Reviewerly/exHarmony huggingface dataset:
Mineralogical and geochemical features of oxidized zone formation

In this paper we present the results of an investigation of the Salair deposit oxidized zone. Barite-sulfide ore bodies were formed in the Early Cambrian. Later during Permian tectono-magmatic activation of these ore bodies were cross-cut carbonate gold-silver stockworks. The oxidized zone is evident to a depth of 130m. Complexly constructed basement and variable physico-chemical conditions during element migration give rise to the formation of secondary ore bodies which have sharply varied composition. Massive smithsonite lenses are present. Significant quantities of silver are present in iron hydroxide accumulations. Gold content increases in the barite sypuchka and cementation zone. Physics-chemical modeling of the mobility of a large group of metals (Pb, Zn, Cu, FE, Au, Ag) was based on the results of mineralogical and geochemical investigations. The general relationships between some of the common primary and secondary minerals, of the composition of ores and wall-rocks and the surface water regimes.
Foreword to the special section on Computer Graphics education in the time of Covid

• Challenges and needs to enhance the computer graphics learning experience. • Strategies for effective mentoring and evaluation at distance. • Guidelines for teaching computer graphics in a hybrid synchronous context. • Educational computer graphics material in the time of pandemic for visual computing novices. • GPGPU through the development of cloud-based path tracer on inexpensive H/W at home.
Analysis of Smartphone Application Mastery in Distance Learning for Deaf Children in Secondary School

Abstract: Distance learning is a learning model that is applied as one of the policies of the world government in the field of education to break the chain of the spread of the covid-19 virus. Many problems occur during distance learning, including in special education, one of which is deaf children, where deaf children have barriers in communicating but must follow learning without meeting in person in the classroom. This study aims to find out what information technology is most mastered by deaf children as a means of supporting distance learning. This research was conducted on deaf children at the high school level in extraordinary schools that during the covid 19 pandemic the school implemented an online learning system. This study uses descriptive analysis with the questionnaire filling method. Data collection is done by disseminating questionnaires containing questions related to the mastery of applications on smartphones aimed at deaf children. Furthermore, the data is analyzed using inductive and thematic analytics. Dai this research can be known that from the use of Whatsapp Application as a means of communication used to receive distance learning materials is 94.74%, zoom meeting application mastery rate 0.00%, then youtube application mastery level in receiving information 100.00%. And the most preferred time for deaf children to learn is free, which is 65.79%. So it can be concluded that the smartphone application that is most mastered by deaf children in high school in learning and receiving information is a Youtube application and the preferred time for deaf children in learning is free or unspecified. Keywords: Mastery of Smartphone Applications; Deaf Children; Distance Learning
3D MSSD: A multilayer spatial structure 3D object detection network for mobile LiDAR point clouds

Note: Since the pseudo-image of each level is processed by the point cloud of the corresponding layer, in order to better reflect the corresponding relationship, we use the same color and different shapes (rectangle, cuboid) in the legend to represent the point cloud of the corresponding with pseudo-image. • 3D-MSSD retains the three dimensional structural characteristics of point clouds. • Our method is proposed to improve the accuracy of 3D object detection. • An experiment based on the KITTI shows that our model can efficiently detect object. Point cloud-based object detection is vital and essential for many real-world applications, such as autonomous driving and robot vision. The PointPillars model has achieved the efficient detection of objects in front of a vehicle. However, the algorithm does not consider the spatial structures semantic information stored in the three-dimensional point cloud for a given spatial structure, thus leading to missed or false detections for objects with complex spatial structures or singular structures. We propose an approach based on PointPillars, which considers the spatial structure characteristics of 3D point clouds to enhance the detection accuracy. First, based on the specified range of the z-axis coordinates, the entire point cloud scene is divided into several layers so that the point cloud areas in the same height interval form one layer. Data from several layers are obtained. Second, the point clouds of several layers are processed with Pillar Feature Net to obtain several pseudoimages. Each pseudoimage represents the semantic information from the corresponding level of the point cloud. Third, the obtained pseudoimages from each level are merged with the pseudoimages of the entire scene to obtain a feature map with spatial structure characteristics. We apply a Region Proposal Network, and an object detection operator processes the feature map and obtains the result of object detection. Experiments show that the proposed method has a highly accurate detection effect for objects with complex spatial structures. In addition, the proposed method does not erroneously detect objects with similar semantic information after vertical dimension projection.
MEASUREMENT INVARIANCE OF PISA 2018 MOTIVATIONAL CONSTRUCTS ACROSS EU COUNTRIES

