Reviewerly/exHarmony huggingface dataset:
Assessing Security of Twitter by Sentimental Analysis using Deep Learning

Abstract: In order to make an approximation that is as accurate as feasible, sentiment analysis or opinion analysis is essential. Given that thoughtfully designed and carried out sentiment analysis can produce better and more accurate projections in both politics and business, this is a very essential component. At its most fundamental, feeling Analysis is based on the views that users and individuals share or express.There is a vast amount of material posted and exchanged over the internet every day by the users on various platforms. Understanding how different products, services, political figures, businesses, governments, and other entities are considered and perceived could be gained by being able to find the pattern in such data. This can handle a range of challenges, including being more reliable.Although we have numerous methods for sentiment analysis, a successful plan for regularly extracting and producing reliable sentiment analysis needs to be constructed. Despite the fact that machine learning algorithms have significantly improved—Naive Bayes, Support Vector Machine, and Maximum Entropy are the three that stand out as being particularly popular techniques for analysis, including good and bad views,is very much under study.
A Symbolic GeoPositioning System to Allow e-Health Enhanced Usages

Indoor positioning has had a bright future for many years. However, the deployment of such systems is struggling to develop rapidly. We propose here, within the framework of e-health, a particularly simple system to implement, based on a so-called “symbolic” approach which no longer seeks to provide precise positioning, but which strives to provide very high reliability. The principle of the approach is described. Then the practical implementation as well as the results obtained both in the home of an elderly person (in order to analyze their daily behavior) and in a hospital (for the monitoring and analysis of stretcher movements) are discussed. We show in particular that it is then very simple, in real time if necessary, to trigger an alarm in case of detection of an anomaly at home (unusual “geographical” behavior) or to estimate, after the analysis of many trips by Machine Learning techniques for example, the moment when stretchers will be available at the emergency service. We also propose other types of uses at the end of the article in slightly broader environments, objectives and user targets.
Twenty years of IDC Reviewed Event Bulletin (REB) statistics using data from a sparse IMS network to one reaching near completion

As of 14 December 2020, almost 90% of the IMS facilities (including radionuclide laboratories) were built and certified, data is transmitted in either real-time or on request from IMS stations to IDC for processing and analyzing. IDC analysts review automatic bulletins generated continuously and release the Reviewed Event Bulletin (REB) on a daily basis since February 2000. We present the statistics of mostly natural seismicity waveform events processed and analyzed over the past 20 years, as the network grew in size and became established. Multiple parameters including magnitude for those events associated with detections from seismic, hydroacoustic and infrasonic stations are analyzed. Techniques and rules related to waveform data analysis and the need to correct the automatic bulletin are discussed. This discussion should be beneficial for analysts work and data processing system optimization.
Alternative applications of dense geophysical measurements in plot-scale digital soil mapping

&amp;#160;In the hilly south-western region of Hungary a 23 hectare, slightly sloping agricultural plot was surveyed with various proximal sensing techniques, basically to support its precision management. An UAV-based hyperspectral &amp;#160;sensor (Cubert UHD185) provided the spectral characteristics (in the form of a hyperspectral ortho-mosaic) together with the Digital Elevation Model of the plot. Detailed geophysical measurements were also carried out. Electrical conductivity was surveyed by a GF Instruments CMD-Explorer and a CMD-Explorer Mini. Gamma-ray data (U, Th, K &amp;#233;s Total Count) was collected using a GF Instruments Gamma Surveyor Vario 2048-channel geo-physical gamma-ray spectrometer. Magnetic field was measured with a Geometrics G-858 magnetometer. Soil samples were collected in a 50 x 50 meter regular grid at 86 locations from the upper 0-30 cm. In the laboratory pH(KCl), consistency, salt, CaCO3, SOM, NO2-NO3-N, P2O5, K2O and Na content were measured.Spatial distribution of primary soil properties was predicted using different combinations of predictor datasets and various machine learning methods and with two differing concepts.While airborne hyperspectral data and terrain derivatives are available in spatially exhaustive (quasi-continuous) image format, underground geophysical data are basically generated as densely sampled point-like observations. According to general approach, these geophysical data are &amp;#8220;simply&amp;#8221; rasterized and then also used in spatially exhaustive form. In the recent study, we tested an alternative approach by using dense geophysical measurements as spatially non-exhaustive ancillary information to support the digital mapping of primary soil properties. First, soil features were estimated purely by geophysical auxiliary data as predictors, basically, establishing geophysical pedotransfer functions. Then predictions were used to infer spatially dense, estimated soil property data at geophysical measurement locations, which are more numerous by orders. Finally, soil property maps were derived by using (i) dense, inferred soil estimates as observation data and (ii) high-resolution spectral and terrain-based predictors. The results were evaluated and compared to those, which were produced using rasterized geophysical data.Our paper will present the comparison results, the experienced similarities and dissimilarities and discuss the applicability of dense geophysical measurements as spatially non-exhaustive ancillary information to support the digital mapping of primary soil properties.&amp;#160;Acknowledgment: Our research has been supported by the Hungarian National Research, Development and Innovation Office (NRDI; Grant No: K 131820).
Magnitude Estimation and Ground Motion Prediction to Harness Fiber Optic Distributed Acoustic Sensing for Earthquake Early Warning

