Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre- and In-Service Teachers’ Attitudes Toward Students With Learning Difficulties and Challenging Behavior

The implementation of inclusive policies is largely dependent on teachers’ willingness to accommodate students with special educational needs (SEN) in mainstream classrooms, which is affected by their perceived competence and attitudes. This study investigated attitudes of pre- and in-service teachers toward students with two types of SEN: challenging behavior and learning difficulties. The three components of attitudes (affective, cognitive, and behavioral) were assessed using indirect and direct measures. Results revealed that teachers held negative implicit attitudes toward challenging behavior and learning difficulties, however, implicit attitudes did not vary as a function of the type of SEN. Ratings of the stereotypical dimensions warmth and competence and overall ratings of scholastic achievement were affected by professional status and type of SEN. Professional status, implicit attitudes, and stereotypical knowledge together explained 52 and 43% of the variance in teachers’ ratings of academic proficiency for students with challenging behavior and learning difficulties, respectively. Results are interpreted within the theoretical framework and implications for teacher training are discussed.
Improved Sea State Bias Estimation for Altimeter Reference Missions With Altimeter-Only Three-Parameter Models

This paper presents an in-depth study concerning the development of a sea state bias (SSB) model designed with three parameters exclusively derived from altimeter data and globally applied to all reference altimeter missions. The proposed technique, first tested for the Jason-1 mission, proves to have a good performance for a wide range of ocean conditions when compared with the state-of-the-art SSB corrections currently in use. In addition to the significant wave height (<inline-formula> <tex-math notation="LaTeX">$H_{s}$ </tex-math></inline-formula>) and wind speed (<inline-formula> <tex-math notation="LaTeX">$U~_{{10}}$ </tex-math></inline-formula>), a third predictor acting as a mediator parameter gathered by the mean wave period (<inline-formula> <tex-math notation="LaTeX">$T_{z}$ </tex-math></inline-formula>) has been used. Two different empirical algorithms for altimeter ocean wave period have been tested and implemented, improving the SSB model performance in some ocean regions. The methodology relies on nonparametric modulation and statistical techniques based on smoothing splines embedded in a generalized additive model. This SSB modeling approach shows good performance when applied to all reference missions, in particular to TOPEX and Jason-2 missions, slightly reducing the explained variance of sea-level anomaly (SLA) when compared with the established SSB models. The approach is computationally efficient, capable of generating a stable SSB model using a small training data set when little information is available, as is the case with the recent Jason-3 mission. Model performance is assessed by comparison with existing SSB corrections for each reference mission, intercomparisons during the period of the tandem phases, and by SLA variance analysis, providing a consistent set of SSB corrections for the four reference missions.
Stabilizing Resistive Switching Characteristics by Inserting Indium-Tin-Oxide Layer as Oxygen Ion Reservoir in HfO2-Based Resistive Random Access Memory

This paper investigates the issues of oxygen accumulation and variation in the high-resistance state of HfO2-based resistive random access memory (RRAM), with improvement attained by inserting a thin oxygen-vacancy-rich layer of indium-tin-oxide (ITO) film. By acting as the oxygen ion reservoir, this ITO thin film on the TiN electrode can further stabilize resistance switching (RS) characteristics. In terms of reliability, ac endurance, and retention tests confirm stable RS characteristics for the Pt/HfO2/ITO/TiN device. Finally, a conducting model was proposed to explain the influence of the ITO thin layer and clarify the physical mechanism of electrical improvements.
Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems

For strict-feedback nonlinear systems under full-state constraints, current barrier Lyapunov function (BLF) and integral BLF based control solutions rely on feasibility conditions for virtual controllers. In this work, we present a new solution that completely removes such restrictive conditions. First, we construct a nonlinear state-dependent function that purely depends on constrained states to cope with full state and asymmetric constraints directly; second, we introduce a new coordinate transformation and integrate it into each step of dynamic surface control based backstepping design, completely circumventing the demanding feasibility conditions on virtual controllers. Consequently, there is no need for the tedious offline computations for feasibility verification, allowing the designer more freedom to select design parameters, enabling the system with a larger range of initial conditions to be handled, and rendering the solution more user-friendly in design and implementation. Simulation verification also confirms the benefits and effectiveness of the proposed control method.
Deep Learning-Based Weather Image Recognition

