Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the Illumination Angle on NDVI Data Composed of Mixed Surface Values Obtained over Vertical-Shoot-Positioned Vineyards

Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of illumination direction with canopy orientation, and the corresponding effect on integrated NDVI. The results confirm that factors that intervene in determining the direction of illumination on a VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI proportions affecting the final integrated NDVI value. However, the effect of shading decreases as the row orientation approaches the solar path. Therefore, models of biophysical parameters using moderate-resolution imagery should consider corrections for variations caused by factors affecting the angle of illumination to provide more general solutions that may enable canopy data to be obtained from mixed, integrated vine NDVI.
Learning Models for Urdu Fake News Detection

Detecting fake news from the real news can be modeled as a typical binary text classification problem. Most of the models proposed for fake news detection address the resource rich languages such as English and Spanish but, languages such as Urdu, Persian, Balouchi and many Indian native languages have received very less attention due to unavailability of bench marked corpus. To promote text processing activities on Urdu, which happens to be a resource poor language FIRE 2020 (Forum for Information Retrieval Evaluation) has called for UrduFake, a shared task to detect fake news in Urdu language. High speed of news broadcast and the importance of detecting fake news from the real news made us (team MUCS) to propose three different learning models namely, an ensemble of Machine Learning (ML) models, Transfer Learning (TL) model based on ULMFiT and a hybrid model made up of an ensemble of ML approaches, TL approach and Deep Learning (DL). The proposed methodology utilizes word and character n-grams to train ML model and word embedding vectors to train BiLSTM networks of DL model and for TL model, a pre-trained general domain Urdu Language Model is fine-tuned with the Urdu fake news dataset. Our ML model obtained 5 th place among 9 teams that participated in this task.
SULFATE SORPTION BY ACID FOREST SOILS: 2. SULFATE ADSORPTION ISOTHERMS WITH AND WITHOUT ORGANIC MATTER AND OXIDES OF ALUMINUM AND IRON1

The effects of organic matter and oxides of Al and Fe on sulfate adsorption by an iron podzol and a brown earth were studied by evaluating the differences in sulfate adsorption with and without these soil constituents. The removal of organic matter had little effect on sulfate adsorption but the removal of Al and Fe oxides had a marked reduction in sulfate adsorption capacity of the iron podzol. The reduction in sulfate adsorption by this soil, due to the removal of Al or Fe, was higher at low concentrations of sulfate in solution. The effect of Al and Fe removal on sulfate adsorption in the brown earth was relatively small and observed only at low concentrations of sulfate in solution.
Modelo híbrido fonético-neural para corrección en sistemas de reconocimiento del habla

Resumen. El reconocimiento autom´atico del habla (ASR) es un ´area relevante en diferentes ´ambitos debido a que brinda un mecanismo de comunicaci´on natural entre aplicaciones y usuarios. A menudo los ASR presentan errores en aplicaciones que usan un l´exico propio de dominios espec´ıﬁcos. Se han explorado diversas estrategias para reducir el error en ASR cerrados mediante posprocesamiento, destacando enfoques de correcci´on ortogr´aﬁca y aprendizaje profundo. En el presente art´ıculo se explora el uso de una red neuronal profunda para rectiﬁcar los resultados de un algoritmo de correcci´on fon´etica, aplicado a una base de datos de audios de televenta. Los resultados obtenidos muestran una reducci´on de la tasa de error de palabras (WER), tanto en la transcripci´on original, como en la correcci´on fon´etica, mostrando la viabilidad de los modelos de aprendizaje profundo para trabajar en conjunto con estrategias de correcci´on posprocesamiento en la reducci´on de errores cometidos por ASR en dominios espec´ıﬁcos de lenguaje. Palabras clave
Passive Remote Sensing of Ice Cloud Properties at Terahertz Wavelengths Based on Genetic Algorithm

