Reviewerly/exHarmony huggingface dataset:
An Improved GAN-Based Image Restoration Method for Imaging Logging Images

An improved GAN-based imaging logging image restoration method is presented in this paper for solving the problem of partially missing micro-resistivity imaging logging images. The method uses FCN as the generative network infrastructure and adds a depth-separable convolutional residual block to learn and retain more effective pixel and semantic information; an Inception module is added to increase the multi-scale perceptual field of the network and reduce the number of parameters in the network; and a multi-scale feature extraction module and a spatial attention residual block are added to combine the channel attention. The multi-scale module adds a multi-scale feature extraction module and a spatial attention residual block, which combine the channel attention mechanism and the residual block to achieve multi-scale feature extraction. The global discriminative network and the local discriminative network are designed to gradually improve the content and semantic structure coherence between the restored parts and the whole image by playing off each other and the generative network. According to the experimental results, the average structural similarity measure of the five sets of imaged logging images with different sizes of missing regions in the test set is 0.903, which is an improvement of about 0.3 compared with other similar methods. It is shown that the method in this thesis can be used for the restoration of micro-resistivity imaging log images with good improvement in semantic structural coherence and texture details, thus providing a new deep learning method to ensure the smooth advancement of the subsequent interpretation of micro-resistivity imaging log images.
Investigating the Impact of Different Partial Overlap Levels on the Perception of Visual Variables for Categorical Data

The overlap of visual items in data visualization techniques is a known problem aggravated by data volume and available visual space issues. Several methods have been applied to mitigate occlusion in data visualizations, such as random jitter, transparency, layout reconfiguration, focus+context techniques, etc. This paper aims to present a comparative study of the reading of visual variables values with partial overlap. The study focuses on categorical data representations varying the percentage limits of partial overlap and the number of distinct values for each visual variable: hue, lightness, saturation, shape, text, orientation, and texture. A computational application generated random scenarios for a unique visual pattern target to perform location tasks. Each scenario involved presentation of the visual items in a grid layout with 160 elements (10 × 16), each visual variable had from three to five distinct values encoded, and the partial overlap percentages applied, represented by a gray square in the center of each grid element, were 0% (control), 50%, 60%, and 70%. Similar to the preliminary tests, the tests conducted in this study involved 48 participants organized into four groups, with 126 tasks per participant, and the application captured the response and time for each task performed. The results analysis indicated that the hue, lightness, and shape visual variables were robust to high percentages of occlusion and gradual increase in encoded visual values. The text visual variable showed promising results for accuracy, and the resolution time was a little higher than for the last visual variables mentioned. In contrast, the texture visual variable presented lower accuracy to high levels of occlusion and more different visual encoding values. Finally, the orientation and saturation visual variables exhibited the highest error and worst perfomance rates during the tests.
Diurnal Evolution and Estimates of Hourly Diffuse Radiation Based on Horizontal Global Radiation, in Cerrado-Amazon Transition, Brazil

In the Cerrado-Amazonian ecotone in the State of Mato Grosso, intensely altered by anthropic action, the knowledge and processes of energy conversion and energy balance are still incipient, making the monitoring and modeling of diffuse radiation essential for several applications. The objective of this study was to evaluate the seasonality of the diurnal evolution and estimate the hourly diffuse radiation (Hdh) and incident radiation in the horizontal plane between June 2011 and October 2016. The instantaneous measurements (5 min) of diffuse radiation underwent geometric, astronomical, and anisotropic corrections, with subsequent hourly integrations. The seasonality of diffuse radiation and its radiometric fractions was evaluated. The estimates were made considering total and seasonal data groupings (water stations in the region) and in different cloudiness classes (atmospheric transmissivity index—KTh). The diurnal behavior of diffuse radiation (Hdh) was similar to that of global radiation and at the top of the atmosphere, with maximum values at solar noon. The correlations between Kdh and KTh showed third-order polynomial behavior, with maximum observed values of Kdh ranging from 0.8 to 0.9, for KTh less than 0.2. Estimation equations based on radiometric fractions underestimated the values of diffuse radiation, with a better performance presented by models adjusted in annual data groupings. Among the parameterized models for estimating diffuse radiation obtained in the literature, those calibrated regionally in this study, together with those developed for tropical regions, presented better statistical performances.
Identification and correction of magnification factor deviations of a telecentric fringe projection system