S. Raižienė1, L. Ringienė1, I. Laukaityte 2, A. Jakaitienė1 1Vilnius University (LITHUANIA) 2Umeå University (SWEDEN)
Competitive Policy Optimization

A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties. To tackle this, we propose competitive policy optimization (CoPO), a novel policy gradient approach that exploits the game-theoretic nature of competitive games to derive policy updates. Motivated by the competitive gradient optimization method, we derive a bilinear approximation of the game objective. In contrast, off-the-shelf policy gradient methods utilize only linear approximations, and hence do not capture interactions among the players. We instantiate CoPO in two ways: (i) competitive policy gradient, and (ii) trust-region competitive policy optimization. We theoretically study these methods, and empirically investigate their behavior on a set of comprehensive, yet challenging, competitive games. We observe that they provide stable optimization, convergence to sophisticated strategies, and higher scores when played against baseline policy gradient methods.
Comparing Eye Tracking and Head Tracking During a Visual Attention Task in Immersive Virtual Reality

The use of eye tracking (ET) and head tracking (HT) in head-mounted displays allows for the study of a subject’s attention in virtual reality environments, expanding the possibility to develop experiments in areas such as health or consumer behavior research. ET is a more precise technique than HT, but many commercial devices do not include ET systems. One way to study visual attention is to segment the space in areas of interest (AoI). However, the ET and HT responses could be similar depending on the size of the studied area in the virtual environment. Therefore, understanding the differences between ET and HT based on AoI size is critical in order to enable the use of HT to assess human attention. The purpose of this study was to perform a comparison between ET and HT technologies through the study of multiple sets of AoI in an immersive virtual environment. To do that, statistical techniques were developed with the objective of measuring the differences between the two technologies. This study found that with HT, an accuracy of 75.37% was obtained when the horizontal and vertical angular size of the AoIs was 25°. Moreover, the results suggest that horizontal movements of the head are much more similar to eye movements than vertical movements. Finally, this work presents a guide for future researchers to measure the precision of HT against ET, considering the dimensions of the AoI defined in a virtual scenario.
An ontology-based approach for automatic goal requirements engineering in data warehouse design

Goal-oriented approaches in data warehouse development projects still face two main issues. First, analysts often lack domain knowledge required during goal decomposition. This may lead to identifying erroneous requirements that most likely propagate to the remaining project phases, potentially leading to the project failure. Second, the identification of the data warehouse content from requirements is done manually by the designers in an error-prone process. In this paper, we address these two issues. We propose an ontology that formalises and automates the reasoning about decision-making knowledge, which allows analysts to compensate their lack of domain knowledge during goal decomposition. In addition, to demonstrate the feasibility of our proposal we present a semi-automatic process that assists the construction of the ontology. Furthermore, the proposed ontology ensures the traceability between both decision-making and data warehouse knowledge. Thanks to this traceability, we propose a set of rules that automatically derive a data warehouse schema from requirements specification.
GENERATIVE CONVERSATIONAL NETWORKS

Inspired by recent work in meta-learning and generative teaching networks, we propose a framework called Generative Conversational Networks, in which conversational agents learn to generate their own labelled training data (given some seed data) and then train themselves from that data to perform a given task. We use reinforcement learning to optimize the data generation process where the reward signal is the agent's performance on the task. The task can be any language-related task, from intent detection to full task-oriented conversations. In this work, we show that our approach is able to generalise from seed data and performs well in limited data and limited computation settings, with significant gains for intent detection and slot tagging across multiple datasets: ATIS, TOD, SNIPS, and Restaurants8k. We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data. We also conduct an analysis of the novelty of the generated data and provide generated examples for intent detection, slot tagging, and non-goal oriented conversations.
Time-Series Forecasting and Analysis of COVID-19 Outbreak in Highly Populated Countries

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.
Data Acquisition and Transmission

