Reviewerly/exHarmony huggingface dataset:
A policy-centered analysis of the research trends in Primary English Education using topic modeling

This study aims to analyze the research trend in Primary English Education from Volumes 1 to 27, focusing on its relation with the English education policies to examine the relationship between academic research and education policy. To this end, English abstracts of the entire research in the journal corresponding to the investigation period were collected as digital texts, preprocessed, and analyzed by LDA topic modeling using the R program. The results revealed that research topics such as teacher education and teaching methods were constantly investigated. It also confirmed that the English education policies at a particular time had a direct impact on the research trend (e.g., immersion program and TEE policy) or the concepts derived from the specific policies had an indirect impact on the research (e.g., teacher and learner variables). In addition, this study found that researchers were steadily interested in literacy education, learner assessment, and textbook analysis regardless of the policies, as the topics are significantly related to teaching and learning. Finally, the implications for the necessity of simultaneous interaction with research and policy were discussed based on the findings.
A Survey on Deep Learning Concepts and Techniques

For the last few decades, the deep learning (DL) computing paradigm has been held to be the benchmark in the machine learning (ML) community. As well, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. This review paper represents the major concepts in deep learning and the use of the neural network, the major applications of deep learning such as in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in deep neural networks training.
Real-time Siamese visual object tracking using attention and anchor-free mechanism

Trackers based on Siamese have consistently demonstrated superior performance in tracking visual objects. The majority of existing trackers calculate the features of the target template and search image independently and then estimate the target's scale and aspect ratio using either a multi-scale searching scheme or pre-defined anchor boxes. This paper proposed a Siamese attention network for tracking visual objects. An attention fusion mechanism is generated using pixel-level matching of template and search features. The framework proposed is anchor-free, making it both simple and effective. Extensive experiments on visual tracking benchmark VOT2018 and UAV123 demonstrate that our tracker operates at 42 fps and achieves state-of-the-art performance.
A fine-scale-preserving bias correction approach for global solar irradiance simulated with a numerical weather prediction model over a high-resolution spatial and temporal grid

&amp;lt;p&amp;gt;The feasibility of national and supra-national low carbon power systems (LCPSs) is challenged under high penetration rates of renewables because the inherent variability of renewable sources increases the system&amp;amp;#8217;s operation and transmission costs. Optimizing the operation of the systems to make them efficient and profitable requires adapting their design to the regional solar and wind patterns.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The MET4LOWCAR Spanish project aims at demonstrating the benefits of a synergistic design of the generation and transmission power system that accounts for the regional climatic patterns of both solar and wind renewable resources, using the Spanish territory as a testbed. To that aim, a 30-yr weather integration is being performed with the Weather Research and Forecasting (WRF) model over the Iberian Peninsula and the Balearic Islands throughout a 5-km spatial grid and a 10-min temporal grid. The ultimate goal is to compound a climatic data base of solar- and wind-related variables to simulate the performance of a high-penetration LCPS in multiple scenarios, from long runs to extreme weather events.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we present an original debiasing approach of the WRF global horizontal solar irradiance (GHI) that guarantees a reliable and more realistic representation of the solar power generation in the LCPS. First, the GHI simulated by WRF is compared against radiometric observations from the Spanish National Radiometric Network to demonstrate that it is indeed affected by a seasonal bias, related to a misrepresentation of convective clouds in the WRF model. Then, the spatial and temporal GHI grid is corrected cell by cell and day by day using the monthly GHI SARAH satellite product as a reference. The method is purposedly designed to interfere only at monthly scale (thus using monthly GHI SARAH as a reference) in order to preserve the original fine-scale spatial and temporal structure of GHI. However, it propagates the debiasing to daily steps using a tailored interpolation approach that prevents any spikes and data artifacts in the bounds between consecutive months.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The method will be described in detail, and some preliminary results will be shown at least for the period 2010&amp;amp;#8211;2020, using ground observations and satellite-based GHI data as a reference to assess the debiasing performance.&amp;lt;/p&amp;gt;
Solar resource mapping in Norway