For high-precision measurements through a inspection window, a 3D scanner based on fringe projection profilometry is being researched. The 3D scanner combines a micromirror array projector and two telecentric cameras. The affine camera model is commonly used to calibrate telecentric imaging systems, in which a single magnification factor is introduced and optimized for each lens. However, 3D reconstructions based on this model indicated that reconstruction uncertainties in the peripheral areas of the measuring volume are significanty affected by a possible inspection window. These uncertainties may occur due to the model-based determination of the magnification factor and the reduction in parallelism of the visible rays within the telecentric lens that can occur at larger working distances. To address this issue, a new method for calculating and identifying the influence of the magnification factor on the 3D point scaling for telecentric measuring systems is proposed. First, the measuring system is calibrated using an affine camera model. Then, the reconstructed 3D target points are used to estimate the magnification factor locally and assessing the influence of an inspection window in the optical path. In order to further investigate the influence of the inspection window on the imaging performance of the cameras the focus is estimated locally within the measuring volume. Initial measurements using these methods reveal that scale variations and the reduction of focus can be quantified locally and a model based correction as well as the removal of poorly reconstructed points is feasible.
Computational self-correction of scanning nonlinearities in optical profilometry

Axial scanning is inherent to optical profilometers, which sample a signal and infer the object height as the location of its maximum. Imaging confocal, interferometric or focus variation methods, are common example technologies for acquiring such signal. Of course, in these approaches, the precision and accuracy of any measurement is directly affected by those of the scanning system, usually based on piezo scanners or motorized linear stages. The latter provide an inexpensive solution but are subject to non-linearities between the commanded and executed positioning, yielding pseudo-random errors, which in practice limit the precision and accuracy of the measurement. We propose a computational method to correct such non-linearities on a measurement using data from the very same scan, therefore providing a zero-cost solution to increase the precision of profilometers employing motorized linear stages. The method is based on the reconstruction of two topographies that are axially shifted by a known amount. Assuming that the two topographies are identical, differences between these two topographies reflect only the errors of the positioning system. Our algorithm predicts and corrects the non-linearity errors by iteratively analyzing the differences in the topographies. Here, we explore two methods for obtaining the topography pair: by performing two scans, and by localizing two axially shifted features of the axial response from a single scan. To validate the technique, we measured a variety of samples including step heights and compared the results against the piezo-measured counterparts, obtaining equivalent performance.
Multi-Sensor Fusion Target Tracking Based on Maximum Mixture Correntropy in Non-Gaussian Noise Environments with Doppler Measurements

This paper addresses the multi-sensor fusion target tracking problem based on maximum mixture correntropy in non-Gaussian noise environments exclusively using Doppler measurements. As Doppler measurements are non-linear, a statistical linear regression model is constructed using the unscented transformation. Then, a centralized measurement model is developed, and the mixture correntropy is determined, which contains the high-order statistics of state prediction and the measurement error caused by noise. Then, a robust fusion filter is proposed by maximizing the mixture-correntropy-based cost. To improve numerical stability, the information filter and corresponding square root version are also derived. Furthermore, the performance of the proposed algorithm is analyzed, and the selection of the kernel width is discussed. Experiments are performed using simulated data and automatic driving software. The results show that the estimation performance of the proposed algorithm is better with respect to outliers and mixture Gaussian noise than that of traditional methods.
Simultaneous orthogonal shearography using two pairs of parallel slits and a tetra-split lens

