Reviewerly/exHarmony huggingface dataset:
Performance evaluation for deep-learning based point-source parameter estimation using a well constrained manual database: examples from the Hengill Geothermal Field, Iceland

&amp;lt;p&amp;gt;In this study, we present a new approach based on recent advances in deep learning for rapid source-parameter estimation of microseismic earthquakes. The seismic inversion is represented in compact form by two convolutional neural networks, with individual feature extraction, and a fully connected neural network, for feature aggregation, to simultaneously obtain moment tensor and spatial location of microseismic sources. The neural network algorithm encapsulates the information about the relationship between seismic waveforms and underlying point-source mechanisms and locations allowing rapid inversion (within a small fraction of a second) once input data are available. A key advantage of the algorithm is that it can be trained using synthesized seismic data only, so it is directly applicable to scenarios where there are insufficient real data for training including temporary seismic networks and hydraulic stimulation experiments, for example. Moreover, we find that the method is robust with respect to perturbations such as observational noise and data incompleteness (missing stations). We apply the new approach on a database of small magnitude (M &amp;amp;#8804; 2) earthquakes recorded at the Hellishei&amp;amp;#240;i geothermal field in the Hengill area, Iceland, which is the demonstration site in the EU-GEOTHERMICA project COSEISMIQ (http://www.coseismiq.ethz.ch). For the examined events, the model achieves very good agreement with the inverted solutions determined through standard methodology. The new approach offers great potential for automatic and rapid real-time information on microseismic sources in a deep geothermal context and can be viably used for microseismic monitoring tasks in general.&amp;lt;/p&amp;gt;
A Study on Reading Guidance for Foreign Learners Through Narrative Literature

본 연구는 서사문학의 한 종류인 전래동화와 창작동화를 활용하여 학습자의 읽기 능력 향상을 위한 학습 지도안을 제시하고 수업효과를 정성적 정량적으로 기술하는데 있다. 이 연구는 외국인 학생의 전반적 읽기능력 향상을 위해 구성한 수업사례를 소개하여 그 효과를 제시하는데 연구의 목적이 았다. 2장에서는 여러 선행 연구들을 바탕으로 서사문학 교육의 의의에 대해 살펴보고자 한다. 3장에서는 외국인 학습자를 위한 서사문학의 교수·학습모형에 대해 살펴보고자 한다. 4장에서는 연구 방법 및 자료분석에 대해 살펴본다. 5장에서는 본 연구의 내용을 요약·정리하고 본 연구의 의의를 서술하고 Action Research 즉, 현장 실행연구로서 제언점을 제시한다.The purpose of the study is to present a study guide to improve learners reading ability using two types of narrative literature, traditional fairy tales and creative fairy tales, and to qualitatively and quantitatively describe the effect of lessons based on them. The study presents the effects of lessons by introducing examples of them designed to improve the overall reading ability of foreign students. Chapter 2 examines the significance of narrative literature education based on several previous studies. Chapter 3 examines the teaching and learning model of narrative literature for foreign learners. Chapter 4 examines research methods and data analysis. Chapter 5 summarizes and organizes the contents of the study describes the study s significance, and also presents suggestions as a field practice study.
Preventing Security Breach in Social Media: Threats and Prevention Techniques

Social media is used in many ways from sharing personal data such as text, photos, and messages to sharing leaked or latest news headlines with related photographs in different domains such as education, entertainment, marketing, and music. Social network has become the most popular culture today. So, as the usage is increasing day by day users are not familiar with the risks present in using these applications. Social media has many advantages and hence leaving social media is not a good option, but we need to know the prevention strategy to avoid these types of risks. In this chapter, we will deploy the types of risks and attacks which you may face sooner or later, but with prevention strategy and techniques you could be on a safer side.
Tailoring TBT-S Treatment Modules into All Levels of Care

TBT-S modules can be selected and arranged to supplement outpatient sessions or create multi-day neurobiologically based interactive programs that can be inserted into ongoing ED residential, partial hospital, or free-standing treatment programs.
The new emerging DOI biotope within and around the Open Source Geospatial Foundation (OSGeo)

