Reviewerly/exHarmony huggingface dataset:
Professional Pedagogical Culture: Historical Culturological Aspect

The phenomenon of professional pedagogical culture is analyzed as social pedagogical thing, that integrates historical cultural experience into elaborated coordinate pedagogical activity. Methodology of research are based on dialectic connection of such methods: comparison historical, genetic, cultural historical, synergistic one – for ascertainment of professional pedagogical culture as an open system, that changes becomes available for multi-factor interaction of processes of breeding and self-breeding, education and self-education, justification of processes of teacher's development and self-development. Scholarly novelty is at the analysis of fact of professional pedagogical culture as syntheses of subjective and objective phenomenon, discovering of pedagogical values, technologies, forms and results of pedagogical creativity as specific form of expression of personality's common culture, that bases at determined scholarly approaches. At the article is justified expediency approaches to study of issues of professional and pedagogical culture. During the work was traced the evolution of culturological epochs and models of culturological education that are appropriate for them on a base of culturological approach. It is emphasized, that professional pedagogical culture as consciously purposeful activity is oriented for self-development to professional self-realization with deployment of a personality. Self-organization of a teacher promotes the formation and figuring-out the target of activity, its planning, building individual professional trajectory, self-correction, making reflection.
An Iteration-Time Combination Method to Reduce the Dynamic Degradation of Digital Chaotic Maps

A new and improved method based on the number of iterations is proposed to reduce the dynamical degradation of the digital chaotic map in this study. We construct a control function by introducing iteration time instead of external systems, thereby replacing the control parameters in the original chaotic map. Experimental results show that the chaotic map based on the iteration-time combination method is more complicated and effective. The period is extended without completely destroying the phase space, which indicates that our method is effective and can compete with other proposed techniques. A type of pseudorandom bit generator based on the iteration-time combination method is proposed to demonstrate its simple application.
Can we accurately predict where we look at paintings?

The objective of this study is to investigate and to simulate the gaze deployment of observers on paintings. For that purpose, we built a large eye tracking dataset composed of 150 paintings belonging to 5 art movements. We observed that the gaze deployment over the proposed paintings was very similar to the gaze deployment over natural scenes. Therefore, we evaluate existing saliency models and propose a new one which significantly outperforms the most recent deep-based saliency models. Thanks to this new saliency model, we can predict very accurately what are the salient areas of a painting. This opens new avenues for many image-based applications such as animation of paintings or transformation of a still painting into a video clip.
Multiplicative Complexity of Autosymmetric Functions: Theory and Applications to Security

The multiplicative complexity of a Boolean function is the minimum number of AND gates (i.e., multiplications) that are sufficient to represent the function over the basis {AND, XOR, NOT}. The multiplicative complexity measure plays a crucial role in cryptography-related applications. In fact, the minimization of the number of AND gates is important for high-level cryptography protocols such as secure multiparty computation, where processing AND gates is more expensive than processing XOR gates. Moreover, it is an indicator of the degree of vulnerability of the circuit, as a small number of AND gates corresponds to a high vulnerability to algebraic attacks. In this paper we study a particular structure regularity of Boolean functions, called autosymmetry, and exploit it to decrease the number of ANDs in XOR-AND Graphs (XAGs), i.e., Boolean networks composed by ANDs, XORs, and inverters. The interest in autosymmetric functions is motivated by the fact that a considerable amount of standard Boolean functions of practical interest presents this regularity; indeed, about 24% of the functions in the classical ESPRESSO benchmark suite have at least one autosymmetric output. The experimental results validate the proposed approach.
Effectiveness of a Head Movement Interface for Steering a Vehicle in a Virtual Reality Driving Simulation

Recently, virtual reality (VR) has become popular for a variety of applications, such as manufacturing and entertainment. In this study, considering that a driver’s head moves according to the motion of turning the steering wheel, we explored the effectiveness of head movement as a means for steering a vehicle in a virtual reality driving simulation. First, we analyzed the motion axes that are effective for control and found that the x (horizontal) direction, yaw rotation, and roll rotation are potential candidates. Through the implementation of a simulator, which allows participants to steer the vehicle by means of head movement, it was found that the x-axis movement was the most reliable as it reduced VR sickness while guaranteeing better usability and realistic motion. Human–machine interaction can become conceived of as symmetrical in the sense that if a machine is truly easy for humans to handle, it means that they can get the best out of it.
E-learning: A way out in COVID-19 Crisis

