Reviewerly/exHarmony huggingface dataset:
Professional Training of Teacher 4.0: Developing Digital Competency By Means of ePortfolio

The purpose of this article is to present the experience of trainee teachers in implementing professional activities in the context of Industry 4.0, and developing the education model 4.0, which provides a change in technologies, forms and teaching aids focused on new requirements for educational results, and implementation of a lifelong educational trajectory. The subject of the study is digital competency, which is the basic competency of Teacher 4.0, which allows teachers to systematically implement professional activities in a digital society, integrate knowledge and interdisciplinary skills. The development of ICT competence is described in the process of training tutors (Bachelor degree in Education) at the Institute of Education, Psychology and Sociology of Siberian Federal University by means of electronic portfolio. The article substantiates methodological approaches and methodological solutions for the development of ICT competency, describes the levels of its formation in the electronic educational environment of a federal university by means of electronic portfolios. The described experimental work discloses assessment of the effectiveness of the presented method for developing ICT competency of trainee tutors within mastering the systemically important IT disciplines. The article presents diagnostic tools, including a list of valid techniques for the component-wise assessment of the ICT competence level of trainee tutors. The main results of the study can serve the basis for further developments in the field of effective training in different teaching specializations, taking into account the requirements of academic and professional standards in the context of digitalization of education and the development of electronic information educational environment
Elimination of parasitic reflections for objects with high transparency in phase measuring deflectometry

Abstract In phase measuring deflectometry (PMD), the existence of parasitic reflections at the rear surface of transparent objects will lead to ‘ghosted’ fringe patterns, which results in phase error. Accurately extracting the phase from the 'ghosted' fringe patterns is considered as one of the main problems in PMD. Existing phase extraction methods still remain some drawbacks such as high cost of experiment equipment, the existence of ill-conditioned areas and difficulty in setting up a suitable threshold. In this paper, the envelope curve algorithm based on multi-frequency approach is proposed to eliminate parasitic reflections. The parasitic reflections were eliminated successfully. The PV and the RMS of the front phase error are 0.1085 rad and 0.0072 rad, and those of the rear phase error are 0.1345 rad and 0.0081 rad respectively in the simulation. Experiments also proved the effectiveness of the proposed algorithm. What’s more, the phase information of both the front and the rear surfaces can be attained simultaneously. The proposed algorithm could eliminate parasitic reflections successfully without destroying the tested object or special equipment. Factors which may influence the performance of this algorithm are discussed, and results showed that the proposed method is especially suitable for thick transparent object.
SemanGit: A Linked Dataset from git

The growing interest in free and open-source software which occurred over the last decades has accelerated the usage of versioning systems to help developers collaborating together in the same projects. As a consequence, specific tools such as git and specialized open-source on-line platforms gained importance. In this study, we introduce and share SemanGit which provides a resource at the crossroads of both Semantic Web and git web-based version control systems. SemanGit is actually the first collection of linked data extracted from GitHub based on a git ontology we designed and extended to include specific GitHub features. In this article, we present the dataset, describe the extraction process according to the ontology, show some promising analyses of the data and outline how SemanGit could be linked with external datasets or enriched with new sources to allow for more complex analyses. Resource type: Dataset Website: http://www.semangit.de/ Permanent URL: https://doi.org/10.5281/zenodo.2176047
Active contour model based on LIF model and optimal DoG operator energy for image segmentation

In order to solve the problem that the region-based active contour model is sensitive to the initial contour position, the convergence is poor, and the active contour model can not obtain good segmentation results when segmenting complex background images and severe intensity inhomogeneous images. In this paper, an active contour model which combines Local Image Fitting (LIF) and Difference of Gaussian (DoG) operator energy for image segmentation is proposed. Firstly, an optimal DoG operator is obtained by using the edge energy term, it can enhance the edge while smoothing inhomogeneous regions. Then, using the DoG energy term which is obtained in the first step and the LIF energy term to construct the total function energy terms. In this process, the regularization term is also established, it can control the smoothness of evolution curve, avoiding over-segmentation and re-initialization step. Finally, the variational method and gradient descent flow method are adopted to minimize the total energy functional for segmentation. Compared with other region-based active contour models, the experimental results show that the proposed method can achieve a better segmentation performance with less iterations and high calculation efficiency while segmenting the synthetic and real images with intensity inhomogeneity.
HORAE

