Reviewerly/exHarmony huggingface dataset:
Formation of artistic and aesthetic culture of students in rural social environment

The article deals with a study of the problem of artistic and aesthetic culture formation in students in rural social environment. Presented in the article are the results of an empirical study that reveals the levels of formation of artistic and aesthetic culture in rural schoolchildren. A hypothesis was tested, that the levels and criteria of artistic and aesthetic culture are associated with factors of the rural social environment, such as life and traditions of their village, as well as natural conditions. Students of two schools, rural and urban, took part in the experiment. Different ages of students were represented in the composition of the experimental and control groups. It has been demonstrated that rural social environment has a positive impact on the development of artistic and aesthetic sense, emotional and sensory perception, appreciative attitude to nature and ancestral cultural heritage, as well as on the development of aesthetic norms and preferences. The results of students’ creative work, reflected in decorative and applied activities, are also the indicators of artistic and aesthetic culture formation of students and have a connection with rural society. Artistic and aesthetic culture of students in rural social environment is the result of holistic socio-cultural development.
Blue-green Algae Recognition Based on Rotation Invariant Uniform LBP and Color Information

Aiming at the problem of blue-green algae recognition in digital images, current algorithms ignore the difference between blue-green algae and water plants. In order to effectively distinguish between blue-green algae and other objects of the same color, this paper uses LBP histogram to extract the texture feature. But it takes a long time to recognize blue-green algae with the original LBP. Therefore, using the rotation invariant uniform LBP to extract image features. At this time, the number of feature vectors is reduced, significantly reduced recognition time. But it also led to the lack of some information, and accuracy of blue-green algae recognition is greatly reduced. Therefore, this paper concatenates the 64-dimension RGB histogram and the rotation invariant uniform LBP histogram to improve the recognition accuracy. The extracted features are then inputted into SVM with the INTER kernel function for final recognition. Experiments show that the proposed method is effective and efficient for blue-green algae recognition.
Study on Signal Processing of IES of Tunnel Lining Based on Wavelet Analysis

Abstract As we all know, high-speed rail is a very important business card in China, and the rapid development of high-speed rail is inseparable from the rapid development of tunnel construction, so tunnel construction has become very important. The quality of the tunnel lining directly affects the construction quality of the entire tunnel construction. This paper first introduces the use of IES, one of the most advanced testing methods for non-destructive testing, to achieve non-destructive testing the quality of tunnel lining. The signal is more or less affected by the surrounding environment and human factors, so In this paper, wavelet analysis is used to eliminate the signal noise in order to extract characteristic signals that better reflect the defects of tunnel lining and realize the intelligent diagnosis of the defects of tunnel lining.
Performance analysis of Cubature rule based Kalman filter for target tracking

In this paper, the Cubature Kalman filter (CKF) is implemented for tracking maneuvering targets such as ballistic missiles, aircraft, etc. Its high accuracy at a relatively low computational cost makes it suitable for real-life applications. The coordinated model is adopted for modeling of the maneuvering targets. The simulation results for this filter are observed for maneuvering targets with varying turn rate. The performance is analyzed in terms of the root mean square error (RMSE) computed over a large number of Monte-Carlo runs. The simulation results reveal a successful tracking of the moving targets using the CKF. From simulations, we observe that RMSE increases with the turn rate.
Post-editing Neural Machine Translation Versus Human Translation for Chinese Essays: A Pilot Study

With the rise of neural machine translationNeural Machine Translation (NMT) (NMT) and encouragement of previous research, a pilot study has been conducted to compare post-editingPost-Editing (PE) (PE) Google neural machine translation (GNMT) to human translation (HT) with Selected Modern Chinese Essays: Annotated Bilingual Edition, Volume 1. To this end, six student translators were invited to take part in a voluntary experiment. They were in M.A. Chinese-English translation postgraduate program with three of them having up to two years’ professional translation experience. They were asked to translate two Chinese essays in two workflows: post-editing Google neural machine translation and translating from scratch, respectively. The elapsed translating time was recorded by a stopwatch application and they were asked to finish the pre- and post-experiment questionnairesQuestionnaires online via Survey Monkey. Following that, they were invited to have a one-to-one personal interview on detailed translation problems they encountered during the experiment and some specific questions prepared in advance to take a step further in the perceptions toward post-editing GNMT and SDL Trados StudioSDL Trados Studio 2019. After the experiment, the translation time taken was analyzed; the qualities of the translations were compared by me; the translation performanceTranslation performance was calculated by Pythagorean theoremPythagorean theorem; the student translators’ perceptions in questionnaires and debriefing sessions were gathered and illustrated in tables. Results indicate that PE was faster than translation from scratch for all participants but one. The post-edited translations were found to be of inferior quality compared to human translations produced from scratch, but this should modulate the productivity benefits of PE quite considerably. In addition, most participants showed positive attitudes toward post-editing GNMT of literature, while some of them complained with examples that segments of SDL Trados StudioSDL Trados Studio may not assist with literary machine translationLiterary machine translation post-editing (MTPE) and mistranslationsMistranslation in GNMT are irritating.
MANAGEMENT OF INTANGIBLE INCENTIVES FOR LABOR IN EDUCATIONAL ORGANIZATIONS

