Reviewerly/exHarmony huggingface dataset:
STEM-oriented approach to learning as educational innovation XXI century

The fundamental difference between the STEM-oriented approach to learning from traditional educational models in focusing on everyday life, real problems. This approach involves, on the one hand, ensuring the integrated formation of scientific and practical knowledge through the acquisition of authentic practical experience (personal aspect), and on the other – preparing students for further study (lifelong learning) and employment in accordance with the requirements of XXI century (social aspect). The leading idea of STEM-oriented approach to learning is the design of subjects, disciplines, courses on an interdisciplinary basis (integrated learning, according to certain topics, not individual disciplines), which allows to comprehensively form key professional and socio-personal competencies of youth. Such an integrated approach is natural and appropriate when solving a real problem. A chain of logical questions is built: «What is it?», «How to deal with it?», «How and what to improve?», «How to present it clearly?», during the answer to which there is a combination of scientific method, technology, design and mathematics on the principle of interdisciplinary. Depending on the ultimate goal of learning, the appropriate option of interdisciplinary integration is chosen, taking into account that from the point of view of philosophical principles STEM-oriented approach to learning is a methodological unity of natural, technical and social sciences, which is manifested in the application of common mathematical apparatus, communication technologies, modeling and interdisciplinary interaction, as well as the use of the latest educational technologies: cognitive, social and knowledge transfer.
On the issue of the formation and development of science as a social institution

The relevance of scientific research on the specifics of the institutional environment for the development of the Institute of Science in the Russian Federation is associated with the rapid development of the digital economy, in which the development of science is becoming the most important factor in ensuring Russia's competitiveness on the world stage. The author conducted a comprehensive study of the specifics of the institutional environment of the development of the Institute of Science in the Russian Federation. The article studies the stages of formation and development of science as a certain social institution. The paper presents the criteria of institutionalization and it is established that the Institute of Science meets them. The study highlights the purpose and functions of science as a social institution. The paper also proposes the institutional environment for the functioning of the Institute of Science, taking into account the state and the individual as an integral part of successful development. The author concludes that science as a social institution is a self-organizing, self-regulating and self-developing system based on the realization of human potential through special tools of scientific activity and special organizations and aimed at the production and dissemination of scientific knowledge.
Communication Engineering Application System Based on Artificial Intelligence Technology

In order to overcome a series of problems in the application process of traditional communication engineering in the new era, such as information security, this paper proposes a novel communication engineering application system based on artificial intelligence technology. The application system fully combines the artificial intelligence technology, and applies the artificial intelligence thinking to the reform of traditional communication engineering. Based on this, the application strategy also fully combines the application and development of 5g technology, and strengthens the security of communication engineering in the application process from many aspects. The results show that the application system can give full play to the role of artificial intelligence technology and improve the security of communication process as much as possible, which lays a good foundation for the further development of 5g technology.
Integrative approach - QR-Code on tax relevant documents in Portugal - Validation and Exequibility

The legal framework for the conduct of the work, derives from the Decree Law No. 28/2019 of February 15, combined with Ordinance 195/2020 of 13 August and the VAT Code, as well as the amendment to the proposal of Law No. 61/XIV, which postponed to 2022 the mandatory printing of the QR-Code on the invoice and documents fiscally relevant. The objective of the paper focuses on the presentation of a validation of a multidisciplinary and integrative approach, which was developed to analyze the impact on the different stakeholders, when introducing the QR-Code in the relevant tax documents in Portugal. The methodology used was Design Science Research. The paper's contributions are based on the presentation and validation of the Approach using survey design, with the aim of assessing its suitability and feasibility with the various stakeholders (Software Producers/Installers, Hauliers, Certified Accountants, Tax and Customs Authority, Taxpayers and Buyers). In this way, it was possible to assess the expectations of the stakeholders in face of the new legal framework, as well as the impact that these legislative changes will cause in an organizational context.
Thinking styles as predictors of psychological readiness of students for innovative activities