We introduce in this paper a new dataset of annotated pages from books of hours, a type of handwritten prayerbooks owned and used by rich lay people in the late middle ages. The dataset was created for conducting historical research on the evolution of the religious mindset in Europe at this period, since book of hours represent one of the major sources of information thanks both to their rich illustrations and the different types of religious sources they contain. We first describe how the corpus was collected and manually annotated then present the evaluation of a state-of-the-art system for text line detection and for zone detection and typing. The corpus is freely available for research.
Machine Translation in the Field of Law: A Study of the Translation of Italian Legal Texts into German

Abstract With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy. However, since legal texts have features that pose major challenges to machine translation, the question arises as to what extent machine translation is now capable of translating legal texts or at least certain types of legal text into another legal language well enough so that the post-editing effort is limited, and, consequently, whether a targeted use in translation pedagogy can be considered. In order to answer this question, DeepL Translator, a machine translation system, and MateCat, a CAT system that integrates machine translation, were tested. The test, undertaken at different times and without specific translation memories, provided for the translation of several legal texts of different types utilising both systems, and was followed by systematisation of errors and evaluation of translation results. The evaluation was carried out according to the following criteria: 1) comprehensibility and meaningfulness of the target text; and 2) correspondence between source and target text in consideration of the specific translation situation. Overall, the results are considered insufficient to give post-editing of machine-translated legal texts a bigger place in translation pedagogy. As the evaluation of the correspondence between source and target text was fundamentally worse than with regard to the meaningfulness of the target text, translation pedagogy should respond by raising awareness about differences between machine translation output and human translation in this field, and by improving translation approach and strengthening legal expertise.
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English

African American Vernacular English (AAVE) is a widely-spoken dialect of English, yet it is under-represented in major speech corpora.As a result, speakers of this dialect are often misunderstood by NLP applications.This study explores the effect on transcription accuracy of an automatic voice recognition system when AAVE data is used.Models trained on AAVE data and on Standard American English data were compared to a baseline model trained on a combination of the two dialects.The accuracy for both dialectspecific models was significantly higher than the baseline model, with the AAVE model showing over 18% improvement.By isolating the effect of having AAVE speakers in the training data, this study highlights the importance of increasing diversity in the field of natural language processing.
Cybercrime Business Digital in Indonesia

The development of information technology and especially internet communication has caused significant social, economic and cultural changes to occur so quickly. On one side of information technology can provide benefits, simplify and accelerate access to information that we need in all things and can change the model of the economy and business models. However, even a negative impact cannot be avoided. Along with the development of internet technology, causing the emergence of new crimes called new cybercrime through the internet network. In this study, the authors approached qualitatively with interview methods and supported descriptive data from research sources originating from regulators and current digital business people who aimed to find out cybercrime in Indonesia. The results of this study, both regulators and business people view that critical preventive measures are taken and the law must continue to follow the development of crime in the cyber world.
Geochemistry and Geophysics of Iron Oxide-Apatite Deposits and Associated Waste Piles with Implications for Potential Rare Earth Element Resources from Ore and Historical Mine Waste in the Eastern Adirondack Highlands, New York, USA

Abstract The iron oxide-apatite (IOA) deposits of the eastern Adirondack Highlands, New York, are historical high-grade magnetite mines that contain variable concentrations of rare earth element (REE)-bearing apatite crystals. The majority of the deposits are hosted within sodically altered Lyon Mountain granite gneiss, although some deposits occur within paragneiss, gabbro, anorthosite, or potassically altered Lyon Mountain granite gneiss. The IOA deposits and the waste and/or tailings piles associated with them have potential as an unconventional resource for REEs. Reprocessing of these piles would have the advantage of partial recycling of the waste material to produce a set of critical elements. Thirty-four ore, nine rock, 25 waste-pile, and four tailings-pile samples were collected and analyzed for major, minor, and trace elements. At the tailings- and waste-pile sites, composite samples were collected by combining 30 to &gt;50 subsamples randomly distributed over each pile. The total REE content of the waste and tailings piles varied from approximately 10 to 22,000 ppm, whereas the ore sample concentrations ranged from approximately 15 to 48,000 ppm total REEs. A positive correlation exists between the total REE content of ore and its associated waste pile. Median light REE/heavy REE values were 2.14 for waste/tailings piles and 2.25 for ore, which is a substantial relative enrichment in the heavy REEs in comparison to many developed REE mines, such as the mined carbonatites of Bayan Obo, China, and Mountain Pass, California. Importantly, the ore and waste samples are significantly enriched in both Y and Nd compared to other REEs in the samples. Other minor components such as Th are also elevated. Airborne radiometric surveys show large positive eTh and eU anomalies corresponding to tailings piles. Although it is a limited data set, geochemical data of unaltered and altered host rocks suggest a speculative new model for IOA ore formation in the Adirondack Highlands that is consistent with the geology and previously published data. The ferroan ore-hosting Lyon Mountain granite gneiss underwent localized potassic alteration that enriched the altered rock in Fe, REEs, Th, and other metals. A later sodic alteration event affected the previously potassically altered Lyon Mountain granite gneiss, which increased rock porosity and remobilized Fe, REEs, and other elements from the host rock into the iron ore seams. The sodic fluids responsible for ore formation were enriched in F and Cl.
Daphne: A Virtual Assistant for Designing Earth Observation Distributed Spacecraft Missions