The article is devoted to the study of the management system of intangible incentives used for the personnel of organizations. The authors defined groups of the main tools of intangible motivation of personnel including working conditions, tools of social support, elements of corporate culture and tools of stimulation of self-realization opportunities; revealed the main principles of the system of intangible motivation of personnel representing a cycle including the following managerial stages: planning, design of methods, techniques and measures of intangible motivation of personnel; plan implementation; coordinated actions, monitor The system of intangible motivation tools application is formed and on its basis the complex evaluation of intangible motivation system of personnel in Belgorod higher educational institutions is performed and the most effective methods of personnel motivation in higher educational institutions of Belgorod region are revealed, the satisfaction of respondents with the system of intangible motivation of personnel is revealed. Further, the authors offered the project to improve intangible motivation of personnel, including forms, methods, recommendations for implementing methods of intangible motivation on the example of educational organizations of Belgorod. It was revealed that it is possible to form a parametric system, including the goal and the way of achieving the goal, requirements for the result and the project result, as well as the final users of the project result, aimed at developing a system of intangible motivation of personnel of educational organizations, which will allow to carry out activities that contribute to the implementation of the required effect in the socio-economic sphere.
Hybrid intelligent system of rules extraction for decision making

Abstract This article attempts to give an overview of several algorithms for extracting rules from an artificial neural network. The goal of this article is to find critical links three important parts of artificial intelligence – production models, fuzzy logic and deep learning. Such an approach will stimulate researchers in the field of soft computing to develop applied systems in the field of explanational artificial intelligence and machine learning.
String Diagrams for Modelling Functional Programming

It is currently impossible to model functional programs in the same way that UML is used to model object-oriented programs: no analogous graphical notation exists. Unlike object-oriented programs, however, functional programming is built on a solid mathematical basis and it may be possible to adapt graphical notation from the mathematical domain for such modelling. This work examines string diagrams as a way to model certain functional abstractions. A proposed notation is demonstrated in the contexts of equational reasoning and descriptive modelling, and is found to be suitable for both.
Employees' Cybersecurity Behaviour in the Mobile Context: The Role of Self-Efficacy and Psychological Ownership

Mobile devices are widely known to be a cybersecurity risk for organisations. However, their use for work purposes is largely becoming a common practice as most organisations rather prefer to find security countermeasures than miss out on the momentous benefits they provide. Since the weakest link in cybersecurity is the human element, organisations are increasingly looking for ways to upskill their employees so that they can be able to implement the necessary security safeguards. Yet, a concerning trend is that even individuals with a high-security self-efficacy sometimes fail to take the needed security actions. To better understand why this occurs, the present study proposes and validates psychological ownership as a possible boundary condition for explaining the security self-efficacy to security behaviour nexus in the mobile security context. Using an online survey with 214 respondents, this study showed that both the direct and indirect effects of security self-efficacy on mobile device security behaviour were moderated by psychological ownership such that the effects were more pronounced when psychological ownership was high than when it was low. The paper culminates with a discussion of the implications for theory and practice.
Efficient Computing Platform Design for Autonomous Driving Systems

Autonomous driving is becoming a hot topic in both academic and industrial communities. Traditional algorithms can hardly achieve the complex tasks and meet the high safety criteria. Recent research on deep learning shows significant performance improvement over traditional algorithms and is believed to be a strong candidate in autonomous driving system. Despite the attractive performance, deep learning does not solve the problem totally. The application scenario requires that an autonomous driving system must work in real-time to keep safety. But the high computation complexity of neural network model, together with complicated pre-process and post-process, brings great challenges. System designers need to do dedicated optimizations to make a practical computing platform for autonomous driving. In this paper, we introduce our work on efficient computing platform design for autonomous driving systems. In the software level, we introduce neural network compression and hardware-aware architecture search to reduce the workload. In the hardware level, we propose customized hardware accelerators for pre- and post-process of deep learning algorithms. Finally, we introduce the hardware platform design, NOVA-30, and our on-vehicle evaluation project.
Matching with GUISAC-Guided Sample Consensus

