Reviewerly/exHarmony huggingface dataset:
The formation of primary school teachers’ professional readiness for teaching Russian as a foreign language using the opportunities of cross-border education

The article is devoted to the problem of using the opportunities of cross-border education as a factor of professional training of primary school teachers for tuition Russian as a second language. The authors note the relevance of this problem both in theoretical aspects and at the practical level. The article describes an effective way to form the readiness of primary school teachers to implement cross-border education. In this study, the authors reveal new opportunities for professional development of primary school teachers. Based on the content analysis of the regulatory database, it is shown that this problem is related to an upward trend in the Russian higher education system. The article reveals the relationship between teachers' professional training in the field of tuition Russian language to younger students, for whom Russian is a second language, and the existing problems with using Online learning. The article substantiates the need for training primary school teachers, which contributes to the development of their professional competencies both in the Offline mode of training and the Online training. A set of pedagogical measures related to the professional training of primary school teachers to teach Russian as a non-native language in cross-border education can become one of the foundations for the development of the Russian Federation education system. The article is addressed to the teachers of higher and secondary vocational training institutions, as well as future and certified teachers of primary General education.
Applying of Quranic Glossary Approach to Improve Indonesian Qur'an Translation Search Engine Performance

This study aims to build a search engine for the Indonesian translation of the Qur'an by implementing the term weighting method and the relevance of quranic glossary. The TF-IDF method is a word weighting method used in this study. The search system is built through several stages, including building corpus, text preprocessing, term weighting, indexing, and ranking. Based on the results of tests with recall & precision, the average indicates that the use of TF-IDF and Qur'anic Glossary has good performance. The test results show an average recall value of 79.3% and an average precision value of 42.5%. Compared to search without quranic glossary that has a recall average value of 62.9% and a precision value of 70.9%.
Development Method of Three Kinds of Typical Tree Structure Algorithms and Isabelle-based Machine Assisted Verification

The tree structure algorithms have been widely used in many computer fields. Developing efficient and reliable tree structure algorithms is a challenging problem in the field of software formalization and trusted software. In this paper, initially, the binary tree algorithms are divided into three kinds through induction of the loop invariant structures and output features. Then, PAR method can conveniently develop loop invariants and corresponding non-recursive algorithm programs. Finally, Isabelle is used to formally verify these developed algorithms. This development method not only overcomes the tediousness and error-proneness of traditional manual verification, but also greatly improves the efficiency and reliability of the developed algorithm program. To the best of our knowledge, this is the maiden attempt in the literature to verify a series of non-recursive and efficient binary tree algorithms. The above process forms a theorem proving library that include data types, data structures and lemma related binary tree algorithms, which can significantly reduce the cost of future verification.
<i>Tracking the Loving Gaze</i>

Abstract Tracking the Loving Gaze is a futile attempt to follow, map and capture the way cherished personal photographs are viewed. The authors asked 30 survey subjects to use an eye tracker while looking at a preselected photograph that held a special meaning for them. The raw visual data from this process—heat maps, focus maps and scan paths—became the foundation of a body of work that includes darkroom prints, short videos and a limited-edition artist book. Apart from exploring the invisible viewing processes of personal photography, this article introduces the concepts of the detached and the invested viewer as well as the corresponding concepts of the cold and the loving gaze.
Ethics and accounting information systems

The use of technology has increased the scope of ethical situations that must be considered by accountants. This chapter discusses the roots of ethics in accounting information systems, as well as considers a subset of current technologies and explores the ethical issues surrounding those technologies. Topics considered include big data, privacy, Internet of Things, cloud computing, and blockchain. These current topics are discussed based on the ethics in technology foundation built by progressing through the hierarchy of the related prior literature of computer ethics, information technology and ethics, information systems and ethics, and finally accounting information systems/enterprise systems perspectives. Within the discussion, the authors not only include theoretical perspectives of the topics but also consider codes of ethics from related professional organizations. The chapter concludes by acknowledging that while perfection is not possible, flexibility must be included in an accounting information system to accommodate the accountant’s ethical model.
Accuracy-aware Structured Filter Pruning for Deep Neural Networks