Image data captured from outdoor visual devices are usually degraded by turbid media, such as haze, smoke, fog, rain, or snow. Therefore, weather conditions would usually disrupt or degrade proper functioning of vision-assisted transportation systems or ADAS (advanced driver assistance systems), as well as several other outdoor surveillance-based systems. To cope with these problems, removal of weather effects (or deweathering) from images has been important and received much attention. Hence, it is important to provide a preprocessing stage to automatically determine the weather condition for an input image, and then the corresponding proper deweathering operations (e.g., removals of haze, rain, or snow) would be correctly triggered accordingly. This paper presents a deep learning-based weather image recognition framework by considering the three most common weather conditions, including hazy, rainy, and snowy, in outdoor scenes. For an input image, our method automatically classifies the image into one of the three categories or none of them (e.g., sunny or others). Extensive experiments based on both well-known deep networks, GoogLeNet and AlexNet, are conducted on open weather image dataset to evaluate the proposed method and the feasibility has been verified.
Credit Scoring Based on Transaction Sequence Classification

The growing volume of credit card and personal loan applications has spurred the need for financial institutions to improve their credit scoring methods to make intelligent decisions. In this paper, we introduce a novel credit scoring framework based on transaction sequence classification. This framework involves a CNN-based structure for sequential feature extraction. It can be applied to both raw transaction sequences and feature matrixes aggregated by time window to capture short-term and long-term sequential features. Other non-sequential personal features are also integrated to the model to help making the final decision. In the experiments, evaluations on the performance of model-based, feature-based sequence classification and the whole framework are done respectively. Our sequence classification method as well as the credit scoring framework outperforms other state-of-art methods in the experiments on real-world labelled application and transaction data from a major payment organization.
OBSTRUCTED VEHICLE-TO-VEHICLE CHANNEL MODELING FOR INTELLIGENT VEHICULAR COMMUNICATIONS

In this paper, we model obstructed vehicle-to-vehicle (V2V) channels for the 5-GHz band through measurement-validated ray-tracing (RT) simulations. To begin, we establish a realistic V2V RT simulator through integrating three key channel features: small-scale structures (e.g. lampposts, traffic signs), handled by their approximate radar cross sections; large-scale structures (such as buildings and ground), calibrating their electromagnetic and scattering parameters; and obstructing vehicle effects via V2V channel measurements. Then, based on extensive RT simulations, the target channels are characterized comprehensively. All the parameters are input into and verified by the 3GPP-like quasi deterministic radio channel generator (QuaDRiGa). By adding the obstructed V2V scenario into standard channel model families, this paper provides a foundation for evaluating intelligent vehicular communications in challenging conditions.
Modeling, Design and Test-Bench Validation of a Semi-Active Propulsive Ankle Prosthesis With a Clutched Series Elastic Actuator

Driven by the desire of amputees to reduce their physical shortcomings, prosthetic feet evolved from passive to propulsive bionic feet, actuated by different types of compliant actuators to provide the push-off power and with the advantage of reducing the motor power requirements. The two-latest in-house developed Ankle Mimicking Prosthetic Foot prototypes used explosive elastic actuation. They were proven to be energy efficient, but are bulky and heavy, which increases gait asymmetries and metabolic cost. In this letter, we present the modeling, design, and test-bench validation of a novel semi-active ankle prosthesis using a resettable overrunning clutch with a series elastic actuator. Unlike many designs found in literature, which are only optimized and evaluated in the mechanical domain, we modeled the drive-train dynamics to have a better understanding of the electrical energy consumption. During the performance evaluation, it is shown that the nonpathological gait cycles at different walking speeds are well replicated in both simulations and test-bench experiments. The results reveal that this clutch is a suitable device to add to a series elastic driven ankle since it offers, for an 8% relative weight increase of the prosthesis, besides the assets of natural adaptability to different gaits and terrains, also an electrical energy reduction of up to 22% for the slow walking speed.
Joint channel and Power Allocation for Device-to-Device Communication on Licensed and Unlicensed Band

Device-to-Device communications (D2D) are being used to improve the spectral efficiency and to reduce the load of the base station (BS) by reutilizing the licensed spectrum. However, the primary cellular users may face high interference from D2D users. There is also a scarcity of licensed spectrum due to the dense deployment of smart devices. To alleviate this problem, in this paper, we are extending the D2D users to the unlicensed band. In this scenario, the D2D users are allowed to reuse the licensed spectrum or share the unlicensed spectrum with the incumbent WiFi users. However, cellular and WiFi users experience high interference if an efficient interference management scheme is not used. In this paper, we propose a joint mode selection, channel allocation, and power control algorithm using particle swarm optimization to manage the interference and improve the overall throughput of cellular and D2D users such that the minimum data rate requirement of WiFi users is guaranteed. Through numerical simulations, we show that the proposed algorithm can significantly mitigate the interference and improve the throughput of the system.
Design and Fabrication of a 3-D Printed Metallic Flexible Joint for Snake-Like Surgical Robot

