Reviewerly/exHarmony huggingface dataset:
ONE ARABESQUE IN THE SMALL WORLD OF OD WEBMAPS

Abstract. Arabesque is an application for the exploration and geovisualisation of origin-destination flows (or spatial networks), developed within the framework of the Univ. Gustave Eiffel (ex. IFSTTAR)-funded research project geographic flow visualisation (gflowiz) geoflowiz, in collaboration with the CNRS. It allows both the exploration and the filtering of OD data and their representation, with a strong emphasis on geographic information layering and features' semiology. The key-objective is to propose an easy way to produce a modern cartography (a geovisualisation) of thematic flows (e.g. bilateral flow volume), at several geographic scales, even from your own datasets. The objective of this article is to position Arabesque in the range of geoweb applications for producing flow maps, by comparing its functionalities with those of similar web applications – Magrit, Kepler.gl, flowmap.blue – pointing out their respective advantages and limitations. The analysis of its functionalities is compared on the same flow dataset – MOBSCO, i.e. a dataset describing the school mobility of French pupils and students on a given year – for a practical and empirical “validation” of its contributions. We demonstrate that the configurations and appearances of these tools’ visual output depend largely on the culture of their developers, and on the use and audiences for which they have been developed.
Design patterns for parser combinators (functional pearl)

Parser combinators are a popular and elegant approach for parsing in functional languages. The design and implementation of such libraries are well discussed, but having a well-designed library is only one-half of the story. In this paper we explore several reusable approaches to writing parsers in combinator style, focusing on easy to apply patterns to keep parsing code simple, separated, and maintainable.
A Lens-Based Extension of Raycasting for Accurate Selection in Dense 3D Environments

In mixed environments, the selection of distant 3D objects is commonly based on raycasting. To address the limitations of raycasting for selecting small targets in dense environments, we present RayLens an extended raycasting technique. RayLens is a bimanual selection technique, which combines raycasting with a virtual 2D magnification lens that can be remotely moved in 3D space using the non-dominant hand. We experimentally compared RayLens with a standard raycasting technique as well as with RaySlider an extension of raycasting based on a target expansion mechanism whose design is akin to RayLens. RayLens is considerably more accurate and more than 1.3\(\times \) faster than raycasting for selecting small targets. Furthermore, RayLens is more than 1.6\(\times \) faster than RaySlider in dense environments. Qualitatively, RayLens is easy-to-learn and the preferred technique making it a good candidate technique for general public usage.
Acquiring Digital Proficiency in Teacher Education

In this commentary, we look at digital technology use in teacher education and practice, theoretical foundations, definitions, and digital competence frameworks in an educational context. With rapid changes in the domain of technology classroom teaching, teachers need to learn and adapt quickly. We argue that a more comprehensive approach to understanding how student-teacher learning with technology is needed.
LayoutTransformer: Layout generation and completion with self-attention

We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents, and 3D objects. Most complex scenes, natural or human-designed, can be expressed as a meaningful arrangement of simpler compositional graphical primitives. Generating a new layout or extending an existing layout requires understanding the relationships between these primitives. To do this, we propose LayoutTransformer, a novel framework that leverages self-attention to learn contextual relationships between layout elements and generate novel layouts in a given domain. Our framework allows us to generate a new layout either from an empty set or from an initial seed set of primitives, and can easily scale to support an arbitrary of primitives per layout. Furthermore, our analyses show that the model is able to automatically capture the semantic properties of the primitives. We propose simple improvements in both representation of layout primitives, as well as training methods to demonstrate competitive performance in very diverse data domains such as object bounding boxes in natural images(COCO bounding box), documents (PubLayNet), mobile applications (RICO dataset) as well as 3D shapes (Part-Net). Code and other materials will be made available at this https URL.
Classification Model Considering Misclassification Based on Distributional Distance Between Labels

