Reviewerly/exHarmony huggingface dataset:
VIRTUAL REALITY THERAPY FOR PAIN

Pain is the unpleasant condition raised as a normal response of the body to injury and illness. In the case of Chronic Pain Syndrome, this can ailment can continue even after the cause that provoked it has disappeared. The classical painkiller medicines (like paracetamol, non-steroidal anti-inflammatory drugs and opioids) are used as therapy, but they raise problems of overdose, adverse side-effects and addiction. Virtual Reality Therapy (VRT) is showing promise as a possible drug-free substitute for pain management. The VRT lets patients to navigate through Virtual Reality environments and to execute operations which are often personalized to specific medical conditions. It offers a multisensory experience that captures the subject attention on an innate level and attenuates the discomfort. Besides the lack of painkiller medicine drawbacks, it is well received by the young patients, which usually have supplementary restrictions to the pharmacological agents. We present in the paper the state of the art in the domain and the results we obtained with the two VRT applications we developed in Unity. The first one, Snow Land, was targeted primarily to patients with burn wounds. The action takes place into a cold environment inhabited by penguins and polar foxes and some of the regular activities are walking, throwing fish and snowballing. In the second one, Angling in Virtual Reality, we are trying a more general approach, with a larger scope, represented by the typical relaxation activity of fishing. The degree of immersion is higher, by using better graphics, sounds and the Leap Motion controller. These approaches permit us to understand what type of interactions, environments, mechanics and sound ambience are more beneficial for providing the patient an efficient pain relief effect and to select the direction we intend to continue our efforts.
DIMINISHING THE UNIVERSITY DROP-OUT RATES IN ROMANIAN ENGINEERING PROGRAMS BY USING A SMART CAREER PATH PROFILING SYSTEM

Although knowledge economy came with an increased amount of world-wide career opportunities, major difficulties related to knowledge workers' employment have occurred, among them incapacity of choosing the suitable career. Usually, careers (jobs, occupations) are determined by education, either formal or informal. When a student enrolls to a faculty, he/she in fact makes the first step in choosing one's career. Unfortunately, in Romania, more than a quarter of the students enrolled in a graduate or master program do not finish their studies. This translates into no-return-on-investment for the Romanian government regarding the money spent on the incomplete schooling of so many undecided youths. The career counselling centers, which could diminish the drop-out problem, are under-financed and under-staffed, as one councilor provides such programs for 3400 students, while in high schools a counselor activates for approximative 800 pupils, according to public statistics. In Romania, the university drop-out rate increases from year to year, especially in engineering programs, which are harder, and less known to pupils, conducting to a lack of skilled personnel in the corresponding domains in industry. In the light of the above observations, the scope of the article is to propose a technological model for a Smart Career Path Profiling System (CPPS) with the aim of providing career counseling and recommendations based on advanced analytics of users' profile, extracted automatically from various data sources, e.g. the school situation, social media profiles, specific answers etc. CPPSs are not a new thing. Usually they are built based on psychometric instruments and psychological tests. Unfortunately, these tests are time-consuming and don't take into consideration all aspects of a person behavior. As a follow-up of the technological opportunities, researchers have tried to improve the CPPSs by exploiting recommendation algorithms, data mining techniques and social media technologies: a new generation of CPPSs occurred. Our model is based on recent technologies and customized for Romanian environment and for engineering domains and presented in the context of the new generation of CPPSs.
AN EDUCATIONAL APPROACH TO THE NATIONAL SECURITY ISSUES IN A DIGITAL SOCIETY

Recently, there has been a significant increase in the number of cyber incidents triggered by the vulnerabilities of human, technical and procedural factors. Nowadays, security of communications and information technology systems correlated with the confidence they offer throughout the entire public services sector and those specific to the defence domain, represents a national priority in most countries. In order to avoid undermining national security or creating certain prejudices to the state, all institutions have the key responsibility to protect their cyber infrastructures. Today we participate in a digital society in which all aspects of our lives are influenced by the way in which we use technology to communicate, socialize, work and study. Through numerous threats concerning national security, digitalization of data generates a series of problems that need to be analyzed and if possible, offered immediate solutions. Undergoing radical changes, the field of education is at a point where its existing infrastructure needs to be reorganized and should aim for future learning environments that will meet tomorrow's expectations. This article reveals possible answers to the problems related to national security generated from the fact that society has now transferred all its content in a giant virtual space that has vulnerabilities and needs to be secured. An advanced digitalization of society requires that most of its citizens should have some minimal knowledge related to using technology. A necessary preliminary step towards helping our contemporary society is to become highly digitalized and, in the meantime, achieving cyber safety through educational solutions.
MorphoGen: Full Inflection Generation Using Recurrent Neural Networks