The paper deals with the problem of thinking styles and creativity as predictors of students psychological readiness for innovation. The aim of the study was to identify the influence of students thinking styles on their psychological readiness for innovation. The study involved 100 students of the Don State Technical University, aged from 17 to 27 years. The research methods were the survey method, statistical analysis methods (descriptive statistics, multiple regression analysis (step-by-step)). The following methods were used as psychodiagnostic tools: the questionnaire Psychological readiness for innovation (V.E. Klochko, O.M. Krasnoryadtseva); the method of measuring the style of thinking (A.K. Belousova); the method of determining the type of thinking of J. Bruner in the modification of G.V. Rezapkina; the questionnaire Styles of thinking (R. Bramson, A. Harrison, adapted by A. Alekseev, L. Gromova). The study found that students psychological readiness for innovation is influenced by thinking styles and creativity. Predictors of psychological readiness for innovation are creativity, realistic and critical thinking styles. The preference for activities that require innovation is influenced by creativity, initiative, idealistic and realistic thinking styles. Predictors of readiness for change are creativity and critical thinking styles. The practical significance of the research lies in the possibility of applying in the educational process the knowledge about the influence of students thinking styles and creativity on their psychological readiness for innovation.
A Flexible Planning Approach Using Label Member

In this paper, we present a novel approach to flexible planning based on a two-stage paradigm of label member generation and solution extraction. The approach provides a new perspective on the flexible planning problem and speeds up the process of the problem solving in the artificial intelligence system plan. On the basis, we propose a novel algorithm based on label member by thoroughly exploring the structure of flexible planning graph, and apply the memorization strategy and heuristic information to prune the search space. The algorithm is provably sound, complete and polynomial-time and polynomial-space of label member generation. And the problems handled by the algorithm are more complex than the classical ones and much closer to the real world, which makes the solution plan safer. Else, because of wide application of intelligent planning, our research is very helpful to the development of artificial intelligent, robotology, intelligent agent, machine learning and so on.
Fitness value curves prediction in the evolutionary process of genetic algorithms

The evolutionary process of a Genetic Algorithm (GA) depends on several factors, the initialization parameters are some of them [4]. One way to inspect the evolutionary process of a GA is to analyze the maximum, average, and minimum fitness of each generation. This article focused on the use of a machine learning model to predict the maximum, average, and minimum fitness values during the evolutionary process of a GA, and this, only with the knowledge of its initialization parameters. In order to accomplish this goal, a Random Forest model was trained with data from different GA executions for a given problem. The prediction process was performed with a very promising performance, where the challenge of predicting the evolutionary process of a GA was fulfilled with low error rates. This approach opens up several opportunities for advances in the segment, and in a way, contributes to the investigation and improvement of GAs, as well as demonstrating the importance of monitoring and storing the information generated during their evolutionary process.
Research introduction of 3D measurement technology based on grating projection

With the rapid development of contemporary science and technology, the requirements for non-contact precision measurement technology for many industrial equipment are getting higher and higher, and the application of optical threedimensional measurement in industrial manufacturing is becoming more and more extensive. Among various optical threedimensional measurement methods, the grating projection three-dimensional measurement method has the advantages of high accuracy and easy control. This paper summarizes the current research status in the field of raster projection, and compares and analyzes the difficulties and research directions of this technology.
A Three-stage Method for Classification of Binary Imbalanced Big Data

In the real world, there are many imbalanced data classification problems, such as extreme weather prediction, software defect prediction, machinery fault diagnosis, spam filtering, etc. It has important theoretical and practical value to study the problem of imbalanced data classification. In the framework of binary imbalanced data classification, a three-stage method for classification of binary imbalanced big data was proposed in this paper. Specifically, in the first stage, the negative class big data was clustered into K clusters by K-means algorithm on Hadoop platform. In the second stage, we use instance selection method to select important samples from each cluster in parallel, and obtain K negative class subsets. In the third stage, we first construct K balanced training sets which consist of negative class subset and positive class subset, and then train K classifiers, and finally we integrate these classifiers to classify the unseen samples. Some experiments are conducted to compare the proposed method with two state-of-the-art methods on G-means. The experimental results demonstrate that the proposed method is more effective and efficient than the compared approaches.
Design and Implementation of a New Intelligent Compression Trash Tank

Garbage can is a common daily necessities in our life, so garbage can is indispensable. It can not only keep the living environment comfortable and tidy, but also make us feel relaxed and happy. The function of garbage bin is to store garbage temporarily, and sanitation workers collect, transport and carry garbage in garbage bin every day for centralized destruction or secondary waste utilization. For part of the garbage fluffy, large volume, easy to overflow, is not conducive to garbage collection, transportation and handling. The degree of automation of the existing compressed trash can is not very high. In some links (such as opening the lid or starting compression), it is usually necessary to manually click and press the twist operation, so it is difficult to match with the robot to complete the garbage picking task in special scenarios. Based on the shortcomings and shortcomings of the prior art, we design an intelligent compressed garbage can based on STM32. The trash can is divided into garbage input room and garbage waiting room. When the garbage in the garbage input room reaches a certain height, the garbage carries on a series of operations to compress the garbage and push it into the garbage waiting room. The garbage waiting room is equipped with garbage height detection device and pressure detection device. When there is garbage in the waiting room, the indicator light is on. When the garbage in the waiting room reaches a certain height, the indicator light begins to flicker. The trash can be connected to Internet devices such as mobile phones. Users can always check the status of the trash can. When the trash can indicator lights flicker, it will give a reminder to the mobile phone. In order to achieve the trash bin automation, intelligence, in line with the concept of green development. Our design can not only realize the automation and intelligence of the trash can, but also in line with the concept of green development.
Tuning Parameters of ANFIS Model Using Chaotic Particle Swarm Optimization Algorithm