COVID-19 pandemic has not only negatively impacted the socio-economic attributes worldwide1 but also led to the confrontation of the education system with enormous challenges. This necessitated the immediate transition from face-to-face teaching to e-learning.2 This abrupt shift was primarily attributed to the need for social distancing due to the contagiousness of this disease and hence was intended to save humanity from coronavirus infection.3 Availing the e-learning alternatives for academic continuity in response to pandemic was the only solution. The teachers shared educational resources in the form of presentations, audio-visual videos, and documents, etc. by using assorted modalities like Microsoft Teams, Zoom, and Webinar.4 Although this exercise demanded stringent efforts of our teachers this bestowed them with an exhilarating opportunity to modify their pedagogical strategies.5 Lockdown imposition and abrupt closure of academic organizations considerably impacted the domain of higher studies. Everybody adapted to deliver their services remotely. The lectures, practicals, tutorials, examinations all were suspended with immediate effect amidst the COVID-19 pandemic.6 Although most of our students managed to attend online classes this modality was perceived inefficient in coverage of the whole curriculum in terms of acquisition of practical skills.7 No doubt synchronous and asynchronous teachings were done by using diverse instructional methods but adjustment of teaching pace, constructive feedback to the students’ assignments, and activation of students’ prior knowledge were reported to be immensely deficient.8 Resource-constrained countries profoundly faced problems in the smooth execution of virtual learning.9 Considerable uncertainties pertinent to COVID-19 along with limited facilities in the execution of prosperous e-learning academia made the scenario quite cumbersome. Tutors implicated in e-learning universally were dealt with issues of online connectivity, capacity building for proficient content delivery, and efficient interactivity with students.10 Designing an appropriate framework to bridge the gaps in e-learning conduction is deemed necessary for the provision of quality education without compromising the established standards amidst rampant outbreaks.
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines

Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents with commonsense knowledge. Such knowledge would allow agents to efficiently act in the world by pruning out implausible actions, and to perform look-ahead planning to determine how current actions might affect future world states. We design a new text-based gaming environment called TextWorld Commonsense (TWC) for training and evaluating RL agents with a specific kind of commonsense knowledge about objects, their attributes, and affordances. We also introduce several baseline RL agents which track the sequential context and dynamically retrieve the relevant commonsense knowledge from ConceptNet. We show that agents which incorporate commonsense knowledge in TWC perform better, while acting more efficiently. We conduct user-studies to estimate human performance on TWC and show that there is ample room for future improvement.
Fuzzy Type Relations and Transformation Operators Defined by Monads

Using the theory of monads in categories and the theory of monadic relations, the concept of general transformation operator defined by a monadic relation is introduced.It is proven that a number of standard relations used in categories of fuzzy structures are monadic relations for monads defined in these categories.It is also proven that a number of standardly used transformation operators in fuzzy sets, fuzzy rough sets, or fuzzy soft sets are in the form of general operators defined by monadic relations.
Approximate Learning of High Dimensional Bayesian Network Structures via Pruning of Candidate Parent Sets

Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks of moderate or higher complexity. In general, approximate solutions tend to sacrifice accuracy for speed, where the aim is to minimise the loss in accuracy and maximise the gain in speed. While some approximate algorithms are optimised to handle thousands of variables, these algorithms may still be unable to learn such high dimensional structures. Some of the most efficient score-based algorithms cast the structure learning problem as a combinatorial optimisation of candidate parent sets. This paper explores a strategy towards pruning the size of candidate parent sets, and which could form part of existing score-based algorithms as an additional pruning phase aimed at high dimensionality problems. The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.
Web-Based Student Opinion Mining System Using Sentiment Analysis

Collecting feedback from a few students after the exams has been the norm in educational institutions.Forms are given to students to assess the course the lecturer has taught.The main purpose of developing student opinion mining system is to create a faster and easier method of collecting feedback from student, and also give lecturers and school administrators an easier way of analysing the feedback collected from students.The significance of this application is that it is less expensive and present a more confidential way of getting students opinion.The major tools used in developing this application are Python, Scikit learn, Textblob, Pandas and SQLite.. Django provides an in-built server that allows the application to run on the localhost..In this project dataset gotten from online feedback form distributed to students was used for the sentiment analysi ,Chi-square was used for feature selection and the support vector machine algorithm was used for sentiment classification.The application will help the university administrators and lecturers to identify the strengths and weaknesses of the lecturer based on the textual evaluation made by the students.
Collaborative Item Recommendations Based on Friendship Strength in Social Network