&amp;lt;p&amp;gt;Dramatically reduced costs, increased electrification and a growing resistance to onshore wind power, makes solar energy, and photovoltaics increasingly relevant in Norway. However, there is a lack of accurate knowledge on the available solar resource. Of key importance is solar irradiance, and the accuracy of this is paramount for the accuracy of energy production estimates. Global horizontal irradiation (GHI) for a location can be found in several different databases, but they are less reliable for high latitudes such as Norway. In our study of the solar resource in Norway we assess surface measurements, satellite data, re-analysis and weather prediction model data. Surface measurements are available for about 100 locations in Norway, but the quality of the data is often not well documented, and quality assurance is necessary. Due to the lack of coverage of geostationary satellite data over the Northernmost part of Norway polar orbiting satellites are preferred, but the passages of the polar satellites are temporally irregular. Model GHI data has regular temporal resolution and availability, but the accuracy is uncertain and needs further exploitation. In this work we present a quality assurance of surface GHI measurements including visual inspection tools, and a comparison of the quality assured surface measurements with satellite and model derived GHI. A discussion of the three data sets is given and a preliminary solar resource map for Norway is also presented.&amp;amp;#160; Our study shows that locations with the highest annual GHI of ca. 1000-1100 kWh/m2 are encountered in high mountain areas with GHI peaking in late spring early simmer when the mountains still are covered with snow. Slightly lower GHI values of&amp;amp;#160;about 1000 kWh/m2 are found at southern coastal sites reaching the peak GHI in mid-summer. Lower values of typically 700-800 kWh/m2 are seen at the west coast and 600-700 kWh/m2 are in northern Norway.&amp;amp;#160;&amp;lt;/p&amp;gt;
An institutional approach to translating and synthesising science for different audiences

&amp;lt;p&amp;gt;The Met Office Hadley Centre has a dedicated stategic area, known as Knowledge Integration, which focuses on translating and communicating science from across its research. Made up of people with expertise across both science and communications, the team works directly with scientists and a wide variety of end users to co-create tailored outputs for specific needs. These outputs synthesise information from many different programmes of work from both the Met Office, as well as from a range of partners both across the UK and internationally . This includes working with collaborators across multiple disciplines, for example taking on best practice from social science approaches to communication (and the team is also working to help contribute to that evidence base). The outputs take the form of anything from written policy briefs, to infographics, to workshops and teach-ins &amp;amp;#8211; with the final package of media determined by the needs and preferences of users.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;While the work of the team is primarily focused at policy and decision-making audiences, they provide support to those who work directly with the media and public by sharing their work and expertise to ensure scientific rigour and credibility. Our work also feeds into a programme of education and outreach which reaches a large and diverse audience.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this session members of the Knowledge Integration team will explain how they work with scientists and end-users to maximise the potential value and impact of research findings. We will talk about the role we play at an institutional level, how we operate, who we work with, how we learn lessons to keep learning and evolving, and how we monitor the impact of what we do.&amp;lt;/p&amp;gt;
Open-data licenses for meteorological datasets - a detailled overview

&amp;lt;p&amp;gt;The license is the fundamental contract of open-data. It defines the rights and obligations of both the producers and the re-users. But, more than that, a carefully chosen license can be a powerful tool to develop and enrich a dataset.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Too often, the choice of the license is neglected. A wrong, but really popular approach is &amp;amp;#160;: &amp;quot;We need a license. Creative commons is trendy, let's choose that one!&amp;quot;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We propose a small workshop around the matter of licensing, intended both for producers, re-users and policy-makers. This intervention is suitable for the curious and novice as well as for legal experts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;First, we will define what a license is and what makes a license &amp;quot;open&amp;quot;, according to the OKF Open Definition.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Then, we will explain the most common concepts and clauses of open-data licenses.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We will have a quick overview of popular open-licenses and their characteristics, applied to software, hardware, creative contents and datasets (GPL, LGPL, MIT, BSD, CC-BY, CC-BY-SA, ODbL and more...).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We will analyze licenses used for institutional meteorological data, such as Germany's GeoNutzV, France's Licence Ouverte or even USA's public domain notice.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We will study how a license can be a powerful tool to make the product improve by itself, with the examples of Wikipedia, OpenStreetMap, Arduino and Blitzortung.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;And finally, we will compile and sort all those licenses, concepts and clauses to focus on our field : dissemination and build-up of open meteorological datasets. The conclusion is that a lot of common licenses aren't really fitted for our need, and the selection must be wise. And the second conclusion is that public institutions owns valuable data. And by using the right clauses and right licensing schemes those datasets can be leveraged to be even more valuable for all users in order to push private sector to contribute to them opening their own datasets.&amp;lt;/p&amp;gt;
On hand available predictors for operational satellite-based forecast