Abstract In recent years, researchers have worked to find multiple methods to overcome the classification problems they encountered. In this paper, both the Chaotic Particle Swarm Optimization (CPSO) algorithm are connected with the Adaptive Fuzzy Inference System (AFIS) model, through two stages, the first stage, CPSO algorithm is used to tune parameters of the fuzzy inference system model. In the second stage, the fuzzy inference system model is constructed according to the optimum parameters that are found by the CPSO algorithm. The proposed CPSO-AFIS algorithm demonstrated efficacy and efficiency compared to the standard algorithm Adaptive Neuro Fuzzy Inference System (ANFIS).
Multi-resource Task Scheduling for Minimum Task Completion Time in Cloud Computing Using Credit-Based Assignment Problem

Task scheduling process problem is a focus and challenging issue in the Cloud Computing. The Cloud enumerate resources skillful and gain the most profits with scheduling process is the ultimate objective in the one of the Cloud enumerate overhaul providers. In this paper, we used multi-resource credit-based assignment problem to assess the entire group of multi-resource task scheduling in task get in line and finding the minimal completion time of all tasks in the cloud enumerate. Here cost/time matrix has been created as the reasonable tendency of the task to be the assigned in multi resources. Here we concern Cloud enumerate is a latest new assessment method of delivering the technology in the consumer by means of Internet servers for dealing out and data storage, while the client process uses the data.
Fraud Detection in Social Commerce: combining structured attributes and images

Social Commerce has risen and evolved in the last years due to changes either in e-commerce or social networks applications. On top of that, the number of online ads and transactions in Social Commerce has grown. This environment is attractive to either good users and bad users. The bad users cause harm to their victims by making them lose money or suffer psychological damage. Since the volume of transactions is high and the fraud occurrence is low, the manual detection is highly inefficient (too much resource required for low detection) and unscalable. The existing solutions for automatic fraud detection in Social Commerce are based on structured information available in ads such as price, product type, brand, new/used, among others. However, such solutions ignore possible fraud signs from the ads’ images that exhibit the products sold. Therefore, this work aims to evaluate if combining structured information and images available in the ads provides more effective models than the ones considering only structured information. To this end, it proposes FDSC, a method that combines information obtained from ads’ images through deep learning with structured information available in the corresponding ads, in order to detect fraud in Social Commerce. Experimental evidence shows an incremental opportunity of 7% in F-score by the adoption of FDSC.
LAENet: Light-weight asymmetric encoder-decoder network for semantic segmentation

Abstract Encode-decoder structure is used in deep learning for real-time dense segmentation task. On account of the limitation of calculation burden on mobile devices, we present a light-weight asymmetric encoder-decoder network in this paper, namely LAENet, which quickly and efficiently accomplish the task of real-time semantic segmentation. We employ an asymmetric convolution and group convolution structure combined with dilated convolution and dense connectivity to reduce computation cost and model size, which can guarantee adequate receptive field and enhance the model learning ability in encoder. On the other hand, feature pyramid networks (FPN) structure combine attention mechanism and ECRE block are utilized in the decoder to strike a balance between the network complexity and segmentation performance. Our approach achieves only have 0.84M parameters, and is able to reach 66 FPS in a single GTX 1080Ti GPU. Experiments on Cityscapes datasets demonstrate that superior performance of LAENet is better than the existing segmentation network, in terms of speed and accuracy trade-off without any post-processing.
Frequency Portraits in Authorship Identification