Deep neural networks (DNNs) have several technical issues on computational complexity, redundancy, and the parameter size – especially when applied in embedded devices. Among those issues, lots of parameters require high memory capacity which causes migration problem to embedded devices. Many pruning techniques are proposed to reduce the network size in deep neural networks, but there are still various issues that exist for applying pruning techniques to DNNs. In this paper, we propose a simple-yet-efficient scheme, accuracy-aware structured pruning based on the characterization of each convolutional layer. We investigate the accuracy and compression rate of individual layer with a fixed pruning ratio and re-order the pruning priority depending on the accuracy of each layer. To achieve a further compression rate, we also add quantization to the linear layers. Our results show that the order of the layers pruned does affect the final accuracy of the deep neural network. Based on our experiments, the pruned AlexNet and VGG16 models’ parameter size is compressed up to 47.28x and 35.21x with less than 1% accuracy drop with respect to the original model.
Fiscal Scam Illuminating Through Analyzing Human Behaviour

Abstract Of these days, money fraud is turning into the employment of criminal practices wherever they will get involved, from senior managers till payroll workers, turning into a criminal sin. There are a vast number of methods that came into existence to analyze, find, and stop this behavior, which is the primary essential thing that is the fraud triangle linked with the standard evidence information model. By performing this study, a review of the connection works among the current literature was administered to build our framework. From this perspective, this paper explains about deception grasp; this framework allows the quality and exactness and disarray of people within a banking association who consign deception, reinforced by the fraud or deception triangle concept. This approach works amongst the permanent review, which can be liable for aggregating the agents to put into user’s assistance and encourage cybersecurity and the dropping of causes linked to financial fraud.
Chapter 8. Prosodic annotation of the Rhapsodie corpus

This chapter provides an overview of the theoretical background that piloted prosodic processing in Rhapsodie. We explain what the components of an autonomous data-driven usage-based perceptual model are, as distinct from the standard approach in which data are used to validate phonological theories, and how the methodological principles applied for prosodic annotation clearly distinguish Rhapsodie annotation from standard approaches using the ToBi system. This specificity is first expressed by our view of the relationships between syntax and prosody: we highlight the role of syntax, or rather the absence thereof, in the generation of the prosodic structure, and, instead, the central function of prominences and disfluencies in this respect. Finally, we recall how important acoustic processing is in order to properly achieve the prosodic modeling of everyday speech, which is often full of noise and, consequently, unusable.
Developing a Configuration System for a Simulation Game in the Domain of Urban $$\text {CO}_{2}$$ Emissions Reduction

In order to help decision-makers find ways to reduce \(\text {CO}_{2}\) emissions of Swiss cities, a simulation game is being developed within the “Post-fossil cities” project. During the game, participants take on different roles in which they together explore pathways to a future, post-fossil city. An important requirement to the software system of the game was to be easily configurable in order to keep the game adaptive to different target groups of players. We describe a User Interface Management System (UIMS) that has been designed and implemented to realise the flexibility demanded from the game designers’ side. The system allows game facilitators to configure the game and decide what kinds of visualisations are used during game sessions. The paper describes how the configuration system was conceptualised, implemented and integrated into the overall system architecture of the simulation game.
Ontology and Rules for Characterization of Sensors and Actuators Devices in AADL Models

This paper presents a set of ontologies to organize concepts, categories, properties, and relationships that are typically used by designers for characterizing sensing and actuation devices that compose a given system architecture (SA). From the characterization of such devices, modeling can be done using the Architecture Analysis and Design Language (AADL). AADL supports modeling and analysis of functional and nonfunctional properties to evaluate quality attributes from ontology models. The paper also proposes an AADL property set that can be applied to sensors and actuators (S&A), based on previous researches with ontologies applied to sensor networks. Specific characteristics are provided to describe systems' quality attributes. This aims to be used for analyzing the designed architecture, and also its probable future evolution.
Cognitive Interaction of Robot Communities, Simulation Modeling

The aim of the article is to present some features of cognitive simulation of complex systems, which are proposed to use at the stage of pre-design studies of robot groups (communities) interaction. The structure of interacting robot communities with a leader can be represented in a hierarchical cognitive map. To develop such a map, it is necessary to use theoretical knowledge, expert and domain statistical data. Minimal theoretical information of complex systems cognitive modeling is presented. It is necessary to explain the possibility of cognitive modeling using in the context of robot groups interaction. An example which illustrates the development of a hierarchical cognitive map of three robot groups interaction is given in this paper. A study of the structural properties of the model was made. Its structural stability and resistance to disturbances have made it possible to conclude that the model does not contradict possible real situations. Scenario modeling results of situations development for various internal and external environment variations are presented on the model. The possibility of adjusting the structure of a cognitive model to provide the desirable qualities of situations development processes on the model is also shown.
Nominate of significant features for unknown internet traffic applications filtering based on a neural network algorithm