The traditional RANSAC samples uniformly in the dataset which is not efficient in the task with rich prior information. This letter proposes GUISAC (Guided Sample Consensus), which samples with the guidance of various prior information. In image matching, GUISAC extracts seed points sets evenly on images based on various prior factors at first, then it incorporates seed points sets into the sampling subset with a growth function, and a new termination criterion is used to decide whether the current best hypothesis is good enough. Finally, experimental results show that the new method GUISAC has a great advantage in time-consuming than other similar RANSAC methods, and without loss of accuracy.
Deep Learning-based Syllable Recognition Framework for Korean Children

Recently, speech recognition has been very widely used in our life. With this trend, this work introduces a new recognition system, called SCKC (Syllable Classifier for Korean Children), for Korean syllable, especially spoken by children. In particular, to design the recognition system, we first collected 10,000 Korean syllable data from approximately 150 children. Based on observations on the dataset, we optimize each procedure of SCKC, including pre-processing, feature extraction, and deep CNN model construction. A prototype implementation of SCKC provides a reasonable degree of accuracy (85.4% on average) in syllable recognition.
CHARACTERIZING EXISTENCE OF A MEASURABLE CARDINAL VIA MODAL LOGIC

Abstract We prove that the existence of a measurable cardinal is equivalent to the existence of a normal space whose modal logic coincides with the modal logic of the Kripke frame isomorphic to the powerset of a two element set.
Address Collision Attacks on ECSM Protected by ADPA

Using the physical characteristics of the encryption device, an attacker can more easily obtain the key, which is called side-channel attack. Common side-channel attacks, such as simple power analysis (SPA) and differential power analysis (DPA), mainly focus on the statistical analysis of the data involved in the encryption algorithm, while there are relatively few studies on the Hamming weight of the addresses. Therefore, a new method of address-based Hamming weight analysis, address collision attack, is proposed in this research. The collision attack method (CA) and support vector machines algorithm (SVM) are used for analysis, meanwhile, the scalar multiplication implemented by protected address-bit DPA (ADPA) can be attack on the ChipWhisperer-Pro CW1200.
A Secure Data Request and Sharing Model Based on Consortium Blockchain in Vehicular Edge Computing Environment

Data sharing among vehicles enhances driving safety and traveling experience. Nevertheless, computational intensive and low latency data sharing tasks impose strict requirements on the system. Vehicular edge computing (VEC) is envisioned as a promising paradigm. However, it cannot solve the security and privacy problems in nature. In this paper, we propose a secure data request and sharing model based on consortium blockchain. Specifically, we first present a novel data request and sharing architecture in VEC environments, which utilizes consortium block-chain and smart contracts technologies to achieve secure high-quality data sharing among vehicles. Further, we propose a reputation management scheme based on trust chain and weighted recommended opinion for subjective logic reputation management (TCWRSL), which comprehensively considers the vehicular reputation, time decay effect of reputation and interaction reputation to establish the weight of the trust chain. On this basis, it assists the data requestor to refer to the recommended opinions to calibrate the local opinion. Moreover, we propose the weighted recommended opinion which helps to reduce the destructive impact of malicious vehicles that participate in the recommended opinions. Simulation results based on real data sets demonstrate the superiority of TCWRSL for obtaining more credible vehicular reputation.
An Impossible Trinity in Blockchain-based Transactions: Decentralization, Privacy, and Lower Transaction Costs

Decentralized blockchain-based transactions benefit users through their openness, transparency, and immutability, which may come at the expense of privacy concerns. This paper shows that if there is a dilemma between decentralization and privacy protection, such that a more decentralized system increases the risks of data leakage, allowing additional privacy protection within the blockchain network at a cost, such as hiring mixers, does not resolve the initial dichotomy. However, if non-protected users benefit from positive externalities from the additional protection provided to protected users, such that all users enjoy less privacy concerns with double protection, the dilemma of decentralization and privacy risks can be resolved, despite the higher transaction costs required. Thus, double privacy protection at a cost transforms the problem with a dilemma into one with a trilemma. Given the trilemma, blockchain-based services, such as payment gateways, need to have effective privacy protections for consumers at a sufficiently low cost, which results in a less serious initial effort for stable and large-scale adoption.
Automatic Threat Detection in Single, Stereo (Two) and Multi View X-Ray Images

