Reviewerly/exHarmony huggingface dataset:
Numerical simulation of inverse geochemistry problems by regularizing algorithms

Currently, methods and approaches of scientific visualization based on additional data analysis are being intensively developed due to the rapid development of computer technology in geology. The general concept is that the main data field on the day surface and additional conditions are specified at the input. Further, the methods of mathematical geophysics are used for their analysis and processing, as a result of which new information is obtained for deep exploration. Then visualization tools are applied to the obtained information and main data field in an information system. Thus, the information system is based on the synthesis of visual representation methods, methods of mathematical geophysics, computational mathematics, and various branches of science. This paper presents a description of the geoinformation system module for deep forecast search modeling of deposits developed on the base of the methods of intelligent anomalies detection of hidden deposits. Operation of the software module is based on the application of the theory of mathematical geophysics inverse problems using geological data on the Earth’s surface, geophysical measurements and geochemical analyses as input data. The software module for the inverse problem of the continuation of potential fields in the direction of perturbing masses is used for real data of a particular mineral deposit.
Keynote Speech 1: Blockchain-enabled Trust in Edge-based Internet of Things Architectures: State of the art and Research Challenges

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Internet of Things (IoT) aims to enable a world where physical objects, integrated into information networks, provide smart services for human beings. The introduction of edge computing in IoT can reduce the decision-making latency, save bandwidth resources, and extend the cloud services to be distributed at the edge of the network. However, edge-based IoT systems currently faces challenges in their decentralized trust management. Trust management plays an essential role for reliable data fusion and mining, provisioning of services with context-awareness, and enhanced user privacy and information security. In this talk, we analyze the edge-based IoT architectures that are available in the literature. Then, a comprehensive review of trust requirements in edge-enabled IoT systems is presented. We notably discuss about blockchain as a key enabler to address many trust related issues in IoT and consider closely the complementary interrelationships between blockchain and edge computing. Finally, we provide a detailed analysis of trusted edge computing based IoT systems' performance and recommend promising research directions for future investigations.
Promoting Effective Managerial Communication on Banking Sector in Kathmandu Valley: Cross Sectional Analysis

Purpose: Efficient managerial level is crucial to any organization for well function and betterment of the organization. Likewise, managerial communication is essential to build good relationship among managers to their subordinates. Proper communication in banks further helps to assist employees, creating sense of mutual respect and favorable working environment. This study aims to identify strategy for promoting effective managerial communication in banking sector in Kathmandu valley. Research Design/ Methodology: A descriptive cross-sectional research design has been applied for this study where 405 managers (383 branch managers and 22 operating managers) of commercial Banks in Kathmandu Valley were taken as a sample using structured questionnaire survey using KOBO Toolbox. This study develops and uses Managerial communication index to identify the communication skills of managers in banking communication and decision making. Findings: Managerial Communication Index value shows that managers of banking sector in Kathmandu valley are highly aware of managerial communication in terms of identity factors, negotiation and adaptation, and decision making. Brining clarity while giving speech or talking (69.13%), regularly gathering feedback for improvement (16.54%) and changing behavior and perception when necessary (12.34%) are some of the management strategies identified for promoting effective managerial communication. Conclusion: Hence, the study concludes that bank managers must properly disseminate the information to its stakeholders which will help to convey message effectively leading clarity in the work and enhancing productivity. Recommendations: The paper recommends that there should be inclusive climate in the workplace. Further, policies supporting communication should be forwarded. Originality: This article is original and there is no potential conflict of interest with respect to research and publication of this article.
Fluid Inclusions and Metallogenic Conditions of the Dashuigou Tellurium Deposit, Tibet Plateau, Southwest China

tellurium mineralization is related to Yanshanian alkaline intrusive rocks, while Luo [5,6] believed that the mineralization is related to Yanshanian granitic magma activity.Yin [3,7,8] proposed that scattered elements including tellurium and bismuth originated from gas blown off from the deep Earth and enriched through nano-effect.Wang [9] summarized the metallogenic process of the Dashuigou tellurium deposit as follows: a volcanic eruption deposit was formed on the ancient seafloor with magmatic eruption in the late Proterozoic.Then, the deposit was strongly superimposed and reconstructed by the Mesozoic multistage regional metamorphic hydrothermal activities. Regional GeologyThe Dashuigou tellurium deposit is located in the transitional belt between the Yangtze Platform and Songpan-Ganzi folded belt, as part of the Tibetan Plateau (Figure 1).The deposit is nestled in the convergence between the Indian, Eurasian and Pacific Plates.The crust-mantle structures and properties in the region are the result of tectogenesis through various geological times.It implies the turning boundary of the Earth's crust's thickness.It is also a gravity gradient zone which controls not only the production and development of earthquakes and tectonomagmatic events, but also the distribution of a series of mineral deposits.Geophysical data indicates that the upper mantle below the region uplifts obviously.As a result, the area possesses high heat flow geophysical characteristics [3,5,6,10,11].
Covert Distributed Training of Deep Federated Industrial Honeypots

