Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05° spatial resolution for the period 2001–2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R2) = 0.58, root mean square error (RMSE) = 2.74 g C·m−2, bias = –0.34 g C·m−2) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future.
Hub@HASOC 2020: Fine-tuning Pre-Trained Transformer Language Models for Hate Speech and Offensive Content Identification in Indo-European Languages

This paper presents the system description of the Hub team participating in HASOC2020: Hate Speech and Offensive Content Identification in Indo-European Languages. The focus of this shared task research is to identify hateful or offensive content in English, German, and Hindi comments posted on Twitter. Each language consists of two tasks, the first of which can be seen as a coarse-grained binary classification task, and the other can be seen as a fine-grained quaternary classification task. We only participated in the English task and the German task. According to our analysis of the task description and data set, we use two fine-tuned pre-trained transformer models ALBERT and BERT for the English task and the German task. In this paper, we will discuss the experiments and results of the English task and the German task.
Embedding-based Authorship Identification of Source Code

Authorship analysis has played an important role in identifying the true writers of literary texts. Of late, there is an increased interest in authorship identification of source codes. This is partially due to a rise in anonymous malware, code injections, and digital forensics. This paper presents the system implemented to identify authors of compilable source codes written using C++. The model was developed for the track Authorship Identification of SOurce COde in Forum for Information Retrieval Evaluation, 2020. The proposed work implements word embedding modeling along with convolutional neural networks (CNN) to achieve an accuracy of 90.64%.
Frequency Variability Feature for Life Signs Detection and Localization in Natural Disasters

The locations and breathing signal of people in disaster areas are significant information for search and rescue missions in prioritizing operations to save more lives. For detecting the living people who are lying on the ground and covered with dust, debris or ashes, a motion magnification-based method has recently been proposed. This current method estimates the locations and breathing signal of people from a drone video by assuming that only human breathing-related motions exist in the video. However, in natural disasters, background motions, such as swing trees and grass caused by wind, are mixed with human breathing, that distort this assumption, resulting in misleading or even no life signs locations. Therefore, the life signs in disaster areas are challenging to be detected due to the undesired background motions. Note that human breathing is a natural physiological phenomenon, and it is a periodic motion with a steady peak frequency; while background motion always involves complex space-time behaviors, their peak frequencies seem to be variable over time. Therefore, in this work we analyze and focus on the frequency properties of motions to model a frequency variability feature used for extracting only human breathing, while eliminating irrelevant background motions in the video, which would ease the challenge in detection and localization of life signs. The proposed method was validated with both drone and camera videos recorded in the wild. The average precision measures of our method for drone and camera videos were 0.94 and 0.92, which are higher than that of compared methods, demonstrating that our method is more robust and accurate to background motions. The implications and limitations regarding the frequency variability feature were discussed.
Phthalocyanine Research and Applications

Introduction. Synthesis. Analysis. Crystals. Reactions: Their Mechanisms and Rates. Organic Chemistry. Solution Chemistry. Radionuclides. Catalysis. Pc and M Pc Colorants. Coloring Cement, Mortar, Sand, and Gypsum. Pcs in Detergent and Dispersant Compositions. Adsorption. Gas and Water Purification. Pc Dyes and Pigments as Water Pollutants. Corrosion Prevention. Physical Chemistry. Coatings. Inks. Spectroscopy. Pc Use in Energy Systems. Conduction, Semiconduction, and Photoconduction. Magnetic Phenomena. Pcs in Electrochemistry. Pc and Metal, M, Pc Polymers. Pcs in Biology. Thermal, Thermal Transfer, and Optical Recording Media. Laser and Other Optical Recording Applications. Studies and Topics in Photochemistry. Chromism. Photoreduction of Viologens. Coatings, Films, and Photographic Products. Photopolymer and Photoresist Compositions. Resists. Credit and ID Cards. Color Filters, Optical Filters. Electrophoretic Devices. Electroradiographic and Electrorecording Material. Toners. Lithographic Printing Plates and Planographic Plates. Electrophotography and Electrophotographic Plates: Electrophotographic Photoreceptors and Photosensitizers. Electrophotographic Developers. Index.
IRLab@IITBHU@Dravidian-CodeMix-FIRE2020: Sentiment Analysis for Dravidian Languages in Code-Mixed Text

This paper describes the IRlab@IITBHU system for the Dravidian-CodeMix - FIRE 2020: Sentiment Analysis for Dravidian Languages pairs Tamil-English (TA-EN) and Malayalam-English (ML-EN) in Code-Mixed text. We submitted three models for sentiment analysis of code-mixed TA-EN and MA-EN datasets. Run-1 was obtained from the BERT and Logistic regression classifier, Run-2 used the DistilBERT and Logistic regression classifier, and Run-3 used the fastText model for producing the results. Run-3 outperformed Run-1 and Run-2 for both the datasets. We obtained an 𝐹 1 -score of 0.58, rank 8/14 in TA-EN language pair and for ML-EN, an 𝐹 1 -score of 0.63 with rank 11/15.
Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: (1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; (2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; (3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and (4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR).
Non-neural Structured Prediction for Event Detection from News in Indian Languages