Sport, and specifically team sports, are a clear example of the application of technology, and how it has been adapted and achieved great improvements, offering all kinds of relevant and interesting information to improve the performance of end users (or the team's game strategy), and in this case, of players of certain sports. This chapter focuses on Electronic Positioning and Tracking Systems (EPTS), which are systems used for positioning and tracking players, offering data such as average speed, distance covered, position, etc. The data transmission carried out by these devices is different depending on the sport, the location and characteristics of the field, being transmitted by GPS, GNSS or LPS. The described systems have been tested and are currently in use in official competition matches of different high-level sports, comparing the different errors produced in obtaining the position, acceleration, speed and total distance, among others.
Identity-Based Encryption in the Internet of Things

Internet of Things (IoT) has been developing rapidly with the use of new technologies today. In addition to offering new applications that make our lives easier, it also reveals serious security and privacy problems. Hundreds of sensors around us or the applications we use can share our private data with other objects without our knowledge. Encryption systems can be used to prevent this emerging security problem. However, it is difficult to determine the appropriate encryption system due to limited power resources and computational capabilities. In this study, an identity-based encryption system has been proposed for secure communication between objects included in the Internet of Things.
Video Compression Based on FPGA Using SIMD Architecture

Recently, video files and images have became the dominant media material for transmitting or storing across different applications that are used by different people. So, there was a serious need to find more effective and efficient video compression techniques to reduce the large size of such multimedia files. This paper proposes SIMD based FPGA lossless JPEG video compression system with the facility of scalability. Generally, the proposed system consists of a software side and a hardware side. The digital video file is prepared to be processed by the hardware side frame by frame on the software side. The hardware side is proposed to consist of two main processing circuits, which are the prediction circuit for calculating the predicted value of each pixel in the certain frame and the encoding circuit that was represented by a modified RLE (Run-Length-Encoder) to encode the result obtained through subtracting the predicted value from the real value for each pixel to produce the final compressed video file. The compression ratio obtained for the proposed system is equal to 1.7493. The throughput improvement for the two and four processing units basing on SIMD architecture was 100 MP/s and 200 MP/s, respectively. The clock results showed that the number of clocks required had become 50% and 25% when using two processing units and four processing units, respectively, from the number of clocks using single processing units. Index Terms— Video Compression, Lossless JPEG, RLE, FPGA.
Deep Learning Approaches to Time Series Forecasting

Ever-growing time series data and brute computing power availability are causing a paradigm shift towards deep learning approaches; they are structured to require a minimum amount of feature engineering from the user. The problem with forward neural networks is that they have no quantification of the sequence's information. Therefore, despite being highly useful for classification and regression problems, they are not suitable for sequence prediction. On the other hand, the statistical methods involve a large amount of preprocessing, like making the series stationary, differencing the series and identifying the appropriate model and its order. The primary focus of this study will be on recurrent neural networks (RNNs), the de-facto candidate for sequence learning. However, RNNs cannot learn long sequences as they face the problem of vanishing gradients due to error flow in long chains. Long short-term memory (LSTM) is therefore considered to prevent the gradients from disappearing and allow the model to learn the long-term dependencies in the data sequence. The work is also embellished with examples and comparison with the traditional statistical time series modeling techniques: linear regression, non-linear regression and Box-Jenkins models. France's electricity consumption data is considered for this study.
A Generic CbITS Authoring Tool Using xAPI

Intelligent Tutoring Systems (ITSs) are considered among the most effective and efficient learning systems. The difficulty of authoring content limits the use of ITSs and creates a bottleneck for most ITS researchers and related industries. AutoTutor, a conversation-based ITS, faces the same problem. This paper will introduce the new improvement of AutoTutor Lite using xAPI and its potential as a generic CbITS authoring tool.
On the Quantum Performance Evaluation of Two Distributed Quantum Architectures

