Reviewerly/exHarmony huggingface dataset:
Evaluation of two climate production satellites over the region of Marrakesh Safi Morocco

Although weather stations provide accurate measurements of ground climate conditions in close proximity to the station, they are expensive and require periodic recording of measurements. In addition, weather stations are therefore distributed sparsely, especially in developing countries. Conversely, satellites see entire landscapes and are therefore able to offer precise measurements at each location. This paper compares the performance of satellite products with weather station observations at three sites characterized by different climates Ounagha, Chichawa, and R3 in Morocco.Precipitation and temperature data over the period of 2 years (2018 - 2019) at the three sites were collected. Data based on satellite imagery were collected for two satellite products, namely ERA5 and POWER over a similar period. The data were compared and analyzed through inferential statistics such as the root-mean-square error (RMSE), and the coefficient of determination (R2&amp;#160; ). The results showed that the temperature minimum daily simulated using the ERA5 satellite reached the highest coefficient of determination R2 = 0.92, with RMSE=1.34 (daily for Ounagha), R2 = 0.94, and with RMSE=1.27 (daily for Chichawa), R2 = 0.96, and with RMSE=1.13 (daily for R3). The temperature maximum daily simulated through the POWER satellite showed the best coefficient of determination R2 =0.924 with RMSE=2.174 (Ounagha daily). In contrast, the ERA5 satellite presents a better coefficient of determination R2 =0.97 in Chichawa and R3 stations. The results of comparing the observed weather stations and the satellite data in terms of precipitation show that the acceptable performance was attributed to the ERA5 data for cumulative, decadaire, and monthly precipitation in the three sites.The use of satellite products is a good way to solve the lack of weather stations and to make data available to the scientific community for further investigation. Furthermore, since our interest in monitoring drought in the Smimou region and our need for climate data in this area, which lacks a meteorological station, the results of this study encourage us to use the ERA5 satellite to collect climate data for the region.
Assessing method for meta-professional competencies in Russian educational institutions

University graduates may face challenges when solving actual business problems because of their lack of soft skills, which are often inferior to their hard skills. Their knowledge (hard skills) may be at a good level, but they often lack developed universal competencies (soft skills). The paper discusses the methodology for assessing the meta-professional competencies of university students based on the results of the pilot year of the project “Assessment and development of managerial competencies in Russian educational institutions”. The research developed psychometric methods and applied as assessment tools: a universal personality questionnaire, a vitality questionnaire, tests for the analysis of numerical and verbal information. The paper provides descriptions and justifications for the validity and tests reliability used to diagnose universal competencies, as well as the procedure for creating a composite construct. The authors propose a method for creating a composite score to combine the results of assessment using a battery of tests and questionnaires. The resulting composite score makes it possible to consider the links between the evaluated constructs and simplify the interpretation of the evaluation results.
The combined surveying of soil CO2 flux and air CO2 concentration for gas hazard mitigation at Vulcano, Italy

Among extreme geological events, people feel the effects of volcanic eruptions through fear and wonder. Several volcanoes erupt across the world with sheer physical power, but a large part of them is either dormant or quiescent. Risks for explosions at active volcanoes are well-recognized, while the gas hazard which is correlated with almost continuous gas emissions from either the crater cone or the soils is still a masked risk. Volcanic gas emissions impact people at settled zones which lay around the main quiescent volcanic structure. In addition, the level of the gas hazard correlates with changes in the gas emissions, which agrees with variations of the magmatic degassing at depth.This study reports on the results of the continuous monitoring for soil CO2 flux, air CO2 concentration, and some weather variables (i.e., temperature, pressure, relative humidity, wind speed, and wind direction). Tailored monitoring stations were deployed in an anomalous degassing zone of the island of Vulcano, Italy (i.e., Faraglione) for mitigating the gas hazard. This area hosts many infrastructures and several tourist facilities. The deployment of the monitoring network occurred in June 2021 in the framework of the agreement between the Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Palermo, and the Dipartimento Regionale di Protezione Civile (DRPC &amp;#8211; Sicilia).Since late September 2021, the volcanic degassing increased at Vulcano due to a probable increase of the magmatic degassing at depth. The results of this study show a distinct increase in the soil CO2 flux at Faraglione which correlated with the high CO2 concentration in the air. These variations caused increases in the gas hazard at Vulcano. Besides various evidence points to the climax of the volcanic unrest in autumn 2021, redundancy for monitoring stations in the anomalous degassing zone allowed capturing of some minor variations of the magmatic activity at depth. Some remarkable increases occurred in soil CO2 emissions during spring 2022, which culminated in the whitening of the seafloor at Baia di Levante. Minor variations are reported for both summer and early autumn 2022. These results point out as both the accurate monitoring of the air CO2 concentration and the weather variables complement efficiently the continuous monitoring of the soil CO2 flux resulting in a suitable strategy for mitigating the gas hazard at Vulcano.
Internet search trends about the Metaverse

