Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-teaching difficult subjects: critical autoethnography and pedagogy

ABSTRACT Building on existing studies on co-teaching and teaching difficult subjects, this article examines the development of our co-teaching relationship, as educators with marginalized identities, teaching on difficult subjects in a graduate critical consciousness course. The authors used the theoretical and methodological intersection of critical performance pedagogy and critical autoethnography to examine and situate their experiences in co-teaching. Three pedagogical practices were identified in co-teaching difficult subjects that included: 1) the negotiation of individual and collective space, 2) the navigation of voice between collaboration and personal expression, and 3) and the role of affect in the classroom environment. The study contributes to an emerging discussion on what it means to transform teaching in higher education around difficult subjects. By focusing on co-teaching, this article sets the stage to reimagine graduate teaching as a collaborative process between more than one instructor and students with a focus on power, privilege, oppression, and social change.
Distributed synchronous cooperative tracking algorithm for ground moving target in urban by UAVs

ABSTRACT In this paper, a cooperative path planning method is proposed to track the unknown ground moving target by multiple unmanned aerial vehicles (UAVs). The kinematic UAV model with dual-input including commanded speed and heading rate is adopted, and the unknown target state is forecasted by the Extended Kalman Filter (EKF) with probability estimate. The cooperative path planning with multiple objectives and priorities is designed, where the infrastructure occlusion to line of detection sight and energy consumption are considered. Three optimisation objectives are fuzzified, and the priority is modelled by the relaxed order of satisfactory degrees. With the synchronous communication, the collision avoidance constraints and compatibility constraints under communication delay are designed. Moreover, the obstacle avoidance constraints with high buildings are addressed. Based on the distributed receding horizon control (RHC), the fuzzy multi-objective synchronous cooperative tracking model is built, and the optimal path of UAVs can be obtained by regulating the parameter. The algorithm to find the bound of regulation parameter is developed based on Karush–Kuhn–Tucker (KKT) condition. Finally, simulation results verify that the proposed method is effective and reliable.
Development of an Open‐Access and Explainable Machine Learning Prediction System to Assess the Mortality and Recurrence Risk Factors of Clostridioides Difficile Infection Patients

Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence facilitates prevention, timely treatment, and improves clinical outcomes. The aim herein is to establish an open‐access web‐based prediction system, which estimates CDI patients’ mortality and recurrence outcomes and explains machine learning prediction with patients’ characteristics. Prognostic models are developed using four various types of machine learning algorithms and the statistical logistics regression model utilizing over 15 000 CDI patients from 41 hospitals in Hong Kong. The boosting‐based machine learning algorithm gradient boosting machine (GBM) (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperforms statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. As the difficulty to interpret complex machine learning results limits their use in the medical area, Shapley additive explanations (SHAP) are adapted to identify which features are crucial to the machine learning models and associate them with clinical findings. SHAP analysis shows that older age, reduced albumin levels, higher creatinine levels, and higher white blood cell count are the most highly associated mortality features, which is consistent with existing clinical findings. The open‐access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml. care/.
A transform based local RBF method for 2D linear PDE with Caputo–Fabrizio derivative

The present work aims to approximate the solution of linear time fractional PDE with Caputo Fabrizio derivative. For the said purpose Laplace transform with local radial basis functions is used. The Laplace transform is applied to obtain the corresponding time independent equation in Laplace space and then the local RBFs are employed for spatial discretization. The solution is then represented as a contour integral in the complex space, which is approximated by trapezoidal rule with high accuracy. The application of Laplace transform avoids the time stepping procedure which commonly encounters the time instability issues. The convergence of the method is discussed also we have derived the bounds for the stability constant of the differentiation matrix of our proposed numerical scheme. The efficiency of the method is demonstrated with the help of numerical examples. For our numerical experiments we have selected three different domains, in the first test case the square domain is selected, for the second test the circular domain is considered, while for third case the L-shape domain is selected. Manuscript received 13th August 2019, revised and accepted 20th July 2020.
The relationship between managers’ goal-setting styles and subordinates’ goal commitment