&amp;lt;p&amp;gt;Photovoltaic production is highly variable with a limited predictability due to stochastic variation of cloud attenuation of surface solar irradiance. Forecasting this irradiance helps to manage the safe and stable operation of the grid and thus enables to increase solar electricity penetration for a lower carbonated energy mix. In particular, intraday horizon forecasts (typically up to 6 h) are more and more required for the grid power reserve management, the increase of intra-day auctions in the electricity market and the current worldwide development of micro-grids.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Forecast irradiance using images from geostationary meteorological satellite is particularly appropriate for intraday horizon. It gives better performance than NWP models, does not require instrumentation, or costly computing resource. However, the accuracy of such methods is very sensitive to the cloud cover state and its short-term evolution. For instance, methods based on the temporal extrapolation of cloud motion present better results for passing cloud events than for sudden cloud appearance or disappearance. In several studies, reliability predictors have been identified for satellite-based irradiance forecast. They showed clear signals when uncertainty is computed as a function of season, solar zenith angle, cloud albedo and more recently synoptic weather regimes. This anticipation of error range would help grid managers to prepare the sizing of storage capacity or ancillary electricity resource.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;A pertinent predictor must have a significant influence on forecast error. It also must be easy to obtain in operational forecast conditions. In this work, we propose to exploit the only observation source required in satellite-based forecast: the satellite image itself. Using 5 years (2017-2021) of image-derived forecasts at 15 min time step over Palaiseau (France), we computed the forecast uncertainties as a function of multiple parameters derived from the HRV channel of Meteosat Second Generation satellite. We highlighted the influence of cloud albedo spatial variance and cloud motion vector field spatial-temporal features in the forecast uncertainties. In addition to the error range provided to users, this work can help forecasters to better characterize their sources of error and to select new predictors for machine-learning approaches.&amp;lt;/p&amp;gt;
Entrepreneurship Education and the Need For a School Environment That Promotes Creativity and Risk-Taking

Entrepreneurship education is among the investments that can have the highest returns for any country in the world. Research has shown that students who have undergone entrepreneurship are three to six times more likely to take part in a later life experience than those who have not received such training.The Bulgarian state must provide all young people with practical entrepreneurial experience before completing compulsory education, stressing the importance of practical exercises in education and training. The Program "Student Company" of Junior Achievement Young Enterprise (JA-YE) Europe is one of the most popular among students at Baba Tonka High School of Mathematics, Ruse. In it, students set up a mini corporation under the leadership of a teacher and a voluntary business consultant.This article examines the role of teachers who have a strong impact on the achievements of students, critically minded teachers who tailor their practice to the individual needs of students; shares our experience who offered students training under the program "Student Company"; introduces you to the methodology developed by Junior Achievement Bulgaria within the international project GREENT for teaching environmentally friendly entrepreneurship in high school through a blended learning methodology.To inspire their students and help them develop entrepreneurship, teachers must possess a wide range of creative and entrepreneurial skills; they need a school environment that promotes creativity and risk-taking, and mistakes are seen as an opportunity to learn. Cooperation between schools, state institutions and business are key to success.
Taxation of Cryptocurrencies with Income Tax and Corporate Income Tax

Cryptocurrencies are a completely new concept that changes not only the way we pay but also the way we experience money. Currently, different member states of the European Union define cryptocurrencies differently and tax the income from cryptocurrency trading differently. Most of the European Union’s member states income from cryptocurrency trading is taxed as capital gains. Therefore, this paper will analyze the legal issues of taxation of cryptocurrencies with income tax and corporate income tax. The de lege ferenda will also analyze the introduction of universal rules for the whole European Union to protect all investors, equal rules within the European Union for all cryptocurrency issuers and all service providers, remove legislative barriers to innovation and cover technological development and future types of cryptocurrencies.
Future change of global photovoltaic power potential in a warming world