Since the introduction of automation technologies in the Industrial field and its subsequent scaling to horizontal and vertical extents, the need for interconnected industrial systems, supporting smart interoperability is ever higher. Due to this scaling, new and critical vulnerabilities have been created, notably in legacy systems, leaving Industrial infrastructures prone to cyber attacks, that can some times have catastrophic results. To tackle the need for extended security measures, this paper presents a Federated Industrial Honeypot that takes advantage of decentralized private Deep Training to produce models that accumulate and simulate real industrial devices. To enhance their camouflage, SCENT, a new custom and covert protocol is proposed, to fully immerse the Federated Honeypot to its industrial role, that handles the communication between the server and honeypot during the training, to hide any clues of operation of the honeypot other that its supposed objective to the eye of the attacker.
Decentralized IoT Platform for Flexibility Service Providers in Power Systems

The power system is undergoing a fundamental transformation to become low-carbon and sustainable, and to reduce its adverse impact on the climate. To meet this challenge, power generation is shifting towards intermittent renewable energy sources such as wind and solar, leading to an increasing demand for additional flexibility on the side of end consumers who are becoming active participants in electricity markets. These goals imply that traditional electricity suppliers will have to upgrade their services and models or be replaced by a new type of supplier - aggregator. An aggregator will allow end consumers to be active and adaptive through a variety of price signals, such as dynamic pricing, consumption adjustment, reward fees, etc. In this paper, we present an architecture for a platform envisioned as a decentralized market for flexibility service providers in the power system, based on Internet of Things (IoT) and distributed ledger technologies. The platform will enable controllable and verifiable interaction of aggregators with end consumer devices in a trustless environment, as well as functionalities such as secure data exchange, search, history analysis, flexibility, and reputation management. The technical feasibility of implementing such a platform is demonstrated using the IOTA network.
Artificial speech detection using image-based features and random forest classifier

The ASVspoof 2015 Challenge was one of the efforts of the research community in the field of speech processing to foster the development of generalized countermeasures against spoofing attacks. However, most countermeasures submitted to the ASVspoof 2015 Challenge failed to detect the S10 attack effectively, the only attack that was generated using the waveform concatenation approach. Hence, more informative features are needed to detect previously unseen spoofing attacks. This paper presents an approach that uses data transformation techniques to engineer image-based features together with random forest classifier to detect artificial speech. The objectives are two-fold: (i) to extract image-based features from the melfrequency cepstral coefficients representation of the speech signal and (ii) to compare the performance of using the extracted features and Random Forest to determine the authenticity of voices with the existing approaches. An audio-to-image transformation technique was used to engineer new features in classifying genuine and spoof voices. An experiment was conducted to find the appropriate combination of the engineered features and classifier. Experimental results showed that the proposed approach was able to detect speech synthesis and voice conversion attacks effectively, with an equal error rate of 0.10% and accuracy of 99.93%.
Exploring narrative fiction: corpora and digital humanities projects

The corpus linguistic study of narrative fiction is not a simple application of existing corpus methods to just another set of texts. It requires consideration of the properties of the texts under analysis, as well as the nature of the questions that can be addressed. The focus of this chapter is on novels as a specific type of narrative fiction. The chapter is particularly concerned with how corpus methods can be used to study novels as fiction, i.e. with an emphasis on the fictional worlds in the texts rather than exclusively on the linguistic features that define a register compared to other registers. The chapter outlines a variety of approaches to fiction by relating corpora to other digital resources and considers how to narrow down a starting point for a corpus linguistic study. To understand what corpus linguistics can do for the study of novels, the chapter reflects on what is special about narrative fiction and discusses patterns and functions of the verb form looking as an example of body language descriptions of fictional characters. The chapter concludes by considering directions for the future of corpus research and its relationship to the wider digital humanities.
Solving a traveling salesman problem using meta-heuristics

In this article, we have introduced an advanced new method of solving a traveling salesman problem (TSP) with the whale optimization algorithm (WOA), and K-means which is a partitioning-based algorithm used in clustering. The whale optimization algorithm first was introduced in 2016 and later used to solve a TSP problem. In the TSP problem, finding the best path, which is the path with the lowest value in the fitness function, has always been difficult and time-consuming. In our algorithm, we want to find the best tour by combining it with K-means which is a clustering method. In other words, we want to divide our problem into smaller parts called clusters, and then we join the clusters based on their distances. To do this, the WOA algorithm, TSP, and K-means must be combined. Separately, the WOA-TSP algorithm which is an unclustered algorithm is also implemented to be compared with the proposed algorithm. The results are shown through some figures and tables, which prove the effectiveness of this new method.
Federated Learning for an IoT Application

