Reviewerly/exHarmony huggingface dataset:
Uncertainty Estimation for Magnetic Maps

&amp;lt;p&amp;gt;Magnetic maps depict spatial variations in the Earth&amp;amp;#8217;s magnetic field.&amp;amp;#160; These variations occur at a wide range of scales and are produced via a variety of physical processes related to factors including structure and evolution of the Earth&amp;amp;#8217;s core field and the geologic distribution of magnetic minerals in the lithosphere.&amp;amp;#160; Mankind has produced magnetic maps for 100&amp;amp;#8217;s of years with increasing fidelity and accuracy and there is a general understanding (particularly among the geophysicists who produce and use these maps) of the approximate level of resolution and accuracy of these maps.&amp;amp;#160; However, few magnetic maps, or the digital grids that typically underpin these maps, have been produced with accompanying uncertainty quantification.&amp;amp;#160; When uncertainty is addressed, it is typically a statistical representation at the grid or survey level (e.g., +- 10 nT overall uncertainty based on line crossings for a modern airborne survey) and not at the cell by cell local level.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As magnetic map data are increasingly used in complex inversions and in combination with other data or constraints (including in machine learning applications), it is increasingly important to have a handle on the uncertainties in these data.&amp;amp;#160; An example of an application with need for detailed uncertainty estimation is the use of magnetic map information for alternative navigation.&amp;amp;#160; In this application data from an onboard magnetometer is compared with previously mapped (or modeled) magnetic variations.&amp;amp;#160; The uncertainty of this previously mapped information has immediate implications for the potential accuracy of navigation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We are exploring the factors contributing to magnetic map uncertainty and producing uncertainty estimates for testing using new data collection in previously mapped (or modeled) map areas.&amp;amp;#160; These factors include (but are likely not limited to) vintage and type of measured data, spatial distribution of measured data, expectation of magnetic variability (e.g., geologic or geochemical environment), statistics of redundant measurement, and spatial scale/resolution of the magnetic map or model.&amp;amp;#160; The purpose of this talk is to discuss the overall issue and our initial results and solicit feedback and ideas from the interpretation community.&amp;lt;/p&amp;gt;
The DRuKoLA project

DRuKoLA, the accompanying project in the making of the Corpus of Romanian Language, is a cooperation between German and Romanian computer scientists, corpus linguists and linguists, aiming at linking reference corpora of European languages under one corpus analysis tool able to manage big data. KorAP, the analysis tool developed at the Leibniz Institute for the German Language (Mannheim), is being tailored for the Romanian language in a first attempt to reunite reference corpora under the EuReCo initiative, detailed in this paper. The paper describes the necessary steps of harmonization within KorAP and the corpus of Romanian language and discusses, as one important goal of this project, criteria and ways to build virtual comparable corpora to be used for contrastive linguistic analyses.
<i>Deep Rhythms VIII</i>Wood block music*

Deep Rhythms are normally constructed by choosing a length (l), and the difference (d) between one basic note and the next. If one begins at 0, and one wishes to construct rhythms in measures conta...
BETTERGEOEDU: THE USE OF MINECRAFT AS AN INTERACTIVE TOOL TO FACILITATE THE KNOWLEDGE ON ROCKS AND MINERALS IN PRIMARY SCHOOL STUDENTS

J.L. Costafreda Mustelier1, C. Peña Narciso1, J. Herrera Herbert1, P. Westrin2, F.P. Luque1, L. Piovano1 1Universidad Politécnica de Madrid (Technical University of Madrid) (SPAIN) 2Sveriges Geologiska Undersökning (Geological Survey of Sweden) (SWEDEN)
A VR-based learning system of tuna dismantling for fisheries high school students

Among characteristic club activities in Japanese fisheries high schools, demonstration of dismantling tunas and other fishes dismantling is attractive to consumers as well as to students. In spite of the extracurricular education, the school has difficulties to provide enough chances the students to practice of the dismantling due to their cost. To improve the situation, this article proposes a virtual reality support system of self-study for tuna dismantling. The system provides visual sense through Head-Mounted-Display (HMD) with interactive manipulation of some kinds of knives by both hands. Through the experience of the virtual world, students can study correct procedure with safety and accuracy of the dismantling. From the user test for fisheries high school students, the system shows its effectiveness on the learning outcome of the dismantling.
A real camera interface enabling to shoot objects in virtual space

Virtual Reality technology has been becoming popular. As fascinating virtual worlds have become more accessible, we may feel compelled to photograph the virtual world. However, real world photography and virtual world photography are not the same experiences. For example, traditional virtual world photography methods do not allow for failures to occur, which is a factor in enjoying the photographic experiences. In this paper, we propose a virtual space photography system that can simulate shooting with a real camera as an interface and allow for shooting failures. We specifically generate images that consider the motion blur of the exposure by the shutter. We demonstrate the possibilities of generating failed photos that function as a kind of memory by simulating the effect of taking a picture with a real world camera.
A Comparative Study of Lexical and Semantic Emoji Suggestion Systems

