Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams

Grey relational analysis (GRA) is a part of the Grey system theory (GST). It is appropriate for solving problems with complicated interrelationships between multiple factors/parameters and variables. It solves multiple-criteria decision-making problems by combining the entire range of performance attribute values being considered for every alternative into one single value. Thus, the main problem is reduced to a single-objective decision-making problem. In this study, we developed a decision support system for the evaluation of written exams with the help of GRA using contextual text mining techniques. The answers obtained from the written exam with the participation of 50 students in a computer laboratory and the answer key prepared by the instructor constituted the data set of the study. A symmetrical perspective allows us to perform relational analysis between the students’ answers and the instructor’s answer key in order to contribute to the measurement and evaluation. Text mining methods and GRA were applied to the data set through the decision support system employing the SQL Server database management system, C#, and Java programming languages. According to the results, we demonstrated that the exam papers are successfully ranked and graded based on the word similarities in the answer key.
Agglomeration Economies: An Analysis of the Determinants of Employment in the Cities of Ecuador

The objective of this investigation is to study the role of agglomeration economies (manufacturing) in urban employment growth, as a proxy for economic growth, between 1980 and 2010 in Ecuador. The three measures of agglomeration-specialization, diversity, and density-are tested to determine their effect on employment growth in industries. The empirical analysis is based on firm- and city-level data from manufacturing activities. A model is proposed to estimate the effect of agglomeration economies on the growth of employment and a regression is conducted using instrumental variables. In particular, the two-stage least squares (2SLS) estimator is used. We conclude that localization economies measured by a specialization index have a positive impact on the growth of employment in the period analyzed. The results are similar to those obtained by other work carried out both in developed and developing countries.
Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors

In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initializeexpand-merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.
Evapotranspiration Data Product from NESDIS GET-D System Upgraded for GOES-16 ABI Observations

Evapotranspiration (ET) is a major component of the global and regional water cycle. An operational Geostationary Operational Environmental Satellite (GOES) ET and Drought (GET-D) product system has been developed by the National Environmental Satellite, Data and Information Service (NESDIS) in the National Oceanic and Atmospheric Administration (NOAA) for numerical weather prediction model validation, data assimilation, and drought monitoring. GET-D system was generating ET and Evaporative Stress Index (ESI) maps at 8 km spatial resolution using thermal observations of the Imagers on GOES-13 and GOES-15 before the primary operational GOES satellites transitioned to GOES-16 and GOES-17 with the Advanced Baseline Imagers (ABI). In this study, the GET-D product system is upgraded to ingest the thermal observations of ABI with the best spatial resolution of 2 km. The core of the GET-D system is the Atmosphere-Land Exchange Inversion (ALEXI) model, which exploits the mid-morning rise in the land surface temperature to deduce the land surface fluxes including ET. Satellite-based land surface temperature and solar insolation retrievals from ABI and meteorological forcing from NOAA NCEP Climate Forecast System (CFS) are the major inputs to the GET-D system. Ancillary data required in GET-D include land cover map, leaf area index, albedo and cloud mask. This paper presents preliminary results of ET from the upgraded GET-D system after a brief introduction of the ALEXI model and the architecture of GET-D system. Comparisons with in situ ET measurements showed that the accuracy of the GOES-16 ABI based ET is similar to the results from the legacy GET-D ET based on GOES-13/15 Imager data. The agreement with the in situ measurements is satisfactory with a correlation of 0.914 averaged from three Mead sites. Further evaluation of the ABI-based ET product, upgrade efforts of the GET-D system for ESI products, and conclusions for the ABI-based GET-D products are discussed.
Integrating Water Observation from Space Product and Time-Series Flow Data for Modeling Spatio-Temporal Flood Inundation Dynamics

Periodic inundation of floodplains and wetlands is critical for the well being of ecosystems. This study proposes a simple but efficient model that integrates time series daily flow data and the Landsat-derived Water Observation from Space (WOfS) product to model the spatio-temporal flood inundation dynamics of the Murray-Darling Basin. A zone-gauge framework is adopted in order to reduce the hydrologic complexity of the large river basin. Under this framework, flood frequency analysis was conducted at each gauge station to identify historical peak flows and their annual exceedance probabilities. The results were then linked with the WOfS dataset through date to model the inundation probability in each zone. Inundation frequency was derived by simply overlaying the yearly inundation extent from 1988 to 2015 and counting the inundation times. Both the resultant inundation frequency map and inundation probability map are of ecological significance for the survival and prosperity of riparian ecosystems. The assumptions of the model were validated carefully to enhance its theoretical basis. The WOfS dataset was also compared with another independent water observation dataset to cross-validate its reliability. It is hoped that with the development of more and more global high-resolution surface water datasets, this study could inspire more studies that integrate surface water datasets with hydrological observations for flood inundation modeling.
A CNN-Based Pan-Sharpening Method for Integrating Panchromatic and Multispectral Images Using Landsat 8

