Reviewerly/exHarmony huggingface dataset:
MALMI: towards combining machine learning and waveform migration for fully automated earthquake detection and location

&amp;lt;p&amp;gt;Automatic event detection and location is key to real-time earthquake monitoring. With the increase of computing power and labeled data, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, classical workflows using ML as phase pickers still face challenges for seismic events of short inter-event time or low signal-to-noise ratio (SNR). Full waveform methods that do not rely on phase pick and association are suitable for processing these events, but are computationally costly and can lack clear event identification criteria, which is not ideal for real-time processing. To leverage the advantages of both methods, we propose a new workflow, MALMI, which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pre-trained ML model to generate continuous phase probabilities that are then back-projected and stacked to locate seismic sources using migration.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We applied the workflow to a microseismic monitoring dataset collected in a borehole at the Utah FORGE geothermal laboratory site. The proposed workflow can automatically detect and locate induced microseismic events from continuous geophone recordings. Different ML models are evaluated for detection capability and phase classification accuracy. We expect that better performance should be possible if a customized ML model re-trained using local dataset would be used in the MALMI workflow. Further comparison with conventional migration methods confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing a complementary tool for automated event detection and location from continuous data.&amp;lt;/p&amp;gt;
Replicate, Reuse, Repeat: Capturing Non-Linear Communication via Session Types and Graded Modal Types

Session types provide guarantees about concurrent behaviour and can be understood through their correspondence with linear logic, with propositions as sessions and proofs as processes.However, a strictly linear setting is somewhat limiting, since there exist various useful patterns of communication that rely on non-linear behaviours.For example, shared channels provide a way to repeatedly spawn a process with binary communication along a fresh linear session-typed channel.Non-linearity can be introduced in a controlled way in programming through the general concept of graded modal types, which are a framework encompassing various kinds of coeffect typing (describing how computations make demands on their context).This paper shows how graded modal types can be leveraged alongside session types to enable various non-linear concurrency behaviours to be re-introduced in a precise manner in a type system with a linear basis.The ideas here are demonstrated using Granule, a functional programming language with linear, indexed, and graded modal types.
Charticulator Expressions

In this chapter, we take a break from designing charts and instead take a detailed look at the expressions used by Charticulator when you bind data to an attribute. You also may have been at a loss as to how Charticulator’s rather unusual numeric formatting method works. This is where you edit characters inside the curly braces. It’s a syntax known as “d3-format.” Have you ever heard of it? No? Well, neither had I nor indeed have any of my more “techie” friends. It doesn’t help that Charticulator’s documentation doesn’t throw much light on how you’re meant to use this syntax (it took me some time to find out how to insert a comma separator). Just to fill you in, “d3-format” is used by JavaScript programmers to format numbers, apparently for “human consumption,” according to GitHub ( ), and is based on Python 3’s formatting specifications. However, I don’t suppose knowing this really helps a lot, so in this chapter I will throw some light on this rarefied syntax language. What we will also discover is that there is an alternative approach to formatting numerical fields using DAX, and we will explore this possibility at the end of this chapter.
Quantum Ordering Points to Identify the Clustering Structure and Application to Emergency Transportation

Studies exploring the use of artificial intelligence (AI) and machine learning (ML) are knowing an undeniable success in many domains. On the other hand, quantum computing (QC) is an emerging field investigated by a large expanding research these last years. Its high computing performance is attracting the scientific community in search of computing power. Hybridizing ML with QC is a recent concern that is growing fast. In this paper, we are interested in quantum machine learning (QML) and more precisely in developing a quantum version of a density-based clustering algorithm namely, the Ordering Points To Identify the Clustering Structure (QOPTICS). The algorithm is evaluated theoretically showing that its computational complexity outperforms that of its classical counterpart. Furthermore, the algorithm is applied to cluster a large geographic zone with the aim to contribute in solving the problem of dispatching ambulances and covering emergency calls in case of COVID-19 crisis.
The dynamic Coulomb stress changes caused by remoteearthquakes based on the borehole strainmeter data