Introduction: The metaverse phenomenon is vast and is in continuous growth and development. Knowing what position the metaverse has been occupying as a theme on the web allows us to achieve precise results on information needs. Objective: to characterize the trends in internet searches on the metaverse, in the period from 2020 to 2022. Methods: a retrospective longitudinal descriptive study was carried out on internet searches for the term "Metaverse". The research was carried out with the support of the Google Trends tool. Results: the interest on the web for the metaverse was high as of October 2021. A tendency to decrease interest in the subject is observed. The country with the greatest interest was China and the term that has been most related in searches was “non-fungible token”. Conclusions: It can be concluded that the term highlighted a high interest that has decreased, in line with the slow reception that technology is having.
The Bitcoin–Macro Disconnect

This paper investigates the link between Bitcoin and macroeconomic fundamentals by estimating the impact of macroeconomic news on Bitcoin using an event study with intraday data. The key result is that, unlike other U.S. asset classes, Bitcoin is orthogonal to monetary and macroeconomic news. This disconnect is puzzling as unexpected changes in discount rates should, in principle, affect the price of Bitcoin even when interpreting Bitcoin as a purely speculative asset.
BaSSL: Boundary-aware Self-Supervised Learning for Video Scene Segmentation

Self-supervised learning has drawn attention through its effectiveness in learning in-domain representations with no ground-truth annotations; in particular, it is shown that properly designed pretext tasks bring significant performance gains for downstream tasks. Inspired from this, we tackle video scene segmentation, which is a task of temporally localizing scene boundaries in a long video, with a self-supervised learning framework where we mainly focus on designing effective pretext tasks. In our framework, given a long video, we adopt a sliding window scheme; from a sequence of shots in each window, we discover a moment with a maximum semantic transition and leverage it as pseudo-boundary to facilitate the pre-training. Specifically, we introduce three novel boundary-aware pretext tasks: 1) Shot-Scene Matching (SSM), 2) Contextual Group Matching (CGM) and 3) Pseudo-boundary Prediction (PP); SSM and CGM guide the model to maximize intra-scene similarity and inter-scene discrimination by capturing contextual relation between shots while PP encourages the model to identify transitional moments. We perform an extensive analysis to validate effectiveness of our method and achieve the new state-of-the-art on the MovieNet-SSeg benchmark. The code is available at https://github.com/kakaobrain/bassl .
TSEM: Temporally-Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series

Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome’s trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called post-hoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data: model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps’ temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality.
A Novel Adaptive Fuzzy Logic Controller for DC-DC Buck Converters

This paper is concerned with design and implementation of a direct adaptive fuzzy logic controller (DAFLC) to stabilize the output voltage of the DC-DC buck converter under the conditions of input voltage variation, changing of load and uncertainty of system model. The DAFLC is designed with the Takagi and Sugeno’s fuzzy logic system and Lyapunov stability theory to bring out remarkable stability and outstanding transient quality to the DC-DC buck converter. Experimental results shown that the DC-DC buck converter with the proposed DAFLC has effective performance in term of stability and transient quality. Morever, the proposed system does not require any requirement of mathematical model.
Smart Home and Machine Learning as a Sustainable Healthcare Solution: Review and Perspectives

Over the past two years, the world has witnessed one of the worst pandemics due to the outbreak of coronavirus (covid19), which has infected hundreds of millions and claimed the lives of millions across the globe. If we have learned anything from this pandemic, it is that the actual healthcare systems are unreliable under situations of enormous pressure. Accordingly, the present investigation tackles smart healthcare paradigm as a solution to transform the classical healthcare model into a sustainable one. Therefore, this paper reviews the most advances on remote healthcare monitoring technologies and introduces a novel smart home architecture combined with cloud computing and machine learning to create a sustainable solution for healthcare. Furthermore, a case study of a patient with heart disease is suggested to highlight the importance of using machine learning to automate medical monitoring at home. Additionally, an investigation of human behavior using neural network transformers is suggested as a perspective of the research in hand to examine patients’ activities at home using surveillance camera thus constructing a resilient remote healthcare model.
Color Image Encryption Using Improved Vigenère Method Followed by a Permutation