There are a growing number of industrial applications for shearography, especially in non-destructive testing of composite materials. Its tolerance to hostile environments is probably the most relevant success factor for industrial applications. The introduction of carrier fringes for phase recovery, replacing temporal phase shift techniques, allows measurements with a single exposure for each loading state, making shearography even more robust. Some defects are better evident when the shearing direction is horizontal. Others are best viewed with a vertical shearing direction. Thus, it is desirable to inspect in both directions. Fortunately, the literature reports some configurations capable of using carrier fringes to perform simultaneous measurements in more than one shearing direction. These systems work well, but since they use a mask with circular holes, they require a high amount of light. An alternative is the use of a pair of oblong slits, which increases the amount of light captured. This paper extends the use of oblong slit pairs for simultaneous measurement in two orthogonal shearing directions. Two pairs of parallel oblong slits are arranged orthogonally on the system mask. Although the Fourier transform of the resulting signal at the camera sensor contains the desired signals as well as a crosstalk between the slit pairs. However, the mask can be designed in such a way that the orthogonal signals are separable. To promote shearing in orthogonal directions, a polymeric lens was sectioned, forming four quadrants of 90&deg;. The quadrants were radially displaced and arranged so that each oblong slit covers only one quadrant of the lens. As a result, four laterally shifted images are formed, two of them with horizontal shearing and the other two with vertical shearing. A proof-of-concept prototype was developed and the first results were evaluated by the authors in the laboratory environment and are presented in this article. The paper also shows details of the resulting Fourier spectrum and the strategy used to separate the orthogonal components and recover the phase using carrier fringes. The performance of the prototype met expectations and validated the concept. Future works involve miniaturization of the prototype and tests in industrial environments.
A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

The CO2 reduction promise must be widely adopted if governments are to decrease future emissions and alter the trajectory of urban mobility. However, from a long-term perspective, the strategic vision of CO2 mitigation is driven by inherent uncertainty and unanticipated volatility. As these issues emerge, they have a considerable impact on the future trends produced by a number of exogenous and endogenous factors, including Political, Economic, Social, Technological, Environmental, and Legal aspects (PESTEL). This study’s goal is to identify, categorize, and analyze major PESTEL factors that have an impact on the dynamics of urban mobility in a rapidly changing environment. For the example scenario of the city of Larissa, Greece, a Fuzzy Cognitive Map (FCM) approach was employed to examine the dynamic interactions and behaviors of the connected criteria from the previous PESTEL categories. An integrative strategy that evaluates the interaction of linguistic evaluations in the FCM is used to include all stakeholders in the creation of a Decision Support System (DSS). The methodology eliminates the uncertainty brought on by a dearth of quantitative data. The scenarios in the study strands highlight how urbanization’s effects on sustainable urban transportation and the emergence of urban PESTEL actors impact on CO2 reduction decision-making. We focus on the use case of Larissa, Greece (the city of the CIVITAS program), which began putting its sustainable urban development plan into practice in 2015. The proposed decision-making tool uses analytics and optimization algorithms to point responsible authorities and decision-makers in the direction of Larissa’s sustainable urban mobility and eventually the decarbonization of the urban and suburban regions.
Thermal single-shot 3D shape measurement of transparent objects: optimization of the projected statistical LWIR pattern

Fast and non-contacting 3D shape measurements of objects for quality assurance, human machine interaction, or robot handling, e.g., in the industrial sector, have become well established. Recently, we have successfully combined thermography and triangulation to tackle the challenge of measuring the 3D shape of uncooperative materials, i.e., materials with optical properties such as being glossy, transparent, absorbent, or translucent. Therefore, we have developed the principle of thermal 3D measurements, a two-step process consisting of (1) the projection and absorption of projection patterns in the thermal infrared and (2) the stereo recording of heat patterns re-emitted by the object surface. We match image points by evaluating the temporal normalized cross correlation between pixels in both camera image stacks. In order to measure dynamic scenes, the previously achieved measurement times of a few seconds must be reduced by at least one order of magnitude to the range &lt; 0.1 s. For this purpose, we apply established single-shot methods from the visible spectral range to our thermal 3D approach. Instead of temporal sequences of multi-fringe patterns or scanning single fringes, we now project statistical point patterns and record only one thermal stereo image pair. In this paper, we theoretically investigate our approach by using a simulation model for thermal point pattern generation on static measurement objects. We analyze the temporal and spatial behavior of the heat patterns taking the material parameters into account. Finally, we show a first thermal single-shot 3D measurement.
IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection against contagion. Hence, innovative systems and algorithms were deployed to rapidly screen a large number of people with faces covered by masks. In this article, we analyze the current state of research and future directions in algorithms and systems for masked-face recognition. First, the paper discusses the importance and applications of facial and face mask recognition, introducing the main approaches. Afterward, we review the recent facial recognition frameworks and systems based on Convolution Neural Networks, deep learning, machine learning, and MobilNet techniques. In detail, we analyze and critically discuss recent scientific works and systems which employ machine learning (ML) and deep learning tools for promptly recognizing masked faces. Also, Internet of Things (IoT)-based sensors, implementing ML and DL algorithms, were described to keep track of the number of persons donning face masks and notify the proper authorities. Afterward, the main challenges and open issues that should be solved in future studies and systems are discussed. Finally, comparative analysis and discussion are reported, providing useful insights for outlining the next generation of face recognition systems.
Cognitive and emotional statements as components of reciprocal interaction in network thinking