&amp;lt;p&amp;gt;We report on the adoption of persistent identifiers by community-driven geospatial open source communities and their umbrella organisation OSGeo. After a ramp up process, which included the introduction and evaluation of Digital Object Identifiers (DOI) for OSGeo conference recordings, a growing number of OSGeo project communities have started to adopt DOI for their respective code bases (e.g. MOSS GIS https://doi.org/10.5281/zenodo.5825144, GRASS GIS https://doi.org/10.5281/zenodo.5810537), enabling scientific reference and citation for distinct versions of code and also the overall software project code base. In addition, the use of persistent IDs for persons is accelerating. This presentation provides an overview over the latest state of DOI use by projects, lessons learned, emerging challenges (e.g. DOI-based reference for software versions predating the DOI minting) and emerging opportunities for the OSGeo communities (e.g. integration of DOI for code, presentations, manuals and video) and beyond.&amp;lt;/p&amp;gt;
Software Product Lines for Industrial Robots: A Pilot Case with Arduino

Software reuse approaches in industrial robots are little exploited by industry, which often leads programmers to continue coding the same solutions, wasting time, effort, and cost. This paper describes the construction of a Software Product Line (SPL) to determine the feasibility of this approach as a reuse strategy for industrial robots. For the definition of the SPL, we followed the fundamental activities of domain engineering and application engineering. In addition, we identified its limitations and possible future work. As a result, it was got that the SPL approach is viable because a derivation of a product was made from the SPL applying some software reuse metrics, where it was found that 33% of the code needed for the operation of the robot was generated by the proposal, which represents a potential improvement in the time that developers take to program industrial robotics solutions with Arduino. In addition, it identified the potential of this approach in solutions to support the elderly in their homes and care centers, as well as some limitations related to the SPL and the domains involved.
Improving Relevance in a Recommendation System to Suggest Charities without Explicit User Profiles Using Dual-Autoencoders

This work explores the effect of the quality of inferred user profiles on the accuracy of charitable recommendations when using an item-based collaborative filter algorithm. A gap was identified in the literature with respect to the application of charitable recom-mendation systems in the absence of rich user profiles. This paper introduces an approach to generate relevant recommendations when neither user profiles nor feedback on donation preferences is available. The discovery of user preferences is achieved via the construction of implicit ratings computed from custom feature engineering, while the sparsity of item and user ratings was addressed with a dimension reduction strategy based on dual-autoencoders from a commercial machine learning platform. Our analysis shows the magnitude and sensitivity of the relationship between the relevance of the recommendations and the average number of donations per user. Raw data for this research was provided by a leading online donation platform and contains 24 million anonymous donations to 165 thousand unique causes from over 1.2 million users. We find that the most effective way to increase the relevance of recommendations by a factor of 2 at any top- k value is to train the collaborative filter with users that have at least 50 donations in the data set. As a result, the training set for the collaborative filter is restricted to 8% of the original users, 70% of the companies, 49% of the causes, and 70% of the original countries where users making donations reside.
Relationship between seismic swarms and episodic inflations at Akutan Volcano in Alaska

&amp;lt;p&amp;gt;Seismic swarms at volcanic regions are important manifestations of volcanic unrest. While they are often inferred to be related to fluid or magma movements, their underlying process remains an active research topic. In particular, quantifying the proportion of seismic swarms that are related to magma movement can potentially improve their utility for eruption forecasting. To better understand the relationship between seismic swarms and magma movement, we focus on the Akutan volcano where episodic inflations have been recorded every 2-3 years since 2002. We first applied template matching on continuous seismic waveforms between 2005-2017 to improve the earthquake catalog&amp;amp;#8217;s magnitude of completeness. We further classified the events as long-period (LP) or regular volcano-tectonic (VT) events based on their frequency content. After waveform-based double-difference relocation, we find that the VT and LP events are concentrated above and below the shallow magma reservoir respectively. We clustered the VT and LP events based on their spatiotemporal evolution and find that most clusters are swarm-like with no clear mainshock-aftershock sequences. Based on their temporal relation to the inflation episodes, we infer that the LP swarms are related to ascending magma into the shallow reservoir, which sometimes triggers VT swarms through stress transfer.&amp;lt;/p&amp;gt;
Pengembangan Gamifikasi Pada Modul E - Learning Service Excellence Untuk Karyawan Frontliner