Ice clouds play a critical role in the balance of the earth–atmosphere radiation system, but there are some limitations in the existing remote sensing methods for ice clouds. Terahertz wave is expected to be the best waveband for retrieving ice clouds, with terahertz wavelengths in the order of the size of typical ice cloud particles. An inversion method for the remote sensing of ice clouds at terahertz wavelengths based on genetic algorithm is proposed in this paper. First, suitable channel sets in the terahertz band, which are mainly a combination of absorption lines and window regions, are determined. Then, to improve the efficiency of the generation of the retrieval database, based on the brightness temperature simulated by the atmospheric radiative transfer simulator (ARTS) for different cloud parameters, a fast forward operator is constructed using three-dimensional interpolation to simulate the brightness temperature difference between clear sky and a cloudy scene. Finally, an inversion model to retrieve the ice cloud base height, the effective particle diameter and the ice water path is established based on the genetic algorithm, and an analysis of the inversion errors is performed. The results show that the forward operator, constructed by the nearest interpolation, can accurately calculate the brightness temperature difference at a high speed. The proposed inversion method at terahertz wavelengths based on the genetic algorithm can achieve the expected scientific requirement. The absolute error of the cloud height is around 0.2 km, and the absolute error of the low ice water path (below 20 g/m2) is small, while the relative error of the high ice water path is generally maintained at around 10%, and the absolute error of the effective particle diameter is mostly around 4 μm.
Preliminary Investigation on Causality Information Retrieval

Causalityreferstotheassociationof variablesinasystem. Humanscancommunicatecausalinteractions directly via natural language and this helps them to gain insight into how the system works. In any general context, causality is the study of the specific connection which allows the action of one event to impact others. There are several approaches that have been developed for this but still, the door is open for the inclusion of new technologies and concepts. The main focus of this work is the extraction of causal relations from unstructured text data. In which we have implemented a word embedding approach using a universal sentence encoder model has trained with a deep averaging network encoder. In which, the data of the articles are split into a new instance for each separate text body of news articles and create embeddings. For similarity measurement, cosine similarity is used. This is our primary investigation to developed the baseline system for the retrieval of causally related articles/documents.
Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System

At present many researchers pay attention to a combination of spectral features and spatial features to enhance hyperspectral image (HSI) classification accuracy. However, the spatial features in some methods are utilized insufficiently. In order to further improve the performance of HSI classification, the spectral-spatial joint classification of HSI based on the broad learning system (BLS) (SSBLS) method was proposed in this paper; it consists of three parts. Firstly, the Gaussian filter is adopted to smooth each band of the original spectra based on the spatial information to remove the noise. Secondly, the test sample’s labels can be obtained using the optimal BLS classification model trained with the spectral features smoothed by the Gaussian filter. At last, the guided filter is performed to correct the BLS classification results based on the spatial contextual information for improving the classification accuracy. Experiment results on the three real HSI datasets demonstrate that the mean overall accuracies (OAs) of ten experiments are 99.83% on the Indian Pines dataset, 99.96% on the Salinas dataset, and 99.49% on the Pavia University dataset. Compared with other methods, the proposed method in the paper has the best performance.
Unsupervised Machine Learning on Domes in the Lunar Gardner Region: Implications for Dome Classification and Local Magmatic Activities on the Moon

Lunar volcanic domes are essential windows into the local magmatic activities on the Moon. Classification of domes is a useful way to figure out the relationship between dome appearances and formation processes. Previous studies of dome classification were manually or semi-automatically carried out either qualitatively or quantitively. We applied an unsupervised machine-learning method to domes that are annularly or radially distributed around Gardner, a unique central-vent volcano located in the northern part of the Mare Tranquillitatis. High-resolution lunar imaging and spectral data were used to extract morphometric and spectral properties of domes in both the Gardner volcano and its surrounding region in the Mare Tranquillitatis. An integrated robust Fuzzy C-Means clustering algorithm was performed on 120 combinations of five morphometric (diameter, area, height, surface volume, and slope) and two elemental features (FeO and TiO2 contents) to find the optimum combination. Rheological features of domes and their dike formation parameters were calculated for dome-forming lava explanations. Results show that diameter, area, surface volume, and slope are the selected optimum features for dome clustering. 54 studied domes can be grouped into four dome clusters (DC1 to DC4). DC1 domes are relatively small, steep, and close to the Gardner volcano, with forming lavas of high viscosities and low effusion rates, representing the latest Eratosthenian dome formation stage of the Gardner volcano. Domes of DC2 to DC4 are relatively large, smooth, and widely distributed, with forming lavas of low viscosities and high effusion rates, representing magmatic activities varying from Imbrian to Eratosthenian in the northern Mare Tranquillitatis. The integrated algorithm provides a new and independent way to figure out the representative properties of lunar domes and helps us further clarify the relationship between dome clusters and local magma activities of the Moon.
Morphological Characteristics of 1,3:2,4-Dibenzylidene Sorbitol/Poly(propylene glycol) Organogels

