Miracle-dz/UniFAR-FTUs huggingface dataset:
Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.
Unsupervised Change Detection from Remotely Sensed Images Based on Multi-Scale Visual Saliency Coarse-to-Fine Fusion

Unsupervised change detection(CD) from remotely sensed images is a fundamental challenge when the ground truth for supervised learning is not easily available. Inspired by the visual attention mechanism and multi-level sensation capacity of human vision, we proposed a novel multi-scale analysis framework based on multi-scale visual saliency coarse-to-fine fusion (MVSF) for unsupervised CD in this paper. As a preface of MVSF, we generalized the connotations of scale as four classes in the field of remote sensing (RS) covering the RS process from imaging to image processing, including intrinsic scale, observation scale, analysis scale and modeling scale. In MVSF, superpixels were considered as the primitives for analysing the difference image(DI) obtained by the change vector analysis method. Then, multi-scale saliency maps at the superpixel level were generated according to the global contrast of each superpixel. Finally, a weighted fusion strategy was designed to incorporate multi-scale saliency at a pixel level. The fusion weight for the pixel at each scale is adaptively obtained by considering the heterogeneity of the superpixel it belongs to and the spectral distance between the pixel and the superpixel. The experimental study was conducted on three bi-temporal remotely sensed image pairs, and the effectiveness of the proposed MVSF was verified qualitatively and quantitatively. The results suggest that it is not entirely true that finer scale brings better CD result, and fusing multi-scale superpixel based saliency at a pixel level obtained a higher F1 score in the three experiments. MVSF is capable of maintaining the detailed changed areas while resisting image noise in the final change map. Analysis of the scale factors in MVSF implied that the performance of MVSF is not sensitive to the manually selected scales in the MVSF framework.
Neighbor-Based Label Distribution Learning to Model Label Ambiguity for Aerial Scene Classification

Many aerial images with similar appearances have different but correlated scene labels, which causes the label ambiguity. Label distribution learning (LDL) can express label ambiguity by giving each sample a label distribution. Thus, a sample contributes to the learning of its ground-truth label as well as correlated labels, which improve data utilization. LDL has gained success in many fields, such as age estimation, in which label ambiguity can be easily modeled on the basis of the prior knowledge about local sample similarity and global label correlations. However, LDL has never been applied to scene classification, because there is no knowledge about the local similarity and label correlations and thus it is hard to model label ambiguity. In this paper, we uncover the sample neighbors that cause label ambiguity by jointly capturing the local similarity and label correlations and propose neighbor-based LDL (N-LDL) for aerial scene classification. We define a subspace learning problem, which formulates the neighboring relations as a coefficient matrix that is regularized by a sparse constraint and label correlations. The sparse constraint provides a few nearest neighbors, which captures local similarity. The label correlations are predefined according to the confusion matrices on validation sets. During subspace learning, the neighboring relations are encouraged to agree with the label correlations, which ensures that the uncovered neighbors have correlated labels. Finally, the label propagation among the neighbors forms the label distributions, which leads to label smoothing in terms of label ambiguity. The label distributions are used to train convolutional neural networks (CNNs). Experiments on the aerial image dataset (AID) and NWPU_RESISC45 (NR) datasets demonstrate that using the label distributions clearly improves the classification performance by assisting feature learning and mitigating over-fitting problems, and our method achieves state-of-the-art performance.
Technology-Enhanced Learning for Student Agency in Higher Education: a Systematic Literature Review

From an educational perspective, student agency is a construct that refers to external and internal factors involved in taking responsibility for learning and the possibility of making choices in learning. Although there are studies that back the idea of using educational technologies to support the development of student agency in higher education, there is still a lack of frameworks that relate student agency with technology-enhanced learning. In this study we present a systematic literature review addressing this gap, with emphasis on educational sciences. The results from the mapping of 29 studies show a focus on the micro level of learning design and clear relationships with other concepts, such as ownership of learning or self-regulated learning. The analysis of the results enabled us to develop an own model approach connecting student agency and technology-enhanced learning. Future work will involve iterative phases of revision and validation of the developed model through empirical studies.
Assessment of Landsat Based Deep-Learning Membership Analysis for Development of from-to Change Time Series in the Prairie Region of Canada from 1984 to 2018

