Miracle-dz/UniFAR-FTUs huggingface dataset:
LaeNet: A Novel Lightweight Multitask CNN for Automatically Extracting Lake Area and Shoreline from Remote Sensing Images

Variations of lake area and shoreline can indicate hydrological and climatic changes effectively. Accordingly, how to automatically and simultaneously extract lake area and shoreline from remote sensing images attracts our attention. In this paper, we formulate lake area and shoreline extraction as a multitask learning problem. Different from existing models that take the deep and complex network architecture as the backbone to extract feature maps, we present LaeNet—a novel end-to-end lightweight multitask fully CNN with no-downsampling to automatically extract lake area and shoreline from remote sensing images. Landsat-8 images over Selenco and the vicinity in the Tibetan Plateau are utilized to train and evaluate our model. Experimental results over the testing image patches achieve an Accuracy of 0.9962, Precision of 0.9912, Recall of 0.9982, F1-score of 0.9941, and mIoU of 0.9879, which align with the mainstream semantic segmentation models (UNet, DeepLabV3+, etc.) or even better. Especially, the running time of each epoch and the size of our model are only 6 s and 0.047 megabytes, which achieve a significant reduction compared to the other models. Finally, we conducted fieldwork to collect the in-situ shoreline position for one typical part of lake Selenco, in order to further evaluate the performance of our model. The validation indicates high accuracy in our results (DRMSE: 30.84 m, DMAE: 22.49 m, DSTD: 21.11 m), only about one pixel deviation for Landsat-8 images. LaeNet can be expanded potentially to the tasks of area segmentation and edge extraction in other application fields.
FGATR-Net: Automatic Network Architecture Design for Fine-Grained Aircraft Type Recognition in Remote Sensing Images

Fine-grained aircraft type recognition in remote sensing images, aiming to distinguish different types of the same parent category aircraft, is quite a significant task. In recent decades, with the development of deep learning, the solution scheme for this problem has shifted from handcrafted feature design to model architecture design. Although a great progress has been achieved, this paradigm generally needs strong expert knowledge and rich expert experience. It is still an extremely laborious work and the automation level is relatively low. In this paper, inspired by Neural Architecture Search (NAS), we explore a novel differentiable automatic architecture design framework for fine-grained aircraft type recognition in remote sensing images. In our framework, the search process is divided into several phases. Network architecture deepens at each phase while the number of candidate functions gradually decreases. To achieve it, we adopt different pruning strategies. Then, the network architecture is determined through a potentiality judgment after an architecture heating process. This approach can not only search deeper network, but also reduce the computational complexity, especially for relatively large size of remote sensing images. When all differentiable search phases are finished, the searched model called Fine-Grained Aircraft Type Recognition Net (FGATR-Net) is obtained. Compared with previous NAS, ours are more suitable for relatively large and complex remote sensing images. Experiments on Multitype Aircraft Remote Sensing Images (MTARSI) and Aircraft17 validate that FGATR-Net possesses a strong capability of feature extraction and feature representation. Besides, it is also compact enough, i.e., parameter quantity is relatively small. This powerfully indicates the feasibility and effectiveness of the proposed automatic network architecture design method.
Differential Beamforming From the Beampattern Factorization Perspective

Differential beamformers have demonstrated a great potential in forming frequency-invariant beampatterns and achieving high directivity factors. Most conventional approaches design differential beamformers in such a way that their beampatterns resemble a desired or target beampattern. In this paper, we show how to design differential beamformers by simply taking advantage of the fact that the beampattern is actually a particular form of an exponential polynomial. Thanks to this quite obvious formulation, a target beampattern is not really needed while the zeros of the exponential polynomial and/or its factorization are fully exploited. The advantage of this factorization is twofold. First, it gives the relation between the beamformer and the roots of the polynomial, so the former can be directly determined from the latter, which seems natural and convenient. Second, based on this factorization, we propose a new formulation of the beamforming filter, which decomposes the filter into shorter ones with the Kronecker product. This formulation is very general, and many well-known beamformers such as the differential and delay-and-sum (DS) ones can be derived from it. Furthermore, the new formulation allows one to combine different kinds of beamformers together, which gives a great flexibility in forming different beampatterns and achieving a compromise among the directivity factor (DF), white noise gain (WNG), and frequency invariance.
Rice-Field Mapping with Sentinel-1A SAR Time-Series Data

