Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameswari_faith_nagaraju@Dravidian-CodeMix-FIRE: A machine-learning approach using n-grams in sentiment analysis for code-mixed texts: A case study in Tamil and Malayalam

Sentiment analysis is a fast growing research positioned to uncover the underlying meaning of a text by categorizing it into different levels. This paper is an attempt to decode the deeply entangled code-mixed Malayalam and Tamil datasets and classify its interlined meaning at five various levels. Along with the corpus creation, [1] propose a five-level classification for Malayalam and Tamil code-mixed datasets. In this paper, we follow the five-level annotated datasets and aim to solve the classification problem by implementing unigram and bigram knowledge with a Multinomial Naive Bayes model. Our model scores an F1-score of 0.55 for Tamil and 0.48 for Malayalam.
Query Revaluation Method For Legal Information Retrieval

In this paper, we introduced in detail the method of implementing the task of identifying relevant prior cases in artificial intelligence for legal assistance. For the task, we transformed the problem into a retrieval task and used the BM25 retrieval model to try to make it perform better in this task. The improved method wins second place on MAP and the second place on BPREF.
Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss

Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China.
Geomorphological and Spatial Characteristics of Underwater Volcanoes in the Easternmost Australian-Antarctic Ridge

Underwater volcanoes and their linear distribution on the flanks of mid-ocean ridges are common submarine topographic structures at intermediate- and fast-spreading systems, where sufficient melt supplies are often available. Such magma sources beneath the seafloor located within a few kilometers of the corresponding ridge-axis tend to concentrate toward the axis during the upwelling process and contribute to seafloor formation. As a result, seamounts on the flanks of the ridge axis are formed at a distance from the spreading axis and distributed asymmetrically about the axis. In this study, we examined three linearly aligned seamount chains on the flanks of the KR1 ridge, which is the easternmost and longest Australian-Antarctic Ridge (AAR) segment. The AAR is an intermediate-spreading rate system located between the Southeast Indian Ridge and Macquarie Triple Junction of the Australian-Antarctic-Pacific plates. By inspecting the high-resolution shipboard multi-beam bathymetric data newly acquired in the study area, we detected 20 individual seamounts. The volcanic lineament runs parallel to the spreading direction of the KR1 segment. The geomorphologic parameters of height, basal area, volume, and summit types of the identified seamounts were individually measured. We also investigated the spatial distribution of the seamounts along the KR1 segment, which exhibits large variations in axial morphology with depth along the ridge axis. Based on the geomorphology and spatial distribution, all the KR1 seamounts can be divided into two groups: the subset seamounts of volcanic chains distributed along the KR1 segment characterized by high elevation and large volume, and the small seamounts distributed mostly on the western KR1. The differences in the volumetric magnitude of volcanic eruptions on the seafloor and the distance from the given axis between these two groups indicate the presence of magma sources with different origins.
Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method

Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform.
Estimating Net Primary Productivity (NPP) and Debris-Fall in Forests Using Lidar Time Series

Temporal series of lidar, properly field-validated, can provide critical information allowing in-ferences about the dynamics of biomass and carbon in forest canopies. Forest canopies gain carbon through net primary production (NPP) and lose carbon through canopy component damage and death, such as fine and coarse woody debris and litterfall (collectively, debris-fall). We describe a statistical method to extract gamma distributions of NPP and debris-fall rates in forest canopies from lidar missions repeated through time and we show that the means of these distributions covary with ecologically meaningful variables: topography, canopy structure, and taxonomic composition. The method employed is the generalized method of moments that applies the R package gmm to uncover the distribution of latent variables. We present an example with eco-logical interpretations that support the method’s application to change in biomass estimated for a boreal forest in southcentral Alaska. The deconvolution of net change from remote sensing products as distributions of NPP and debris-fall rates can inform carbon cycling models of can-opy-level NPP and debris-fall rates.
yasuo at HASOC2020: Fine-tune XML-RoBERTa for Hate Speech Identification

In recent years, people are more concerned about hate speech identification and identification than ever. This paper describes our system for English and German Sub-Task A in HASOC2020. For these subtasks, we fine-tune the XLM-RoBERTa pre-training model for sentence embedding and extract the layer with the best performance for slicing and splicing. In order to make full use of both English and German corpus, we propose a multi-task method to optimize two classification tasks at the same time. Our model has achieved 0.9076 for F1 score in English Sub-Task A and 0.8165 in German Sub-Task A.
Fragment-based lead design