Distributed quantum applications impose requirements on the quality of the quantum states that they consume. When analyzing architecture implementations of quantum hardware, characterizing this quality forms an important factor in understanding their performance. Fundamental characteristics of quantum hardware lead to inherent tradeoffs between the quality of states and traditional performance metrics such as throughput. Furthermore, any real-world implementation of quantum hardware exhibits time-dependent noise that degrades the quality of quantum states over time. Here, we study the performance of two possible architectures for interfacing a quantum processor with a quantum network. The first corresponds to the current experimental state of the art in which the same device functions both as a processor and a network device. The second corresponds to a future architecture that separates these two functions over two distinct devices. We model these architectures as continuous-time Markov chains and compare their quality of executing quantum operations and producing entangled quantum states as functions of their memory lifetimes, as well as the time that it takes to perform various operations within each architecture. As an illustrative example, we apply our analysis to architectures based on Nitrogen- Vacancy centers in diamond, where we find that for presentday device parameters one architecture is more suited to computation-heavy applications, and the other for networkheavy ones. We validate our analysis with the quantum network simulator NetSquid. Besides the detailed study of these architectures, a novel contribution of our work are several formulas that connect an understanding of waiting time distributions to the decay of quantum quality over time for the most common noise models employed in quantum technologies. This provides a valuable new tool for performance evaluation experts, and its applications extend beyond the two architectures studied in this work.
The Influence of Entrepreneurial Leadership in the Performance of Micro and Small Enterprises the Case for Eastern Tigray, Ethiopia

Micro and Small Enterprises (MSEs) play a vital role significantly in a country’s economic development and employment. But the volatile economic environment with intensified competition makes the survival of the MSEs a tough one. Hence the sustainable performance of the MSEs is an essential need today. Among the many factors that stimulates the performance of the MSEs entrepreneurial leadership and its effect on the performance is not explored well by the researchers. Hence this study attempts to find the relationship between the entrepreneurial leadership and the performance, effect of entrepreneurial leadership on the performance of the MSEs in Eastern Tigray Region of Ethiopia. The study throws a light on the performance of MSEs and how it is influenced by the entrepreneurial leadership of the entrepreneurs which may add values to the entrepreneurs. The respondents are the employees of the MSEs, the total no of enterprises in the region is 19718 and the sample size was derived as 392. Well structured questionnaires were administered to collect the required data. The data were collected during 2017. SPSS software was applied to compute the data. Mean and standard deviation are the statistical tools applied to describe the variables under the study. The statistical tools correlation and regression have been applied to test the hypothesis. The results decipher that there is a positive correlation between entrepreneurial leadership amd the performance of the MSEs. The study also has found that the entrepreneurial leadership considerably affects the performance of the entrepreneurs. The study results imply that there is a scope to the entrepreneurs to improve their entrepreneurial leadership which may result in higher level of performance of the MSEs in Eastern Tigray, Ethiopia.
LogPicker: Strengthening Certificate Transparency Against Covert Adversaries

Abstract HTTPS is a cornerstone of privacy in the modern Web. The public key infrastructure underlying HTTPS, however, is a frequent target of attacks. In several cases, forged certificates have been issued by compromised Certificate Authorities (CA) and used to spy on users at large scale. While the concept of Certificate Transparency (CT) provides a means for detecting such forgeries, it builds on a distributed system of CT logs whose correctness is still insufficiently protected. By compromising a certificate authority and the corresponding log, a covert adversary can still issue rogue certificates unnoticed. We introduce LogPicker, a novel protocol for strengthening the public key infrastructure of HTTPS. LogPicker enables a pool of CT logs to collaborate, where a randomly selected log includes the certificate while the rest witness and testify the certificate issuance process. As a result, CT logs become capable of auditing the log in charge independently without the need for a trusted third party. This auditing forces an attacker to control each participating witness, which significantly raises the bar for issuing rogue certificates. LogPicker is efficient and designed to be deployed incrementally, allowing a smooth transition towards a more secure Web.
Transformer-Based Direct Hidden Markov Model for Machine Translation

The neural hidden Markov model has been proposed as an alternative to attention mechanism in machine translation with recurrent neural networks.However, since the introduction of the transformer models, its performance has been surpassed.This work proposes to introduce the concept of the hidden Markov model to the transformer architecture, which outperforms the transformer baseline.Interestingly, we find that the zero-order model already provides promising performance, giving it an edge compared to a model with first-order dependency, which performs similarly but is significantly slower in training and decoding.
PROJECT-ORIENTED APPROACH TO DIAGNOSTICS OF THE FORMING OF COMPETENCIES IN PROSPECTIVE MATHEMATICS TEACHERS

E. Sukhovienko , S. Sevostyanova , R. Nigmatulin , E. Martynova South Ural State Humanitarian Pedagogical University (RUSSIAN FEDERATION)