Background: Convincing employees to set aside their self-interests and commit to collective goals is essential for the effective functioning of organisations. It is critical that the impact of different managerial goal-setting styles, and the associated impressions of fair interpersonal treatment in the workplace, is understood from subordinates' perspective. This might clarify the psychological mechanisms involved in motivating subordinates to commit to organisational goals. Aim: The primary aim of this article is to determine the relationship between managers' goalsetting styles and subordinates' goal commitment. The secondary aim is to determine whether this relationship is mediated by interactional justice. Setting: A total of 451 working adults completed an online or paper-and-pen survey. Methods: A mediator model was conducted in structural equation modelling with maximum likelihood estimation and Bollen-Stine bootstrapping, with 5000 bootstrap resamples, to test the hypotheses. Results: The perception that managers are deliberative had the greatest positive direct relationship with subordinates' goal commitment, followed by the directive style. Subordinates' perception of managers as complaisant, in turn, were unrelated to goal commitment (amotivational), whereas the perception of managers as hostile had a negative relationship with goal commitment. Informational justice, not interpersonal justice, emerged as the only mediating variable. Conclusion: Managers should be encouraged to actively seek feedback from subordinates on their goal-setting styles. Managers can accordingly adapt their behaviour to effectively motivate subordinates to commit to organisational goals.
When Does Source Information Help? Content Vs. Source-Based Validation as a Function of Readers' Prior Knowledge

Undergraduate students read true, false, or uncertain statements attributed to either competent or less competent sources. Participants rated the statements as true or false. Statements attributed to competent sources were more likely to be rated as true, but the effect was much larger when the statement was uncertain than when it was either true or false. Implications for validation processes are discussed.
Malicious PowerShell Detection Using Attention against Adversarial Attacks

Currently, hundreds of thousands of new malicious files are created daily. Existing pattern-based antivirus solutions face difficulties in detecting such files. In addition, malicious PowerShell files are currently being used for fileless attacks. To prevent these problems, artificial intelligence-based detection methods have been suggested. However, methods that use a generative adversarial network (GAN) to avoid AI-based detection have been proposed recently. Attacks that use such methods are called adversarial attacks. In this study, we propose an attention-based filtering method to prevent adversarial attacks. Using the attention-based filtering method, we can obtain restored PowerShell data from fake PowerShell data generated by GAN. First, we show that the detection rate of the fake PowerShell data generated by GAN in an existing malware detector is 0%. Subsequently, we show that the detection rate of the restored PowerShell data generated by attention-based filtering is 96.5%.
Forecasting Global Ionospheric TEC Using Deep Learning Approach

Global ionospheric total electron content (TEC) maps are widely utilized in research regarding ionospheric physics and the associated space weather impacts, so there is a great interest in the community in short‐term ionosphere TEC forecasting. In this study, the long short‐term memory (LSTM) neural network (NN) is applied to forecast the 256 spherical harmonic (SH) coefficients that are traditionally used to construct global ionospheric maps (GIM). Multiple input data, including historical time series of the SH coefficients, solar extreme ultraviolet (EUV) flux, disturbance storm time (Dst) index, and hour of the day, are used in the developed LSTM NN model. Different combinations of the above parameters have been used in constructing the LSTM NN model, and it is found that the model using all four parameters performs the best. Then the best performing LSTM model is used to forecast the SH coefficients, and the global hourly TEC maps are reproduced using the 256 predicted SH coefficients. A comprehensive evaluation is carried out with respect to the CODE GIM TEC. Results show that the first/second hour TEC root mean square error (RMSE) is 1.27/2.20 TECU during storm time and 0.86/1.51 TECU during quiet time, so the developed model performs well during both quiet and storm times. Moreover, typical ionospheric structures, such as equatorial ionization anomaly (EIA) and storm‐enhanced density (SED), are well reproduced in the predicted TEC maps during storm time. The developed model also shows competitive performance in predicting global TEC when compared to the persistence model and two empirical models (IRI‐2016 and NeQuick‐2).
Assessing Suitable Areas of Common Grapevine (Vitis vinifera L.) for Current and Future Climate Situations: The CDS Toolbox SDM

