Reviewerly/exHarmony huggingface dataset:
A Study on Estimating the Elements of Card Game Fun Using Biological Information

In this study, we conduct research to estimate the elements of fun in card games.Previously, we tried to estimate the elements of fun by conducting a questionnaire to players, but the results were not good.Therefore, we propose an analysis using the player's biological information to make a more accurate estimation.Specifically, we try to elucidate the elements of fun by having a player who is playing a game wear a smart watch, and measuring and analyzing the heart rate of that player.This paper conducts an experiment to determine whether our intended data can be collected.As a result, it was found that there is a response to the heart rate in a specific scene, and there is a possibility that the intended data can be collected.We plan to conduct larger experiments in the future.
Features of students’ perception of a university’s organizational culture depending on their organizational identification

In the current reality, the higher education as the trajectory for the academic career faces particular difficulties. There is a downward trend regarding the number of young academic specialists&amp;nbsp;— a negative factor for the entire educational system. The discussed problem may arise according to various reasons: changes in the law sphere or peculiarities of the educational organization activity. Both Western and Russian researchers conclude that the relations between an organization and its staff members are the key aspect of their loyalty. Thus, this article aims to examine the connection of a university organizational culture perception by the students with different types of identification demonstrated. The authors study the peculiarities of students’ perception of the university organizational culture. Altogether, 118 students participated in questionnaires that measure organizational culture with Organizational Culture Assessment Instrument and four types of identification with the university. The findings show the gap between students’ current and preferred perception of the university organizational culture. Consequently, the dominance of market culture that contradicts the desired “clan” culture orientation with the values of friendly and close relations environment, may influence the choice of academic career and postgraduate studies.
On the Typology of the Information Ethos

The paper provides a discussion on the understanding of information ethics as a research field which scrutinizes ethical problems of social communication in close connection with the analysis of cyber informatization process. It is proposed to consider information ethics as a generic concept and interpret computer ethics as one of its types due to computer communication technologies. Historical types of information ethos and its psychological characteristics are revealed in accordance with the main types of information paradigms. The basis of this paradigm is a specific type of system ‘human – information technology’ and information exchange technology. In the structure of the information exchange several components can be distinguished: (a) content component, or the actual exchange of the information (content); (b) technological basis of communication which generates the ethics of technological methods of information exchange; (c) psychological aspects of information exchange which change together with each new technological method of communication. Its changes raise the specific ethical problems and form a specific type of information morality – information ethos. Ethics of information exchange and ethics of information technologies are distinguished.
ATTEMPT OF COMPILING COMPETENCE-BASED ASSIGNMENTS BY THE EXAMPLE OF THE DISCIPLINE “SAFETY ON THE ROAD AND IN PUBLIC TRANSPORT”

The article is devoted to the relevant problem of assessing students’ competences. The author proposes her own model of structural and meaningful content of competences being formed based on the content of the teacher’s labour functions. The assessment procedure includes different types of competence-based assignments. The researcher shares her experience of compiling such assignments. The proposed technique and the assignments themselves can be used by teachers in all areas of training when organizing the assessment procedure and assessing students’ competences.
How to implement the basic educational program based on the three requirements of the Federal State Educational Standard

The article makes an attempt to comprehend the main points presented in the works of the Academician of the Russian Academy of Education, Doctor of Sciences (Education), Professor Alexander A. Kuznetsov, devoted to the introduction of the FSES of second generation in schools’ practice and the role of a teacher in the context of the introduction of the FSES. Particular attention is paid to such issues as: the development of the basic educational program, which is considered as the basis for the organization of the educational process; educational program evaluation; the role and importance of the explanatory note of the basic educational program; the main methodological idea of the work educational program on the subject; content of information educational environment; textbook, including electronic, as part of information educational environment. The analysis of the problems of the implementation of the FSES of second generation given in the article will be useful to teachers, methodologists and other pedagogical staff to improve the educational process both at school and at a pedagogical university when teaching future teachers.
Derivation of a Conceptual Framework to Assess and Mitigate Identified Customer Cybersecurity Risks by Utilizing the Public Cloud

