is the process of identification of the printed text using photoelectric devices and computer software. It converts the inscribed text on the stones into machine encoded format. OCR is widely used in machine learning process like cognitive computing, machine translation, text to speech conversion and text mining.OCR is mainly used in the research fields like Character Recognition, Artificial Intelligence and Computer Vision. In this research, the recognition process is done using OCR, the inscribed character is processed using Raspberry Pi device on which it recognizes characters using Artificial Neural Network. This work mainly focuses on the recognition of ancient Tamil characters inscribed on stones to modern Tamil characters belong to 9th and 12th century characters. The input image is subjected to gray scale conversion process and enhanced using adaptive thresholding process. The output image is subjected to thinning process to reduce the pixel size of the image. Then the characters are classified using Artificial Neural Network Architecture and the classified characters are mapped to modern Tamil character using Unicode. The Artificial Neural Network has input layer, hidden layer of 15 neurons and output layer of 1 neuron to classify the characters. The accuracy of the constructed system for the recognition of epigraphical inscriptions is calculated. The above process is carried out in raspbian environment using python process.
Introduction

The Introduction to <italic>Sonic Overload</italic> explains the motivations for the book and sets in play its general themes. Taking stock of the contemporary overabundance of information, the introduction asks how we reached this point. It focuses on the late USSR for an answer by first looking at paradoxical accounts about information overload in the Soviet Union of the 1970s and early 1980s. Valentin Silvestrov and Alfred Schnittke serve as guides for considering further how information overload affected and was affected by music in the USSR. Schnittke’s and Silvestrov’s evocations of the past range across a spectrum from overmuch to not enough. Each composer engaged with overabundance, using music as a means to articulate a sense of self amid the often overwhelming sensations of too much. The introduction presents the main premises of the book by defining polystylism and style before tying style to fundamental senses of identity, purpose, and meaning both within and against society. The remainder of the introduction discusses the overall argument of the book, from embracing to rejecting polystylism, as well as the contents of each chapter and its role in the ongoing narrative.
Discovering Relative High Utility Itemsets in Very Large Transactional Databases Using Null-Invariant Measure

High utility itemset mining is an important model in data mining. It involves discovering all itemsets in a quantitative transactional database that satisfy a user-specified minimum utility (minUtil) constraint. MinUtil controls the minimum value that an itemset must maintain in a database. Since the model evaluates an itemset’s interestingness using only the minUtil constraint, it implicitly assumes that all items in the database have similar utility values. However, some items have high utility, while others may have relatively low utility in a database. If minUtil is set too high, the user will miss all itemsets containing low utility items. To find itemsets that involve both high and low utility items, minUtil has to be set very low. However, this may cause a combinatorial explosion as the items with high utility may combine with others in all possible ways. This dilemma is called the low utility item problem. This paper proposes a flexible model of relative high utility itemset to address this problem. We introduce a new null-invariant measure, called utility ratio, to evaluate the interestingness of an itemset in the database. We also present a fast single scan algorithm to find all desired itemsets in the database. Experimental results demonstrate that the proposed algorithm is efficient. Finally, a case study on Yahoo! JAPAN retail data shows that the proposed model is useful.
Mass Testing With Contact Tracing Compared to Test and Trace for the Effective Suppression of COVID-19 in the United Kingdom: Systematic Review (Preprint)

<sec> <title>BACKGROUND</title> Making testing available to everyone and tracing contacts might be the gold standard to control COVID-19. Many countries including the United Kingdom have relied on the symptom-based test and trace strategy in bringing the COVID-19 pandemic under control. The effectiveness of a test and trace strategy based on symptoms has been questionable and has failed to meet testing and tracing needs. This is further exacerbated by it not being delivered at the point of care, leading to rising cases and deaths. Increases in COVID-19 cases and deaths in the United Kingdom despite performing the highest number of tests in Europe suggest that symptom-based testing and contact tracing might not be effective as a control strategy. An alternative strategy is making testing available to all. </sec> <sec> <title>OBJECTIVE</title> The primary objective of this review was to compare mass testing and contact tracing with the conventional test and trace method in the suppression of SARS-CoV-2 infections. The secondary objective was to determine the proportion of asymptomatic COVID-19 cases reported during mass testing interventions. </sec> <sec> <title>METHODS</title> Literature in English was searched from September through December 2020 in Google Scholar, ScienceDirect, Mendeley, and PubMed. Search terms included “mass testing,” “test and trace,” “contact tracing,” “COVID-19,” “SARS-CoV-2,” “effectiveness,” “asymptomatic,” “symptomatic,” “community screening,” “UK,” and “2020.” Search results were synthesized without meta-analysis using the direction of effect as the standardized metric and vote counting as the synthesis metric. A statistical synthesis was performed using Stata 14.2. Tabular and graphical methods were used to present findings. </sec> <sec> <title>RESULTS</title> The literature search yielded 286 articles from Google Scholar, 20 from ScienceDirect, 14 from Mendeley, 27 from PubMed, and 15 through manual search. A total of 35 articles were included in the review, with a sample size of nearly 1 million participants. We found a 76.9% (10/13, 95% CI 46.2%-95.0%; &lt;i&gt;P&lt;/i&gt;=.09) majority vote in favor of the intervention under the primary objective. The overall proportion of asymptomatic cases among those who tested positive and in the tested sample populations under the secondary objective was 40.7% (1084/2661, 95% CI 38.9%-42.6%) and 0.0% (1084/9,942,878, 95% CI 0.0%-0.0%), respectively. </sec> <sec> <title>CONCLUSIONS</title> There was low-level but promising evidence that mass testing and contact tracing could be more effective in bringing the virus under control and even more effective if combined with social distancing and face coverings. The conventional test and trace method should be superseded by decentralized and regular mass rapid testing and contact tracing, championed by general practitioner surgeries and low-cost community services. </sec>
Manifold Regularized Convolutional Neural Network