Earthquake Early Warning (EEW) systems provide seconds to tens of seconds of warning time before potentially-damaging ground motions are felt. For optimal warning times, seismic sensors should be installed as close as possible to expected earthquake sources. However, while the most hazardous earthquakes on Earth occur underwater, most seismological stations are located on-land; precious seconds may go by before these earthquakes are detected. In this work, we harness available optical fiber infrastructure for EEW using the novel approach of Distributed Acoustic Sensing (DAS). DAS strain measurements of earthquakes from different regions are converted to ground motions using a real-time slant-stack approach, magnitudes are estimated using a theoretical earthquake source model, and ground shaking intensities are predicted via ground motion prediction equations. The results demonstrate the potential of DAS-based EEW and the significant time-gains that can be achieved compared to the use of standard sensors, in particular for offshore earthquakes.
How good can we get? – An analysis of systematic errors in common models for all-sky imager based irradiance nowcasting for solar energy

Solar energy from photovoltaics (PV) is a major contributor to the power production, e.g., in Germany, with a growing share. It is a major contributor to renewable power production but highly volatile as it is heavily influenced by atmospheric conditions. Especially shading by clouds can change within seconds to minutes and cause ramps in irradiance and solar power production. Accurate short-term predictions (nowcasts) of irradiance for the next minutes can help to alleviate the impact of this volatility and improve the integration of solar power into energy grids. One approach for nowcasting is the use of all-sky imagers (ASI), ground based fisheye cameras which capture the current cloud situation. Therefore, cloud information is extracted from current images, future cloud states are extrapolated and converted into an irradiance nowcast. Despite substantial progress in the quality of the applied methods, current ASI nowcasting models still exhibit significant nowcast errors and struggle to reliably outperform persistence nowcasts for all situations. Therefore, we assessed the implications for nowcast performance of two common fundamental simplifications of ASI nowcasting models. Firstly, cloud evolution is often modelled by advection, i.e. simple displacement over time. Growth, shrinking or reshaping of clouds is usually neglected in the models. Additionally, the ASI viewing geometry may introduce a misrepresentation of the depicted cloud scene, which is also commonly neglected. The ASI views surrounding clouds from a single ground position and under varying angles. For direct irradiance however, the horizontal distribution of clouds and their intersection in the direction of the sun is essential. While ASI images are usually reprojected to comply with the required horizontal representation, the original difference in actual and required viewing geometry cannot be fully compensated. E.g., breaks between distant clouds may not be clearly visible by the ASI although modulating the irradiance. Kurtz et al. (2017) demonstrated a major impact by this geometric limitation. We applied a nowcasting model to synthetic ASI images of a simulated cloud scene to extend this previous study and analyze the errors introduced by both of the two commonly used simplifications of ASI nowcasting models. A large fraction of the nowcasting error is attributable to the simplifications, which implies a systematic baseline error of common ASI nowcasting models. While the implementation of more evolved cloud evolution and a better representation of relevant cloud geometry are challenging, this work indicates, that efforts to implement these improvements in ASI nowcasting models are a chance for a leap in performance of future nowcasting models.&amp;#160;Kurtz, B., Mejia, F., and Kleissl, J.: A virtual sky imager testbed for solar energy forecasting, Solar Energy, 158, 753&amp;#8211;759, https://doi.org/10.1016/j.solener.2017.10.036, 2017.
Using text corpora for volcanic eruption impact assessment and resilience planning: The first steps of the CorVo project

