Reviewerly/exHarmony huggingface dataset:
Hierarchical Mapping for Crosslingual Word Embedding Alignment

Abstract The alignment of word embedding spaces in different languages into a common crosslingual space has recently been in vogue. Strategies that do so compute pairwise alignments and then map multiple languages to a single pivot language (most often English). These strategies, however, are biased towards the choice of the pivot language, given that language proximity and the linguistic characteristics of the target language can strongly impact the resultant crosslingual space in detriment of topologically distant languages. We present a strategy that eliminates the need for a pivot language by learning the mappings across languages in a hierarchical way. Experiments demonstrate that our strategy significantly improves vocabulary induction scores in all existing benchmarks, as well as in a new non-English–centered benchmark we built, which we make publicly available.
Conceptual Modeling in Product Design within Virtual Reality Environments

Serial plates are not reasonable in material saving and stress dispersion.To design orthopedic plates ideally and conveniently, this paper proposes a method to optimize plates through editing semantic parameters based on average bone model.Firstly, for the reasonable distribution of serial plates in number and size, an average bone model is created from the existing bones, among which each bone has a contribution to the average model.Secondly, a common orthopedic plate with semantic parameters is constructed on average bone model and it can be conveniently modified.Lastly, optimizing the thickness of the plate through finite element analysis and genetic algorithm to meet the stress condition and use as little material as possible.The simulation results indicate that the method can save material and disperse the stress of the plates so that it can effectively optimize the orthopedic plates.
Deep Learning Based Automatic Speech Recognition for Turkish

Using Deep Neural Networks (DNN) as an advanced Artificial Neural Networks (ANN) has become widespread with the development of computer technology. Although DNN has been applied for solving Automatic Speech Recognition (ASR) problem in some languages, DNN-based Turkish Speech Recognition has not been studied extensively. Turkish language is an agglutinative and a phoneme-based language. In this study, a Deep Belief Network (DBN) based Turkish phoneme and speech recognizer is developed. The proposed system recognizes words in the system vocabulary and phoneme components of out of vocabulary (OOV) words. Sub-word (morpheme) based language modelling is implemented into the system. Each phoneme of Turkish language is also modelled as a sub-word in the model. Sub-word (morpheme) based language model is widely used for agglutinative languages to prevent excessive vocabulary size. The performance of the suggested DBN based ASR system is compared with the conventional recognition method, GMM (Gaussian Mixture Method) based Hidden Markov Model (HMM). Regarding to performance metrics, the recognition rate of Turkish language is improved in compare with previous studies.
On the issue of digitalization of education.

Introduction. The article is devoted to a new problem in pedagogical science – the digi- talization of education – which is becoming increasingly important. In our opinion, it is the concept of digitalization of education that will determine the course of further psy- chological and pedagogical research. Methods. The scientific article provides an overview of modern analytical sources that describe the phenomenon of digitalization of education. The history of the development of digitalization of education is described, various aspects of this phenomenon are con- sidered, special attention is paid to the changes in the organization of the educational process that digitalization entails. Results. The scientific article considers new terms that came into use with the advent of digitalization: “Big Data”, “Educational Data Mining”, “lifelong-learning”, “advanced- learning-technologies”. Conclusions. Digitalization of education is a multifaceted phenomenon, covering all spheres of human activity. A phenomenon that needs to be comprehended, developed, as well as actively applied in practice, the advanced training technologies that result from it in order to fit into a new, emerging digital, global system.
The ‘dark data’ conundrum

Unstructured ‘dark' data is both an opportunity and a threat to every business. How your organisation manages this unique category of data will potentially define its chances of success or failure over the years to come. This article explores the risk and opportunity inherent in dark data and suggests ways of enhancing your organisation's approach to managing this enigmatic data type. Unstructured dark data is both an opportunity and a threat to every business. How your organisation manages this data will potentially define its chances of success or failure over the years to come. Given the potential afforded by illuminating dark data, it is worth looking at the value of your own dark data and what you can do to reduce the long-term cost of hoarding data of no value, mitigate the risks of a data breach, drive efficiencies in business processing, prepare your organisation to exploit AI, increase your competitiveness and deliver tangible improvements in services, says Mark Hobart of Infoboss.
A Survey of Algorithms for Geodesic Paths and Distances