This paper is to develop teaching materials in the form of developing gamification teaching materials in the service excellence module that will be used for frontliner employees. The purpose of this research is to design the development of gamification teaching materials in the service excellence module that can be used for frontliner employees. The research results from the results of designing and testing gamification teaching materials for the service excellence module for frontlinner mall employees on a limited scale, that the feasibility of the content of the material and the feasibility of presenting gamification obtained an average score of 80%. Validation of the content of the Service Excellence module using gamification obtained an average score of 81%, and validation of the feasibility of presenting the Service Excellence module using gamification obtained an average score of 78%, so the survey results from the trial show that the teaching materials in the E-learning Service module Excellence carried out using gamification is suitable for use in training activities
A new machine learning-based data assimilation technique&amp;#160;to detect volcanic unrest from tremor

&amp;lt;p&amp;gt;Linking geophysical and geochemical observables with subsurface processes is crucial to detect volcanic unrest and better anticipate eruptions. One of the most important observables to monitor pre-eruptive volcanic activity is tremor, a more or less persistent, highly periodic, ground vibration recorded near active vents. Tremor is commonly being monitored in near real-time by volcano observatories to anticipate unrest, as it may emerge, or change properties, when subsurface pressure varies. For example, it has been observed that the dominant frequency of tremor may glide towards higher or lower values before eruptions; overtones may appear or disappear; and seismic amplitude may increase or decrease. However, similar variations can be also observed during quiescence and when activity decreases. This leads to the following questions: How does tremor actually reflect the overpressure of the subsurface? Can we infer when and where the pressure beneath active vents increases or decreases by monitoring volcanic tremor? In this work, we present a new data assimilation technique that combines new physics-based models of volcanic tremor with a machine learning-based inversion algorithm to track pressure changes beneath volcanic craters in near-real time. In particular, our inversion algorithm is based on a supervised random forest classifier trained with synthetic data, whereas our physics-based model extends from Girona et al. (2019) and is based on a stop-and-go mechanism, i.e., tremor is assumed to emerge when: (i) gas is supplied randomly to shallow levels of the volcanic plumbing system; (ii) accumulates temporarily beneath permeable caps (e.g., beneath a dome or in a leaky fracture); and (iii) transfers via permeable flow to the surface. Using this machine learning-based data assimilation technique, we find that the recent 2013 unrest phase of Kawah-Ijen volcano (Indonesia) was driven by a pressure increase in the subsurface of a factor 2-to-5. This technique is currently also being applied to unveil the pressure history of the shallow vents of Pavlof and Veniaminof volcanoes (Alaska).&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Girona, T., C. Caudron, C. Huber (2019). Origin of shallow volcanic tremor: the dynamics of gas pockets trapped beneath thin permeable media. &amp;lt;em&amp;gt;J. Geophys. Res.&amp;lt;/em&amp;gt;, doi: 10.1029/2019JB017482.&amp;lt;/p&amp;gt;
Beating the bias in AI-based biometrics

OneSpan research scientist Ismini Psychoula outlines the many types of bias that can exist in face and other biometric systems – and how to counter them.
Fast and Furious

Article Share on Fast and Furious: How the web got turbo charged just in time…ASPLOS 2022: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating SystemsFebruary 2022 https://doi.org/10.1145/3503222.3527481Online:31 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher Site
Contribution in the scientific production of Peruvian universities against COVID-19

Humankind is living more than a year in a COVID-19 pandemic. Faced with this international health emergency situation, scientific research is a tool to combat it. Universities, which must contribute human knowledge, are called upon to do research to combat COVID-19 in different aspects. This work analyses which Peruvian universities have contributed the most to developing projects and publications related to COVID-19. The results show that it is mainly the universities in the capital, Lima, that have won funds from the government to develop projects Keywords: COVID-19, Coronavirus, SARS-Cov2, Scientific Research, Peru, Funding, Research projects.
Analysis and Improvement of the Security Strength of a Secure Computation for Polynomial