The CorVo project[1] (Corpora for Volcanoes) aims at building an innovative tool for volcanic risk forecasting, impact assessment, and resilience planning. Exploiting technologies coming from the digital humanities and computational linguistics, a flexible digital interface will be developed to query a body of documents (the CorVo corpus) containing extensive descriptions of the past activity of one of Italy&amp;#8217;s most high-risk volcanoes: the Vesuvius. The methodology proposed is an innovative type of approach and the resulting prototype is likely to be extended to other volcanoes in multi-hazard settings.By querying the linguistically annotated corpus, end users (such as Civil Protection units and other stakeholders) will be able to quickly obtain important information from past eruptive scenarios, such as precursors, phenomenology, deposit distribution, and damages, as well as their social impact and the reactions they provoked in the institutions. In this way, they will be able to tackle future emergency scenarios, assess area vulnerability, and plan their response strategies in the best way.In order for the CorVo tool to return useful information, the CorVo corpus linked to it needs to contain a balanced selection of documents that describe the different types of eruptions the volcano may experience: Plinian-type, fissural or mixed (explosive-effusive). Therefore, the first three eruptions that are going to be included in the pilot are going to be the Plinian-type eruption of 1631, the fissural eruption of 1794, and the most recent mixed eruption of 1944. In order to test the validity of the obtained tool, the selected eruptions are in the number of the best-studied Vesuvius eruptions, also from the point of view of the information retrieved from historical sources. The bulk of the documents to be included in the corpus is going to be selected from the BIBV Database (http://libero.area.pi.cnr.it/libbiv/aboutBIV.html). The testing phase of the tool will be carried out in cooperation with other units of the PNRR EPOS MEET project. [1] The CorVo project is funded as Operating Unit CNR-IGG, WP11 Action 11c of the MUR PNRR EPOS MEET project, which started November, 1st 2022.
How PostgreSQL and QField Cloud can streamline data collection and improve data security: experience from a collaborative, interdisciplinary water reuse project in Flanders, Belgium.

Data is at the heart of every project, and its quality determines the reliability of research outcomes. In ever more collaborative geoscientific research, where data comes from many sources and in various formats, the right tools must be used to ensure data integrity and seamless access for all involved.Since 2019, in the project Grow, we routinely monitored groundwater quality and levels within an agricultural field where water is reused for irrigation and groundwater recharge. With multiple participants and analysis factors and fine temporal resolution of the monitoring, problems were encountered with a large volume of unstructured data with poor version control. Standard tools for collaborative research, such as cloud-based Excel spreadsheets, proved ineffective and threatened data integrity. A more robust data management system was urgently needed.Here we provide an overview of a framework based on a popular, open-source PostgreSQL relational database management system deployed in Amazon Web Services that helps to overcome data management issues in groundwater monitoring projects. Among main features are user-based, minimum privilege access which protects the data from, e.g., accidental deletions, and a hardcoded set of data correctness checks decreasing the likeliness of data input errors. Field data is collected using QField Cloud mobile application running a preconfigured QGIS project, sending the data directly to the database. Users also have access to all historical records, helping them detect anomalies on the spot. Laboratory analysis results and data from automatic data loggers without Internet of Things (IoT) modules are processed and uploaded to the database using custom-developed, open-source Python software, providing full transparency. Several IoT devices upload data directly to the database.So far, the new management system has proved a far superior platform for collaborative data analysis compared to existing tools. It significantly improved fieldwork efficiency and provided assurance of the data quality by improving the collection and handling process transparency. Thanks to the hard work of the QGIS and QField communities, as well as the developers and maintainers of PostgreSQL, we are better equipped for the future of geodata analysis.
Re-evaluating Caledonian magmatism and associated base metal mineralisation: a case study of the Black Stockarton Moor porphyry copper system

Following the British Geological Survey&amp;#8217;s (BGS) 1970s &amp;#8211; 1990s Mineral Reconnaissance Programme (MRP), there has been limited characterisation and quantification of base and precious metal mineralisation in the UK, with the notable exception of Au. Data gaps still exist regarding mineral paragenesis, geochronology, deportment of critical raw materials (CRM), and ore forming processes. With increased focus on CRM, NetZero, and supply risk we must improve our knowledge of deportment in base metal systems. The BGS Critical Minerals Intelligence Centre (CMIC) was recently established to aid the UK in meeting projected future CRM demand and will act as a nexus for industry and academia. Here, we establish a workflow and document a case study where academia and the CMIC have partnered to re-evaluate a potential mineral resource, a starting point for renewed studies elsewhere in the UK.&amp;#160; The Black Stockarton Moor (BSM) post-subduction porphyry Cu system is thought to have formed by interaction of Devonian plutonic to sub-volcanic complexes with Silurian turbidites in the&amp;#160;Southern Uplands of Scotland. No study of the BSM has been undertaken since the&amp;#160;1979 MRP report, thus&amp;#160;whether it is of any modern value remains unproven. Field sampling and utilising the National Geological Repository at BGS will allow for&amp;#160;optical and scanning electron microscopy (SEM) to quantitatively establish paragenesis and primary mineralogy. Sites will then be identified for chemical mapping to quantify CRM deportment in base metals using SEM-energy dispersive X-ray analysis (EDX), with areas of particular interest further quantified by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS).&amp;#160;Focused ion beam (FIB) nano-tomography will be used to identify the cm to nano-scale distribution of CRM.&amp;#160;Finally, magmatism and mineralisation will be fully temporally constrained using U-Pb analysis of zircon, titanite, calcite and epidote and/or Re-Os analysis of sulphides as appropriate.&amp;#160;On a large scale, this study will address one set of data gaps by re-invigorating our knowledge of the geology and geodynamic associations of mineralisation. However, by also identifying the quantities and associations of metals at the cm to micron scale, it addresses another, by constraining the extent and nature of processes responsible for the distribution of metals in such deposits. This workflow is to be refined for application to mineralisation elsewhere in the UK including work underway on the Strontian Caledonian granite and associated Pb-Zn mineralisation in the Northern Scottish Highlands.
The set up of the “UNO” project relational database for Stromboli volcano