Working in the world of the 21st century requires ever closer cooperation, more and more people. At the same time, paradoxically, the people of this society are increasingly striving for independence and feel lonely, despite the increased possibilities of digital technologies that greatly facilitate information interaction. All this could not but affect human thinking, which, together with network technologies, was forced to change significantly, turning into network thinking. As thinking changes, so does the interaction between people. To study this interaction, a study was conducted to analyze the components of reciprocity in network thinking. The method used in obtaining primary results was content analysis. Mathematical processing of the obtained data was carried out by means of multivariate analysis of variance. The study determined the parameters of the initial conditions of network thinking that affect various forms of reciprocity, as well as emotional and cognitive statements. Combinations of conditions in the greatest and least degree influencing the quantitative and qualitative component of interaction in solving mental problems are revealed. The influence of various forms of reciprocity on emotional and cognitive statements realized in the process of network thinking by participants in problem solving has been established. Combinations of two forms of reciprocity are determined that contribute to a change in the number of emotional and cognitive statements. Fundamental differences in the influence of various forms of reciprocity on utterances in the process of joint mental activity are revealed. The results obtained as a result of the study can be used to assess the features of interdependent interaction in the implementation of network thinking and its correction by changing the initial conditions for the implementation of mental activity.
Unsuccessful pedagogical situations

On the basis of the previous theoretical and practical research conducted by the author, in this paper the concept of pedagogical situation is studied. The suggested classification of pedagogical situations is based on the ability of a pedagogical situation to solve pedagogical tasks, reach pedagogical problems, achieve a pedagogical effect and thus to become a pedagogical event. On these basis as well as on the logic and practice of a teacher's everyday work, the three types of situations were previously identified. One type of these situations is described in this paper. The goal of such situations is unachievable, which means that the situation can not change from a potential situation to an actual situation. The goal-oriented principle of analysis opens up the prospect of establishing relationships between all the parameters of the situation and the personal characteristics of its participants. The scheme for describing pedagogical situations logically includes the following components: participants (teachers and students a potential pedagogical agent), goal/task, conditions (locus).
Role of information and communication technologies in the development of listening skills of non-linguistic students at a technical university

This paper focuses on a particular type of modern technologies, i.e. information and communication technology which has emerged as part of digital didactics. These technologies have brought about dramatic changes in all walks of life as well as in our expectations of what students should learn to survive in a new technological world. In their future work students will have to deal with large volumes of information obtained digitally, to use digital tools to work not only in Russian, but also in a foreign language (English). Consequently, it is necessary to teach them how to operate digital tools while teaching them the language. Among the most challenging speech activities, it is listening comprehension that challenges learning the most and requires longer time to develop at technical university due to insufficient number of hours given to the discipline called Foreign (English) Language in engineering faculties. Thus, the object of the research is the process of developing listening skills of nonlinguistic students in foreign (English) language classes. The subject of the research is interactive tasks based on web-services, aimed at developing their listening skills. The aim of the research is to create and test listening comprehension tasks on the platform of web services during lessons of foreign (English) language for students of non-language direction at technical university. To follow up listening skills and search for new audio texts the authors suggest using Randall's ESL Cyber Listening Lab resource and AI-based Andy application which simulates native speech. Based on a four-month (one semester) experiential study these resources have proven themselves effective.