Climate Data Science (CDS) Toolbox Species Distribution Model (SDM) aims identifying the suitable areas for species, community of species and landscape units. This model is based on the use of 23 variables available over the Internet, for which any assumptions are formulated about their relationships with the spatial distribution of species. The application of CDS Toolbox SDM on the assessment of the potential impact of two scenarios of climate change (Representative Concentration Pathways RCP4.5 and RCP6.0) on the suitability of grapevine crops in France shows a general decrease of the most suitable areas for grapevine crops between 41% and 83% towards 2070 according to the current location of the vineyard parcels. The results underline a potential shift of the suitable areas in northern part of the French territory. They also show a potential shift of the most suitable areas in altitude (60 m in average) for RCP6.0 scenario. Finally, the model shows that RCP4.5 scenario should be more drastic than RCP6.0 scenario by 2050 and 2070. In effect, the model underlines a significant potential decrease of cultivated crops in the areas of high probably of suitable areas, according to the baseline scenario. This decrease would be of 630,000 ha for 2070 RCP4.5 scenario and 330,000 ha for 2070 RCP6.0 scenario.
Methodological Innovation in Research: Participatory Theater with Migrant Families on Conflicts and Transformations over the Politics of Belonging

Abstract This paper introduces notions of conviviality as both a research practice and a research outcome through an exploration of the racialised and gendered experiences of migrant mothers and young girls in the current hostile environment for migrants in the UK. We argue that innovative, participatory theater and walking methods constitute a convivial practice, particularly helpful for addressing the everyday lives of migrant families within the current racist climate in the UK, characterized by the effects of the hostile environment on migrant families. Furthermore, the innovative participatory arts and action research methods in this project allowed the creation of relations between research participants and with the research team. These methodological and conceptual tools, we argue can strengthen research that challenges and goes beyond current xenophobic and racist conflicts.The innovative methods support research for social transformation, challenging prevalent racist discourses on migrant families, through building creative groups to express and publicly share their lived experiences.
What future for school business leadership? Recent change and looming possibilities

Fundamental widespread changes affecting education’s purposes, policies and practices have had transformational repercussions for school business across the developed world. Subsequently, school business demands and accountabilities continue to escalate in scope and complexity and governments, education authorities and school communities are acknowledging the primacy and imperative of proficient school business leadership. International research chronicling the subsequent rapid professionalisation of school business leaders demonstrates pervasive policy moves that have re-focused school business priorities. Drawing on research conducted in Australia, USA, UK, Canada and New Zealand this article describes recent widespread changes before discussing issues and trends portending future professional adaptation for school business leaders whose work lies at the cross hairs of macro pressures and micro necessities.
A regularized logistic regression based model for supervised learning

In this work, we introduce a new regularized logistic model for the supervised classification problem. Current logistic models have become the preferred tools for supervised classification in many situations. They mostly use either L1 or L2 regularization of the weight vector of parameters. Here we take a different approach by applying regularization not to the weight vector but to the gradient vector of the function representing the separating hyper-surface. We present the mathematical analysis of the model in its continuous setting and provide experimental evidence to show that the new model is competitive with state of the art models.
An Adjoint‐Free Alternating Direction Method for Four‐Dimensional Variational Data Assimilation With Multiple Parameter Tikhonov Regularization

Tikhonov regularization is critical for accurately specifying both the background (B) and observational (R) error covariances in four‐dimensional variational data assimilation (4DVar). The ratio of the background and observation error variances (referred to as the B‐R ratio) is the key to ensuring that 4DVar maximizes the information extracted from the observations. However, it is difficult to specify the regularization parameters in a high‐dimensional variational data assimilation (VDA) system for both the single‐ and multiple‐parameter regularization schemes. In this study, we used a maximum likelihood estimation (MLE)‐based inflation scheme that originated from the ensemble Kalman filter (EnKF) community and proposed an alternating direction method (ADM) to minimize the 4DVar cost function with the iterative application of multiple regularization parameters to simultaneously optimize the regularization parameters and model states under the framework of the nonlinear least‐squares 4‐D ensemble variational data assimilation method (NLS‐4DVar). The big‐data‐driven version of NLS‐4DVar (BD‐NLS4DVar) with multiple‐parameter Tikhonov regularization was able to adjust the B‐R ratios more accurately. Several groups of observing system simulation experiments (OSSEs) based on 2‐D shallow‐water equations demonstrated that BD‐NLS4DVar with multiple‐parameter Tikhonov regularization produced a substantial performance improvement over the standard BD‐NLS4DVar method with no regularization.
Evaluating the impact of psychological entitlement on cyberloafing: the mediating role of perceived organizational justice