Emoji suggestion systems based on typed text have been proposed to encourage emoji usage and enrich text messaging; however, such systems’ actual effects on the chat experience are unknown. We built an Android keyboard with both lexical (word-based) and semantic (meaning-based) emoji suggestion capabilities and compared these in two different studies. To investigate the effect of emoji suggestion in online conversations, we conducted a laboratory text-messaging study with 24 participants and a 15-day longitudinal field deployment with 18 participants. We found that participants picked more semantic suggestions than lexical suggestions and perceived the semantic suggestions as more relevant to the message content. Our subjective data showed that although the suggestion mechanism did not affect the chatting experience significantly, different mechanisms could change the composing behavior of the users and facilitate their emoji-searching needs in different ways.
Fine-Tuning a Pre-trained CAE for Deep One Class Anomaly Detection in Video Footage

In recent years, abnormal event detection in video surveillance has become a very important task mainly treated by deep learning methods taken into account many challenges. However, these methods still not trained on an anomaly detection based objective which proves their ineffectiveness in such a problem. In this paper, we propose an unsupervised method based on a new architecture for deep one class of convolutional auto-encoders (CAEs) for representing a compact Spatio-temporal feature for anomaly detection. Our CAEs are constructed by added deconvolutions layers to the CNN VGG 16. Then, we train our CAEs for a one-class training objective by fine-tuning our model to properly exploit the richness of the dataset with which CNN was trained. The first CAE is trained on the original frames to extract a good descriptor of shapes and the second CAE is learned using optical flow representations to provide a strength description of motion between frames. For this purpose, we define two loss functions, compactness loss and representativeness loss for training our CAEs architectures not only to maximize the inter-classes distance and to minimize the intra-class distance but also to ensure the tightness and the representativeness of features of normal images. We reduce features dimensions by applying a PCA (Principal Component Analyser) to combine our two descriptors with a Gaussian classifier for abnormal Spatio-temporal events detection. Our method has a high performance in terms of reliability and accuracy. It achieved abnormal event detection with good efficiency in challenging datasets compared to state-of-the-art methods.
Tests and Analysis of Health States for Graduate Students Based on the Random Matrix Theory

In this paper, the health status is studied for 26 volunteers of graduate students by calculating the average spectral radius of the finger pulse under the random matrix theory. It has been found that the average spectral radius of the pulse is higher for health status than that for unhealth status. This results are compared with the phase diagram of health which is filled in by volunteers actively.
The Development of a Comprehensive Data Set for Systematic Studies of Machine Translation

This paper presents our on-going efforts to develop a comprehensive data set and benchmark for machine translation beyond high-resource languages. The current release includes 500GB of compressed parallel data for almost 3,000 language pairs covering over 500 languages and language variants. We present the structure of the data set and demonstrate its use for systematic studies based on baseline experiments with multilingual neural machine translation between Finno-Ugric languages and other language groups. Our initial results show the capabilities of training effective multilingual translation models with skewed training data but also stress the shortcomings with low-resource settings and the difficulties to obtain sufficient information through straightforward transfer from related languages.
Research on Multimedia Swimming Teaching under Informatization

Computer technology, network technology and multimedia technology are developing rapidly and being used more and more widely, which brings hope for the modernization of educational technology and provides power for the reform of physical education. The use of multimedia courseware has solved many problems that conventional teaching can't solve or can't solve well, changed the monotonous, dull and dull atmosphere in classroom teaching in the past, and easily stimulated students' enthusiasm and interest in learning, so as to improve the comprehensive quality of college students. By means of teaching experiments, this paper analyzes the application effect of multimedia in college sports swimming class, and studies the application value of multimedia in college sports swimming class, hoping to have a certain positive impact and reference significance on improving the teaching quality of college sports swimming class.
A support system for tracheal suction skill acquisition based on augmented reality technology

This paper describes a support system for tracheal suction skill acquisition based on augmented reality technology. A few tracheal suction skill training tools have already been developed and used, but its operating cost is expensive, such as maintenance, storage, and cleaning/drying/sterilization after use as well as its purchase cost. Besides, the tracheal suction skill is a procedure inside the trachea, it is difficult to observe and evaluate the skill because the state of the procedure is to be invisible directly. Our proposed system solves these existing tools problems using augmented reality technology and compact 3D printing models and sensors. The system validity has been verified by evaluation experiments.
Financial Statement Audit Utilising Naive Bayes Networks, Decision Trees, Linear Discriminant Analysis and Logistic Regression