Numerical computation of shortest paths or geodesics on curved domains, as well as the associated geodesic distance, arises in a broad range of applications across digital geometry processing, scientific computing, computer graphics, and computer vision. Relative to Euclidean distance computation, these tasks are complicated by the influence of curvature on the behavior of shortest paths, as well as the fact that the representation of the domain may itself be approximate. In spite of the difficulty of this problem, recent literature has developed a wide variety of sophisticated methods that enable rapid queries of geodesic information, even on relatively large models. This survey reviews the major categories of approaches to the computation of geodesic paths and distances, highlighting common themes and opportunities for future improvement.
Funnel Activation for Visual Recognition

We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition. The forms of ReLU and PReLU are y = max(x, 0) and y = max(x, px), respectively, while FReLU is in the form of y = max(x,T(x)), where T(x) is the 2D spatial condition. Moreover, the spatial condition achieves a pixel-wise modeling capacity in a simple way, capturing complicated visual layouts with regular convolutions. We conduct experiments on ImageNet, COCO detection, and semantic segmentation tasks, showing great improvements and robustness of FReLU in the visual recognition tasks. Code is available at https://github.com/megvii-model/FunnelAct.
Development of Management and Evaluation System for Realistic Virtual Reality Field Training Exercise Contents : A Case Study

Realistic training contents utilizing intensive immersion of virtual reality are being used in various fields such as industry, education, and medical care.High-risk, high-cost education training, in particular, is difficult to conduct in reality, but it can be applied with the latest virtual reality technology that enhances educational effectiveness by efficiently and safely experiencing it in an environment similar to reality.This study introduces a management system that systematically manages realistic virtual training contents and visualizes training results in schematic pictures based on defined evaluation elements.The management system can store the information generated from the content in the database and manage the training records of each trainee in a practical way.In addition, a content-based scenario can be created in multiple scenarios by setting training goals, number of participants, and methods for applying evaluation elements.This paper describes the management system's production method and the results based on the virtual reality training content as an application example.
A Comprehensive Study on Sign Language Recognition Methods.

In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted. By implementing the most recent deep neural network methods in this field, a thorough evaluation on multiple publicly available datasets is performed. The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. For this task, two new sequence training criteria, known from the fields of speech and scene text recognition, are introduced. Furthermore, a plethora of pretraining schemes is thoroughly discussed. Finally, a new RGB+D dataset for the Greek sign language is created. To the best of our knowledge, this is the first sign language dataset where sentence and gloss level annotations are provided for a video capture.
Global Trends in Marketing Technologies to Promote Library Websites

In recent decades, the scope of library activities has changed. In addition to providing traditional resources and services, today libraries themselves are becoming developers of digital content and providers of access to electronic content. The user base of libraries is also undergoing significant changes: there are changing the user generations, employment trends, areas of interest and habits. Realizing that marketing activities can increase user loyalty, form public opinion about both — a particular institution and libraries in general, increase the visibility of library resources and enlarge market share, the most active specialists are adapting marketing to the conditions of libraries operation. In 2019, there was organized the study on the websites of libraries in Russia, Australia, the United States and Canada, Western and Eastern Europe, Latin America, as well as in a number of countries in South and South-East Asia in order to identify the main trends in the organization of library promotion in the Internet environment. There were considered the websites of public, national, University and academic libraries. Library websites were analysed for the use of 10 most common tools in Internet marketing, such as: the transition to a modern web site design, effective linking with social networks, marketing in social networks (Social Media Marketing, SMM), the use of banner advertising, the availability of subscription to e-mailing, the publication of press and post releases, the presence of a blog in the library domain, the availability of content evaluation and sharing tools in social networks. The study conclusion was the identification of trends in the development of library sites over a large area and the compilation of heat maps, which clearly demonstrate such trends, where the libraries of the countries of North America, Australia and Northern Europe showed greater consistency with the parameters selected for the study; Central and Southern Europe, Russia and part of the countries of Latin America are in the transition zone, and the least compliance are found in the countries of South Asia. The most common means of promotion is the publication of press and post releases. Among the least used marketing technologies are blogs in the library domain, embedded video and Internet broadcasts, as well as work on optimizing sites for an audience from social networks.
SysML Models for Discrete Event Logistics Systems

System models and model-based engineering methods have the promise of transforming the way that industrial engineers interact with production and logistics systems. Model-based methods play a role in improving communication between stakeholders, interoperability between systems, automated access to consistent analysis models, and multi-disciplinary design methods for complex systems. However, there remains a need for a foundation for modeling these kinds of systems – a foundation that tailors methods and tools developed in other engineering domains to the unique concepts and semantics of production and logistics. This foundation is the topic of these models.
CROWD ABNORMAL BEHAVIOUR DETECTION USING DEEP LEARNING