In this paper we will develop a new method of color image encryption, based on a deeply improved Vigenère scheme, using two large substitution matrices designed from the most used chaotic maps in cryptography. After vectorizing the image, we begin by computing an initial value to modify the first pixel of the image, then we apply the new Vigenère technique followed by a chaotic permutation to increase the complexity of the algorithm. To overcome the differential, a diffusion function was established between the encrypted pixel and the adjacent clear pixel. Simulations performed on a large number of images of different sizes and formats ensure that our approach is not exposed to known attacks. $$Notation \{{G}_{t}={Z}/{tZ}^{ring}$$
Magnetic resonance imaging enhancement using prior knowledge and a denoising scheme that combines total variation and histogram matching techniques

Introduction Brain perfusion-weighted images obtained through dynamic contrast studies play a critical and clinical role in diagnosis and treatment decisions. However, due to the patient's limited exposure to radiation, computed magnetic resonance imaging (MRI) suffers from low contrast-to-noise ratios (CNRs). Denoising MRI images is a critical task in many e-health applications for disease detection. The challenge in this research field is to define novel algorithms and strategies capable of improving accuracy and performance in terms of image vision quality and computational cost to process data. Using MRI statistical information, the authors present a method for improving image quality by combining a total variation-based denoising algorithm with histogram matching (HM) techniques. Methods The total variation is the Rudin–Osher–Fatemi total variation (TV-ROF) minimization approach, TV-L2, using the isotropic TV setting for the bounded variation (BV) component. The dual-stage approach is tested against two implementations of the TV-L2: the split Bregman (SB) algorithm and a fixed-point (FP) iterations scheme. In terms of HM, the study explores approximate matching and the exact histogram matching from Coltuc. Results As measured by the structural similarity index (SIMM), the results indicate that in the more realistic study scenarios, the FP with an HM pairing is one of the best options, with an improvement of up to 12.2% over the one without an HM. Discussion The findings can be used to evaluate and investigate more advanced machine learning-based approaches for developing novel denoising algorithms that infer information from ad hoc MRI histograms. The proposed methods are adapted to medical image denoising since they account for the preference of the medical expert: a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction.
CRYSTAL CHEMICAL FEATURES OF GREEN AND LIGHT-BLUE AMAZONITE AND GEOCHEMICAL ASPECTS OF THEIR FORMATION PROCESSES

A study was carried out in order to identify the aspects of geochemical processes occurring during the formation of green and light-blue amazonite crystals. Green, light-blue and greenish-blue amazonites from rare-metals deposits of Ukraine (Perzhanske, Ukrainian Shield), rf (Gora Parusna, Ploskogirske, Kola Peninsula; Orlovske, Western Transbaikalia), and USA (Rutherford, Virginia) were investigated using X-ray luminescence (XRL), infrared (IR) spectroscopy and X-ray diffraction. The rock-forming microcline from the Perzhanske, Orlovske and Katuginske (rf) deposits was also studied. It was found that the multi-colored crystals of amazonite have similar degrees of Si/Al ordering. They are represented by the maximum microcline with 2t1 = 0.959-1.0. The various samples only differ significantly in their lead contents that range between 2000 and 10000 ppm in green amazonite and 200 ppm in light-blue colored crystals. Differences between untreated crystals and those annealed in air (1173 K) or under a stream of He (923 K) samples were observed in terms of the composition, ratio of optically active centers and oxygen-hydrogen defects. Their composition and concentration in the untreated natural crystals reflect the genesis conditions of parent rocks, and, first of all, the redox and fO2 conditions of water-containing fluids, which affect the isomorphism of plumbum in the structures of the green and light-blue crystals, mechanisms of crystal chemical compensation of Pb2+ ions and correlates with different degree of ferum oxidation (Fe3+ → Fe2+). Changes in redox and fO2 parameters of the mineral-forming fluid are the most important factors affecting the activity and acid-base properties in the residual water fluids, the process of reduction following 2H2O + 2e– → H2 + 2OH– or oxidation via 2H2O – 4e– → O2 + 4H+. A complex combination of these factors allows the formation of certain nanosized defects in the structures of the amazonite crystals. These include impurity ([Pb2+ – Pb+]3+) and impurity-vacancy (Pb2+ – VK) clusters that serve as chromophore centers for green and light-blue color, respectively.
Prototypical Blockchain Application for Mapping Complex Products in Dynamic Supply Chains