The evolution of the internet into a large, complex service-based network has posed tremendous challenges for network monitoring and control in terms of how to collect massive volumes of data, in addition to the accurate classification of new emerging applications, such as peer-to-peer networks, streaming content and online games. In this work, machine learning algorithms are used for the classification of traffic into their corresponding applications. Furthermore, this research uses our customized training data set collected from the three institutions' campuses. The effect on the size of the training data set has been considered before examining the accuracy of various classification algorithms and selecting the best from a large amount of data traffic in the network, which has led to delays in performance; therefore, to solve this problem we suggested a distinct approach using multiple neural networks with the feature selection in order to predict and identify known and unknown applications. By applying the proposed method, we get excellent accuracy in the classification of data traffic in the network of up to 99.11%, which leads to improved data traffic in the network and avoids delays.
Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score

Scene graph parsing aims to detect objects in an image scene and recognize their relations. Recent approaches have achieved high average scores on some popular benchmarks, but fail in detecting rare relations, as the highly long-tailed distribution of data biases the learning towards frequent labels. Motivated by the fact that detecting these rare relations can be critical in real-world applications, this paper introduces a novel integrated framework of classification and ranking to resolve the class imbalance problem in scene graph parsing. Specifically, we design a new Contrasting Cross-Entropy loss, which promotes the detection of rare relations by suppressing incorrect frequent ones. Furthermore, we propose a novel scoring module, termed as Scorer, which learns to rank the relations based on the image features and relation features to improve the recall of predictions. Our framework is simple and effective, and can be incorporated into current scene graph models. Experimental results show that the proposed approach improves the current state-of-the-art methods, with a clear advantage of detecting rare relations.
Automation of Functional and Measurement Control of Spacecrafts' Command and Measurement Systems

In this article we describe automation methods of functional and measurement control of the spacecraft onboard equipment's technological production process. We formalized the methods in form of syntax diagrams showing the key elements of test procedures. Highlighted are the main tasks solved at each stage of autonomous and comprehensive testing of the system's operation. For autonomous testing, a scenario method of test procedures' building is used. A scenario includes configuration of test equipment, parameters of measurement operations or effects towards the object of control, as well as the actions of the paired systems' simulators. For comprehensive tests, we suggest the method of automation of the object of control's interaction with test software of an industrial enterprise. Our methods have been realized in software and implemented in production allowing to make full study of the onboard equipment's properties.
Preparing for the roll-back of Covid-19 emergency legislation: what needs to be done?

The Covid-19 pandemic has led many countries across the world to pass emergency legislation, but is there a danger that this legislation could lead to a permanent loss of civil liberties? Franklin De Vrieze explains what has to be done to prepare for the roll-back of these measures.
Research on Radar Signal Sorting Algorithm Based on Supervised Learning

Aiming at the main sorting stage of known radar signals in the radar signal sorting system, using supervised learning algorithms in machine learning to replace the traditional sorting algorithms which based on pulse repetition interval. The experimental results show that the supervised learning algorithms can successfully sort overlapping multi-type radar pulse signals, and the sorting accuracy of some algorithms exceeded 95%. It is feasible to apply the supervised learning algorithm to the main sorting stage of known radar signals.
Android based position tracking for car condition monitoring

Many applications could be developed in Android Phone for the improvements of modern car's condition monitoring. That could be a simple user interface for an On-Board Diagnostics (OBD) tools for a car condition monitoring in nowadays manual driving, or up to replacement of its driver for further control in an autonomous driving. However all of them will be so much dependent on its position tracking. In this work, a position tracking system would be designed started from the system's architecture with telecommunication network until its behavior with UML Diagrams. Overall, the system consisted of two parts. They were an android application (app) and the nodeJs web server. The app would deal with the data collection. Then, the web server would show the result using map interface from Open Street Map. From a testing trip, 611 position points were obtained. From those points, it was found also that the shortest processing time between the records was 5 seconds and the longest could be more than 3 minutes.
Training system for designing computer games for early career guidance of schoolchildren (Junior Skills)