The increasing availability of sensors enables the combination of a high-spatial-resolution panchromatic image and a low-spatial-resolution multispectral image, which has become a hotspot in recent years for many applications. To address the spectral and spatial distortions that adversely affect the conventional methods, a pan-sharpening method based on a convolutional neural network (CNN) architecture is proposed in this paper, where the low-spatial-resolution multispectral image is upgraded and integrated with the high-spatial-resolution panchromatic image to produce a new multispectral image with high spatial resolution. Based on the pyramid structure of the CNN architecture, the proposed method has high learning capacity to generate more representative and robust hierarchical features for construction tasks. Moreover, the highly nonlinear fusion process can be effectively simulated by stacking several linear filtering layers, which is suitable for learning the complex mapping relationship between a high-spatial-resolution panchromatic and low-spatial-resolution multispectral image. Both qualitative and quantitative experimental analyses were carried out on images captured from a Landsat 8 on-board operational land imager (LOI) sensor to demonstrate the method’s performance. The results regarding the sensitivity analysis of the involved parameters indicate the effects of parameters on the performance of our CNN-based pan-sharpening approach. Additionally, our CNN-based pan-sharpening approach outperforms other existing conventional pan-sharpening methods with a more promising fusion result for different landcovers, with differences in Erreur Relative Globale Adimensionnelle de Synthse (ERGAS), root-mean-squared error (RMSE), and spectral angle mapper (SAM) of 0.69, 0.0021, and 0.81 on average, respectively.
Hybrid Scene Structuring for Accelerating 3D Radiative Transfer Simulations

Three-dimensional (3D) radiative transfer models are the most accurate remote sensing models. However, presently the application of 3D models to heterogeneous Earth scenes is a computationally intensive task. A common approach to reduce computation time is abstracting the landscape elements into simpler geometries (e.g., ellipsoid), which, however, may introduce biases. Here, a hybrid scene structuring approach is proposed to accelerate the radiative transfer simulations while keeping the scene as realistic as possible. In a first step, a 3D description of the Earth landscape with equal-sized voxels is optimized to keep only non-empty voxels (i.e., voxels that contain triangles) and managed using a bounding volume hierarchy (BVH). For any voxel that contains triangles, within-voxel BVHs are created to accelerate the ray–triangle intersection tests. The hybrid scheme is implemented in the Discrete Anisotropic Radiative Transfer (DART) model by integrating the Embree ray-tracing kernels developed at Intel. In this paper, the performance of the hybrid algorithm is compared with the original uniform grid approach implemented in DART for a 3D city scene and a forest scene. Results show that the removal of empty voxels can accelerate urban simulation by 1.4×~3.7×, and that the within-voxel BVH can accelerate forest simulations by up to 258.5×.
Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

Systematic Regression Testing is essential for maintaining software quality, but the cost of regression testing is high. Test case prioritization (TCP) is a widely used approach to reduce this cost. Many researchers have proposed regression test case prioritization techniques, and clustering is one of the popular methods for prioritization. The task of selecting appropriate test cases and identifying faulty functions involves ambiguities and uncertainties. To alleviate the issue, in this paper, two fuzzy-based clustering techniques are proposed for TCP using newly derived similarity coefficient and dominancy measure. Proposed techniques adopt grouping technology for clustering and the Weighted Arithmetic Sum Product Assessment (WASPAS) method for ranking. Initially, test cases are clustered using similarity//dominancy measures, which are later prioritized using the WASPAS method under both inter- and intra-perspectives. The proposed algorithms are evaluated using real-time data obtained from Software-artifact Infrastructure Repository (SIR). On evaluation, it is inferred that the proposed algorithms increase the likelihood of selecting more relevant test cases when compared to the recent state-of-the-art techniques. Finally, the strengths of the proposed algorithms are discussed in comparison with state-of-the-art techniques.
Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.
Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra

Oil palm is rapidly expanding in Southeast Asia and represents one of the major drivers of deforestation in the region. This includes both industrial-scale and smallholder plantations, the management of which entails specific challenges, with either operational scale having its own particular social and environmental challenges. Although, past studies addressed the mapping of oil palm with remote sensing data, none of these studies considered the discrimination between industrial and smallholder plantations and, furthermore, between young and mature oil palm stands. This study assesses the feasibility of mapping oil palm plantations, by typology (industrial versus smallholder) and age (young versus mature), in the largest palm oil producing region of Indonesia (Riau province). The impact of using optical images (Sentinel-2) and radar scenes (Sentinel-1) in a Random Forest classification model was investigated. The classification model was implemented in a cloud computing system to map the oil palm plantations of Riau province. Our results show that the mapping of oil palm plantations by typology and age requires a set of optimal features, derived from optical and radar data, to obtain the best model performance (OA = 90.2% and kappa = 87.2%). These features are texture images that capture contextual information, such as the dense harvesting trail network in industrial plantations. The study also shows that the mapping of mature oil palm trees, without distinction between smallholder and industrial plantations, can be done with high accuracy using only Sentinel-1 data (OA = 93.5% and kappa = 86.9%) because of the characteristic backscatter response of palm-like trees in radar scenes. This means that researchers, certification bodies, and stakeholders can adequately detect mature oil palm stands over large regions without training complex classification models and with Sentinel-1 features as the only predictive variables. The results over Riau province show that smallholders represent 49.9% of total oil palm plantations, which is higher than reported in previous studies. This study is an important step towards a global map of oil palm plantations at different production scales and stand ages that can frequently be updated. Resulting insights would facilitate a more informed debate about optimizing land use for meeting global vegetable oil demands from oil palm and other oil crops.
Deep learning acceleration based on in-memory computing