The number of end points connecting to the cloud can increase distributed attack vectors due to vulnerable devices connecting from the front end. The risk is also enhanced due to the technological abstractions associated with public cloud computing models at the back end. On the one hand, cloud service providers make sets of defined service criteria and supporting documentation, publicly available to assist customers with their public cloud deployments. However, on the other hand, a cacophony of security incidents over the past five years involving vulnerable cloud customer instantiations reveals that cloud security risks may not be completely comprehended. Essentially, the fundamental principle of cloud computing is the ‘shared security responsibility’ model. It is argued in this paper that from a cloud customer perspective, there is either too much reliance upon legacy risk assessment methods and/or standards orientated compliance-mapping approaches when trying to apply due diligence for cybersecurity. This can be amplified by different cloud service providers using terms like ‘core services’ and ‘managed services’ rather than traditional terms such as Infrastructure-as-a-Service and Platform-as-a-Service. This extended paper describes the myriad of techniques used to derive a conceptual framework through post-graduate research. Based around a defense-in-depth model, the proposed conceptual framework is a proof of concept to enable customers to focus on the contextualized risks when using the public cloud. A method of reducing the risks using mitigation categories is also proposed. Consequently, a method of calculating residual risk against the identified risks levels is theoretically defined and dependent upon the rigor of counter-measure selection.
Simplifying Graph Convolutional Networks as Matrix Factorization

In recent years, substantial progress has been made on Graph Convolutional Networks (GCNs). However, the computing of GCN usually requires a large memory space for keeping the entire graph. In consequence, GCN is not flexible enough, especially for large scale graphs in complex real-world applications. Fortunately, for transductive graph representation learning, methods based on Matrix Factorization (MF) naturally support constructing mini-batches, and thus are more friendly to distributed computing compared with GCN. Accordingly, in this paper, we analyze the connections between GCN and MF, and simplify GCN as matrix factorization with unitization and co-training. Furthermore, under the guidance of our analysis, we propose an alternative model to GCN named Unitized and Co-training Matrix Factorization (UCMF). Extensive experiments have been conducted on several real-world datasets. On the task of semi-supervised node classification, the experimental results illustrate that UCMF achieves similar or superior performances compared with GCN. Meanwhile, distributed UCMF significantly outperforms distributed GCN methods, which shows that UCMF can greatly benefit complex real-world applications.
The Development and Implementation of M-Glossary in the System of Content and Language Integrated Learning

The purpose of this article is to present the results of the ongoing study aimed at development and implementation of the specific mobile application – “aviation glossary” within the educational CLIL model on the basis of Saint-Petersburg State University of Civil Aviation, Saint Petersburg. The authors examined the phenomenon of content and language integrated learning, its features and components. At the same time the authors appealed to the newly-developed branch of e-learning – mobile learning, its models based on the “Bring your own device” concept and its key advantages for everyone involved in educational process. The presented article is also devoted to the description of lexicographical work of the m-glossary composition and its further usage with the “autonomous learning” model to develop students’ linguistic skills, e.g. vocabulary knowledge, reading comprehension of the professionally-oriented text. The findings revealed a stable increase in the rate of students’ vocabulary knowledge which is required in order to perform professional and communicational activities successfully.
A Novel Attention Enhanced Dense Network for Image Super-Resolution

Deep convolutional neural networks (CNNs) have recently achieved impressive performance in image super-resolution (SR). However, they usually treat the spatial features and channel-wise features indiscriminatingly and fail to take full advantage of hierarchical features, restricting adaptive ability. To address these issues, we propose a novel attention enhanced dense network (AEDN) to adaptively recalibrate each kernel and feature for different inputs, by integrating both spatial attention (SA) and channel attention (CA) modules in the proposed network. In experiments, we explore the effect of attention mechanism and present quantitative and qualitative evaluations, where the results show that the proposed AEDN outperforms state-of-the-art methods by effectively suppressing the artifacts and faithfully recovering more high-frequency image details.
Analysis of the System Engineering Problems in the Automated Dispatch Control Systems for Oil and Gas Production Technological Processes

Automated dispatch control systems (ADCS) is the main direction of development of process control systems in the oil and gas industry. Being part of the man-machine control systems, the development of ADCS is largely determined by new knowledge in the field of information technology. Based on this, we present an analysis of current problems of system engineering for each life cycle stage along with an analysis of the relevance of the study and accounting of the human factor in the development of ADCS.
A Study on the Improvement of the Course for the Education Program of Medical Manager's Qualification : Focus on the Maritime and Fisheries High School Students

This study is a descriptive research that aimed to analyze educational satisfaction, difficulty level, teaching ability, learning flow and scores between maritime and fisheries high school students and ordinary people. The subjects in this study were 127 maritime and fisheries high school students and 195 ordinary people. And a questionnaire survey for factors affecting score was conducted. The data were analyzed by descriptive statistics, chi-square test, t-test, and Multiple Linear Regression with SPSS WIN 23.0 program. As a result, maritime and fisheries high school students were evaluated to have lower educational satisfaction, higher difficulty level, lower learning flow than the ordinary people. In terms of the evaluation score, maritime and fisheries high school students were significantly lower than the ordinary people, and the pass rate also showed a great difference. The difficulty level was found to be a factor that affects the score of maritime and fisheries high school students. The education program of medical manager
Security on Cloud Computing Using Pseudo-random Number Generator Along with Steganography