Accurate X-ray screening systems are of paramount importance in the present day. Most existing systems predict only on the basis of a single image, which could lead to false positives and false negatives due to limited information present. We implemented several approaches using single, two and multiple X-Ray views to make a reliable and practical model with varying levels of success in threat object detection. These approaches include long-established methods such as Bag of Visual Words (BOVW), 3D Object Recognition, Adaptive Implicit Shape Model and Deep Neural Networks. The approaches took in dual inputs to make more informed predictions. Varying levels of success are obtained in these methods ranging from 73% using BOVW to 87% using Deep CNN. It was observed that, when two views of an object are considered, an improvement of 5% to 15% in performance took place (considering various approaches) compared to a single view.
Sentiment Analysis of Tamil-English Code-Switched Text on Social Media Using Sub-Word Level LSTM

Social media are the ultimate platforms to express the opinion and to facilitate the creation and sharing of information, ideas, career interests and other forms of expression via virtual communities and networks. Analysing the sentiment features in these ideas in the public posts of social media users will lead to building more accurate behavioural patterns. Importance of these behavioural patterns with respect to the marketing and business perspective has been focused here. When considering the traditional Facebook marketing platform, efficiency and effectiveness of the marketing are very low since the advertisers do not happen to have a proper understanding of the customers that they should address. Thus, to overcome this issue, a system is proposed to identify the behavioural patterns of Facebook users by analysing their social media contents such as posts, comments, interactions, and also reviews and critics on products to enhance the effectiveness of the Facebook marketing. This system mainly focuses on Facebook users in Sri Lanka. Natural language processing is used to process text-based posts (uploaded and shared) and comments of users in order to build a behavioural profile for the users. This system process text data which is composed by using both English and Tamil languages, in code-switching language pattern.
S4: Simple, Secure, Survivable Systems Human-first crisis technology design principles

Information technology has become embedded in almost every area of modern life. The many complex digital systems that support modern societies are now highly dependent on the correct function of complex and highly interdependent technological systems. Digital tools are increasingly becoming part of traditional crisis response efforts by government and non-government organisations. While digital tools have substantial capabilities to enhance crisis response efforts, they also pose significant risks to user communities when deployed in time-sensitive, vulnerable and fragile crisis contexts - as part of an already complex system. These risks and inefficiencies have been demonstrated in the contact-tracing application debate in the response to the COVID-19 pandemic.Technology must be intentionally designed and implemented, both to help solve the problem at hand and support end user com-munities. The principles of simple, secure and survivable systems (S4) offer a framework for technology that serves the interests of end-users and maintains human dignity, especially in crisis situations. The S4 principles are already evident in a number of technology projects, across research, design, build and deployment phases. Instead of high-risk, ad hoc, reactive digital solutions, crisis responders can pre-emptively share information, invest and work with existing technology design and development experts that reflect the S4 principles for efficient, effective solutions that enhance response capabilities both now and in future scenarios.
Data consistency method of heterogeneous power IOT based on hybrid model

The data of the power Internet of Things (IOT) system is transferred from the IaaS layer to the SaaS layer. The general data preprocessing method mainly solves the problem of big data anomalies and missing at the PaaS layer, but it still lacks the ability to judge the high error data that meets the timing characteristics, making it difficult to deal with heterogeneous power inconsistent issues. This paper shows this phenomenon and its physical mechanism, showing the difficulty of building a quantitative model forward. A data-driven method is needed to form a hybrid model to correct the data. The research object is the electricity meter data on both sides of a commercial building transformer, which comes from different power IOT systems. The low-voltage side was revised based on the high-voltage side. Compared with the correction method based on purely using neural networks, the combined method, Linear Regression (LS) + Differential Evolution (DE) + Extreme Learning Machine (ELM), further reduces the deviation from approximately 4% to 1%.
Learning Graph Representations

Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible. Some of the interesting and useful applications on these graphs are graph classification, node classification, link prediction, etc. The Graph Neural Networks have evolved over the last few years. Graph Neural Networks (GNNs) are efficient ways to get insight into large and dynamic graph datasets capturing relationships among billions of entities also known as knowledge graphs. In this paper, we discuss the graph convolutional neural networks graph autoencoders and spatio-temporal graph neural networks. The representations of the graph in lower dimensions can be learned using these methods. The representations in lower dimensions can be used further for downstream machine learning tasks.