Abstract The article deals with the development of a training system for the design of computer games based on the use of information technologies for early vocational training and vocational guidance of schoolchildren (Junior Skills). The peculiarity of vocational guidance work with schoolchildren is primarily associated with the need to overcome the contradictions between the state of the labor market, the needs of the economy and the subjective professional preferences of young people. The solution to youth career guidance issues is offered in the Junior Skills project, which includes students, employer representatives, and the expert community.
FA3D: Fast and Accurate 3D Object Detection

Fast and accurate detection of objects, in 3D, is one of the critical components in an advanced driver assistance system. In this paper, we aim to develop an accurate 3D object detector that runs in near real-time on low-end embedded systems. We propose an efficient framework that converts raw point cloud into a 3D occupancy cuboid and detects cars using a deep convolutional neural network. Even though the complexity of our proposed model is high, it runs at 7.27 FPS on a Jetson Xavier and at 57.83 FPS on a high-end workstation that is $$18\%$$ and $$43\%$$ faster than the fastest published method while having a comparable performance with state-of-the-art models on the KITTI dataset. We conduct a comprehensive error analysis on our model and show that two quantities are the principal sources of error among nine predicted attributes. Our source code is available at https://github.com/Selameab/FA3D .
Evolution of practical-oriented development of socialization of children in conditions of integration of general and extracurricular education of children

We presented and briefly described stages of evolution of practical-oriented development of socialization of children in conditions of integration of general and extracurricular education of children. The study bases on the theory of social partnership, the concept of integration of general and extracurricular education. We established three main stages of evolution of practical-oriented development of socialization of children in conditions of integration of general and extracurricular education of children. We described the main stages of evolution by presenting the main models of integration, key regulatory documents. We found that the key resource for integrating general and complementary education is the ideological, substantive and organizational unity of key components of the educational process in the school. The integration of extra-time activities and the development of extracurricular education programmes creates favourable conditions for the fuller self-realization of students. A modern school needs a holistic project of instrumentally equipped educational and methodological support for combining extracurricular education and out-of-hours activities of students during the socialization of schoolchildren. One of the possible mechanisms for combining extracurricular and general education in the socialization of schoolchildren may be the formation, discussion and approval of an educational program of general education levels of a general education organization.
Isomorphism in Wavelets II

A scaling function φ A associated with a d × d expansive dyadic integral matrix A can be isomorphically embedded into the family of scaling functions associated with a s × s, d ≤ s, expansive dyadic integral matrix B. On the other hand, a scaling function φ A associated with a d × d expansive dyadic integral matrix A and a finite two scaling relation can be isomorphically embedded into the family of scaling functions associated with expansive dyadic integral s × s matrix B, for any s. In particular, for s = 1 and B = [2]. We provide examples for such isomorphisms.
Harvest shopping advice: Neural Question Generation from multiple information sources in E-commerce

The success of recent efforts in Question Generation (QG) has amazed scientists from academia and industry. In this paper, we explore to harvest shopping advice through a novel QG engine for e-commerce platforms. Unlike traditional QG methods conditioned on factual data, generating purchase-oriented questions depends on open-ended product properties and customer reviews. Besides, these questions should follow not only natural expressions but also user-interested aspects simultaneously. For this challenging task, an innovative generative adversarial net-based QG model is proposed – a generator featuring multi-source attention mechanism is employed to yield questions from multiple information sources; a discriminator featuring quality control is applied to fine-tune generated questions in terms of both language performance and aspect compatibility. We conduct extensive experiments on a new dataset comprised of Question-Review-Aspect-Property (Q-RAP) tuples from a real e-commerce site. Our experimental results demonstrate that the proposed approach achieves a significant superiority over seven state-of-the-art QG solutions. Meanwhile, this study indicates that customer reviews play a critical role in generating purchase-oriented questions, which confirms the validity of previous practices using buyer feedback to address natural language generation in e-commerce.
Learning to Play Robot Soccer from Partial Observations

Reinforcement learning (RL) has undergone unprecedented evolution in the last few years, managing to surpass the human baseline in several high-profile board and computer games. Despite these successes, deep neural agents remained absent in several challenging fields, such as robot soccer, which is a rather complex task involving simultaneous cooperation and competition between multiple agents. In this paper, we investigate the feasibility of playing robot soccer via deep reinforcement learning using an environment of our own making. This environment provides the agent with imperfect and incomplete observations to simulate the errors of a real vision pipeline. To improve the agent's performance on this task, an intrinsic reward module is proposed based on self-supervised learning. Our experiments show that the proposed extension significantly improves the agent.