Performing computations on conventional von Neumann computing systems results in a significant amount of data being moved back and forth between the physically separated memory and processing units. This costs time and energy, and constitutes an inherent performance bottleneck. In-memory computing is a novel non-von Neumann approach, where certain computational tasks are performed in the memory itself. This is enabled by the physical attributes and state dynamics of memory devices, in particular, resistance-based nonvolatile memory technology. Several computational tasks such as logical operations, arithmetic operations, and even certain machine learning tasks can be implemented in such a computational memory unit. In this article, we first introduce the general notion of in-memory computing and then focus on mixed-precision deep learning training with in-memory computing. The efficacy of this new approach will be demonstrated by training the MNIST multilayer perceptron network achieving high accuracy. Moreover, we show how the precision of in-memory computing can be further improved through architectural and device-level innovations. Finally, we present system aspects, such as high-level system architecture, including core-to-core interconnect technologies, and high-level ideas and concepts of the software stack .
Mapping and Monitoring Fractional Woody Vegetation Cover in the Arid Savannas of Namibia Using LiDAR Training Data, Machine Learning, and ALOS PALSAR Data

Namibia is a very arid country, which has experienced significant bush encroachment and associated decreased livestock productivity. Therefore, it is essential to monitor bush encroachment and widespread debushing activities, including selective bush thinning and complete bush clearing. The aim of study was to develop a system to map and monitor fractional woody cover (FWC) at national scales (50 m and 75 m resolution) using Synthetic Aperture Radar (SAR) satellite data (Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics, 2009, 2010, 2015, 2016) and ancillary variables (mean annual precipitation—MAP, elevation), with machine learning models that were trained with diverse airborne Light Detection and Ranging (LiDAR) data sets (244,032 ha, 2008–2014). When only the SAR variables were used, an average R2 of 0.65 (RSME = 0.16) was attained. Adding either elevation or MAP, or both ancillary variables, increased the mean R2 to 0.75 (RSME = 0.13), and 0.79 (RSME = 0.12). The inclusion of MAP addressed the overestimation of FWC in very arid areas, but resulted in anomalies in the form of sharp gradients in FWC along a MAP contour which were most likely caused by to the geographic distribution of the LiDAR training data. Additional targeted LiDAR acquisitions could address this issue. This was the first attempt to produce SAR-derived FWC maps for Namibia and the maps contain substantially more detailed spatial information on woody vegetation structure than existing national maps. During the seven-year study period the Shrubland–Woodland Mosaic was the only vegetation structural class that exhibited a regional net gain in FWC of more than 0.2 across 9% (11,906 km2) of its area that may potentially be attributed to bush encroachment. FWC change maps provided regional insights and detailed local patterns related to debushing and regrowth that can inform national rangeland policies and debushing programs.
Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST–NDVI relations over Europe, using long-term (2000–2017) MODIS images. Overlaying the LST–NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March–August). During the spring season (March–May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June–August), most of the biomes—except the northern ones—turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST–NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes.
Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution

The phenomenon of periodical gapping in Synthetic Aperture Radar (SAR), which is induced in various ways, creates challenges in focusing raw SAR data. To handle this problem, a novel method is proposed in this paper. Complex deconvolution is utilized to restore the azimuth spectrum of complete data from the gapped raw data in the proposed method. In other words, a new approach is provided by the proposed method to cope with periodically gapped raw SAR data via complex deconvolution. The proposed method provides a robust implementation of deconvolution for processing azimuth gapped raw data. The proposed method mainly consists of phase compensation and recovering the azimuth spectrum of raw data with complex deconvolution. The gapped data become sparser in the range of the Doppler domain after phase compensation. Then, it is feasible to recover the azimuth spectrum of the complete data from gapped raw data via complex deconvolution in the Doppler domain. Afterwards, the traditional SAR imaging algorithm is capable of focusing the reconstructed raw data in this paper. The effectiveness of the proposed method was validated via point target simulation and surface target simulation. Moreover, real SAR data were utilized to further demonstrate the validity of the proposed method.