The Internet of Things (IoT) is having a great impact in different areas of our modern life. Various IoT applications and services are getting evolved day by day. In parallel, artificial intelligence (AI) is a rapidly growing technology. AI makes our system execution smart. A subdomain of AI is known as machine learning (ML). ML focuses on extracting knowledge from the data. Federated learning (FL) is a branch of ML. FL aims at training a machine learning program. The training data needs to be centralized in case of ML. This is considered a drawback in an IoT environment because IoT works in a highly distributed manner. FL is best suitable here because it takes the learning of different devices executing under different applications and running under a single IoT environment. These may be dependent or independent. The FL needs to unify the outcome of heterogeneous applications before applying any rule. This chapter will cover major challenges that come in the way of FL for intelligent IoT applications.
Federated Machine Learning with Data Mining in Healthcare

Electronic medical records (EMRs) uphold the advancement of machine learning calculations for predicting malady occurrence, understanding reaction to therapy, and other medical care occasions, as machine learning (ML) is a data-driven technique which can help build accurate and robust statistical models from electronic medical data, which can be collected in huge volumes as well due to ever-growing population of the world. Privacy concerns or being stuck in a data silo is causing inaccessibility to the researchers for the EMRs, which is potentially harming the learning of the aforementioned ML models. This chapter is about a study of how federated learning can be used on fewer datasets and still make accurate and robust statistical models. Apart from the study on how federated ML can solve the situation, the federated ML technique has been extensively studied in this chapter.
Minerals Matter: Science, Technology, and Society

The expertise of earth scientists is crucial for solving some of today's most pressing challenges, including global warming and the climate crises, natural hazard reduction, water and soil quality, exploration for critical elements for our technological society, and many others.
Detection and Implementation of Blood Group and Hb Level by Image Processing Techniques

In an emergency, an urgent blood transfusion from a person to the patient is required and blood group identification is the first process to do so. In addition, a hemoglobin test is often required to make decisions about blood transfusion as well as to check anemia. Hemoglobin testing is also required for complete blood count and monitoring a number of diseases. These blood tests are almost difficult in rural areas where lab facilities are not sufficient. Researchers proposed a number of methods to identify blood groups using computer vision techniques. However, no study was conducted to identify blood group and hemoglobin level in a work using image processing techniques and an android mobile application which shows high detection accuracy. In this paper, manual clinical experiments have been replaced by an android app using image processing techniques to detect blood groups and hemoglobin levels except users require using antigen before taking samples. The proposed technique is divided into two portions. The first portion is blood group detection, which is done by taking a blood sample and performing the grayscale conversion, binary conversion, segmentation, edge detection, and computation to make the decision. The second section describes how to determine hemoglobin levels by comparing a blood sample image to a hemoglobin color scale (HCS). Here, the Hemoglobin value is determined from their RGB values. It has been discovered that the proposed approaches are capable of detecting hemoglobin levels and blood groups in a cost-effective and error-free manner. As a result, the tests can be conducted in a remote area without adequate lab facilities and the proposed work can solve major steps in blood transfusion difficulties and anemia.
Comparative Study of Short-Term Photovoltaic Power Generation Forecasting Methods

An accurate forecasting scheme is essential to optimize efficiency when planning a distribution grid with a photovoltaic system. This article proposed a comparative study of the efficiency and advantages between supervised photovoltaic power forecasting methods that can be appropriately applied to various systems. For comparative study, Feedforward Neural Network (FNN) was compared with Long Short-Term Memory (LSTM) and Nonlinear Autoregressive with External Input (NARX) that use the previous output as input via MATLAB program. The forecasting dataset consists of time, cell temperature, irradiance, and power output from the PV system in 8,760 samples (for each hour). The dataset was divided into three parts following: (1) training set is 70%, (2) validating set 20% and (3) testing 10 % The simulation results show that, compared to the three methods, LSTM had the highest accuracy, followed by FNN and NARX, respectively. However, NARX is closer to the peak at high temperatures and irradiance. For data set analysis, the difference in power output ranges between the training and test sets affects forecasting accuracy.
Volunteering as One of the Ways of Developing Engineering Students’ Soft Skills

The paper discusses the involvement of engineering students in volunteering and skills they acquire as a result. The authors talk about the soft skills in the context of volunteering and how it can actually help students’ professional life. Before COVID-19, student’s volunteering included cleaning up parks or visiting the elderly and orphanages. While more than 90% of schools and universities around the world were closed, students of Kazan National Research Technological University continued developing their skills and education. In lockdown period students were involved in new ways of community volunteering as masks sewing, food supply, shopping for the elderly and more. With the global social distancing measures, they took volunteering online. Student’s initiative was mobilizing their groups to support their communities in dealing with COVID-19. There are many skills that can be learned through volunteering such as teamwork, creativity, self-management, work ethics, flexibility etc. While hard skills are necessary for completing technical tasks, soft skills will make you the kind of worker employers want to hire, keep and promote. Students succeeded in developing their soft skills during this challenging time. The experimental work aimed at checking the effectiveness of the integrated volunteer activities to develop soft skills and it was proved by a comparative analysis of the data obtained at the initial stage of the study and after its completion. The study involved 85 engineering major Bachelor students from engineering university.
A Novel Hybrid Clustering Analysis Based on Combination of K-Means and PSO Algorithm