In this paper, I give a description of my submission, with team name 3Idiots, to the Event Detection from News in Indian Languages (EDNIL) 2020 shared task. My method is based on structured prediction using only word n-gram and regex features and does not rely on any latest deep learning or neural network methods. The methods was used for both tasks across all languages. It was the best performing system on all tasks and languages, outperforming other best methods by a clear ˜10 to 15 F1-score points. The approach presented here is quite fast to train and do inference. The code will be open sourced at https://github.com/socialmediaie/EDNIL2020.
Spectral and Spatial Global Context Attention for Hyperspectral Image Classification

Recently, hyperspectral image (HSI) classification has attracted increasing attention in the remote sensing field. Plenty of CNN-based methods with diverse attention mechanisms (AMs) have been proposed for HSI classification due to AMs being able to improve the quality of feature representations. However, some of the previous AMs squeeze global spatial or channel information directly by pooling operations to yield feature descriptors, which inadequately utilize global contextual information. Besides, some AMs cannot exploit the interactions among channels or positions with the aid of nonlinear transformation well. In this article, a spectral-spatial network with channel and position global context (GC) attention (SSGCA) is proposed to capture discriminative spectral and spatial features. Firstly, a spectral-spatial network is designed to extract spectral and spatial features. Secondly, two novel GC attentions are proposed to optimize the spectral and spatial features respectively for feature enhancement. The channel GC attention is used to capture channel dependencies to emphasize informative features while the position GC attention focuses on position dependencies. Both GC attentions aggregate global contextual features of positions or channels adequately, following a nonlinear transformation. Experimental results on several public HSI datasets demonstrate that the spectral-spatial network with GC attentions outperforms other related methods.
Retrieval Model and Classification Model for AILA2020

In this paper, we introduce our methods used in the Task1 and Task2 evaluations of AILA2020 (Artificial Intelligence for Legal Assistance). For Task1a, we used BM25 and Cosine Similarity to rank the documents. And the language models with Jelinek-Mercer smoothing method and Dirichlet smoothing method are used for Task1b. For the result we submitted for task2, Logistic Regression model based on TF-IDF feature and BERT is used.
Sampling-Based Path Planning for High-Quality Aerial 3D Reconstruction of Urban Scenes

Unmanned aerial vehicles (UAVs) can capture high-quality aerial photos and have been widely used for large-scale urban 3D reconstruction. However, even with the help of commercial flight control software, it is still a challenging task for non-professional users to capture full-coverage aerial photos in complex urban environments, which normally leads to incomplete 3D reconstruction. In this paper, we propose a novel path planning method for the high-quality aerial 3D reconstruction of urban scenes. The proposed approach first captures aerial photos, following an initial path to generate a coarse 3D model as prior knowledge. Then, 3D viewpoints with constrained location and orientation are generated and evaluated, according to the completeness and accuracy of the corresponding visible regions of the prior model. Finally, an optimized path is produced by smoothly connecting the optimal viewpoints. We perform an extensive evaluation of our method on real and simulated data sets, in comparison with a state-of-the-art method. The experimental results indicate that the optimized trajectory generated by our method can lead to a significant boost in the performance of aerial 3D urban reconstruction.
Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.
Instrumental conditions and procedure for multielement analysis of soils and plant tissue by ICP‐AES

Abstract Four standard soils prepared by the Canadian Certified Reference Materials Project and one standard NBS plant tissue sample were analysed by Inductively Coupled Plasma‐Atomic Emission Spectroscopy (ICP‐AES) to evaluate the spectral interferences and subsequent correction required for the bulk elemental analysis of the soil and plant digests. Elemental interferences were identified and resolved through use of groups of liquid standards coupled with application of correction factors using the system's software. Accurate results for the standard soils were obtained for 16 elements while content of 19 elements determined in standard orchard leaves were within certified limits. Acceptable results for As, Cd and B in soil could not be obtained, in part due to interferences by Ti.
Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

Satellite altimetry and tide gauges are the two main techniques used to measure sea level. Due to the limitations of satellite altimetry, a high-quality unified sea level model from coast to open ocean has traditionally been difficult to achieve. This study proposes a fusion approach of altimetry and tide gauge data based on a deep belief network (DBN) method. Taking the Mediterranean Sea as the case study area, a progressive three-step experiment was designed to compare the fused sea level anomalies from the DBN method with those from the inverse distance weighted (IDW) method, the kriging (KRG) method and the curvature continuous splines in tension (CCS) method for different cases. The results show that the fusion precision varies with the methods and the input measurements. The precision of the DBN method is better than that of the other three methods in most schemes and is reduced by approximately 20% when the limited altimetry along-track data and in-situ tide gauge data are used. In addition, the distribution of satellite altimetry data and tide gauge data has a large effect on the other three methods but less impact on the DBN model. Furthermore, the sea level anomalies in the Mediterranean Sea with a spatial resolution of 0.25° × 0.25° generated by the DBN model contain more spatial distribution information than others, which means the DBN can be applied as a more feasible and robust way to fuse these two kinds of sea levels.
Application of Sentinel-1 and-2 Images in Measuring the Deformation of Kuh-e-Namak (Dashti) Namakier, Iran