The prairie region of Canada is a dynamically changing landscape in relation to past and present anthropogenic activities and recent climate change. Improving our understanding of the rate, timing, and distribution of landscape change is needed to determine the impact on wildlife populations and biodiversity, ultimately leading to better-informed management regarding requirements for habitat amount and its connectedness. In this research, we assessed the viability of an approach to detect from–to class changes designed to be scalable to the prairie region with the capacity for local refinement. It employed a deep-learning convolutional neural network to model general land covers and examined class memberships to identify land-cover conversions. For this implementation, eight land-cover categories were derived from the Agriculture and Agri-Food Canada Annual Space-Based Crop Inventory. Change was assessed in three study areas that contained different mixes of grassland, pasture, and forest cover. Results showed that the deep-learning method produced the highest accuracy across all classes relative to an implementation of random forest that included some first-order texture measures. Overall accuracy was 4% greater with the deep-learning classifier and class accuracies were more balanced. Evaluation of change accuracy suggested good performance for many conversions such as grassland to crop, forest to crop, water to dryland covers, and most bare/developed-related changes. Changes involving pasture with grassland or cropland were more difficult to detect due to spectral confusion among classes. Similarly, conversion to forests in some cases was poorly detected due to gradual and subtle change characteristics combined with confusion between forest, shrub, and croplands. The proposed framework involved several processing steps that can be explored to enhance the thematic content and accuracy for large regional implementation. Evaluation for understanding connectivity in natural land covers and related declines in species at risk is planned for future research.
Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis

In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to reliably detect and track individual leaves of a maize plant based on a graph theoretic approach for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking, and generation of a leaf status report. The method accepts an image sequence of a plant as the input and automatically generates a leaf status report containing the phenotypes, which are crucial in the understanding of a plant’s growth, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf ceased to grow, and the length and relative growth rate of individual leaves. Based on experimental study, three types of leaf intersections are identified, i.e., tip-contact, tangential-contact, and crossover, which pose challenges to accurate leaf tracking in the late vegetative stage. Thus, we introduce a novel curve tracing approach based on an angular consistency check to address the challenges due to intersecting leaves for improved performance. The proposed method shows high accuracy in detecting leaves and tracking them through the vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset.
GECORIS: An Open-Source Toolbox for Analyzing Time Series of Corner Reflectors in InSAR Geodesy

Artificial radar reflectors, such as corner reflectors or transponders, are commonly used for radiometric and geometric Synthetic Aperture Radar (SAR) sensor calibration, SAR interferometry (InSAR) applications over areas with few natural coherent scatterers, and InSAR datum connection and geodetic integration. Despite the current abundance of regular SAR time series, no free and open-source software (FOSS) dedicated to analyzing SAR time series of artificial radar reflectors exists. In this paper, we present a FOSS Python toolbox for efficient and automatic estimation of: (i) the clutter level of a particular site before a corner reflector installation, (ii) the Radar Cross Section (RCS) to track a corner reflector’s performance and detect outliers, for example, due to damage or debris accumulation, (iii) the Signal-to-Clutter Ratio (SCR) to predict the positioning precision and the InSAR phase variance, (iv) the InSAR displacement time series of a corner reflector network. We use the toolbox to analyze Sentinel-1 SAR time series of the network of 23 corner reflectors for InSAR monitoring of landslides in Slovakia.
Characterizing polarized autofluorescence of normal and benign tissues using singular value decomposition and wavelet transform

A systematic investigation of the polarization characteristics of the auto-fluorescence of normal and benign human breast tissues is carried out complementing our earlier studies on normal and cancer tissues. Co- and cross-polarized auto-fluorescence are collected in the 500 to 700nm range through excitation at 488nm using laser as excitation source. A number of parameters, capturing spectral variations are extracted in the co- and cross-polarized channels through singular value decomposition and wavelet decomposition, which differentiate normal and benign tissues. The correlation matrix differs significantly in normal and benign tissues reflecting the presence of different fluorophores. The eigenvectors corresponding to the dominant eigenvalues reveal differences between tissue types. The co-polarized component being sensitive to intrinsic fluorescence shows different behavior for normal and benign tissues in the emission domain of known fluorophores. Interestingly, the benign tissue samples show correlation properties intermediate to malignant and normal cases. In the wavelet domain the standard deviation of percentage fluctuation reveal differences between tissues type. The correlation characteristics manifest prominently in the wavelet low pass (average) domain.
Corneal reconstruction in chemically damaged cornea using temperature sensitive surface assisted mesenchymal stem cell transplantation in rabbits