Frequency methods of text content analysis are mainly used for decrypting cipher messages. In addition, these methods allow you to identify the features of both individual languages and topics. But not only for the language or topic, but also for each person is characterized by a personal set of features of speech behavior, which can be used to identify authorship. Methods of such analysis can be divided into two groups - expert and formal. Based on mathematical methods, you can create a model that differentiates one author of a text from another. This model is proposed to be called the Frequency Portrait. It includes single-and bigram frequency distributions, confidence interval, and probabilities. Algorithms and software have been developed to implement the frequency portrait model. Specific examples show the effectiveness of author identification. A method for forming a model of a set of single - and bigram frequencies for the author of text content and for a specific text is proposed. A system of terminology for such models is proposed. The concept of forming frequency portraits at various levels, from language to a specific text, is presented. The models of frequency portraits of the author of text content and frequency portraits of specific texts are presented. The possibility of identifying the authorship of text content on the basis of the presented concept is shown. The research was conducted within the framework of the Russian alphabet.
Automatic speech recognition by using local adaptive thresholding in continuous speech segmentation

Abstract Speech recognition of continuous speech will be greatly influenced by the word segmentation process of the speech input. Proper segmentation will result in better speech recognition. This study proposed automatic speech recognition by applying local adaptive thresholding in the segmentation process. The segmentation method used is an enhanced blocking block area method whose input is a spectrogram image of the speech signal. While the locally adaptive thresholding method used is the Niblack method which is the best method compared to other methods, namely Sauvola, Bradley, Guanglei Xiong, and Bernsen when applied to the enhanced blocking block area method. For the speech recognition process, using Mel-frequency cepstral coefficients (MFCC) as a feature extraction method and Hidden Markov Model (HMM) as speech recognition. The experimental results show that by using 400 sentences consisting of 80 testing data and 320 as training data and using K-fold cross-validation, the highest accuracy is 60,8%. This result has no significant difference with the use of global thresholding in the segmentation process.
Randomly Rotate Qubits, Compute and Reverse for Weak Measurements Resilient QKD and Securing Entanglement

Homomorphic encryption (HE) schemes enable the processing of encrypted data and may be used by a user to outsource storage and computations to an untrusted server. A plethora of HE schemes has been suggested in the past four decades, based on various assumptions, which achieve different attributes. In this work, we assume that the user and server are quantum computers and look for HE schemes of classical data. We set a high bar of requirements and ask what can be achieved under these requirements. Namely, we look for HE schemes which are efficient, information-theoretically secure, perfectly correct, and which support homomorphic operations in a fully compact and non-interactive way. Fully compact means that decryption costs $$\mathcal {O}(1)$$ time and space. We suggest an encryption scheme based on random bases and discuss the homomorphic properties of that scheme. The main advantage of our scheme is providing better security in the face of weak measurements (WM). Measurements of this kind enable collecting partial information on a quantum state while only slightly disturbing the state. We suggest here a novel QKD scheme based on our encryption scheme, which is resilient against WM-based attacks. We bring up a new concept we call securing entanglement. We look at entangled systems of qubits as a resource used for carrying out quantum computations and show how our scheme may be used to guarantee that an entangled system can be used only by its rightful owners. To the best of our knowledge, this concept has not been discussed in previous literature.
IntelliJML: a JML plugin for IntelliJ IDEA

Java code can be annotated with formal specifications using the Java Modelling Language (JML). Previous work has provided IDE plugins intended to help write JML, but mostly for the Eclipse IDE. We introduce IntelliJML, a JML plugin for IntelliJ IDEA, with a focus on ease of use and maintainability. Features such as syntax, semantic, and type checking, as well as syntax highlighting and code completion are integrated into the plugin. The plugin can also be extended in the future to add more features. The source code for the plugin can be found at https://gitlab.utwente.nl/fmt/intellijml.
CUDA Implementation of an Algorithm for Batch Mode Detection of Collisions

The collision detection problem arises in many different areas, such as games, computer graphics, physics simulations, robot control systems, and others. There exist many different setups of this problem. We consider a setup associated with a special case of the collision detection problem that may arise during the process of optimization of robot control. The number of geometries of rigid bodies in this setup is relatively small (of the order of tens or hundreds), but the number of configurations (i.e., bodies in different positions) can be substantial (of the order of thousands or tens of thousands). In some cases, these configurations (scenes) may be pregenerated together, while in other cases, new scenes can appear only after the collision detection in previous ones. Several scene sources may participate simultaneously. There are well-known flexible implementations of algorithms for collision detection (for instance, Flexible Collision Library, FCL) designed for many-core CPUs. However, efficient GPU implementations (at least for the described setup) are still missing. In this paper, we outline a GPU implementation and compare it with the FCL. We measure the acceleration of our implementation against a single-threaded CPU version (which is the FCL in our case) under different regimes. In some cases, the acceleration reaches a value of the order of 60 to 70.