This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions.
Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP outperforms the SVR to capture satellite Chl (correlation of 0.6 vs. 0.17 on a global scale, respectively) along with its seasonal and interannual variability, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series.
Detection of Lung Cancer Tumor in CT Scan Images Using Novel Combination of Super Pixel and Active Contour Algorithms

Lung cancer is a problem that has become increasingly widespread in recent years due to smoking, poor nutrition and other factors. If lung cancer cells are identified at an early stage, they will be crucial in saving lives. Machine learning-based approaches to detecting lung cancer tumors have reduced the need for manpower, reduced human error and reduced medical costs. CT scan images are one of the efficient image types to identify these tumors in the lung. However, the random location and shape of the tumors and poor quality of CT scans are biggest challenges in lung cancer tumor detection. In this paper, a multi-step method for detecting cancer tumors in CT scans is proposed. In the proposed method, the images are first clustered using the super pixel algorithm. The morphological operators are then used to cut the unconnected parts. Finally, the cancerous nodules and tumors are identified using the active contour algorithm. The performance of the proposed approach is evaluated on benchmark LIDC database in terms of Dice similarity measure which is 84.88%. Results show the higher performance of the proposed approach in comparison with state-of-the-art methods in this area.
Board of Directors and Intellectual Capital Disclosure: Evidence from GCC Countries

This study examines the firm board of directors' characteristics influences on intellectual capital disclosure (ICD) in the Gulf Cooperation Council (GCC) listed firms' annual reports. By using content analysis and the ordinary least-square regression method it was found that the board size, meeting, and experience are drivers for the level of intellectual capital disclosure. The results of this study are undoubted of great concern to regulatory bodies in setting guidelines and procedures to encourage the disclosure of intellectual capital in annual reports. This paper's contribution to the literature is as follows: it examines the relationship between board characteristics and the ICD for six years (2014–2019) and provides new evidence on the importance of the corporate governance variables and their impact on the firm's ICD in emerging stock markets.
Automatic Classification of Glaucoma Stages Using Two-Dimensional Tensor Empirical Wavelet Transform

Glaucoma is a chronic eye disease, causing damage to the optic nerve; it may cause permanent vision loss. The conventional instrument methods for glaucoma detection are manual and time-consuming. Many approaches have recently been proposed for automatic glaucoma classification using retinal fundus images. However, none of the existing methods can efficiently use for early-stage glaucoma detection. In this letter, we proposed a novel method for glaucoma classification based on the newly introduced two-dimensional tensor empirical wavelet transform (2D-T-EWT). In this study, the pre-processed images are decomposed into sub-band images (SBIs) using 2D-T-EWT. Then, texture-based grey level co-occurrence matrix (GLCM), chip histogram, and moment invariant features have been extracted from decomposed SBIs. Afore, robust features have been selects and ranked using the student'st-test algorithm. Finally, trained multi-class least squares-support vector machine (MC-LS-SVM) classifier has been used for the classification. The experimental results show that our method outperformed state-of-the-art approaches for glaucoma classification. The proposed method achieved the highest classification accuracy of 93.65% using tenfold cross-validation.
New Combining Rules for Spatial Clustering Methods Using Sigma-Count for Spatial Epidemiology

In Epidemiology, the study of several diseases is related to the geographical territory in which it occurs. Some spatial clustering methods are able to perform statistical decision about the significance of territories. However, each method is based on a different methodology, providing different results as well. Recently, some authors proposed combining spatial clustering methods in order to provide more accurate results. This paper propose two new combining rules based on Cardinality of Fuzzy Sets, generalizing the classical Majority Voting and Plurality Voting. A study of case using real Dengue Fever epidemiological data and combining five spatial clustering methods was performed. In this study the Fuzzy Plurality Voting provided better decision map than the classical ones, when compared to a reference map.
DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud

3D (3-Dimensional) object recognition is a hot research topic that benefits environment perception, disease diagnosis, and the mobile robot industry. Point clouds collected by range sensors are a popular data structure to represent a 3D object model. This paper proposed a 3D object recognition method named Dynamic Graph Convolutional Broad Network (DGCB-Net) to realize feature extraction and 3D object recognition from the point cloud. DGCB-Net adopts edge convolutional layers constructed by weight-shared multiple-layer perceptrons (MLPs) to extract local features from the point cloud graph structure automatically. Features obtained from all edge convolutional layers are concatenated together to form a feature aggregation. Unlike stacking many layers in-depth, our DGCB-Net employs a broad architecture to extend point cloud feature aggregation flatly. The broad architecture is structured utilizing a flat combining architecture with multiple feature layers and enhancement layers. Both feature layers and enhancement layers concatenate together to further enrich the features’ information of the point cloud. All features work on the object recognition results thus that our DGCB-Net show better recognition performance than other 3D object recognition algorithms on ModelNet10/40 and our scanning point cloud dataset.
Adaptation of the Whale Optimization Algorithm to the Optimal Sizing of Analog Integrated Circuit: Low Voltage Amplifier Performances

Metaheuristics have been successfully proposed in the past years for sizing the circuits containing large number of transistors, and it has been shown that they can replace traditional approaches. Among the most well-known metaheuristics, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) have been used successfully to optimize analog circuits. Although PSO, ACO and ABC are robust and have a good research strategy to solve problems, they suffer from their need for computation time and accuracy in the case of multi-variable problems. In this paper, we discuss the application of a new algorithm called Whale Optimization Algorithm (WOA) to resolution and optimization of single-objective and multi-objective problems. The performances of WOA in the field of integrated electronic circuits (IEC) are highlighted through a more interesting and complex integrated circuit (IC) called low voltage amplifier. This algorithm is coded in MATLAB software and the obtained results are validated by Cadence Virtuoso simulations using CMOS 0. 18µm technology. Comparison with other metaheuristics shows the interest of the proposed algorithm.
Stock price forecast with deep learning

In this paper, we compare various approaches to stock price prediction using neural networks. We analyze the performance fully connected, convolutional, and recurrent architectures in predicting the next day value of S&P 500 index based on its previous values. We further expand our analysis by including three different optimization techniques: Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation. The numerical experiments reveal that a single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test Mean Absolute Error of 0.0150 and 0.0148 respectively.
Victoria Land, Antarctica: An Improved Geodynamic Interpretation Based on the Strain Rate Field of the Current Crustal Motion and Moho Depth Model

In Antarctica, the severe climatic conditions and the thick ice sheet that covers the largest and most internal part of the continent make it particularly difficult to systematically carry out geophysical and geodetic observations on a continental scale. It prevents the comprehensive understanding of both the onshore and offshore geology as well as the relationship between the inner part of East Antarctica (EA) and the coastal sector of Victoria Land (VL). With the aim to reduce this gap, in this paper multiple geophysical dataset collected since the 1980s in Antarctica by Programma Nazionale di Ricerche in Antartide (PNRA) were integrated with geodetic observations. In particular, the analyzed data includes: (i) Geodetic time series from Trans Antarctic Mountains DEFormation (TAMDEF), and Victoria Land Network for DEFormation control (VLNDEF) GNSS stations installed in Victoria Land; (ii) the integration of on-shore (ground points data and airborne) gravity measurements in Victoria Land and marine gravity surveys performed in the Ross Sea and the narrow strip of Southern Ocean facing the coasts of northern Victoria Land. Gravity data modelling has improved the knowledge of the Moho depth of VL and surrounding the offshore areas. By the integration of geodetic and gravitational (or gravity) potential results it was possible to better constrain/identify four geodynamic blocks characterized by homogeneous geophysical signature: the Southern Ocean to the N, the Ross Sea to the E, the Wilkes Basin to the W, and VL in between. The last block is characterized by a small but significant clockwise rotation relative to East Antarctica. The presence of a N-S to NNW-SSE 1-km step in the Moho in correspondence of the Rennick Geodynamic Belt confirms the existence of this crustal scale discontinuity, possibly representing the tectonic boundary between East Antarctica and the northern part of VL block, as previously proposed by some geological studies.
Android Malware Classification Approach Based on Host-Level Encrypted Traffic Shaping