Background: The study aims to assess the potential of corneal reconstruction using mesenchymal stem cells (MSCs) transplantation using temperature sensitive membranes as carriers and compare with fibrin glue method in chemically damaged rabbit cornea. Methods: MSCs were cultured from rabbit’s bone marrow and transplanted over rabbit’s cornea, using either temperature sensitive membranes or fibrin glue method. Endogenous levels of MSCs were assessed to decide the optimal time point for transplanting cells. MSC transplanted corneas were harvested 15, 30 and 60 days post transplantation. Corneal repair markers were evaluated using histopathology, immunohistochemistry (IHC) and real time PCR. The quality of cornea reconstructed was evaluated using corneal opacity scoring and IHC. Results: Endogenous levels of MSCs were significantly higher 48 hours post corneal injury. Transplantation of MSCs using temperature sensitive method resulted in uniform and homogenous spread of MSCs over the damaged surface. Corneal transparency improved 7 days onward in rabbit chemical injured cornea after MSC transplantation. Immunohistochemistry (IHC) of damaged cornea showed complete absence of corneal epithelium as seen by CK3 expression, while re-epithelialization was observed after MSC transplantation. Histopathology showed complete re-epithelialization 15 days of transplantation. Evaluation of corneal quality using epithelial and stromal markers showing restoration of vimentin, α-smooth muscle actin and collagen levels in MSC transplanted cornea compared to damaged corneas. CK3 expression showed differentiation of MSC into corneal epithelial cells. MSC transplanted corneal clarity seemed a little better with temperature sensitive method compared to fibrin glue method. No toxic effects of MSC transplantation were observed on other organs or on resident corneal cells. Conclusions: Temperature sensitive surface MSC transplantation method results in uniform and homogenous delivery of cells on corneal surface and a transparent clear cornea. Epithelial and stromal markers showing restoration of vimentin, α-smooth muscle actin and collagen levels in MSC transplanted cornea compared to chemically damaged corneas. Our data also show the differentiation of MSC into corneal epithelial cells on corneal surface. Overall temperature surface MSC transplantation is an easy and safe alternative method to fibrin glue for exploring corneal reconstruction.
Surface plasmons in layered structures with semiconductor and metallic films

The complete spectrum of eigenwaves including surface plasmon polaritons (SPP), dynamic (bulk) and complex waves in the layered structures containing semiconductor and metallic films has been explored. The effects of loss, geometry and the parameters of dielectric layers on the eigenmode spectrum and, particularly, on the SPP modes have been analysed using both the asymptotic and rigorous numerical solutions of the full-wave dispersion equation. The field and Poynting vector distributions have been examined to identify the modes and elucidate their properties. It has been shown that losses and dispersion of permittivity qualitatively alter the spectral content and the eigenwave properties. The SPP counter-directional power fluxes in the film and surrounding dielectrics have been attributed to vortices of power flow, which are responsible for the distinctive features of SPP modes. It has been demonstrated for the first time that the maximal attainable slow-wave factor of the SPP modes guided by thin Au films at optical frequencies is capped not by losses but the frequency dispersion of the actual Au permittivity.
Cyber-Hero: A Gamification framework for Cyber Security Awareness for High Schools Students

People are considered the weakest point in the information systems which causes most of the cyber-attacks successful. The increasing numbers of cyber-attacks indicate that the traditional methodologies of training and awareness are still insufficient to build the necessary cybersecurity skills and capabilities. Gamification represents a promising methodology to change the behavior of people in the early stages. The objective of this research is not only to verify the effectiveness of gamification but also to provide a constructive application and to propose a framework that helps eventually and iteratively in improving the cyber-security skills and capabilities for high school students in early stages. In this research, we propose a novel framework for information security awareness and training programs, known as Cyber-Hero, that will help fight one of the most popular weaknesses in cybersecurity which is human error. The framework helps in transferring the learning of a narrative set of instructions to be acquired in a serious game, in which students in schools can engage with at an early stage. Such an engagement will educate students on how to create a strong password to protect themselves from cyber-attacks. The preliminary results show incremental progress in the skills and capabilities of cybersecurity for the students using the proposed framework.
A Hybrid Method of Rounded Hexagon Flux Direct Torque Control for Traction Motor in Mid-Speed Range