Sub-word level alternations during inflection (apophonies) are an common linguistic phenomenon present in morphologically-rich languages, like Romanian. Inflection learning, or predicting the inflection class of a partially regular or fully irregular verb or noun in such a language has been a widely studied task in NLP, but generative models are limited to capturing the most common ending patterns and apophonies. In this paper, we show how to train a character-level Recurrent Neural Network language model to be able to accurately generate the full inflection of verbs in Romanian, Finish, and Spanish and model stem-level phonological alternations triggered by inflection in an unsupervised way. We also introduce a method to evaluate the accuracy of the generated inflections.
Interpreting Machine Learning Models for Geochemistry Data Classification using Decision Boundary Maps

Machine learning has been shown to be a highly effective method for classifying geochemistry data, such as mineral forming environments and rock tectonics. However, it can be difficult to understand the decision-making processes of these models. To address this issue, we propose the use of Decision Boundary Maps (DBMs) as a visualization tool for interpreting machine learning models. These maps project high-dimensional geochemistry data onto a 2D plane and depict the decision boundaries in the projected space, providing a visual representation of the algorithm's decision-making processes. In addition, DBMs can reveal trends, correlations, and outliers in the data, helping to interpret the results obtained from machine learning-based geochemistry data classification. Seeing the positions of data points, rather than just class labels, is especially valuable because samples in geological categories often follow a sequence, such as a magmatic to hydrothermal transition. Observing the positions of data points allows for the identification of trends from one class to an adjacent class.
Soil and soil-gas surveys for geothermal exploration at Cumbre Vieja volcano, La Palma, Canary Islands

The most common types of survey in known geothermal areas are water and gas sampling of natural discharges. However, where w&amp;#225;ter and gas discharge features are few and where the extent of the field is not known, soil and soil-gas surveys can prove useful. These surveys can identify areas in a field that are permeable and potential upflow or boiling zones. They can also outline the boundaries of a geothermal system, often in conjunction with geophysical surveys to provide more comprehensive understanding, especially when geophysical data is difficult to interpret. For all of the above, a soil and soil-gas survey for geothermal exploration had been carried out at Cumbre Vieja volcano that is characterized by neither having visible manifestations of volcanic gases nor zones of hydrothermal alteration on its surface. We have divided the soil and soil-gas survey for geotermal exploration into four categories based on the number of sampling sites per square kilometer: 1) Regional exploration survey (1 sampling site/km2), 2) Regular exploration survey (about 5 sample sites/km2), 3) Detailed I exploration survey (between 10 and 100 sampling sites/km2) and 4) Detailed II exploration survey (more than 500 sampling sites/km2). We have classified our soil and soil-gas survey for geothermal exploration at Cumbre Vieja volcano as a regular exploration survey due to the fact that 1200 observation or measurement points were selected for its 220 km2 area, resulting in approximately 5.5 sampling sites per km2. Similar soil gas surveys had been performed in other study &amp;#225;reas of the Canarian archipelago (Rodr&amp;#237;guez et al. 2021). At every sampling site, we performed in-situ measurements of soil CO2 efflux and 222Rn activity in the soil gas atmosphere. In addition, soil gas samples were collected at a depth of 40 cm for further chemical and isotopic analysis in our geochem lab. Simultaneously with the soil gas survey, soil samples were collected from the base of the B Horizon-Subsoil (30&amp;#8211;50 cm) which were subsequently dried and sieved (80 mesh) for subsequent leaching in order to analyze those vol&amp;#225;tiles (Hg0, As, B and NH3) of interest that were fixed in the organic-clay fraction of the soils. Some preliminary results of this soil and soil-gas survey for geothermal exploration at Cumbre Vieja are presented.Rodr&amp;#237;guez F., P&amp;#233;rez N. M., Meli&amp;#225;n G. V., Padr&amp;#243;n E., Hern&amp;#225;ndez P. A., Asensio-Ramos M., Padilla G. D., Barrancos J. and D&amp;#8217;Auria L. (2021). Exploration of deep-seated geothermal reservoirs in the Canary Islands by means of soil CO2 degassing surveys. Renewable Energy, https://doi.org/10.1016/j.renene.2020.09.065
The development of a detailed mineralogical database from satellite remote sensing products, towards an improved representation of dust transport in NWP simulations.