&amp;lt;p&amp;gt;Sufficient shreds of evidence have proved the existence of the remote triggering effect of large earthquakes. To understand its mechanism, it is necessary to conduct detailed investigations on the influence of the far-field dynamic stress changes on the stress state of faults. As an important tool of ultra-broadband crustal stress monitoring, a four-component borehole strainmeter can directly record the dynamic changes of horizontal strain and stress caused by seismic waves. These data are of great importance to study the dynamic Coulomb stress changes and related triggering effects, but have not been paid sufficient attention to so far. This paper analyzes the data of the four-component borehole strainmeter at Gaotai and Tonghua stations, which recorded the far-field strain changes of four major earthquake events in the Pacific region in 2018. We successfully identify the seismic phases of P, S, and surface waves, and analyze the characteristics of different phases through the stress petal method. The dynamic stress changes are calculated, demonstrating the feasibility of using borehole strainmeter data to quantitatively study the triggering effect of teleseismic waves of earthquakes with different magnitudes at different epicentral distances. We find that the direction of the principal stress axis of the dynamic stress changes is generally consistent with the azimuth of the earthquake epicenter. We further discuss the Coulomb stress changes on the major faults near the stations. According to the results, the peak values of the dynamic Coulomb stress changes produced by four earthquakes on the fault planes near Gaotai and Tonghua stations are at the magnitude of hundreds of Pa, which are lower than the threshold value of dynamic triggering. This is also consistent with the observation that no dynamically triggered earthquakes are found on the faults. However, the idea and method of this paper provide useful insight into the detection of possible dynamic triggering of large earthquakes.&amp;lt;/p&amp;gt;
ARMA model based on big data is used to study China's coastal coal freight rate

Based on China's coastal coal transportation market, this paper establishes ARMA model, and carries out data analysis and prediction by using China coastal coal transport price index (CBCFI). The analysis results show that the prediction accuracy of the model is high. With the increase of time, the prediction accuracy of ARMA model is also declining. Therefore, ARMA model is suitable for short-term prediction of cbcfi, so as to provide decision-making suggestions for shipping companies and shipping operators and avoid risks.
From the memoirs of G. Kh. Shakhnazarov

In the article, a well-known political figure, a Corresponding Member of the USSR Academy of Sciences and the Russian Academy of Sciences G. Kh. Shakhnazarov shares his memories of his years of study at the postgraduate Institute of the USSR Academy of Sciences (now the Institute of State and Law of the Russian Academy of Sciences), about the atmosphere prevailing within the walls of this scientific institution, as well as about its employees who made up the color of domestic jurisprudence of that period.
Staying Current: Keeping Up with the Latest Literature

CSA NewsEarly View STUDENTS Staying Current: Keeping Up with the Latest Literature Elizabeth Bradley, Elizabeth Bradley Ph.D. Candidate at Auburn UniversitySearch for more papers by this authorRachelle LaCroix, Rachelle LaCroix Ph.D. Candidate at Cornell University and 2022 Chair of the ASA, CSSA, and SSSA Graduate Student CommitteeSearch for more papers by this author Elizabeth Bradley, Elizabeth Bradley Ph.D. Candidate at Auburn UniversitySearch for more papers by this authorRachelle LaCroix, Rachelle LaCroix Ph.D. Candidate at Cornell University and 2022 Chair of the ASA, CSSA, and SSSA Graduate Student CommitteeSearch for more papers by this author First published: 14 March 2022 https://doi.org/10.1002/csan.20705Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Optical kernel computing: a photonic hardware accelerator for AI

We show that spectral mapping of data onto femtosecond optical pulses and a projection into an implicit, higher dimensional space using nonlinear optical transformation of data reduces the latency in data classification by several orders of magnitude. The approach is validated by the classification of various datasets, including brain intracranial pressure, cancer cell imaging, spoken digit recognition, and the classic Exclusive OR (XOR) benchmark for nonlinear classification. Single-shot operation is demonstrated using time stretch data acquisition. Due to the modest degrees of freedom in the optical domain, the classification accuracy is data-dependent.
Data-dependent differentially private publication of horizontally partitioned data