The organic gelator 1,3:2,4-dibenzylidene sorbitol (DBS) self-organizes to form a 3-D network stabilized by hydrogen bonds at relatively low concentrations in a variety of nonpolar organic solvents and polymers. The resultant network consists of nanofibrils and is responsible for the physical gelation of the matrix component. In this work, the morphological characteristics of organogels composed of DBS and poly(propylene glycol) (PPG) are investigated as functions of DBS concentration, PPG molecular weight, and temperature through the use of polarized light microscopy, transmission electron microscopy, X-ray diffractometry, and spectrophotometry. Polarized light microscopy reveals thermally reversible features that become increasingly more pronounced with increasing DBS concentration. Electron microscopy verifies that these features arise due to the presence of a DBS nanofibrillar network, with nanofibrils measuring ca. 10 nm in diameter. Comparison of X-ray diffraction patterns of pure DBS crystals and D.
Shipborne Mobile Photogrammetry for 3D Mapping and Landslide Detection of the Water-Level Fluctuation Zone in the Three Gorges Reservoir Area, China

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods.
HateDetectors at HASOC 2020: Hate Speech Detection using Classical Machine learning and Transfer learning based approaches

In this paper, we describe our models submitted to the HASOC shared task conducted as part of FIRE 2020. We have presented two directions to approach the problem of hate speech detection which are based on the advancements in the field of natural language processing. We present classical machine learning approaches using Support Vector Machine (SVM) and transfer learning approaches at sentence level using BERT. We have shown through experimental results that Transfer learning based approaches beat Machine learning based approaches in identifying the nuances of hate speech in a sentence. We have performed experiments on the English and Hindi datasets. On the public test dataset provided, we obtain a macro F1 score of 0.90 and 0.67 on English and Hindi languages respectively for subtask A and scores of 0.54 and 0.44 for subtask B. We also observe a difference of around 0.05 macro F1 score between BERT based models and SVM based models on English public test data, while we see only a difference of 0.01 on Hindi public test data in subtask A. We have highlighted the importance of a monolingual model over a multi lingual BERT based model for hate speech detection. We also highlight the importance of having a large, balanced training dataset on model performance for hate speech detection.
Kode_Stylers: Author Identification through Naturalness of Code: An Ensemble Approach

Authorship identification plays an important role in detecting undesirable deception of others’ content misuse or exposing the owners of some anonymous hurtful content. The Authorship Identification of SOurce COde (AI-SOCO) competition was held to investigate this task. Our team, namely Kode_Stylers , participated in the competition and used the naturalness of code as the key to our solution. In this working note, we (i) present methods to obtain features such as tokenization, N-gram TF-IDF, warning messages, and coding styles, (ii) implement our framework using Random Forest and Transformer to classify authors through our features, and (iii) apply an ensemble approach to increase the performance of our solutions. The results suggest that the authorship can be identified through the features extracted from source code and selected classifiers with up to an accuracy of 0.82, while the ensemble model outperforms any single model.
Uncertainty Assessment of the Vertically-Resolved Cloud Amount for Joint CloudSat-CALIPSO Radar-Lidar Observations

The joint CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) climatology remains the only dataset that provides a global, vertically-resolved cloud amount statistic. However, data are affected by uncertainty that is the result of a combination of infrequent sampling, and a very narrow, pencil-like swath. This study provides the first global assessment of these uncertainties, which are quantified using bootstrapped confidence intervals. Rather than focusing on a purely theoretical discussion, we investigate empirical data that span a five-year period between 2006 and 2011. We examine the 2B-Geometric Profiling (GEOPROF)-LIDAR cloud product, at typical spatial resolutions found in global grids (1.0°, 2.5°, 5.0°, and 10.0°), four confidence levels (0.85, 0.90, 0.95, and 0.99), and three time scales (annual, seasonal, and monthly). Our results demonstrate that it is impossible to estimate, for every location, a five-year mean cloud amount based on CloudSat–CALIPSO data, assuming an accuracy of 1% or 5%, a high confidence level (>0.95), and a fine spatial resolution (1°–2.5°). In fact, the 1% requirement was only met by ~6.5% of atmospheric volumes at 1° and 2.5°, while the more tolerant criterion (5%) was met by 22.5% volumes at 1°, or 48.9% at 2.5° resolution. In order for at least 99% of volumes to meet an accuracy criterion, the criterion itself would have to be lowered to ~20% for 1° data, or to ~8% for 2.5° data. Our study also showed that the average confidence interval: decreased four times when the spatial resolution increased from 1° to 10°; doubled when the confidence level increased from 0.85 to 0.99; and tripled when the number of data-months increased from one (monthly mean) to twelve (annual mean). The cloud regime arguably had the most impact on the width of the confidence interval (mean cloud amount and its standard deviation). Our findings suggest that existing uncertainties in the CloudSat–CALIPSO five-year climatology are primarily the result of climate-specific factors, rather than the sampling scheme. Results that are presented in the form of statistics or maps, as in this study, can help the scientific community to improve accuracy assessments (which are frequently omitted), when analyzing existing and future CloudSat–CALIPSO cloud climatologies.
What Drives Crop Land Use Change during Multi-Year Droughts in California's Central Valley? Prices or Concern for Water?