A broad spectrum of environmental processes such as radiation, cloud formation, ocean fertilization and human health are affected from the presence of mineral dust. The transport of dust particles is dictated by the prevailing meteorological conditions as well as the composition and physiochemical properties of the particles themselves. Which, in turn, are bound to the soil mineralogy at the source region.Numerical weather prediction models can estimate the transport of dust particles, yet a more refined mineralogical categorization can significantly improve the dust transport estimations and&amp;#160; increase preparedness for implications on weather, biogeochemistry and health. This novel mineralogical representation is derived from multi-spectral satellite remote sensing sensors (Sentinel 2A) over a limited area around Lake Chad in Sahara desert by taking into account dust particle characteristics such as size, composition and optical properties. The mineralogy map will be implemented in WRF/CHEM model to improve the accuracy of atmospheric simulations. The final product will be juxtaposed against current state-of-the-art mineralogical products such as the NASA&amp;#8217;s Earth Surface Mineral Dust Source Investigation (EMIT) mission. Dust transport simulations will be compared against field measurements from Antikythera PANGEA station in the Mediterranean and ASKOS campaign in the Atlantic Ocean.
Preliminary earthquake catalog (2012–2021) of the southern Korean Peninsula by deep learning-based techniques

Earthquake monitoring has been stepped up due to high-density permanent networks in the southern Korean Peninsula, though its relatively low seismicity. With the dramatic increase of data volume, deep learning techniques can be effective ways to process them. In this study, we present a preliminary, but comprehensive earthquake catalog in the southern Korean Peninsula for research purposes by applying a series of deep learning-incorporated methods including for earthquake and phase detection, event discrimination, and focal mechanism determination. We first improved the EQTransformer by re-training it with hybrid local and STEAD datasets to perform earthquake and phase detection for 10-year-long data from 2012 to 2021. Then, the subsequent phase association was carried out using the algorithm based on a Bayesian gaussian mixture model. In the result, 66,855 events were identified and located from 691,077 phase detections. Among them, 27,429 natural earthquakes were separated with a novel CNN model trained using event waveforms and origin time constraints. The natural seismicity suggested various earthquake clusters that constrained by tectonic structures, such as the Okcheon belt and Gyeongsang basin, and showed significantly low rate of occurrence in the Gyeonggi massif. In addition, we developed a CNN model for the determination of focal mechanisms that identify the polarity of initial P-waves in input waveforms, and the application of it resulted in 2,345 reliable solutions. Strike-slip motions were dominant in the inland, while reverse faulting of coastal earthquakes, showing an average P-axis direction of N74E in both areas. Despite the massive volume of data, it took less than a week to perform all of the processes with more cataloged earthquakes than those in the previous one (9,218). The extended earthquake catalog accompanied by focal mechanisms underpins data-driven studies such as tomography, stress field estimation, earthquake hazards assessment, and burial fault mapping.
Comparison of Characteristics of MEMS and Geophone Sensors for Earthquake Detection

Recently, various attempts for earthquake detection and seismic observation using low-cost vibration sensors are being actively conducted, and among them, MEMS and Geophone are the most frequently attempted sensors. MEMS and Geophone sensors have different characteristics and strengths and weaknesses, and it is necessary to understand them to select a sensor suitable for the purpose. In this study, MEMS and Geophone sensors were compared and tested for P-wave detection for earthquake early warning and ground and structure earthquake motion observation. For this purpose, the two types of sensors were mounted together on one aluminum plate, vibration table tests were conducted, and earthquake detection results were compared through real-time earthquake detection over several months. Through this, only for the tested sensor models, we could conclude that Geophone is suitable for P-wave detection and pattern extraction for noise classification, and MEMS is suitable for strong vibration measurement.
Efficacy of automatic detection and classification methods for volcano seismicity

At volcanic islands, seismometers are the main tool for monitoring various active processes including seismicity that could forewarn impending eruptions. However, the sparse seismic networks, high background noise levels, large diversity of seismic signals, and high event rates during unrest episodes make automatic detection and classification of volcano seismicity a difficult challenge. In this paper, we use the Alaska Volcano Observatory&amp;#8217;s catalogue of ~120,000 long-period (LP) and volcano-tectonic (VT) earthquakes at 34 volcanoes from 1989-2018 to evaluate the efficacy of automatic detection and classification methods. For each event, we calculate the frequency index (FI) based on the ratio of mean spectral amplitudes in the higher and lower frequency bands of the recorded waveforms. Using the local minima in the FI distribution at each volcano as the classification boundary, we find that our labels generally agree with the catalogue&amp;#8217;s manual labels. The classification boundaries separating LP and VT earthquakes are also relatively consistent (FI = -1) between volcanoes. Therefore, the FI method is an effective method for automatic classification of volcano seismicity. We then evaluate the performance of two machine-learning-based models (PhaseNet and EQTransformer) and the cross-correlation-based template-matching method for automatic detection. While the template-matching method is computationally more expensive, we find that most of the catalogue events can be detected by using another event as a template, with relatively low false positive rates. In comparison, both machine-learning-based models&amp;#8217; performances are worse than previously reported results and deteriorate systematically with decreasing FI index values. The bias might have resulted from the models having been trained using earthquake catalogues from non-volcanic regions that lack LP events. Therefore, these models should be retrained with a dataset of volcano seismicity before being applied for automatic earthquake detection at volcanic regions.&amp;#160;&amp;#160;
Convolutional neural network for normal fault scarp characterization : application to the Trans-Mexican Volcanic Belt