In this study, we propose deep learning model that not only has a high accuracy, but also a small distance between training and prediction labels for unknown data in the neighbor of the training data. We define a knowledge distribution as probability distribution for each labels. The relationship between the knowledge distributions corresponds to the distance between the labels. In the proposed method, we firstly train a model to minimize distributional distance between internal features and knowledge distribution, then train the model to classify labels correctry. We visually confirmed that the proposed method can predict labels with small distance between labels for unknown data near the training data. In addition, we compared the proposed method with a existing method that uses a typical convolutional neural network. Experiments on MNIST show that the proposed method achieved the same or better accuracy than existing methods. The proposed method also achieved mean absolute error of 0.32, while the existing method achieved 0.374.
Psychological and pedagogical aspects of management of activation of cognitive activity of applicants for higher education

The article presents the results of theoretical, psychological and pedagogical research on the effectiveness of management of cognitive activity of students of higher educational institutions. The notion of the model of education as a mixed type of pedagogical technologies of education and with orientation on educational and professional needs of students is substantiated. The postmodern model of teaching is described through the actualization of the creative potential of the pedagogical process, while preserving the classical educational paradigm. Peculiarities of students' cognitive activity management in the postmodern model of education are analyzed. Important personal and motivational characteristics of the student as a subject of educational activity are reflected.
Building Critical Thinking Skills Through a New Design Mobile-Based Interactive Learning Media Knowledge Framework

Education is expected to be a solution to issues in human resource development. A number of initiatives have been made to overcome educational problems ranging from improving the curriculum, developing and increasing the quality of teachers, developing student abilities, and other efforts related to improving the quality of education. Based on the preliminary observation, students’ critical thinking skills were considered low and a confirmatory study related to building critical thinking skills is necessary. This study analyzed critical thinking skills that are built based on perceptions of mobile media, self-directed learning, and digital literacy. The research method used was a quantitative approach by using a questionnaire instrument.. Data analysis included analyses of prerequisite tests including validity and reliability tests. The data were analyzed using the structural equation model (SEM) analysis. The results showed that 1) there was a positive and significant effect on the variable of perceptions on online learning media on self-directed learning and digital literacy, 2) there was a positive and significant effect on the variable of self-directed learning on critical thinking skills 3) there was a positive and significant effect on the variable of digital literacy on critical thinking skills 4) the variable of the perceptions on mobile-based learning media had a positive and significant effect on critical thinking skills through the mediation of self-directed learning and digital literacy.
Prospect for expansion and replenishment of rare metal resources and reserves in Eastern Kazakhstan

The main rare metal-bearing structures in Eastern Kazakhstan are granitoid belts formed in the Hercynian cycle of tectogenesis in the post-collisional (orogenic) geodynamic environment of the Permian time and confined to the continental blocks of siallitic high-level. The geotectonic position of the Kalba&ndash;Narym rare metal granitoid belt in the geological structure of the Greater Altai is considered. The known rare-metal pegmatite deposits (Ta, Nb, Be, Li, etc.) are spatially and genetically related to Kalba granites (P1). The richest ore-bearing are granite massifs formed in the mobile tectonic environment and under unbalanced P&ndash;T conditions. The distribution of rare alkalis in granitoids of the Hercynian evolutionary series is given. The forecast estimate of prospects is associated with the study of hidden granite massifs on the northwestern flank of the belt, identified based on the geological and geophysical data. The forecasting criteria are the intensive alteration of the above-intrusive zone (granitization, greisenization, silicification), direct signs of tin&ndash;tungsten mineralization on ground surface, identification of ongonite-like dikes and rare metal minerals in metasomatites from the results of electron microscopy. The estimation of the prospects of nontraditional tin&ndash;lithium&ndash;tantalum mineralization associated with albitized and greisenized granites is given. It is recommended to undertake deep mineralogical mapping of the closed reserved areas. The further geological exploration should be aimed at expansion of mineral resources and reserves for rare metal production.The study was supported by the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan, Contract No. 120 dated 1 June 2020, Registration No. 0120RK00135, and Grant IRN AR08856325.
An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining

Abstract The Linked Open Data practice has led to a significant growth of structured data on the Web. While this has created an unprecedented opportunity for research in the field of Natural Language Processing, there is a lack of systematic studies on how such data can be used to support downstream NLP tasks. This work focuses on the e-commerce domain and explores how we can use such structured data to create language resources for product data mining tasks. To do so, we process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating language resources: training word-embedding models, continued pre-training of BERT-like language models, and training machine translation models that are used as a proxy to generate product-related keywords. These language resources are then evaluated in three downstream tasks, product classification, linking, and fake review detection using an extensive set of benchmarks. Our results show word embeddings to be the most reliable and consistent method to improve the accuracy on all tasks (with up to 6.9% points in macro-average F1 on some datasets). Contrary to some earlier studies that suggest a rather simple but effective approach such as building domain-specific language models by pre-training using in-domain corpora, our work serves a lesson that adapting these methods to new domains may not be as easy as it seems. We further analyse our datasets and reflect on how our findings can inform future research and practice.
Use of Augmentation and Distant Supervision for Sentiment Analysis in Russian

In this study, we test several augmentation and distant supervision techniques to increase sentiment datasets in Russian. We use transfer learning approach pre-trained on created additional data to improve the performance. We compare our proposed approach based on distant supervision with existing augmentation methods. The best results were achieved using three-step approach of sequential training on general, thematic and original train samples. The results were improved by more than 3% to the current state-of-the-art methods for most of the benchmarks using data automatically annotated with distant supervision technique.
BOLD.R: A Software Package to Interface with BOLD Through R

DNA barcoding has been established as a reliable system for identifying and classifying species. Data generated from DNA barcoding is continuously cataloged on the Barcode of Life Data System (BOLD) using samples collected from researchers and institutions around the world. Advances in DNA analysis have led to a rapid increase in the amount of data available for researchers to study and modern statistical techniques are consequently playing an increasingly important role in the analysis of such large volumes of data. Existing methods to import data from BOLD into any statistical software can be inconvenient, time-consuming, or provide limited information. One of the most popular software applications for statistical analysis is R and we developed an R packages called BOLD.R which aims to overcome existing barriers currently preventing the ease of access to information on BOLD directly into statistical software. Our package allows users to access public data directly from BOLD into R via the current API maintained by the BOLD system. In this article we discuss the implementation and benefits of BOLD.R for researchers and scientists.
A security scheme based on blockchain and a hybrid cryptosystem to reduce packet loss in IoV

Recently, the focus of smart vehicles that relied on sensors has changed due to advances in the Internet of Things (IoT) and networks.Various information can be exchanged while applying IoT technology to automobiles, which allows drivers to pursue convenience.The Internet of Vehicles (IoV) is a technology that combines Intelligent Transportation Systems (ITS) and IoT, and vehicular ad-hoc network (VANET), the current communication, is being developed into IoV [1].IoV allows autonomous vehicles to communicate with other autonomous vehicles or various road infrastructures to understand road conditions.Autonomous vehicles recognize the road environment with sensing techniques such as cameras, radars, and riders, and identify road topography using High-definition (HD) maps [2] and the Global Navigation Satellite System (GNSS) [3].*Author for correspondence Furthermore, autonomous vehicles communicate with objects on the road using the Vehicle to Everything (V2X) communication, collect information, and plan routes based on it [4].Autonomous vehicles collect various situational information, such as road control areas caused by accidents and roads where rush hour occurs through V2X communication.Using this information, automatic vehicles can avoid road congestion and navigate and drive on roads that allow for the shortest travel time [4].V2X communication that can collect information is a necessary technology for autonomous driving, and the stability of the network must be ensured while the vehicle is communicating.This is because such information can be sent and received in real time to cope with the situation.Therefore, for autonomous driving, a high network transmission speed must be guaranteed and data must be protected from attackers [5].An attacker infiltrates networks used by autonomous vehicles to delete or forge data.Accidents occur because autonomous vehicles receive forged data or fail to
Detection Performance Analysis of Multistatic Sonar System Based on Cumulative Detection

With the development of acoustic stealth technology and underwater acoustic countermeasure technology, the detection performance of monostatic sonar system for low-noise targets is difficult to meet the operational requirements, while the comprehensive detection of multistatic system may greatly improve the detection performance, which provides a new way to solve the detection problem of a quiet target. Therefore, a fusion detection algorithm based on vector hydrophone for multistatic sonar system is proposed in this paper. The detection performance of four kinds of sonar systems is compared, which are monostatic distributed, monostatic centralized, multistatic distributed and multistatic centralized. Detection performance is quantitatively analyzed by the detection range of detection probability above the detection threshold. The research has a certain reference value for large-scale target detection.
Semi-literate Texting (SLT): Survey based text message dataset from digitally semi-literate users in India