Crowd analysis has become an extremely famous research point in the territory of computer vision. Computerized examination of group exercises utilizing reconnaissance recordings is a significant issue for public security since it permits the identification of hazardous groups and where they’re going. We all see how many problems are faced because of the crowd. In our country, many terrorists are there. They plant a bomb in a crowded area which causes a lot of injuries. Thieves are mostly found or always leave in crowded areas so they can easily get an advantage of the crowd. In that situation, crowd analysis is very important. This paper presents the design of the deep learning architecture that provides control over the crowd behavior that will help to avoid violence or any other act which occurs because of the crowd which causes harmful effects to the society. So we are proposing a system that detects abnormal behavior of crowds using deep learning techniques.
Using normal dictionaries to extract multiple semantic relationships

This article proposes a rule-based semantic relationship extraction method for Chinese sentiment lexicon to solve the problem that words in the sentiment dictionary cannot be updated in time, and expand the lexicon and the semantic relationship in the dictionary. Using the massive information on the Internet, the modern Chinese dictionary and sentiment dictionary and other resources, based on the interpretation of words and the characteristics of semantic relationships between words, sentiment lexicon are automatically extracted and their semantic relationships are annotated. At the same time, the semantic relationship extraction rules are defined, and the sentiment lexicon with synonymous relationship and antisense relationship is extracted recursively. The extraction rules and algorithms of emotional intensity relationship are also improved, and the lexicon is extracted according to the level of degree adverbs and their emotional weights are calculated. Compare the extraction effect before and after the improved algorithm and the change of the number of lexicon extracted after increasing the corpus. The experimental results show the effectiveness and accuracy of the proposed method.
On Euclidean Hulls of MDS Codes

In this paper, we propose a mechanism for the construction of MDS codes with arbitrary dimensions of Euclidean hulls. Precisely, we construct (extended) generalized Reed-Solomon (GRS) codes with assigned dimensions of Euclidean hulls from self-orthogonal GRS codes. It turns out our constructions are more general than previous works on Euclidean hulls of (extended) GRS codes.
The age of mineralization of Mayskoe gold ore deposit (Central Chukotka): results of Re-Os isotopic dating

The article presents the results of the sulfide mineralization dating of the Mayskoe gold ore deposit using the Re-Os isotope system and isochron age estimation method of the main sulfide minerals: arsenopyrite, pyrite, and antimonite. The complex multistage formation of the studied sulfides, as well as the close intergrowths of genetically different mineral phases, did not allow obtaining a single rhenium-osmium isochron corresponding to the formation time of sulfide mineralization. Isochrones for single minerals, collected from each sulfide sample, turned out to be the result of isotopically distinct components mixture (radiogenic crustal and non-radiogenic mantle) and do not make sense from the geochronological point of view. In terms of geology, the most significant result of the study is an age estimation of 128.8 ± 4.4 Ma, obtained for the sulfide mineralization of Mayskoe deposit using Re-Os isotope dating of single fractions of pyrite and antimonite of the ore mineralization stage. While arsenopyrite is most closely associated with gold mineralization, one of the arsenopyrite varieties corrodes framboidal pyrite of the pre-ore stage, has a maximum of the crust component in the osmium isotopic composition and forms a mixing line in the isochron diagram with an apparent formation age of 458 ± 18 Ma. The initial osmium isotopic composition of the studied sulfides indicates a mixed mantle-crust source of sulfide mineralization. The issue of simultaneous ore genesis and granitoid magmatism in the Mayskoe deposit remained unresolved (the age of granitoids according to the U-Pb zircon system is 108 Ma). However, a possible solution could be the further determination of the Re-Os isochron age of the ore mineralization sulphides from the single paragenesis of a specific sample containing both arsenopyrite and pyrite (+ antimonite) with gold.
Restrain the losses of the entanglement and the non-local advantage of quantum coherence for accelerated quantum systems

We examined the possibility of recovering the losses of entanglement and the non-local advantage by using the local symmetric operations. The improvement efficiency may be increased by applying the symmetric operations on both qubits. The recovering process of both phenomenon is exhibited clearly when only one qubit is accelerated and the symmetric operations is applied on both qubits. It is shown that, for large acceleration, the non-local coherent advantage may be re-birthed by using these symmetric operations.
Dilemmas, Countermeasures and Enlightenment of Large-scale real-time online teaching in the Context of Epidemic situation