Normal fault markers in the landscape such as scarp are records of fault activity. The scarp morphology is used for exemple to estimate slip rates or rupture behaviors. The scarp morphology varies along strike, and needs to be estimated to assess this variation. Currently this is often a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated profiles, called ScarpLearn. We train the CNN to use high resolution data (&lt; 5m). From a 2D topographic profile across normal fault scarps, ScarpLearn is able to automatically give the scarp height with an uncertainty and to illuminate the area of the profile containing the scarp. We apply ScarpLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. This region is a slow deforming area (~0.2&amp;#177;0.05 mm/yr), which extends over more than 800km, crossed&amp;#160; by more than 600 potentially active faults but less than 5% of those have been correctly characterized by paleoseismological studies. In this context an automatic method to characterize the escarpments in a global, reproductible, robust (not expert-dependent) quantitative way will be highly valuable and a great step towards a better characterization of the seismic hazard of the region. In particular we tested our approach across the Ameca-Ahuisculco fault system, and by comparing ScarpLearn with with other methods based on profile analisis (not based on deep learning), we explore the advantages (computation time, accuracy, uncertainties) that deep learning methods bring, as well as the current limits (such as bias and dependance of the resolution).
Revisiting the Messel palynoflora using a combined LM and SEM approach

The early to middle Eocene maar lake of Messel is a world-renowned fossil locality. Its oil shale deposits are well-known for their exceptional preservation of vertebrates (e.g., crocodiles, early horses) and invertebrates, especially insects. The makro- and microfossil remains of plants also contributed to providing a more holistic snapshot of this Eocene ecosystem. So far, the palynoflora has mostly been investigated using only conventional LM, and the classic taxonomic qualitative/illustrative work on the palynoflora dates back to the late 1980ies. Here we report on the first results from an ongoing study using combined LM and SEM, and in some cases even TEM, to re-investigate the Messel palynoflora qualitatively. The main goals of our study are to conclude (I) if some of the spores/pollen can be systematically placed with more certainty, (II) if additional spore/pollen types can be discovered, (III) to compare our combined method of investigation with the classic LM-based counting method, and finally (IV) to conclude about the composition and species richness of the palynoflora and how it correlates with the macro- and mesofloras. To accomplish this, we first processed a single sedimentary rock sample without fine-mesh sieving and then extracted and investigated every pollen type encountered using the single-grain method. So far, this sample has produced 30 spore types, 5 gymnosperm pollen types, and about 185 different angiosperm pollen types. The previous LM-based qualitative work identified 173 palynomorphs in an accumulative account from numerous rock samples up-trough the oil shale section. Our study, on a single sample from the lowest part of the oil shale revealed every single pollen type (except for Milfordia (Restionaceae) and Pityosporites microalatus (Cathaya, Pinaceae)) discovered in the accumulative approach using the classic LM counting method. Also, in addition to the c. 140 angiosperm pollen types previously recorded, we can now add at least 45 new pollen types for this locality. Our study shows that when taxonomic resolution and diversity are key, a combined LM and SEM investigation produces higher diversity than relying only on the conventional LM counting method when single samples are compared. In addition, a well-preserved single sample extensively studied using the combined approach is likely to provide the same or even higher number of taxa when compared to conventional LM counting methods analyzing an entire stratigraphic section. The qualitative combined approach will provide a more reliable presence/absence of taxa in the accumulation site hinterland. For quantitative analysis and subtle changes in the surrounding environments conventional counting methods are more appropriate because of the universal availability of light microscopes and difficulties with SEM-counting.
Financial Development, New Information Technology, and Economic Growth: An Empirical Validation Test via a Dynamic Panel Data Model

The article aims to analyze and empirically verify what is analyzed in detail in the theoretical part. Our work will aim to empirically validate the effect of the interaction between the two variables (ICT and financial development) and quantify their effects on economic growth for the MENA region.