Probabilistic graphical model (PGM) is a mainstream model for data publishing. Due to its structural characteristics, the higher the accuracy of important nodes in PGM, the higher the utility of published data. This paper proposes a datadependent differentially private publishing method for horizontally partitioned data, including a multi-party data publishing framework which can combine the existing data-dependent parameter learning method and a data-dependent structure learning method in the horizontally partitioned data setting. So that the whole learning process of PGM is datadependent. Thus, different privacy budgets can be allocated to different nodes when learning the PGM for different datasets. The experimental results show that the data published by our scheme has high utility.
PGFLibPy: An Open-Source Parallel Python Toolbox for Genetic Folding Algorithm

Genetic folding (GF) is a robust evolutionary optimization algorithm. For efficient hyper-scale GFs, a hybrid parallel approach based on CPU architecture Parallel GF (PGF) is proposed. It aids in resolving kernel tricks that are difficult to predict using conventional optimization approaches. The regression and classification problems are solved using PGF. Four concurrent CPUs are formed to parallelize the GF, and each executes eight threads. It is also easily scalable to multi-core CPUs. PGFLibPy is a Python-based machine learning framework for classification and regression problems. PGFLibPy was used to build a model of the UCI dataset that reliably predicts regression values. The toolbox activity is used for binary and multiclassification datasets to classify UCI. PGFLibPy’s has 25 Python files and 18 datasets. Dask parallel implementation is being considered in the toolbox. According to this study, this toolbox can categorize and predict models on any other dataset. The source code, binaries, and dataset are available for download at https://github.com/mohabedalgani/PGFLibPy.
Kryptographische Algorithmen

No AccessAlgorithmen und DatenstrukturenMar 2022Kryptographische AlgorithmenRené KrooßRené KrooßSearch for more papers by this authorhttps://doi.org/10.3139/9783446472419.010SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2022Pages: 357-434Print ISBN: 978-3-446-47222-8eISBN: 978-3-446-47241-9 Copyright & Permissions© 2022 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
A Survey of Knowledge Graph Construction Techniques for Scientific Research Archives

Most of the archive resources are in the stage of “preservation-oriented”, and there is a lack of in-depth resource development and sharing in the functions of resource collection and retrieval, resulting in the value of archive data not being activated. The knowledge graph has powerful semantic processing and open interconnection organization capabilities. The construction of the knowledge graph for scientific research archives facilitates the scientific management of information resources and supports the innovative development of scientific research.
UNIVERSITY STUDENTS' EVALUATION OF EDUCATIONAL ACTIVITY RISK

Introduction:Restructuring the education system due to its modernization in the context of the fourth industrial revolution ("Industry 4.0") sets the new challenge of preparing specialists for the future. At the same time, changes in the system of Russian higher education do not always correspond to modern challenges, market requirements and student demands. The purpose of the article is to analyze the opinions of university students about risk zones in the field of educational activities. Authors carried out the empirical typology of risks in the field of education and measured the attitude towards them in two groups of students: with a high and a reduced level of educational success.Research Methods:The study results are based on was the massive online survey of university students, conducted in 2021 in a large industrial region of Russia (Sverdlovsk region). Quota sampling was used, the sample size was 933 people. Quotas were formed according to areas, levels of training of students and types of universities (the error in quota criteria did not exceed 2%). The sample was based on open statistical data on the number of students in the Sverdlovsk region. TwoStep cluster analysis and correlation analysis were implemented on data.Results:The study revealed 4 groups of risks: 1) the risks of inconsistency of the higher education system with the ideas of students about its role in their professional and personal development; 2) the risks of entering the vocational education system; 3) the risks of the implementation of educational activities; 4) the risks of its result. Clustering students on self-assessments of educational success resulted in two clusters. The first group (37%) is characterized by a high level of self-esteem and the second (63%) - by lower level of self-esteem. These clusters of students also differ in their risk ratings. The students of the second cluster demonstrate increased risks of all four types. They believe that higher education in Russia does not guarantee them success in their professional and personal development. They show the increased risks of disappointment in the chosen specialty and the knowledge acquired in the profession. They are less active and effective in the implementation of educational activities and more than others feel the fear for its formal bad results (ratings, points, diploma).Conclusions:Students in both groups do not consider higher education a significant resource for their future. Only 30% of students believe that education gives confidence in the future, makes finding a good job easier, and is an important factor in self-realization. Research shows that we need to deepen the dialogue between students and educational management in modernizing education and delivering its goals and meaning to the student community.
A Study on Risk Issues and Policy for Future Society of Digital Transformation: Focusing on Artificial Intelligence