Kuh-e-Namak (Dashti) namakier is one of the most active salt diapirs along the Zagros fold–thrust belt in Iran. Its surface deformation should be measured to estimate its long-term kinematics. Ten Sentinel-2 optical images acquired between October 2016 and December 2019 were processed by using Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) method. Forty-seven Sentinel-1 ascending Synthetic Aperture Radar (SAR) images acquired between April 2017 and December 2019 were processed by using Small Baseline Subset Synthetic Aperture Radar Interferometry (SBAS-InSAR) method. The deformation of Kuh-e-Namak (Dashti) namakier was measured using both methods. Then, meteorological data were utilized to explore the relationship between the kinematics of the namakier and weather conditions and differences in macrodeformation behavior of various rock salt types. The advantages and disadvantages of COSI-Corr and SBAS-InSAR methods in measuring the deformation of the namakier were compared. The results show that: (1) The flank subsides in the dry season and uplifts in the rainy season, whereas the dome subsides in the rainy season and uplifts in the dry season. Under extreme rainfall conditions, the namakier experiences permanent plastic deformation. (2) The “dirty” rock salt of the namakier is more prone to flow than the “clean” rock salt in terms of macrodeformation behavior. (3) In the exploration of the kinematics of the namakier via the two methods, COSI-Corr is superior to SBAS-InSAR on a spatial scale, but the latter is superior to the former on a time scale.
Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data

Remote sensing (RS) of biophysical variables plays a vital role in providing the information necessary for understanding spatio-temporal dynamics in ecosystems. The hybrid approach to retrieve biophysical variables from RS by combining Machine Learning (ML) algorithms with surrogate data generated by Radiative Transfer Models (RTM). The susceptibility of the ill-posed solutions to noise currently constrains further application of hybrid approaches. Here, we explored how noise affects the performance of ML algorithms for biophysical trait retrieval. We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN). After identifying which biophysical variables can be retrieved from S2 using a Global Sensitivity Analysis, we evaluated the performance loss of each algorithm using the Mean Absolute Percentage Error (MAPE) with increasing noise levels. We found that, for S2 data, Carotenoid concentrations are uniquely dependent on band 2, Chlorophyll is almost exclusively dependent on the visible ranges, and Leaf Area Index, water, and dry matter contents are mostly dependent on infrared bands. Without added noise, GPR was the best algorithm (<0.05%), followed by the MTN (<3%) and ANN (<5%), with the RFR performing very poorly (<50%). The addition of noise critically affected the performance of all algorithms (>20%) even at low levels of added noise (≈5%). Overall, both neural networks performed significantly better than GPR and RFR when noise was added with the MTN being slightly better when compared to the ANN. Our results imply that the performance of the commonly used algorithms in hybrid-RTM inversion are pervasively sensitive to noise. The implication is that more advanced models or approaches are necessary to minimize the impact of noise to improve near real-time and accurate RS monitoring of biophysical trait retrieval.
Toward a Natural-Language Interface in Ontologies for Disambiguation

With natural-language-interfaces-to-ontology (NLI2O) one can query a speciﬁc domain’s data, stored into that particular conceptual structure, coded into specialized languages such as Ontology Web Language, OWL. To access such data, one requires complex query languages, such as SPARQL. NLI2Os translate human-language queries into SPARQL ones. In this work, we describe the architecture of an NLI2O. It receives a spoken question from an Android device. The spoken query becomes text and goes to a Java Server Page (JSP). The core of our interface runs on a JSP server, which receives the user’s text query. It transforms it into a SPARQL query, later used to access, from the ontology to query, the information requested by the user. The interface uses several technologies to achieve that. Those are known as lemmati-zation, part-of-speech tagging, and tokenization. It also uses knowledge inference from the ontology of interest. It also uses clariﬁcation dialogues for disambiguation to answer user questions and revise the query. Besides that, some experiments suggest further studies like the disambiguation of an original text.
Fake News Detection in the Urdu Language using CharCNN-RoBERTa

In this article, we report the solution of the team BERT 4EVER for the Fake News Detection in the Urdu Language task in FIRE 2020, which aims to identify deceiving news articles in the Urdu language spread via digital media. We propose the CharCNN-RoBERTa model to tackle the problem. In addition, we adopt label smoothing and ensemble learning to improve the generalization capability. Experimental results as well as the leading position of our team on the task leaderboard demonstrate the effectiveness of our method