Recommender Systems (RSs) are among the solutions in addressing the information overload problems.One of the RS main problem is cold start users where RSs do not have enough information to identify the user's preferences and thus unable to recommend relevant items.One of the approaches to overcome such problem is through social relationship which can be extracted from social networks.Existing studies used friendship relation to find nearest neighbors combined with implicit data such as tweet content, posts, like or tags to identify user's preference.This study proposed friendship strength through user's interaction and item rating values to identify users' preferences.User-based collaborative filtering methods are used in developing the system.Two datasets are synthetically obtained from MovieLens database for user's rating information.Meanwhile Twitter users' interactions data, was retrieved from Higgs Boson topic available from the Standford University.There are four phases of development namely user profile phase, friendship strength phase, user's preference phase and items recommendation phase.The findings of the study show that there is an improvement in the proposed method compared to the baseline based on the recall (R), precision (P) and F score measures.
Delineation of Copper Mineralization Zones at Wadi Ham, Northern Oman Mountains, United Arab Emirates Using Multispectral Landsat 8 (OLI) Data

Copper deposits in the ultramafic rocks of the Semail ophiolite massifs is found to be enormous in the region of northern Oman Mountains, United Arab Emirates (UAE). For this study, samples of copper were gathered from 14 different sites in the investigation area and were studied in the laboratory using the X-ray diffraction (XRD), GER 3700 spectroradiometer, and Inductively Coupled Plasma-Mass Spectrometer (ICP-MS). Detection and mapping of copper-bearing mineralized zones were carried out using minimum noise fraction (MNF), principal component analysis (PCA), decorrelation stretch (DS), and band ratio (BR) which were applied to Landsat 8 (OLI) data. The spectra of malachite and azurite samples were characterized by broad absorption features in the visible and near infrared region (VNIR) (0.6-1.0 µm). The results obtained from the PCA, MNF, BR, DS, spectral reflectance analyses, and mineralogical and chemical analyses were found to be similar. Thus, it can be concluded that multispectral Landsat 8 data are useful in the detection iron ore deposits in arid and semi-arid regions.
A Heterogeneous IoT Node Authentication Scheme Based on Hybrid Blockchain and Trust Value

Node identity authentication is an essential means to ensure the security of the Internet of Things.Existing blockchain-based IoT node authentication schemes have many problems.A heterogeneous IoT node authentication scheme based on an improved hybrid blockchain is proposed.Firstly, the hybrid blockchain model is designed to make the blockchain and IoT environment more compatible.Then the proxy node selection mechanism is intended to establish a bridge between the ordinary IoT node and the blockchain, building by calculating the trust value between nodes.Finally, based on the improved hybrid blockchain, the node authentication scheme of the model and proxy node selection mechanism establishes a secure connection for communication between nodes.Safety and performance analysis shows proper safety and performance.
Arabic handwritten digits recognition based on convolutional neural networks with resnet-34 model

&lt;span&gt;Handwritten digits recognition has attracted the attention of researchers in pattern recognition fields, due to its importance in many applications in public real life, such as read bank checks and formal documents which is a continuous challenge in the last years. For this motivation, the researchers created several algorithms in recognition of different human languages, but the problem of the Arabic language is still widespread. Concerning its importance in many Arab and Islamic countries, because the people of these countries speak this language, However, there is still a little work to recognize patterns of letters and digits. In this paper, a new method is proposed that used pre-trained convolutional neural networks with resnet-34 model what is known as transfer learning for recognizing digits in the arabic language that provides us a high accuracy when this type of network is applied. This work uses a famous arabic handwritten digits dataset that called MADBase that contains 60000 training and 1000 testing samples that in later steps was converted to grayscale samples for convenient handling during the training process. This proposed method recorded the highest accuracy compared to previous methods, which is 99.6%.&lt;/span&gt;
Job satisfaction in the academic sphere