This paper proposes improved segment direct torque control (DTC) for full speed range in high-power urban rail vehicles engaged in frequent starting and braking. In the mid-speed range, based on a detailed harmonic analysis and comparison, a hybrid method of a rounded hexagon flux for DTC is proposed that can reduce the harmonic distortions in current and voltage. In addition, a circular flux is adopted to eliminate torque ripple in the low-speed range and a hexagon flux is adopted in the high-speed range to eliminate switching frequency. A transition control strategy for different speed segments is presented, and a smooth transition can be achieved. Hence, system stability and security are improved. The operation characteristics and design considerations for the proposed method are also introduced. Finally, a simulation model and a prototype of a full speed range system are built. The simulation and experimental results validate the effectiveness and the advantages of the proposed method.
Towards Ultrasonically Actuated Programmable Polymorphic Soft Actuator

The development of an untethered soft actuator that can perform complex task under external control is highly desirable. In this work, we present an ultrasonically actuated programmable polymorphic soft actuator scheme and demonstrate the untethered polymorphic actuation of the proof-of-concept device. The ultrasonic actuation is enabled through converting the incoming ultrasonic energy to thermal energy by the Joule heating. The temperature increase triggers shrinking of the thermally responsive hydrogel/polymer bimorph. By employing two ultrasonic receivers with different resonant frequencies (frequency coding), we demonstrated 4 different motions of the actuator.
Machine Learning Approach Based on Hybrid Features for Detection of Phishing URLs

Phishing attacks are one of the most widespread problems over the internet. A lot of internet users fall into the hands of attackers every day which accounts into millions of dollars of fraud around the globe every day. The availability of the internet among people who don’t have the knowledge of cyber-attacks adds more to this problem. Thus, there is a need to employ intelligent algorithms to solve these serious problems. In this paper, we present different ways in which phishing URLs can be detected using machine learning algorithms. The URL based features as well as network-based features were used to feed to the machine learning classifiers. Similarly, other features that might add relevance to our problem are also discussed. The unbalanced dataset is made balanced using various oversampling and undersampling techniques and the performance for the various machine learning algorithms is evaluated for the dataset. The evaluation shows that the machine learning algorithms can show promising results in terms of precision, recall, f- score, and ROC AUC.
Nonlinear Model Predictive Control for Trajectory Tracking of a Hexarotor with Actively Tiltable Propellers

This paper presents the dynamic modeling and control of an over-actuated hexarotor with bounded rotor speeds. With actively tiltable propellers, the hexarotor can track six degrees of freedom (DOF) trajectories independently. Based on the state space model, a fast nonlinear model predictive controller (NMPC) using direct multiple shooting is proposed using two different non-linear programming solvers. The performance of the NMPC is analyzed using numerical simulations for step response and trajectory tracking under realistic operational conditions with noisy state estimation, parameter uncertainties, and disturbances. The controller is verified in the Gazebo simulator in a more realistic environment.
A Machine Learning Implementation for Mental Health Care. Application: Smart Watch for Depression Detection

Machine learning is one of the most significant dimensions of artificial Intelligence. It is being used in almost all fields of science and technology. The Healthcare sector is one such realm where the application of machine learning has given excellent results. Moreover, in combination with the Internet of Things (IoT), machine learning has been widely successful in the healthcare sector. Still, there are some areas that remain devoid of the growing technology. Mental illness is one of the areas where there hasn’t been any perfect treatment. Predicting whether a person has a mental illness itself is the big challenge. Psychologists provide assessment and therapy to their clients with one-on-one physical interactions. Still, there is some ambiguity regarding the treatment. Although psychologists prescribe various medications for their clients like anti-depressants, sleeping pills, etc.; still, the medication hasn’t been able to cure or eradicate the sickness. There may be multiple reasons why a person is going through a certain situation like society, work pressure, family, etc. Our research on this topic will be limited to predicting such sickness in the human body and identifying what the person is going through using the previously recorded dataset. We will be using Logistic Regression, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor, and Naïve-Bayes algorithms for creating ensemble models and further compare the models. We have applied the proposed algorithms on the Kaggle dataset having 334 sample sizes with 31 different fields about unemployment and mental illness. In the end, the test result of this application can be an authentic example of IoT in healthcare.