Active volcanoes are complex, poorly predictable systems that can pose a threat to humans and their infrastructures. As such, it is important to improve as much as possible the understanding of their behavior. The Stromboli volcano, in Italy, is one of the most active volcanoes in the world, and its almost persistent activity is documented since centuries. The persistent background activity is sometimes interrupted by much more energetic, dangerous episodes. The Istituto Nazionale di Geofisica e Vulcanologia (Italy) set up the interdisciplinary &amp;#8220;UNO&amp;#8221; project, aimed to understand when the Stromboli volcano is about to switch from the ordinary to the extraordinary activity. The UNO project includes an outstanding variety of research activities, such as sampling in the field, the modeling of Stromboli topography from ALS technique and satellite data, the 3D numerical simulations of ballistic trajectories, or the set up of an ultrasonic microphones system. Key to the success of the project is the collection of integrated high spatial and temporal resolution data and their joint analyses in a shared relational database. We present here the simplified logical model of such database, focusing on the identification of entities and their relationships.
Earthquake emergency and cognitive disabilities: an experience.

The intervention concerns an experience conducted in the north-east of Italy, in a highly seismic territory, with the involvement of people with Down syndrome and intellectual disabilities whom live independently in the city. With the involvement of Fire Brigade and Civil Protection, four residential groups were involved and accompanied in a training course on seismic risk, starting from how it can manifest an earthquake in their homes and the consequent behaviors to be adopted, up to paths to reach the external assembly point indicated in the Civil Protection Plan. The attention was also paid to the relationship between rescuers and people with intellectual disabilities, but also the ability of these people to be able to relate to rescuers during an emergency.
Measuring the Effect of Data Augmentation Methods for Improving the Success of Convolutional Neural Network

With the intensive work done, deep learning finds many useful areas.However, obtaining a sufficient amount of data required by deep learning is not always an easy task.To overcome this difficulty, deep network trainers prefer to develop their datasets by using a set of algorithms.With the increased amount of data, deep networks can be trained more successfully.Data augmentation (DA) is one of the most widely used methods of increasing the amount of data.With DA, the number of sounds and images that a convolutional neural network (CNN) can classify can be increased.In this study, the number of images belonging to 6 classes that do not have enough images to train the CNN successfully enough was increased by DA methods.First, the amount of data was increased by applying three different DA methods separately and all three together.The original dataset and created datasets in which DA methods were used are used to train 15 CNNs with different parameters.Then, their effects on CNN have been investigated.As a result, a success increase of over 5% was observed by increasing the data.
Membangun Frontend Website Pertanian Sebagai Media Edukasi Interaktif Menggunakan Framework Angular Js

Agriculture is one of the pillars of the life of the Indonesian nation. Indonesian agriculture became world famous with the title of the best spice producer in the world and became a Southeast Asian rice barn at that time. Time and history have changed along with the many changes that have occurred in various fields where rice farming has also become a developing field with agricultural techniques that have developed more advanced than conventional agriculture. However, unfortunately the current developments are not accompanied by an even distribution of education received by farmers from cities to remote rural areas who still use conventional processing techniques so that the productivity produced is far in comparison to farmers who use more modern farming techniques supported by broader knowledge. Based on the problems above, the creation of a website containing education in the form of videos and articles on agriculture, especially rice, is more modern so that farmers in remote areas are not left behind byfarmers in urban areas. The choice of website as an educational tool is because it is more effective and efficient in reaching very remote areas. By looking at smartphone usage data which continues to increase and there is no need to install an application to access this website, this is the main reason for choosing a website as the main means of conveying information. It is hoped that with this writing or research, the gap in knowledge of rice cultivation in urban and rural areas will be narrowed so that the productivity of rice farming will increase so that farmers will prosper.