Clustering is an unsupervised data grouping or data mining technique that group data based on similarity and dissimilarity. Hidden patterns provide the basis for developing judgments, and these hidden patterns may be detected by utilizing the Clustering methods. In this work, a hybrid clustering analysis based on the grouping of K-means and the PSO method is presented for performing the clustering process. K-means is commonly used for clustering since it is simple to implement. However, it cannot provide better outcomes; therefore it may be used with the other clustering approach, Particle Swarm Optimization (PSO). By using primary-world datasets, this approach can be solved. Based on the clustering rationality measures, the comparison is accessed between the traditional clustering technique and proposed method. Some of clustering factors are Sum of Squared Error (SSE), quantization error and silhouette index. In the comparison, the proposed system gives more improved results than the K-means, PSO technique.
Quartz Ti/Ge-P discrimination diagram: A machine learning based approach for deposit classification

石英的微量元素记录了石英生长的物理化学条件。通过微量元素对石英原岩进行分类的研究历史已久，经典工作是在以微量元素为坐标轴的图解上绘制各类型石英的分布范围，以区分石英类型。经典图解包括Rusk （2012）提出用于区分三种矿床类型石英的Al-Ti二元图解，和Schrön et al.（1988）提出的用于判别不同岩浆岩类型石英的Ti-Al-Ge三元图解。越来越多的研究表明，上述图解不能满足对更多石英类型进行分类的需求，同时也出现与部分已知产状类型的石英微量元素判别相矛盾的情况。随着石英原位微区测试方法的成熟，高精度石英微量元素数据逐渐丰富为系统开展机器学习提供了大数据基础，为石英微量元素研究提供了新的角度和可能性。本研究运用机器学习分类方法对石英微量元素进行精确数学分析，提出Ti/Ge-P图解为石英成因研究提出新的地球化学指标。本文同时测试了六种经典机器学习分类算法，提高Ti/Ge-P图解在石英成因分类研究上的精度。此Ti/Ge-P图解适用于多种矿床研究，包括但不局限于斑岩型矿床、矽卡岩型矿床、浅成低温热液型矿床、卡林型矿床以及造山型矿床中的石英。这项工作是大数据技术与机器学习技术在地球化学研究中的积极探索。;Trace elements in quartz record quartz physical and chemical growth processes, which so far has been an important tool to investigate geological settings associated with quartz-forming environments. The classic method is to draw the distribution range of various types of quartz on a diagram with trace elements as the coordinate axis. Classical diagrams include the Al-Ti binary diagram of Rusk (2012) distinguishing three-type ore deposits, as well as the Ti-Al-Ge ternary diagram of Schrön et al. (1988) distinguishing quartz from various magmatic rocks. However, these studies cannot fit well with some known types of quartz trace element data, and they in addition cannot support scholars to classify more quartz types. With the maturity of micro-area testing technology, quartz trace element data is gradually enriched. To address it, here we use programming methods to exhaustively combine several elements and their ratios in the published quartz trace element data. Then we design an algorithm to select the best axis, which turns out to be Ti/Ge-Al diagram, for distinguishing quartz from different deposit types. On the basis of the Ti/Ge and P axes, six machine learning classification algorithms were trained and compared, and the best classification algorithm was selected to predict the decision boundary. The Ti/Ge-P diagram is therefore proposed to be used to identify quartz in porphyry deposits, skarn deposits, epithermal deposits, Carlin deposits, and orogenic deposits. Our research is a great active exploration of data technology and machine learning technology in hydrothermal geochemistry of mineral deposits.
Scholar-Course Knowledge Graph Construction Based on Graph Database Storage

Knowledge graph is an effective way to model and represent complex linked data, which have attracted broad research in recent years and have been applied in different fields. Considering the data characteristics and development needs of course platform in SCHOLAT, Scholar-Course Knowledge Graph (SCKG) is built with scholars and courses as the core concept and integrated it into the next version of our course platform. The ontology structure of SCKG is constructed first and then extracted knowledge from different data sources by employing D2R technology, web crawlers, etc. so as to add them to SCKG. There are 110,856 entities and 1,674,961 pairs of relationships in total after the construction of SCKG. 13 b-tree indexes and 3 full-text indexes are created on some key properties to speed up the query and we also defined some constraints on SCKG to ensure data consistency.