The dataset explicates text messages and associated metadata from digitally semi-literate mobile phone users in India. A survey among urban and rural representatives conducted between July 2020 and November 2020 is the origin for this dataset. The data has been collected through face to face interviews and online surveys across urban and rural geographies in India, largely from western region of Maharashtra. A total of 382 respondents, accumulating 3368 messages has been composed (approximately 90% through face to face surveys and 10% from online mode). To the best of our knowledge there is no factual text message data from digitally semi-literate users being available till date. This dataset can be used for bridging the digital divide in human computer interaction using machine learning, data mining, behavioural analysis as well as in other fields.
Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks.

Graph representation learning has made major strides over the past decade. However, in many relational domains, the input data are not suited for simple graph representations as the relationships between entities go beyond pairwise interactions. In such cases, the relationships in the data are better represented as hyperedges (set of entities) of a non-uniform hypergraph. While there have been works on principled methods for learning representations of nodes of a hypergraph, these approaches are limited in their applicability to tasks on non-uniform hypergraphs (hyperedges with different cardinalities). In this work, we exploit the incidence structure to develop a hypergraph neural network to learn provably expressive representations of variable sized hyperedges which preserve local-isomorphism in the line graph of the hypergraph, while also being invariant to permutations of its constituent vertices. Specifically, for a given vertex set, we propose frameworks for (1) hyperedge classification and (2) variable sized expansion of partially observed hyperedges which captures the higher order interactions among vertices and hyperedges. We evaluate performance on multiple real-world hypergraph datasets and demonstrate consistent, significant improvement in accuracy, over state-of-the-art models.
Consistent management of variability in space and time

Development and maintenance of software-intensive systems face major challenges. To cope with an increasing demand for customization, systems need to exist in concurrent variations at a single point in time (i.e., variability in space). Furthermore, as longevity of systems increases, it is necessary to continuously maintain sequential variations due to evolution (i.e., variability in time). Finally, systems are often built from different kinds of artifacts, such as source code or diagrams, that need to be kept consistent. Managing these challenges - the evolution of variable systems composed of heterogeneous artifacts in a consistent and integrated way - is highly demanding for engineers. To tackle the described challenges, we propose an approach for consistent, view-based management of variability in space and time. Therefore, we study, identify, and unify concepts and operations of approaches and tools dealing with variability in space and time to provide a common ground for comparing existing work and encouraging novel solutions. Furthermore, we identify consistency preservation challenges related to view-based evolution of variable systems composed of heterogeneous artifacts, such as the consistent propagation of changes between products, and across the different types of artifacts. We provide a technique for (semi-)automated detection and repair of variability-related inconsistencies. The goal of this doctoral work is to develop an integrated solution for dealing with the described challenges and, thus, advance state of the art towards uniform management of variability in space and time.
Smart Classroom Design on the Basis of Internet of Things Technology

Internet of Things technology is a new type of network technology emerging in the new economic era. It has played a good role in various industries and provided new opportunities for the reform and optimization of the education industry. This paper takes the intelligent teaching base on the basis of Internet of Things technology as the research object, introduces the connotation of the wisdom teaching base with reference to relevant literature materials, analyzes the research background of the wisdom teaching base on the basis of the Internet of Things technology, expounds the design method of smart teaching base on the basis of Internet of Things technology, and analyzes the function realization and application of intelligent teaching base on the basis of Internet of Things technology.
Construction of Flat Domains in the Environment of Dynamic Mathematics When Solving Problems with Parameters

The article is based on the main key ideas of the concept of profile education and the experiment on the implementation of a digital educational environment, one of them is the idea of using digital educational content, including in mathematical education, as a basis for getting ahead at the world level. The article acquaints teachers of mathematics with the possibilities of using dynamic drawings to visualize mathematical objects when solving problems with parameters. The use of multimedia technologies with elements of interactive interaction with the user will make it possible to realize the developmental potential of basic general secondary education, which is a priority area of educational standards.