The blockchain technology represents a decentralised database that stores information securely in immutable data blocks. Regarding supply chain management, these characteristics offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. In this context, first token-based mapping approaches exist to transfer certain manufacturing processes to the blockchain, such as the creation or assembly of parts as well as their transfer of ownership. This paper proposes a prototypical blockchain application that adopts an authority concept and a concept of smart non-fungible tokens. The application enables the mapping of complex products in dynamic supply chains that require the auditability of changeable assembling processes on the blockchain. Finally, the paper demonstrates the practical feasibility of the proposed application based on a prototypical implementation created on the Ethereum blockchain.
Dualities over the cross product of the cyclic groups of order 2

We determine the number of symmetric dualities on the s-fold cross product of the cyclic group of order 2, which is the additive group of the finite field F 2 s .We show that the ratio of symmetric dualities over all dualities goes to 0 as s goes to infinity.We also prove a surprising result that given any two binary codes C and D of the same length n with |C||D| = 2 n , then viewing them as groups there is a symmetric duality M with C M = D, which also relates their weight enumerators as additive codes in a group via the MacWilliams relations.Using this theorem we show that any additive code in this setting can be viewed as an additive complementary dual code of length 1 with respect to some duality.
Transversal focus of STEM education

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
THE CONCEPT OF SMART EDUCATION AS A FACTOR IN ENHANCING DIGITALIZATION AND INTELLECTUALIZATION

The relevance of the study of smart education as a factor in enhancing digitalization and intellectualization is that HEIs are struggling to keep up with the changes in technology in higher education.For the first time, the idea of wisdom formation was proposed and developed by philosophers.Philosophically, the starting point and aim of wisdom formation is the awakening and development of human 'wisdom' .True education should help people to know themselves, eliminate fear and awaken wisdom 1 .The famous British philosopher Whitehead put forward the theory of child wisdom education, believing that the theme of learning is life and the purpose of learning is to discover the wisdom of students.Subsequently, Smart Education attracted the attention of educators, psychologists, and scientists at home and abroad.Max van Manen, the founder of Canadian «phenomenological pedagogy» 2 put forward the concept of wisdom pedagogy focused on children's development, pointing out that educators should create a caring school environment for children and pay attention 1 Buhaychuk Oksana, Nikitenko Vitalina, Voronkova Valentyna, Andriukaitiene Regina, Malysh Myroslava.Iteraction of the digital person and society in the context of the philosophy of politics.
Resection margin assessment in breast lumpectomy specimens using deep learning-based hyperspectral imaging (Conference Presentation)

Achieving adequate resection margins during breast-conserving surgery is crucial for minimizing the risk of tumor recurrence in patients with breast cancer but remains challenging due to the lack of intraoperative feedback. Here, we evaluated the use of hyperspectral imaging to discriminate healthy tissue from tumor tissue in lumpectomy specimens of 121 patients. A dataset on tissue slices was used to develop and evaluate three convolutional neural networks. Subsequently, these networks were fine-tuned with lumpectomy data to predict the tissue percentages on the lumpectomy resection surface. We achieved a MCC of 0.92 on the tissue slices and an RMSE of 9% on the lumpectomy resection surface.
The Earth Sciences and Society: The Needs for the 21st Century

As world population moves to 10–12 billion next century, there will be, must be, vast development of Earth Resources. If there is to be truly sustainable development, we must use all knowledge about our planet with greater wisdom than in the past. We must integrate all knowledge to solve critical problems like energy supply, food security, waste management and the maintenance of quality air and water for all species who live with us and support us. Knowledge from the Earth Sciences is at the core of intelligent development of our life support systems.
Scene-object semantic incongruity across stages of processing: From detection to identification and episodic encoding

Visual processes are assumed to be affected by scene-object semantics throughout the stream of processing, from the earliest processes of conscious object detection to the later stages of object identification and memory encoding. However, very few studies have jointly explored these processes in a unified setting. In this study, we build upon a change detection task to assess the influence of semantic congruity between scenes and objects across three processing stages, as indexed through measures of conscious detection, object identification, and delayed recognition. Across four experiments, we show that semantically incongruent targets are easier to detect than their congruent counterparts, but that the latter are better identified and recognized in a surprise memory test. In addition, we used eye-tracking measures, in conjunction with these three behavioral indexes, to further understand the locus of the advantage observed in each case. The results indicate that (i) competition with other congruent objects modulates the effects of congruity on target detection, but it does not affect identification nor recognition memory, (ii) the detection cost of scene-congruent targets is mediated by earlier fixations on incongruent targets, (iii) neither fixation times, dwell times, nor pupil dilatation are related to the effects obtained in identification and recognition; and (iv) even though congruent targets are both better identified and remembered, the recognition benefit does not depend on the identification demands. The transversal approach taken in this study represents a challenging but exciting perspective that holds the potential to build bridges over the seemingly different but related fields of conscious detection, semantic identification, and episodic memory.