This article describes Daphne, a virtual assistant for designing Earth observation distributed spacecraft missions.It is, to the best of our knowledge, the first virtual assistant for such application.The article provides a thorough description of Daphne, including its question answering system and the main features we have implemented to help system engineers design distributed spacecraft missions.In addition, the article describes a study performed at NASA's Jet Propulsion Laboratory (JPL) to assess the usefulness of Daphne in this use case.The study was conducted with N = 9 subjects from JPL, who were asked to work on a mission design task with two versions of Daphne, one that was fully featured implementing the cognitive assistance functions, and one that only had the features one would find in a traditional design space exploration tool.After the task, they filled out a standard user experience survey, completed a test to assess how much they learned about the task, and were asked a number of questions in a semi-structured exit interview.Results of the study suggest that Daphne can help improve performance during system design tasks compared to traditional tools, while keeping the system usable.However, the study also raises some concerns with respect to a potential reduction in human learning due to the use of the cognitive assistant.The article ends with a list of suggestions for future development of virtual assistants for space mission design.
Blended Learning Syarah: Bagaimana Penerapan dan Persepsi Mahasiswa

This study aims to see how the implementation and students’ perceptions of e-learning type blended learning in Mathematics Capita Selecta course. The e-learning in this study utilized the Syarah portal, an online learning application developed by Universitas Maritim Raja Ali Haji (UMRAH) responding to the 4.0 era. This online learning using Syarah was the first time applied to research subjects, namely 35 students of Mathematics Capita Selecta Course year 2019. The data was collected from the results of the researchers' observations during the learning process and students’ responses to the questionnaire. The data were analyzed qualitatively to describe how the implementation of e-learning and how students’ perceptions about e-learning Syarah in supporting them to learn. The results of the analysis show that the Syarah system and students are ready for the implementation of online learning. Students’ confusion only occurs at the initial meeting of each type of e-learning activities designed by the researcher. Hereinafter, students could carry out learning well, even independently, but with clearly stated instructions. In addition, students were actively involved during the e-learning process. It also reveals that students felt more open to engaging in e-learning activities than when they were in class. In general, students gave positive responses to the use of Syarah in supporting blended learning.
Do Multi-hop Readers Dream of Reasoning Chains?

General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to derive the answer.In this paper we conduct a systematic analysis to assess such an ability of various existing models proposed for multi-hop QA tasks.Specifically, our analysis investigates that whether providing the full reasoning chain of multiple passages, instead of just one final passage where the answer appears, could improve the performance of the existing QA models.Surprisingly, when using the additional evidence passages, the improvements of all the existing multi-hop reading approaches are rather limited, with the highest error reduction of 5.8% on F1 (corresponding to 1.3% absolute improvement) from the BERT model.To better understand whether the reasoning chains could indeed help find correct answers, we further develop a co-matchingbased method that leads to 13.1% error reduction with passage chains when applied to two of our base readers (including BERT).Our results demonstrate the existence of the potential improvement using explicit multi-hop reasoning and the necessity to develop models with better reasoning abilities.
Deep Patient Representation of Clinical Notes via Multi-Task Learning for Mortality Prediction.