The state-of-the-art performance of deep learning models is achieved at the expense of large data sets, computational costs, and overcoming the overfitting problem. These requirements may be alleviated by complementing the learning of network parameters with representational features obtained via manifold learning. This paper investigates the effect of regularizing convolutional neural networks (CNN) with a nonlinear embedding of convolutional response features. A locally linear embedding (LLE) is used to learn the manifold of the convolutional features before the fully connected network performs image classification tasks. Our six experimental cases reveal consistent benefits of introducing manifold regularization over using a baseline CNN model in image classification tasks. After several epochs of training, we introduce the manifold regularization in the objective function after stopping the update of the lowest convolutional layer parameters, which yields the best areas under the receiver operating characteristic curves (AUC) with the CIFAR natural image data set (AUC: 0.920) and a medical imaging data set for macular disease classification (AUC: 0.993) when compared to their performances with the baseline CNN model without manifold regularization (AUC: 0.899 and AUC: 0.975), respectively. The performance improvement over the baseline CNN model is observed across all individual object classification, and it additionally recommends an early stopping of the lowest convolutional layer parameter updates during model training. The performance improvement observed in both global shape (object classification) and local disease pattern (medical imaging) classification is promising to guide the development of more efficient alternatives to regular deep parameter learning.
The nexus between corruption and opportunistic crimes during Covid-19 in South Africa

Covid 19 in South Africa created opportunity for criminals to enrich themselves at the expense of the poor. Public and private sector officials benefited due to irregular tenders and overpricing of personal protective equipments in South Africa as reported on media platforms. This article explores opportunistic crimes associated with Covid 19 and their impact on the fight against the pandemic. This qualitative article adopted a non-empirical research design: Systematic review, indirect observation schedules to identify and describe available research literature ‘using systematic and explicit accountable methods and pre-specified formalised tools for searching and integrating literature.&#x0D; The data was collected from January-July 2021. The collected data was analysed through inductive textual content analysis. Findings revealed that public officials benefited from irregular PPE tenders as well as friends and families of politicians. The findings further indicate that billions of Rands were looted from funds which were meant to fight Covid 19 in South Africa. Lastly the findings show that law enforcement agencies are not effective in dealing with cases of Covid 19 crimes. Based on the findings, the following recommendations were developed: Law enforcement agencies should be equipped with resources to deal with Covid 19 crimes and ensure successful prosecution of those crimes, Competition Commission should investigate the companies which benefited from irregular tenders and overpricing of PPE’s so that they could be held accountable. Public participation should be strengthened to combat crime.
Data representation via refined discriminant analysis and common class structure

The main goal of discriminant embedding is to extract features that form a compact and informative representation of the original feature set. In this paper, we propose an improved hybrid method aimed at extracting linear features for supervised multiclass classification. We implement a unifying criterion that is able to preserve the benefits of robust sparse linear discrimination along with inter-class sparsity. The expected transformation involves two forms of discrimination, namely: common class or group sparsity in addition to robust discriminant analysis with feature ranking. For the purpose of solving the proposed criterion, an iterative alternating minimization framework is used to evaluate the linear transformation and orthogonal matrix. The presented scheme is generic enough that it can be used to consolidate and tune several linear embedding methods. In the light of experiments conducted on various image datasets with different types, the suggested scheme was able to outperform other methods in most cases.