State-of-the-art approaches to the fragment-based design of organic compounds with desired properties are considered. The review covers methods, which are used in different steps of the design, such as computational methods for fragment library design, experimental and computational methods for fragment discovery and methods for the generation of structures of organic compounds. Examples are given of drug candidates, which were constructed using the fragment-based approach. The bibliography includes 156 references.
IIITG-ADBU@HASOC-Dravidian-CodeMix-FIRE2020: Offensive Content Detection in Code-Mixed Dravidian Text

This paper presents the results obtained by our SVM and XLM-RoBERTa based classifiers in the shared task Dravidian-CodeMix-HASOC 2020. The SVM classifier trained using TF-IDF features of character and word n-grams performed the best on the code-mixed Malayalam text. It obtained a weighted F1 score of 0.95 (1st Rank) and 0.76 (3rd Rank) on the YouTube and Twitter dataset respectively. The XLM-RoBERTa based classifier performed the best on the code-mixed Tamil text. It obtained a weighted F1 score of 0.87 (3rd Rank) on the code-mixed Tamil Twitter dataset.
SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe

This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.
Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection

Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.
The Impact of Shale Oil and Gas Development on Rangelands in the Permian Basin Region: An Assessment Using High-Resolution Remote Sensing Data

The environmental impact of shale energy development is a growing concern in the US and worldwide. Although the topic is well-studied in general, shale development’s impact on drylands has received much less attention in the literature. This study focuses on the effect of shale development on land cover in the Permian Basin region—a unique arid/semi-arid landscape experiencing an unprecedented intensity of drilling and production activities. By taking advantage of the high-resolution remote sensing land cover data, we develop a fixed-effects panel (longitudinal) data regression model to control unobserved spatial heterogeneities and regionwide trends. The model allows us to understand the land cover’s dynamics over the past decade of shale development. The results show that shale development had moderate negative but statistically significant impacts on shrubland and grassland/pasture. The effect is more strongly associated with the hydrocarbon production volume and less with the number of oil and gas wells drilled. Between shrubland and grassland/pasture, the impact on shrubland is more pronounced in terms of magnitude. The dominance of shrubland in the region likely explains the result.
Quantifying Optically Derived Two-Dimensional Wave-Averaged Currents in the Surf Zone

Complex two-dimensional nearshore current patterns are generated by feedbacks between sub-aqueous morphology and momentum imparted on the water column by breaking waves, winds, and tides. These non-stationary features, such as rip currents and circulation cells, respond to changing environmental conditions and underlying morphology. However, using fixed instruments to observe nearshore currents is limiting due to the high costs and logistics necessary to achieve adequate spatial sampling resolution. A new technique for processing surf-zone imagery, WAMFlow, quantifies fluid velocities to reveal complex, multi-scale (10 s–1000 s meters) nearshore surface circulation patterns. We apply the concept of a wave-averaged movie (WAM) to measure surf-zone circulation patterns on spatial scales of kilometers in the alongshore and 100 s of meters in the cross-shore. The approach uses a rolling average of 2 Hz optical imagery, removing the dominant optical clutter of incident waves, to leave the residual foam or water turbidity features carried by the flow. These residual features are tracked as quasi-passive tracers in space and time using optical flow, which solves for u and v as a function of image intensity gradients in x, y, and t. Surf zone drifters were deployed over multiple days with varying nearshore circulations to validate the optically derived flow patterns. Root mean square error are reduced to 0.1 m per second after filtering based on image attributes. The optically derived patterns captured longshore currents, rip currents, and gyres within the surf zone. Quantifying nearshore circulation patterns using low-cost image platforms and open-source computer vision algorithms presents the potential to further our understanding of fundamental surf zone dynamics.
NLP-CIC at HASOC 2020: Multilingual Offensive Language Detection using All-in-one Model

We describe our deep learning model submitted to the HASOC 2020 shared task on detection of offensive language in social media in three Indo-European languages: English, German, and Hindi. We finetune a pre-trained multilingual encoder on the combination of data provided for the competition. Our submission received a competitive macroaverage F1 score of 0.4980 on the English Subtask A as well as comparatively strong performance on the German data.
CIA_NITT@Dravidian-CodeMix-FIRE2020: Malayalam-English Code Mixed Sentiment Analysis Using Sentence BERT And Sentiment Features

Code mixing is the mixing of language while writing text. The biggest problem in Malayalam-English code-mixing is that people switch between languages (e.g. Malayalam and English) and use English phonetic typing instead of writing Malayalam words using Dravidian scripts. Traditional NLP models are trained on extensive monolingual tools ( e.g. Malayalam or English ); code-mixing is challenging since they cannot handle data code-mixing. DravidianCodeMix FIRE 2020 is to classify comments into positive, negative, unknown_state, mixed_feelings and not-malayalam categories based on message-level polarity using Malayalam - English dataset. The classification model used for this challenging task is sentence-level BERT. Achieved an F1-score of 0.71 (ranked 4th) for the Malayalam-English (Manglish) comments dataset submitted under the username: CIA_NITT.
Surface Velocity Analysis of Surge Region of Karayaylak Glacier from 2014 to 2020 in the Pamir Plateau