The purpose of this study is to evaluate the impact of psychological entitlement and perceived organizational justice on cyberloafing. ,In this study, a cross-sectional research design based on a questionnaire method was used to collect the required data from a sample of 226 employees working at selected universities in the city of Isfahan, Iran. To test the research hypotheses, structural equation modeling was used. ,According to the findings, psychological entitlement could have a significant impact on perceived organizational justice and consequently perceived organizational justice could significantly influence cyberloafing. Moreover, psychological entitlement could significantly influence cyberloafing and finally, psychological entitlement could have a significant effect on cyberloafing through perceived organizational justice. ,This research provides valuable insight for studying the relationship among psychological entitlement, perceived organizational justice and cyberloafing.
Trialogical Learning as a Theoretical Framework in the Digital Ecosystem of a Maker Community: a Case Study

Digital media is transforming teaching-learning processes. It is necessary to understand the new ecologies of learning that are developing in technological ecosystems. In this article, a learning community is analysed from the perspective of «trialogical learning», and the components of the triad are identified: elements, interactions, and products. The case study offers the possibility of transferring this knowledge for the transformation of formal learning.
Novel noise filter Techniques and Dynamic Ensemble Selection for Classification.

Ensemble selection is one of the most researched topics for ensemble learning. Researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensemble system classifiers. Dynamic Ensemble Selection (DES) is one of the most effective techniques in classification problems. DES systems obtain to select the most appropriate classifiers from the candidate classifier pool. Ensemble models that balance diversity and accuracy in the training process improve performance than the whole classifiers. In this paper, the novel techniques are proposed by combining Noise Filter (NF) and Dynamic Ensemble System (DES) to have better predictive accuracy. In other words, a noise filter and DES make the data cleaner and DES improve the performance of classification. The proposed NF-DES model had been demonstrated on twelve datasets, especially has three credit scoring datasets and a performance measure accuracy. The results show that our proposed model has better than other models. The novel noise filer and dynamic ensemble learning with aim to improve the classification ability are presented. To improve performance of classification, noise filter with dynamic ensemble learning makes the noise data toward the correct class. Then, novel dynamic ensemble learning choose the appropriate subset classifiers in the pool of base classifiers.
Exploring Convection-Allowing Model Evaluation Strategies for Severe Local Storms Using the Finite-Volume Cubed-Sphere (FV3) Model Core

Verification methods for convection-allowing models (CAMs) should consider the finescale spatial and temporal detail provided by CAMs, and including both neighborhood and object-based methods can account for displaced features that may still provide useful information. This work explores both contingency table–based verification techniques and object-based verification techniques as they relate to forecasts of severe convection. Two key fields in severe weather forecasting are investigated: updraft helicity (UH) and simulated composite reflectivity. UH is used to generate severe weather probabilities called surrogate severe fields, which have two tunable parameters: the UH threshold and the smoothing level. Probabilities computed using the UH threshold and smoothing level that give the best area under the receiver operating curve result in very high probabilities, while optimizing the parameters based on the Brier score reliability component results in much lower probabilities. Subjective ratings from participants in the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (SFE) provide a complementary evaluation source. This work compares the verification methodologies in the context of three CAMs using the Finite-Volume Cubed-Sphere Dynamical Core (FV3), which will be the foundation of the U.S. Unified Forecast System (UFS). Three agencies ran FV3-based CAMs during the five-week 2018 SFE. These FV3-based CAMs are verified alongside a current operational CAM, the High-Resolution Rapid Refresh version 3 (HRRRv3). The HRRR is planned to eventually use the FV3 dynamical core as part of the UFS; as such evaluations relative to current HRRR configurations are imperative to maintaining high forecast quality and informing future implementation decisions.
Uplift Mechanism of the Highest Mountains at Eastern Himalayan Syntaxis Revealed by In Situ Dense Gravimetry

Dense gravity surveys at 408 stations are built around eastern Himalayan syntaxis (EHS). Free‐air gravity anomalies (FGAs) and Bouguer gravity anomalies (BGAs) are obtained based on the in situ data, while the modeled and observed FGAs are merged to increase the accuracy of the gravity anomalies. The variation ranges of FGAs and BGAs are −350–100 and −520 to −320 mGal, respectively. The density structures inversed by the BGAs represent a low‐density zone below the Namche Barwa‐Gyala Peri metamorphic massif. The Moho below the metamorphic massif is significantly shallower than the surrounding areas, and the variation range is between 45 and 75 km. A gravity admittance method is used to study the uplift mechanism of the highest mountains at the EHS. Our result shows that rapid uplift is primarily attributed to the upwelling of the mantle.