With the development of mobile terminals, smartphones have attracted a very huge number of users with their powerful functions. Among them, Android system is famous for its open-source and convenience, which occupies a large market share. But this also leads many attackers to use their malware to gain benefits quickly, which make it necessary to design a practical android malware detection approach. At present, there are not many pieces of research on detecting malware by analyzing Android malicious traffic. This paper examines the characteristics of malicious traffic on the host computer to construct a traffic fingerprint. It combines machine learning algorithms to build a practical detection approach which is also suitable for encrypted traffic. To distinguish similar fuzzy traffic, an additional layer named confusion classifier is added to help further malware classification. This paper uses a realworld dataset called CICAndMal2017 and simulates two classification scenarios: malware binary detection and malware category classification. The experimental results show that the accuracy of the malware binary detection reached 98.8% while the accuracy rate of malware category classification is 95.2%.
A Comparative Analysis of Deep Learning Models Applied for Disease Classification in Bell Pepper

Crop diseases are a major cause of degrading the quality and reducing the number of agricultural products. Hence, there is a strong need for the early diagnosis of the disease. The effectiveness of deep learning techniques in pattern matching and image processing motivated the authors to design an automatic tool for the detection of diseases in bell pepper plants. In this manuscript, the authors present the comparative analysis of different deep learning models applied for plant disease classification. They applied the deep learning models namely VGG16, VGG19, ResNet50, ResNet101, ResNet152, InceptionResNetV2, DenseNet121 on the publicly available dataset of the bell pepper plant. The experimental results prove that the model ‘DenseNet’ requires less training time and gives the highest validation accuracy among all the above-stated models. It achieves a training accuracy of 97.49% and the testing accuracy of 96.87% in classifying the bell pepper plants into healthy and diseased categories.
Control The COVID-19 Pandemic: Face Mask Detection Using Transfer Learning

Currently, in the face of the health crisis caused by the Coronavirus COVID-19 which has spread throughout the worldwide. The fight against this pandemic has become an unavoidable reality for many countries. It is now a matter involving many areas of research in the use of new information technologies, particularly those related to artificial intelligence. In this paper, we present a novel contribution to help in the fight against this pandemic. It concerns the detection of people wearing masks because they cannot work or move around as usual without protection against COVID-19. However, there are only a few research studies about face mask detection. In this work, we investigated using different deep Convolutional Neural Networks (CNN) to extract deep features from images of faces. The extracted features are further processed using various machine learning classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN). Were used and examined all different metrics such as accuracy and precision, to compare all model performances. The best classification rate was getting is 97.1%, which was achieved by combining SVM and the MobileNetV2 model. Despite the small dataset used (1376 images), we have obtained very satisfactory results for the detection of masks on the faces.
Constitutive Modeling of Cemented Tailings Backfill With Different Saturation States Based on Particle Swarm Optimization and Support Vector Machine

Mine tailings disposal has been a serious environmental issue for decades. The wide application of cemented tailings backfill (CTB) technology could indirectly abate tailings pollution by recycling the tailings for backfilling. CTB constitutive modeling helps with design by improving the understanding of its compressive behavior. This study focused on CTB intelligent constitutive modeling considering the coupled effects of the cement content and saturation state. An artificial intelligence model was established and utilized based on particle swarm optimization (PSO) and the support vector machine (SVM). CTB samples with different cement contents and water saturation states were prepared, and unconfined compression tests were conducted to obtain the dataset. We verified the feasibility of using integrated PSO and SVM (P-S) in the CTB constitutive model using experimental data. We analyzed model errors. The results showed that the CTB stress strain curve was complex and nonlinear and could be significantly affected by the saturation states. PSO was feasible and efficient for tuning the SVM hyperparameters. The lowest minimum MSE value of 0.0108 was achieved in the eighth iteration. The PSO and SVM modeling was indicated to be accurate in the CTB constitutive model (a high R-square value of 0.9935 and a low mean squared error value of 0.001664 were achieved on the testing set). This model may accelerate the CTB structure design process.