We propose a deep learning-based multi-task learning (MTL) architecture focusing on patient mortality predictions from clinical notes. The MTL framework enables the model to learn a patient representation that generalizes to a variety of clinical prediction tasks. Moreover, we demonstrate how MTL enables small but consistent gains on a single classification task (e.g., in-hospital mortality prediction) simply by incorporating related tasks (e.g., 30-day and 1-year mortality prediction) into the MTL framework. To accomplish this, we utilize a multi-level Convolutional Neural Network (CNN) associated with a MTL loss component. The model is evaluated with 3, 5, and 20 tasks and is consistently able to produce a higher-performing model than a single-task learning (STL) classifier. We further discuss the effect of the multi-task model on other clinical outcomes of interest, including being able to produce high-quality representations that can be utilized to great effect by simpler models. Overall, this study demonstrates the efficiency and generalizability of MTL across tasks that STL fails to leverage.
Development and field test of the articulated mobile robot T<sup>2</sup>Snake-4 for plant disaster prevention

In this work, we develop an articulated mobile robot that can move in narrow spaces, climb stairs, gather information, and operate valves for plant disaster prevention. The robot can adopt a tall position using a folding arm and gather information using sensors mounted on the arm. In addition, this paper presents a stair climbing method using a single backward wave. This method enables the robot to climb stairs that have a short tread. The developed robot system is tested in a field test at the World Robot Summit 2018, and the lessons learned in the field test are discussed.
Learning to Fix Build Errors with Graph2Diff Neural Networks

Professional software developers spend a significant amount of time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new deep learning architecture, called Graph2Diff, for automatically localizing and fixing build errors. We represent source code, build configuration files, and compiler diagnostic messages as a graph, and then use a Graph Neural Network model to predict a diff. A diff specifies how to modify the code's abstract syntax tree, represented in the neural network as a sequence of tokens and of pointers to code locations. Our network is an instance of a more general abstraction that we call Graph2Tocopo, which is potentially useful in any development tool for predicting source code changes. We evaluate the model on a dataset of over 500k real build errors and their resolutions from professional developers. Compared to the approach of DeepDelta (Mesbah et al., 2019), our approach tackles the harder task of predicting a more precise diff but still achieves over double the accuracy.
Wide-Ranging Review Manipulation Attacks

User reviews have become a cornerstone of how we make decisions. However, this user-based feedback is susceptible to manipulation as recent research has shown the feasibility of automatically generating fake reviews. Previous investigations, however, have focused on generative fake review approaches that are (i) domain dependent and not extendable to other domains without replicating the whole process from scratch; and (ii) character-level based known to generate reviews of poor quality that are easily detectable by anti-spam detectors and by end users. In this work, we propose and evaluate a new class of attacks on online review platforms based on neural language models at word-level granularity in an inductive transfer-learning framework wherein a universal model is refined to handle domain shift, leading to potentially wide-ranging attacks on review systems. Through extensive evaluation, we show that such model-generated reviews can bypass powerful anti-spam detectors and fool end users. Paired with this troubling attack vector, we propose a new defense mechanism that exploits the distributed representation of these reviews to detect model-generated reviews. We conclude that despite the success of neural models in generating realistic reviews, our proposed RNN-based discriminator can combat this type of attack effectively (90% accuracy).
Exploring Rust for Unikernel Development

System-level development has been dominated by programming languages like C/C++ for decades. These languages are inherently unsafe, error-prone, and a major reason for vulnerabilities. High-level programming languages with a secure memory model and strong type system are able to improve the quality of the system software. In this paper, we explore the programming language Rust for kernel development and present RustyHermit, which is a unikernel completely written in Rust without any C/C++. We show that the support for RustyHermit can be transparently integratable in the Rust toolchain and common Rust applications are build-able on top of RustyHermit. Previously, we developed the C-based unikernel HermitCore with a similar design to RustyHermit and we are able to compare both kernels. We show that the performance of both kernels is similar and only ~3.27 % of RustyHermit relies on unsafe code, that cannot be checked by the compiler in detail.
BLENDED LEARNING IN UNIVERSITY EDUCATION

Purpose of the study: The article brings forward the definition, based on the analysis of both domestic and foreign scientists’ views; structure components of blended learning with account taken of university-specific character.&#x0D; Methodology: Blended learning process is presented as a model consisting of 9 successive stages, each of them represents a logical sequence of measures ensuring the dynamics of system development.&#x0D; Results: The developed by the author model result in the recommendation to proceed to “blended learning” which is conceptually based on the constructive potential of a teacher, on searching ways of its building on the basis of communicative, person-centered and interactive approaches, increasing organizational changeability of the educational environment.&#x0D; Applications of this study: This research can be used for the universities, teachers, and students.&#x0D; Novelty/Originality of this study: In this research, the model of the Blended Learning in University Education is presented in a comprehensive and complete manner.