The west branch of Karayaylak Glacier (eastern Pamir Plateau) surged in May 2015, significantly impacting on local socio-economic development. This event was also of great significance for studies of surging glaciers. Using Sentinel-1 imagery analyzed by offset tracking, based on normalized cross-correlation (NCC), and with the support of the Google Earth Engine (GEE) platform, we quantified the ice surface velocity of the west branch and terminus of Karayaylak Glacier from 13 October 2014 to 17 October 2020. Sentinel-1 images were acquired at intervals of 12 or 24 days. We also used a three-dimensional (3-D) laser scanner to measure the velocity of 3 ablation stakes and 56 feature points in the study region from 15 August to 6 October 2015, for the purpose of accuracy assessment. We set up an automatic meteorological station to record the air temperature in the same period and combined this with data from Tashkurgan meteorological station from 1957 to 2015. Analysis of this dataset provided insights into the glacier surge mechanism, with the following conclusions. (1) Surface velocity of the west branch and terminus of Karayaylak Glacier increased sharply after October 2014. The velocity then dropped significantly in the two months after the surge, and stayed at low values for nearly a year. After 2017, the velocity was slightly higher than in the previous period. (2) The surge event occurred from 11 April to 17 May 2015; the average surface velocity in this phase attained 2395 m a−1 with a maximum velocity of 4265 m a−1 at the west branch terminus. (3) From 2017 to 2020, the velocity showed periodic annual changes. (4) Based on the meteorological data analysis, we conclude that this surge resulted from the interaction between thermal and hydrological control mechanisms. Simultaneously, we demonstrate the high potential of the GEE platform and Sentinel-1 data to extract glacier surface velocity.
Feature Fusion with Hand-crafted and Transfer Learning Embeddings for Cause-Effect Relation Extraction

Cause-effect relation extraction is the problem of detecting causal relations expressed in a text. The extraction of causal-relations from texts might be beneficial for the improvement of various natural language processing (NLP) tasks including Q/A, text-summarization, opinion mining, and event analysis. However, cause-effect relation in the text is sparse, ambiguous, sometimes implicit, and has a linguistically complex construct. To address these challenges FIRE-2020 introduced a shared task focusing on cause-effect relation extraction (CEREX). We propose a feature based supervised classification model with a naive rule-based classifier. We define a set of rules based on a causal connective dictionary and stop-words. Besides, we use a fusion of hand-crafted features and transfer learning embeddings to train our SVM based supervised classification model. Experimental results exhibit that our proposed method achieved the topnotch performance for cause-effect relation extraction and causal word annotation.
Attention based Anaphora Resolution for Code-Mixed Social Media Text for Hindi Language

Anaphora resolution is a challenging problem in Natural Language Processing. Its complexity is further aggravated when applied in a social media setting, often due to inherent challenges in code-mixed data widely prevalent in such a setting. In this paper, we investigate the problem of anaphora resolution for code-mixed (Hindi and English) Twitter data. We propose a modified encoder-decoder with attention model for anaphora resolution. Results of our preliminary investigations indicate that such attention based approaches are indeed useful for this task, and should be explored further.
Processing Framework for Landslide Detection Based on Synthetic Aperture Radar (SAR) Intensity-Image Analysis

We present an object-based image analysis (OBIA) approach to identify temporal changes in radar-intensity images and to locate land-cover changes caused by mass-wasting processes at small to large scales, such as landslides. Our approach is based upon change detection in SAR intensity images that remain in their original imaging coordinate system rather than being georeferenced and map-projected, in order to reduce accumulation of filtering artifacts and other unwanted effects that would deteriorate the detection efficiency. Intensity images in their native slant-range coordinate frame allow for a consistent level of detection of land-cover changes. By analyzing intensity images, a much faster response can be achieved and images can be processed as soon as they are made publicly available. In this study, OBIA was introduced to systematically and semiautomatically detect landslides in image pairs with an overall accuracy of at least 60% when compared to in-situ landslide inventory data. In this process, the OBIA feature extraction component was supported by derived data from a polarimetric decomposition as well as by texture indices derived from the original image data. The results shown here indicate that most of the landslide events could be detected when compared to a closer visual inspection and to established inventories, and that the method could therefore be considered as a robust detection tool. Significant deviations are caused by the limited geometric resolution when compared to field data and by an additional detection of stream-related sediment redeposition in our approach. This overdetection, however, turns out to be potentially beneficial for assessing the risk situation after landslide events.