The outbreak of COVID-19 in 2020 triggered a large-scale real-time online teaching, which exposed many teaching problems. In order to solve these problems, this paper studies the dilemmas, causes and countermeasures of large-scale real-time online teaching, and obtains the enlightenment of improving teaching quality. Firstly, the background and characteristics of large-scale real-time online teaching are described, combining with sampling survey, through the data analysis model to analyze the user data of the mainstream online teaching platforms in this paper. From the aspects of teachers, students and technology, this paper analyzes the difficulties and causes of teaching, and puts forward some teaching countermeasures, such as strengthening the application of information technology, changing the teaching content and design, and strengthening the construction of teaching interactive scenes. Finally, this article summarizes the enlightenment of large-scale real-time online teaching to the development of modern information-based teaching.
Charades game: does it affect students’ learning on English vocabulary?

The purpose of this study is to determine the effect of the use of charades game on elementary school students in improving English vocabulary as a foreign language. The source of the data in this study were 5th grade students of Madrasah Ibtidaiyah Nahdlatul Ulama Tropodo Sidoarjo, while the data was the application of charades game to 5th grade students of Madrasah Ibtidaiyah Nahdlatul Ulama Tropodo before and after being given a test. Researchers use use pre-experimental quantitative design as the research method . This study uses one group pretest and posttest. The data obtained from the pretest and posttest which is analyzed by using the t-test formula. The results show that at the 5% level the t - test value is higher than t-table (tt) (4.53&gt; 2.024). Based on the test criteria, the results of the study show that there is a significant influence on the students' vocabulary skills after using charades game. It proves that charades game can improve the foreign language vocabulary of Islamic elementary school students, especially in Madrasah Ibtidaiyah Tropodo Sidoarjo . DOI: 10.26905/enjourme.v4i2.4258
Verification of Quantitative Hyperproperties Using Trace Enumeration Relations

Many important cryptographic primitives offer probabilistic guarantees of security that can be specified as quantitative hyperproperties; these are specifications that stipulate the existence of a certain number of traces in the system satisfying certain constraints. Verification of such hyperproperties is extremely challenging because they involve simultaneous reasoning about an unbounded number of different traces. In this paper, we introduce a technique for verifying quantitative hyperproperties based on the notion of trace enumeration relations. These relations allow us to reduce the problem of trace-counting into one of model-counting of formulas in first-order logic. We also introduce a set of inference rules for machine-checked reasoning about the number of satisfying solutions to first-order formulas (aka model counting). Putting these two components together enables semi-automated verification of quantitative hyperproperties on infinite-state systems. We use our methodology to prove confidentiality of access patterns in Path ORAMs of unbounded size, soundness of a simple interactive zero-knowledge proof protocol as well as other applications of quantitative hyperproperties studied in past work.
LEARNING SUPER-RESOLUTION FOR SENTINEL-2 IMAGES WITH REAL GROUND TRUTH DATA FROM A REFERENCE SATELLITE

Abstract. Copernicus program via its Sentinel missions is making earth observation more accessible and affordable for everybody. Sentinel-2 images provide multi-spectral information every 5 days for each location. However, the maximum spatial resolution of its bands is 10m for RGB and near-infrared bands. Increasing the spatial resolution of Sentinel-2 images without additional costs, would make any posterior analysis more accurate. Most approaches on super-resolution for Sentinel-2 have focused on obtaining 10m resolution images for those at lower resolutions (20m and 60m), taking advantage of the information provided by bands of finer resolutions (10m). Otherwise, our focus is on increasing the resolution of the 10m bands, that is, super-resolving 10m bands to 2.5m resolution, where no additional information is available. This problem is known as single-image super-resolution and deep learning-based approaches have become the state-of-the-art for this problem on standard images. Obviously, models learned for standard images do not translate well to satellite images. Hence, the problem is how to train a deep learning model for super-resolving Sentinel-2 images when no ground truth exist (Sentinel-2 images at 2.5m). We propose a methodology for learning Convolutional Neural Networks for Sentinel-2 image super-resolution making use of images from other sensors having a high similarity with Sentinel-2 in terms of spectral bands, but greater spatial resolution. Our proposal is tested with a state-of-the-art neural network showing that it can be useful for learning to increase the spatial resolution of RGB and near-infrared bands of Sentinel-2.