This paper analyzes security strength of Iseki & Hayashi's secure computation which is valid for computation of polynomial as for real numbers, and proposes a new method to improve its security strength. First we derive the fact that Iseki & Hayashi's secure computation is deciphered if the value of single input variables of the computation is known. This implies that the security strength of Iseki & Hayashi's secure computation is bounded by the length of bits which can be inputted to this single variable. To improve this bound to be more secured, we propose a technique to repeat steps of Iseki & Hayashi's secure computation until its security strength becomes sufficiently powerful. A simple relationship about security strength between Iseki & Hayashi's secure computation and our proposal is found, and it shows security strength becomes quickly powerful with the increase of number of repetitions. Finally, numerical examples are demonstrated to show the effectiveness of our proposal.
Effect of Quality Perception for Smart Factory System Construction on Intention to Promote Intelligence

In recent years, in the manufacturing sector, smart factories have emerged as an indispensable phenomenon worldwide. Globally, the industrial structure is shifting from the manufacturing industry to the service industry due to the global long-term economic downturn, labor costs, rising raw materials, and the avoidance of the manufacturing industry. Meanwhile, with the introduction of smart factories through information and communication technology (ICT) convergence, the manufacturing industry is changing into a different paradigm from the past. Above all, the development of various technologies such as Big Data, Artificial Intelligence (AI), Internet of Things (IoT), and 5G is changing all management paradigms. It has been argued that this will become a very important factor in determining the competitiveness of a company in the future. Therefore, this study aims to empirically verify the effect of smart factory quality factors on acceptance intention and intelligence promotion intention for small and medium-sized manufacturing enterprises that are currently using smart factories.BR This study, in particular, was analyzed to be affected by the perceived usefulness of the system of field workers through the use of smart factories in the manufacturing field in this study. It was confirmed that there is a close correlation between education and field-specific customization. In addition, it was confirmed that if field workers
Characterizing induced seismic events in the Groningen gas field using an efficient Hamiltonian Monte Carlo sampler: a case study

&amp;lt;p&amp;gt;In May 2019, an earthquake with a magnitude of 3.4 (local magnitude) hit the area of the Westerwijtwerd village in the province of Groningen, the Netherlands. The event is the result of the gas extraction in the Groningen gas field and is one of the largest events to date. To better understand the source characteristics of the event, we apply a probabilistic full-waveform inversion technique that we recently developed to the event's recordings. Specifically, we use a variant of the Hamiltonian Monte Carlo (HMC) algorithm. When sampling high-dimensional model spaces, HMC is proven to be more efficient than the generic Metropolis-Hasting algorithm. Compared to probabilistic inversions of tectonic events, two main challenges arise while applying the algorithm. First, the prior information of the event is usually incomplete and inaccurate. That is, the only available information is (an estimate of) the hypocenter and origin time. Second, the frequency content of the induced event's seismograms is higher than that of typical tectonic events. This implies a higher non-linearity, which in turn complicates the ability of a probabilistic inversion algorithm to sample the model spaces, particularly when considering the first challenge. Consequently, to address both challenges, first, we develop a procedure to estimate the necessary prior information and use it as input to the HMC variant. Second, we run our HMC algorithm iteratively to mitigate the non-linearity. Using the relatively detailed velocity model of the Groningen gas field, we eventually estimate ten posteriors of the source parameters. The latter being the hypocenter (three parameters), the moment tensor (six independent parameters), and the origin time. &amp;amp;#160;&amp;lt;/p&amp;gt;
V International Conference on Digital Education at Environmental Universities

This is an introductory text to a collection of selected papers from the V International Conference on Digital Education at Environmental Universities (DEEU 2018), held in Kyiv, Ukraine, on October 17–18, 2018.
How to Span the Quantum Gap

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text Edwin Cartlidge, "How to Span the Quantum Gap," Optics & Photonics News 33(4), 34-41 (2022) Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
Geometric Derivation of Camera Equations

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Photogrammetry, camera matching, and model-based image matching are commonly used techniques to analyze photographs and video for accident reconstruction and other forensic applications. Investigators are often tasked with taking measurements from photographs or determining speed from a video. All such calculations are based on fundamental geometric principles governing image projection inside a camera. Most treatments in the literature express the image projection equations in matrix notation rather than closed-form solutions. The purpose of this paper is to present a geometric derivation of the image projection equations in closed form that can be readily applied by a qualified investigator without the need for specialized software. In addition, a simple brute force optimization procedure is described to perform camera matching and model-based image matching. Examples are provided to demonstrate the method.&lt;/div&gt;&lt;/div&gt;