Cloud computing is a convenient and flexible method to use computing assets such as storage, database and networks while “on the cloud”, i.e. to access over the internet. Data communicated is not secure and hence a technique was formulated which included- standard encryption techniques is used to encrypt the data, followed by applying steganography and then finally, splitting it across two or more hosts to transfer it. Steganography refers to encrypting information in the pixels of an image. In the spatial format, an alternative to embedding information sequentially in the picture elements of a cover image, a randomized method- PseudoRandom Number Generator (PRNG) is implemented to improve the overall security and efficacy of the system.
A Security Problem in Cloud Auditing Protocols

In 2013, subversion attack comes to publity again by Mikhail Bellare, who was inspired by PRISM. In this work, we implement this kind of attack on cloud auditing protocols. We show that through subversion attacks, the cloud server can recover the secret information stored by the data owner. Especially, First, we set a general frame of data auditing protocols. This model forms a basic security model of auditing protocols. Then we give a security model of attacker. Finally, we put forward some popular auditing protocols which can be subverted.
Constrained representation learning for recurrent policy optimisation under uncertainty

Learning to make decisions in partially observable environments is a notorious problem that requires a complex representation of controllers. In most work, the controllers are designed as a non-linear mapping from a sequence of temporal observations to actions. These problems can, in principle, be formulated as a partially observable Markov decision process whose policy can be parameterised through the use of recurrent neural networks. In this paper, we will propose an alternative framework that (a) uses the Long-Short-Term-Memory (LSTM) Encoder-Decoder framework to learn an internal state representation for historical observations and then (b) integrates it into existing recurrent policy models to improve the task performance. The LSTM Encoder encodes a history of observations as input into a representation of internal states. The LSTM Decoder can perform two alternative decoding tasks: predicting the same input observation sequence or predicting future observation sequences. The first proposed decoder acts like an auto-encoder that will guide and constrain the learning of a useful internal state for the policy optimisation task. The second proposed decoder decodes the learnt internal state by the encoder to predict future observation sequences. This idea makes the network act like a non-linear predictive state representation model. Both these decoding parts, which introduce constraints to policy representation, will help guide both the policy optimisation problem and latent state representation learning. The integration of representation learning and policy optimisation aims to help learn more complex policies and improve the performance of policy learning tasks.
Strong WQO Tree Theorems

Ordinal (vertex- and/or edge-) labeled finite trees are well-quasi-ordered by homeomorphic embeddability with sound gap-conditions. Such strong generalizations of Harvey Friedman’s tree theorem (abbr.: FT) on trees whose vertices are labeled by bounded natural numbers are (a) provable in second-order arithmetic $$\Pi _{1}^{1}$$-TR$$_{0}$$ (also designated ITR$$_{0}$$ below) that extends ACA$$_{0}$$ by transfinite iteration of $$\Pi _{1}^{1}$$-comprehension along arbitrary countable ordinals but (b) not provable in a subsystem thereof that arises by weakening $$\Pi _{1}^{1}$$-transfinite recursion axiom to $$\Pi _{1}^{1}$$-transfinite recursion rule. In particular, I. Křiž’s tree theorem (abbr.: KřT) referring to ordinal edge-labeled trees [9] is provable in $$\Pi _{1}^{1}$$-TR$$_{0}$$ (that is weaker than theory $$\Pi _{2}^{1}$$-CA implicitly used in [9]), which is the main result of the paper. Moreover KřT is proof-theoretically equivalent to the author’s analogous theorem (abbr.: GT) referring to ordinal vertex-labeled trees under symmetric gap-condition [6]. Namely, both theorems characterize ITR$$_{0}$$ in the sense of ordinal provability over ACA$$_{0}$$. That is, the supremum of proof-theoretic ordinals provable in ACA$$_{0}$$ extended by GT and/or KřT is the proof-theoretic ordinal of ITR$$_{0}$$ [in symbols: $$\left| \mathbf {ACA}_{0}+GT\right| =\left| \mathbf {ACA}_{0}+K\check{r}T\right| =\left| \Pi _{1}^{1}\text {-}\mathbf {TR}_{0}\right| =\psi 0\left( \Phi 10\right) $$ (see [12] for the last equality)]. By contrast, the restricted GT and KřT referring to ordinal labeled intervals (i.e. non-branching trees) both yield analogous characterizations of a weaker (predicative) theory ATR$$_{0}$$, instead of ITR$$_{0}$$ (cf. [5]).
Augmented Eye: From theory to practice