The recent multi-year droughts in California have highlighted the heightened risk of longer and more intense droughts, thus increasing the interest in understanding potential impacts for major economic activities, such as agriculture. This study examines changes in cropping pattern in California’s Central Valley between 2007 and 2016 in response to two consecutive droughts (2007–2009 and 2012–2016), factors driving these changes, and the impact of these changes on groundwater level. Results indicate that Central Valley experienced a shift in cropping pattern from alfalfa, cereals (rice, winter wheat, corn, and oats), and cotton, to nut (almonds, walnuts, and pistachios) and fruit (grapes, oranges, and tomatoes) tree crops. This shift in cropping pattern was likely driven by high crop prices, increasing trend in crop price, and increasing water pumping cost, particularly in the relatively water-stressed southern parts of Central Valley. While the total cropland water use for Central Valley remained the same during 2007–2016 (during both wet and dry years), they vary from county to county. Some counties experienced large reductions in cropland water use, while other counties experienced large increases in cropland water use, indicating the need for county-specific water resource management. The results also indicate that both land management (determining size of fallow land), as well as crop management (choice of crop types), are key factors in water resource management.
Localización de un robot móvil mediante el filtro de Kalman extendido y el simulador Gazebo

Resumen. La localización de un robot móvil permite realizar tareas de planeación de trayectorias. Aunque existen modelos dinámicos para distintos tipos de robots móviles, los efectos dinámicos no lineales en el ambiente, modelo, sensores y mecánica impiden un conocimiento exacto de la posición del robot. Por lo anterior es necesario estimar la posición considerando efectos probabiĺısticos. El Filtro de Kalman Extendido (EKF) permite la estimación de la pose de un robot móvil considerando desviaciones respecto al modelo dinámico no lineal. Se presenta la implementación del EKF para la localización de un robot móvil en la plataforma de simulación Gazebo. La posición del robot es obtenida mediante segmentación por color, la imagen se adquiere a través de una cámara colocada en el entorno de simulación. Se compara la estimación de la posición con respecto a la posición adquirida mediante segmentación por color.
Zeus at HASOC 2020: Hate speech detection based on ALBERT-DPCNN

The use of social media has grown rapidly in the past few years. User generated data often contains objectionable content. Identifying hate speech, cyber-attacks and offensive language is a very challenging sentiment analysis task. In this paper, we participated in HASOC’s English hate speech and offensive content identification task and proposed the ALBERT-DPCNN model based on emotion analysis, which combined ALBERT and DPCNN obtained richer semantic features, ranking third in the task and achieving good results.
Detección de ciberataques a través del análisis de mensajes de redes sociales: revisión del estado del arte

Resumen. Este art´ıculo muestra que de la misma forma como se ha utilizado la informaci´on de redes sociales para extraer ciertos eventos espec´ıﬁcos; por ejemplo, la aceptaci´on de un producto en el mercado, preferencias pol´ıticas del p´ublico, eventos de salud, entre otros. Del mis-mo modo, existen investigaciones
Análisis con aprendizaje automático en el proceso de flotación de burbujas para la obtención de minerales

Resumen. El análisis de capa superficial de espuma ha sido clave para entender el comportamiento general de los sistemas de flotación de burbujas para la obtención de minerales en la industria minera. En el estado del arte se han desarrollado varios analizadores de capa superficial de espuma de las burbujas en las celdas de flotación con el objetivo de clasificar mejor el proceso, adición de reactivos y control del flujo de aire. El uso de la inteligencia artificial, siendo más específico el aprendizaje automático, en la industria ha tenido un uso creciente y ha demostrado su aplicabilidad y buenos resultados mediante aplicación de técnicas para la clasificación de imágenes. En este artículo se presenta un caso aplicado a la industria minera donde se hace uso de métodos y herramientas de aprendizaje automático para la clasificación de imágenes, con el fin de obtener el modelo que brinde mejores resultados para estimar el estado del proceso a partir de las imágenes tomadas en las celdas de flotación, con lo que se pretende mejorar el proceso