The article analyzes job satisfaction among the academic staff in Russia. The materials on the social and professional well-being of the scientific and pedagogical personnel of the Southern Federal University provide empirical evidence for the study. The authors justify the new meanings for such concepts as «job satisfaction», «satisfaction with work results», «subjective metrics», «subjective value». The theoretical and methodological framework of the study includes indicators of the general level of job satisfaction, satisfaction with work-role inputs and factors that characterize work-role outputs. Based on empirical data, the results on job satisfaction for the Southern Federal University are compared with the data provided by the Russia Longitudinal Monitoring Survey, RLMSHSE, for the Russian education system. The case of the Southern Federal University is suggested to test the method of cross-national comparisons authored by Alfonso SousaPoza and Andres Sousa-Poza, focused on the level of work satisfaction relying on workrole inputs and work-role outputs. This methodology allows the authors to build a comparative matrix of satisfaction with factors that ensure the work process and factors that assess satisfaction with work results. This matrix can serve as a theoretical basis for defining benchmarks for policies related to the academic personnel management through motivation.
Framework for Incorporating Artificial Somatic Markers in the Decision-Making of Autonomous Agents

The somatic marker hypothesis proposes that when a person faces a decision scenario, many thoughts arise and different “physical consequences” are fleetingly observable. It is generally accepted that affective dimension influences cognitive capacities. Several proposals for including affectivity within artificial systems have been presented. However, to the best of our knowledge, a proposal that considers the incorporation of artificial somatic markers in a disaggregated and specialized way for the different phases that make up a decision-making process has not been observed yet. Thus, this research work proposes a framework that considers the incorporation of artificial somatic markers in different phases of the decision-making of autonomous agents: recognition of decision point; determination of the courses of action; analysis of decision options; decision selection and performing; memory management. Additionally, a unified decision-making process and a general architecture for autonomous agents are presented. This proposal offers a qualitative perspective following an approach of grounded theory, which is suggested when existing theories or models cannot fully explain or understand a phenomenon or circumstance under study. This research work represents a novel contribution to the body of knowledge in guiding the incorporation of this biological concept in artificial terms within autonomous agents.
A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing

Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph.To train most AMR parsers, one needs to segment the graph into subgraphs and align each such subgraph to a word in a sentence; this is normally done at preprocessing, relying on hand-crafted rules.In contrast, we treat both alignment and segmentation as latent variables in our model and induce them as part of end-to-end training.As marginalizing over the structured latent variables is infeasible, we use the variational autoencoding framework.To ensure end-to-end differentiable optimization, we introduce a differentiable relaxation of the segmentation and alignment problems.We observe that inducing segmentation yields substantial gains over using a 'greedy' segmentation heuristic.The performance of our method also approaches that of a model that relies on the segmentation rules of Lyu and Titov (2018), which were hand-crafted to handle individual AMR constructions.
The Blended Learning In Higher Education

In the article, the author defines the possibility of integrating modern learning technologies in the conditions of digitalization of higher education. Blended learning in the preparation of university students includes a combination of mobile learning, e-learning, distance learning, and mass open courses. The prospects of implementing the most common models of blended learning are considered. The use of digital learning technologies with the inclusion of modern approaches in training: project training, team building, case studies allows you to get a synergistic effect in the preparation of a modern specialist. In the Russian Federation, the process of digitalization of education is being activated as part of the implementation of the state program "development of education". Teachers of the higher education system of Russian universities are actively involved in the process of integrating e-learning technologies in courses of disciplines. The article presents the experience of implementing models of blended learning in the electronic educational environment of higher education institutions. There is an increase in the involvement and motivation of students during the course of the discipline using a blended approach to learning. The advantages, difficulties and key performance criteria of each blended learning model presented in the study are identified. The role of mobile learning for the modern generation of university students and teachers is defined, the advantages of mobile learning in the implementation of blended models in the electronic educational environment of universities are presented.
Actual Issues for Relevant Logics

In this paper, I motivate the addition of an actuality operator to relevant logics.Straightforward ways of doing this are in tension with standard motivations for relevant logics, but I show how to add the operator in a way that permits one to maintain the intuitions behind relevant logics.I close by exploring some of the philosophical consequences of the addition.Restall (2002) or Bimbó (2006) for reader-friendly overviews of relevant logics. See Dunn and2. How to understand the connection at issue is a delicate matter.Anderson and Belnap (1975) suggest a kind of connection based on use.An alternative conception is the sufficiency view of Routley (1977: 895ff.).A different approach to relevance is offered by Tennant (2017).