&amp;lt;p&amp;gt;Photovoltaic power production strongly depends on meteorological conditions. Influencing factors are the air temperature and wind speed at the photovoltaic module, as well as changes in irradiance caused by humidity, clouds and aerosols.&amp;amp;#160;Here we analyse the global photovoltaic (PV) power potential and compare the changes therefore between a historical time period (1985 to 2014) and possible futures (SSP1-2.6, SSP2-4.5 and SSP5-8.5) at the end of the 21st century (2071-2100). To that end, we use an ensemble of more than 12 CMIP6 models with interactive aerosol schemes and estimate the potential photovoltaic power generation following Crook et al. (2011). We calculate the change in the PV power potential associated with surface irradiance, temperature, and wind speed between the future and the past, and divide the change in PV power generation into contributions from changes in temperature, cloud cover, humidity, aerosols and wind speed.&amp;lt;br&amp;gt;Our results point to a global decrease in future PV power potentials due to the rising air temperatures and the associated increase of humidity by -1.2% to more than -3.5% depending on the future scenario. The contribution from changes in cloud cover and aerosols have heterogeneous spatial patterns, with typically stronger influences in SSP5-8.5 compared to SSP1-2.6, e.g. for the clouds. The contribution by clouds is mostly positive, but has a negative effect for PV power potential in the pole regions, over India, and equatorial oceans. The models also show a large spread in their contributions from clouds, especially over Europe and North America. The future change in the contributions from aerosols has beneficial effects for PV power production over Europe and China in all scenarios assesses, but over South-America, Australia and Africa, the contributions from aerosols are positive in SSP1-2.6 but negative in SSP5-8.5 owing to the different behaviour in anthropogenic aerosols and of natural desert-dust emissions.&amp;amp;#160;Our study highlights the impacts of CMIP6 model uncertainty in the future development of aerosol burden, the magnitude of future warming, and the unclear response of clouds to warming on estimating future PV power potentials.&amp;lt;/p&amp;gt;
A CRYPTOGRAPHIC HASH FUNCTION BASED ON A MODIFIED SPONGE SCHEME

In this paper, a new version of the cryptographic hash function based on the modified Sponge scheme is considered. According to this scheme, a function f is selected from a given set of internal functions using a selection function. The constructed algorithm uses three different internal functions. The first internal function builds on the generalised AES design methodology. This methodology makes it easy to design block ciphers to encrypt large blocks of plaintext with small components, exposing the processed data as multidimensional arrays. The internal function is a block cipher that processes 2048 bits, represented as a 9-dimensional array of 512 4-bit elements of size 2 × 2 × 2 × 2 × 2 × 2 × 2 × 2 × 2. Each round of encryption consists of three transformations (S-boxes, linear transformation and permutation), similar to the three round transformations of AES SubBytes, MixColumns and ShiftRows. The second internal function is based on the use of word ring feedback with carry shift registers. The third internal function is similar to the Keccak permutation, but with a few major differences. The internal function operates on a 2048-bit state S, which can be thought of as a 4 × 8 × 64 three-dimensional bitmap. This internal function uses an 8-bit one instead of a 5-bit S-box. In this regard, the parameters of the three-dimensional representation of the state have been changed. Instead of a linear feedback shift register, a word ring feedback with carry shift register is used to generate round constants.
A Sensor based Data Analytics for Patient Monitoring Using Data Mining

Remote sensor networks have been broadly utilized in medical care applications, like emergency clinic and home patient observing. A great deal of work has been done to get remote clinical sensor organizations. The current arrangements can safeguard the patient information during transmission, yet cant stop within assault where the manager of the patient data set uncovers the touchy patient information. In this paper, we propose a useful way to deal with forestall within assault by utilizing different information servers to store patient information. The fundamental commitment of this paper is safely disseminating the patient information in various information servers and utilizing the AES to perform measurement investigation on the patient information without compromising the patients security. Remote clinical sensor networks surely work on understandings nature of-care without upsetting their solace. Nonetheless, there exist numerous potential security dangers to the patient delicate physiological information communicated over the public diverts and put away in the back-end frameworks. Average security dangers to medical care applications with WSNs can be summed up as follows. Snoopping is a security danger to the patient information protection A busybody, having a strong beneficiary recieving wire, might have the option to catch the patient information from the clinical sensors and accordingly realizes the patients medical issue. He might even post the patients medical issue on interpersonal organization, which can represent a genuine danger to patient security. Pantomime is a security danger to the patient information credibility.