Initial big data analytics (BDA) implementation regarding data mining mechanisms in the audit process, remain un-der-researched and hence, more investigation is needed in developing classification prediction models utilising data mining analytics to identify appropriate auditing opinion. Empirical results show that the decision tree technique provides a balanced model for choosing the right audit opinion, with it achieving higher evaluation rates for two datasets (for the years 2018 and 2019) than Naive Bayes Networks (NBN), Linear Discriminant Analysis (LDA), and Logistic Regression (LR). In addition, for both datasets the LR model achieved higher evaluation rates than NBN and LDA. Moreover, the evaluation results for NBN exhibited a gap between 2019 and 2018 datasets, which means that it is an unbalanced model and hence, is not a good predictor of audit opinion. This paper is a pioneering effort utilising the sample of a company that have big data, based on financial and non-financial data to develop a model able to predict the correct audit opinion.
Study of Athlete’s Motion Identification Method using Gymnastics Apparatus with an Accelerometer

In gymnastics, the skeleton of the athletes can be estimated from many points having three-dimensional coordinate data by measurement control using LiDAR (Light Detection and Ranging), and the motion can be known. However, the system cannot know what kind of load is being put on the athletes' bodies. Also, it is not possible to know in detail how top-level athletes handle apparatus. It is considered that if we can measure apparatus' motion and grasp the athlete's motion using the data, it will be possible to evaluate the load by the exercise and the accurate use of the apparatus. Therefore, it is important to understand the dynamic response of the apparatus to the athlete’s motion. However, it is not desirable to directly attach a sensor to the bar because it interferes with the competition. For example, if it is possible to grasp the deformation and stress of the apparatus by attaching sensors to the post end, the measurement can be carried out without affecting the competition. In this study, we measured the displacement of both ends of the bar with an accelerometer that can sense how the bar is deformed by applying a static load to the bar. It was shown that the deformation identification of the bar was possible by substituting these measurement results to both ends of the bar of the model using the finite element method as displacement quantity.
Survey on Deep Learning Approaches for Aspect Level Opinion Mining

The the task of Aspect-based opinion mining (AbOM) is an emeraging research area, where aspects are mined, the corresponding opinion are scrutinized and sentiments are continuously changed, is gaining increased attention with growing feedback of clients and community across various social media streams. The gigantic improvements of deep learning (DL) techniques in natural language processing (NLP) tasks motivated research community to introduce a novel DL models and for AbSA, each investigate a diverse research points from different perspective, that cope with imminent problems and composite circumstances of AbOM. Consequently, in this survey paper, we concentrate on the limitations of the current studies and challenges relevant to mining of various aspects and their pertinent opinion, interrelationship delineations among different aspects, interactions, dependencies and contextual-semantic associations among various entities for enhanced opinion precision, and estimation of the automaticity of opinion polrity development. A laborious investigation of the later advancement is discussed depending on their contribution in the direction of spotlighting and alleviating the shortcomings related to Aspect Extraction (AE), AbOM, opinion progression (OP). The reported performance for each scrutinized study of Aspect Extraction and Aspect opinion Analysis is also given, revealing the numeriacal evaluation of the presented approach. Future research trends are introduce and delibrated by critically analysing the existing recent approaches, that will be supportive for researchers and advantageous for refining aspect based opinion classification.
Дистанционное образование в условиях действия ограничительных мер и его влияние на готовность студентов к такому формату проведения занятий

Due to coronavirus infection (COVID-19) declared pandemic in the past 2019-2020 academic year urgent safety restrictions and lockdown were introduced in many countries including Russian Federation to minimize the risk of infection. Because of these restrictions educational organizations had to switch to remote form of learning and use remote education technologies to guarantee education continuity. This article proposes to consider the remote format education implementation results in the second semester of the past academic year, its impact on the students’ readiness for such a method of conducting classes, on the possibility of self-organization, as well as the students learning motivation. The preparatory stage encapsulated two main items. The first of them included the questionnaire formation. The questionnaire supposed to allow assessing students’ attitude to remote education forms, to using distance education technologies and their self-organization level. Formed questionnaire and the T.I. Ilyina' questionnaire were used in the second stage for data collecting. The survey participants were Voronezh State University Faculty of Applied Mathematics, Informatics and Mechanics students. The questionnaire answers were given by bachelor's degree 2-4 years, specialty 2-5 years and magistracy 2 year students. Further, the obtained questionnaires were processed. On the basis of this information, conclusions were drawn about the attitude of students to distance learning used for various types of disciplines, their readiness for distance learning and the ability to self-organize. The second questionnaire data set processing allowed us to analyze respondents' learning motivation, as well as to compare results with similar ones, obtained before the restriction measures.
PROJECT-BASED MATHEMATICS LEARNING: FRUIT SALAD RECIPES IN JUNIOR HIGH SCHOOL