This paper describes the prototype development of a pilot instructor support tool: Augmented Eye. This tool aims to support instructors by enabling them to monitor students’ scan behaviour in real-time, without bothering the student. Although little is known about optimal pilot scan behaviour, some scan behaviour can yet be quantified and translated to scan performance. This led to the construction of a tunneling analysis and gap analysis. Augmented Eye has a modular set-up, including a fusion of simulator data, eye tracking data and real-time analyses. The tool seems promising for pilot training.
SYG-Net

To improve the accuracy of vehicle detection, a vehicle detection neural network SYG-Net with YOLOv3 network as the main body was proposed and combined with generalized Intersection over Union (GIoU) and spatial pyramid pooling (SPP) module. The backbone network of SYG-Net network is the basic network structure of YOLOv3. However, a layer of SPP was added before the main network structure of feature extraction, namely, darknet and YOLO layers. In this manner, the features before the input of YOLO layer can obtain spatial features. GIoU was used as the regression loss of BBox at the end of the network layer and tested on UA-DETRAC data set. Results showed that the map and recall values of SYG-Net network increased substantially. Meanwhile, loss and average GIoU converged quickly and had good effect. SYG-Net was 0.75% and 0.75% more accurate than YOLOv3 and 0.7 YOLOv3-SPP, respectively. Results showed that SYG-Net was effectively detects vehicle. This paper looks forward to the combination of SYG-Net and other modules.
A Multi-modal Spatio-temporal Data Semantic Association Model Based on Ontology

Multi-modal spatio-temporal data is mixed data with spatio-temporal information, various types and complex modalities. Different modal data are usually related to each other. Effective organization and associated spatio-temporal data are the focus of massive data management and data information mining. Aiming at the characteristics of multidimensional, multi-scale, multi-temporal and multi-modality of spatio-temporal data, by analyzing its rich spatio-temporal information and semantic information, this paper proposes a multi-modal spatio-temporal data semantic association model with time information, spatial information and content object semantic information as three related factors. At the same time, based on the characteristics of multi-modal spatio-temporal data features low-level features heterogeneity, the ontology model of spatio-temporal data semantic expression that can describe data information uniformly is defined and constructed based on Ontology theory. The association query is realized by constructing multiple instances of different modal spatio-temporal data. The experimental analysis shows that the multi-modal spatio-temporal data semantic association model can effectively correlate spatio-temporal data of different modalities and has certain scalability.
Short-Term Memory Effects in Subjective Image Quality Assessment of Natural Images

In this paper we discuss the influence of short-term memory in judging image quality of natural images. For this, a category judgement experiment with 15 observers has been carried out on a set of natural images. To analyze the presence of memory effect the autocorrelation and correlation between the ratings given by each observer have been studied. Many different orders of the ratings have been considered to draw a meaningful conclusion on the presence of short-term memory effect while evaluating the quality of natural images.
Promoting Scientific Researches of IHE Scientists: Comprehensive Solutions

Objective. One of the main tasks of the University Library today is to create a new additional effective virtual environment for research activities - its own open access electronic information resources. The work is aimed at considering virtual communication forms of representing scientific achievements of researchers of the National Technical University “Kharkiv Polytechnic Institute” (NTU “KhPI”) in various fields of science, production and industry. Methods. Using the example of specific bio bibliographic scientific projects (schools), which are being developed in KhPI Scientific and Technical Library, a comprehensive approach to their realization is considered. Results. Due to a comprehensive approach, the concept of information integration was first implemented – the electronic resources of the Library site, the University repository and electronic and paper versions of the journal were combined. Conclusions. Only through joint efforts of scientists and librarians can we promote and disseminate the results of university research in the modern scientific and educational space and raise the ranking and image of our university among higher education institutions of Ukraine and the world.
Skin friction measurement on NASA Common Research Model using oil film in JAXA

In the JAXA 2 m × 2 m low speed wind tunnel, the skin-friction distributions on the wing surface of the NASA Common Research Model (CRM) were measured using oil film interferometry method and luminescent oil method. In addition, the temperature distributions on the surface were measured with an IR camera to improve the measurement accuracy by eliminating the temperature dependence of the oil. Simultaneous measurement for surface temperature and skin friction can be achieved by using a polyimide tape attached to the surface. Furthermore, markers were adhered on the surface to combine the results of the oil film interferometry method, the luminescent oil method, and the temperature distributions. As a result, it was found that the measurement system is normally performed in the oil-temperature correction and 3D mapping technique.
Modelling the Semantics for Model-Driven Interactive Visualizations

The following topics are dealt with: business data processing; ontologies (artificial intelligence); data mining; security of data; cryptography; cloud computing; software architecture; query processing; storage management; distributed databases.