Digital transformation refers to the economic and social effects of digitisation and digitalisation. Although digital transformation acts as a useful tool for economic/social development and enhancing the convenience of life, it can have negative effects (misuse of personal information, ethical problems, deepening social gaps, etc.). The government is actively establishing policies to promote digital transformation to secure competitiveness and technological hegemony, however, understanding of digital transformation-related risk issues and implementing policies to prevent them are relatively slow. Thus, this study systematically identifies risk issues of the future society that can be caused by digital transformation based on quantitative analysis of media articles big data through the Embedded Topic Modeling method. Specifically, first, detailed issues of negative effects of digital transformation in major countries were identified. Then detailed issues of negative effects of artificial intelligence in major countries and Korea were identified. Further, by synthesizing the results, future direction of the government's digital transformation policies for responding the negative effects was proposed. The policy implications are as follows. First, since the negative effects of digital transformation does not only affect technological fields but also affect the overall society, such as national security, social issues, and fairness issues. Therefore, the government should not only promote the positive functions of digital transformation, but also prepare policies to counter the negative functions of digital transformation. Second, the detailed issues of future social risks of digital transformation appear differently depending on contexts, so the government should establish a policy to respond to the negative effects of digital transformation in consideration of the national and social context. Third, the government should set a major direction for responding negative effects of digital transformation to minimize confusion among stakeholders, and prepare effective policy measures.
Sparse Signal Models

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Introduction

This chapter lays out the plan of the book and introduces the key concepts that are deployed in subsequent chapters, including natural logic, polarity, natural deduction, and the difference between characterizing inferences syntactically (by virtue of their forms) and characterizing them semantically (for example by use of model theory).
Nonconvex Methods for Low-Dimensional Models

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Poesia e musica nella <i>Theophoroumene</i> di Menandro

No AccessPoesia e musica nella Theophoroumene di MenandroLuca BettariniLuca BettariniSearch for more papers by this authorhttps://doi.org/10.13109/9783949189326.69SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book coverStudia ComicaVolume 14 1st editionISBN: 978-3-949189-30-2 eISBN: 978-3-949189-32-6HistoryPublished online:March 2022 PDF download
APPLICATION OF DIGITAL GAMES INTO THE EDUCATIONAL PROCESS IN THE KINDERGARTEN

The rapidly-growing world requires selection and application of innovative approaches that meet the needs of 21st century child. The idea of implementing technologies into the educational process is increasingly gaining ground. This research is focused at examining the application of digital games into the educational process in the kindergarten. Digital games can be used as an educational tool to supplement the traditional methods used in preschool education. The first part of the study reveals some theories that support the use of digital games in the educational process. Some benefits of digital game based education are also described. By using digital games in their lessons, teachers have the potential to apply more innovative methods of teaching which give wide range of opportunities to enrich the experience of the young learners as well as to teach other skills such as following rules, adaptation, problem solving, interaction, critical thinking, creativity, cooperation and acquisition of a set of competencies - knowledge, skills and attitudes, necessary for the overall development of the child's personality and essential for the successful transition of the child to school education. The second part of the research is aimed at studying teachers' attitude and experience in applying digital games into the educational process. For this purpose a questionnaire was created and conducted with teachers working in the field of preschool education. The data presented in this report cover teachers and kindergartens in Bulgaria.