Mathematics learning is associated with 21st-century skills such as communication, collaboration, critical thinking in problem-solving, creativity, and innovation. To help students obtain these skills, a learning project was developed through Pendidikan Matematika Realistik Indonesia (PMRI) approach by using fruit salad recipes and collaborative learning based on the Lesson Study for Learning Community (LSLC) system. The primary purpose of this study was to develop fruit salad recipes to assist junior high school students in solving problems using mean, social arithmetic, and data presentation. It employed design research type validation studies using photos, fruit salad products, and document reviews as data collection techniques. The research subjects were 27 students of grade 8 from a junior high school in Palembang. This study resulted in a learning trajectory consisting of two activities and post-test questions. In the first activity, the students can analyze and solve problems in planning fruit salad recipes with material averaging, social arithmetic, and data presentation. In the second activity, the students can make fruit salads and write recipes based on skills to make fruit salad products. The results of this study demonstrate that in project-based learning through PMRI with the context of fruit salad recipes and the LSLC system, students can learn collaboratively. The learning helps them solve problems by using average material, social arithmetic, and data presentation in developing fruit salad recipes.
Causality in Econometric Modeling. From Theory to Structural Causal Modeling

This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these approaches. Traditional structural modeling is first discussed. A distinction is then drawn between model-based and design-based approaches. Some more recent developments are examined next, namely history-friendly simulation and information-theory based approaches. Finally, in a constructive perspective, structural causal modeling (SCM) is presented, based on the concepts of mechanism and sub-mechanisms, and of recursive decomposition of the joint distribution of variables. This modeling strategy endeavors at representing the structure of the underlying data generating process. It operationalizes the concept of causation through the ordering and role-function of the variables in each of the intelligible sub-mechanisms.
E-LEARNING IN THE CONDITIONS OF THE UNIVERSITY OF ŽILINA DURING THE CORONAVIRUS PANDEMIC

Appears in: INTED2021 Proceedings Publication year: 2021Pages: 5725-5731ISBN: 978-84-09-27666-0ISSN: 2340-1079doi: 10.21125/inted.2021.1155Conference name: 15th International Technology, Education and Development ConferenceDates: 8-9 March, 2021Location: Online Conference
Building language technology infrastructures to support a collaborative approach to language resource building

Digital infrastructures are a vital part of support for providing a research framework and platform in engineering their digital lexicography and grammars and deploying the to end-users as real NLP software products.
On the list decodability of rank-metric codes containing Gabidulin codes

Wachter-Zeh (IEEE Trans Inf Theory 59(11):7268–7276, 2013), and later together with Raviv (IEEE Trans Inf Theory 62(4):1605–1615, 2016), proved that Gabidulin codes cannot be efficiently list decoded for any radius $$\tau $$ , providing that $$\tau $$ is large enough. Also, they proved that there are infinitely many choices of the parameters for which Gabidulin codes cannot be efficiently list decoded at all. Subsequently, in Trombetti and Zullo (IEEE Trans Inf Theory 66(9):5379–5386, 2020) these results have been extended to the family of generalized Gabidulin codes and to further family of MRD-codes. In this paper, we provide bounds on the list size of rank-metric codes containing generalized Gabidulin codes in order to determine whether or not a polynomial-time list decoding algorithm exists. We detect several families of rank-metric codes containing a generalized Gabidulin code as subcode which cannot be efficiently list decoded for any radius large enough and families of rank-metric codes which cannot be efficiently list decoded. These results suggest that rank-metric codes which are $${{\mathbb {F}}}_{q^m}$$ -linear or that contains a (power of) generalized Gabidulin code cannot be efficiently list decoded for large values of the radius.
A Real-Time Whole Page Personalization Framework for E-Commerce

E-commerce platforms consistently aim to provide personalized recommendations to drive user engagement, enhance overall user experience, and improve business metrics. Most e-commerce platforms contain multiple carousels on their homepage, each attempting to capture different facets of the shopping experience. Given varied user preferences, optimizing the placement of these carousels is critical for improved user satisfaction. Furthermore, items within a carousel may change dynamically based on sequential user actions, thus necessitating online ranking of carousels. In this work, we present a scalable end-to-end production system to optimally rank item-carousels in real-time on the Walmart online grocery homepage. The proposed system utilizes a novel model that captures the user's affinity for different carousels and their likelihood to interact with previously unseen items. Our system is flexible in design and is easily extendable to settings where page components need to be ranked. We provide the system architecture consisting of a model development phase and an online inference framework. To ensure low-latency, various optimizations across these stages are implemented. We conducted extensive online evaluations to benchmark against the prior experience. In production, our system resulted in an improvement in item discovery, an increase in online engagement, and a significant lift on add-to-carts (ATCs) per visitor on the homepage.