Snake-like robots have numerous applications in minimally invasive surgery. One important research topic of snake-like robots is the flexible joint mechanism and its actuation. This letter describes the design and fabrication of a new type of flexible joint mechanism that is enabled by metal powder bed additive manufacturing technique. Kinematics and static models of the flexible joint are presented, which can help in designing and controlling the flexible joint. As a compliant mechanism, the fatigue characteristics of the flexible joint is investigated. Finite element analysis (FEA) is performed aiming for optimizing the design process. In the experiment section, model estimation, FEA, and experimental validation are conducted for further understanding the characteristics of the flexible joint. An example design that can survive after 100 000 full loading cycles is demonstrated. Finally, different design variations of the proposed method and a multi-section flexible endoscope using the proposed design are introduced. The proposed flexible joint has the potential not only in reducing the cost of manufacturing and assembling a snake-like surgical robot, but also benefits for developing of more sophisticated three-dimensional snake robotic structure that has an optimized space for embedded sensing and actuation.
Degradation and Recovery of n-Type Multi-Crystalline Silicon Under Illuminated and Dark Annealing Conditions at Moderate Temperatures

Recently, an <italic>n</italic>-type multi-crystalline silicon (mc-Si) was observed to be susceptible to degradation under illumination at elevated temperatures with similarities to carrier-induced degradation in <italic>p</italic>-type mc-Si. In this study, we demonstrate degradation and regeneration of the effective lifetime of non-diffused <italic>n</italic>-type mc-Si wafers using illuminated and dark annealing conditions at moderate temperatures. Under illuminated annealing conditions, the degradation and regeneration rates of the <italic>n</italic>-type mc-Si are observed to be slower than those of the <italic>p</italic>-type mc-Si; however, the opposite trend was observed under dark annealing conditions. The carrier-induced degradation kinetics of the <italic>n</italic>-type wafers can be described by degradation and regeneration that occur simultaneously, and the activation energies have been identified to be 1.23 ± 0.16 eV for the degradation process and 1.34 ± 0.08 eV for the regeneration. Surprisingly, no degradation was observed in <italic>n</italic>-type mc-Si under dark annealing above 160 °C. Rather, at these conditions, a two-stage improvement in the lifetime was observed. Although degradation occurs after a subsequent laser treatment, the stable lifetime at the end of the degradation is still slightly higher than its initial value.
Role of the Serum Calcium in Erythropoietin Responsiveness in Anaemic Haemodialysis Chronic Renal Failure Patients

The current study is devoted to examine the effect of the serum calcium level (in mmol/dl) on the rHuEPO EPIRAX) in 100 patients of different age group (20-65 years mean 42.5)of both sexes (35 female and 65 male) in haemodialysis program. Experiments were carried out to examine the responsiveness of anaemic CRF patients in HD program to the rHuEPO by the level of the PCV. Additionally, a comparison was conducted in terms of differences in the results according to the changes of the level of the serum calcium, which may be fluctuated from patients to patients according to the different causes. The results obtained showed that the serum calcium level in all studied cases has a direct effect on erythropoietin treatment to anaemic HD CRF patients (p<0.05), which in turns indicates that the calcium plays a vital role in the anaemia management.
Face anti-spoofing based on projective invariants

The most common security authentication systems rely on automatic face recognition, which is particularly vulnerable to various spoofing attacks. Often these attacks include attempts to deceive a system by using a photo or video recording of a legitimate user. Recent approaches to this problem are based on pure machine learning techniques that require large training datasets and generalize or scale, poorly.By contrast, we present a geometric approach for detecting spoofing attacks in face recognition based authentication systems. By locating planar regions around racial landmarks, our method distinguishes between genuine user recordings and recordings of spoofed images such as printed photos and video replays.The proposed algorithm is based on projective invariant relationships that are independent of the camera parameters and lighting conditions. Unlike previous geometric approaches, the input to our system is a stream of two RGB cameras. Comparing with methods implemented by a single RGB camera, our approach is significantly more accurate and is completely automatic, since we do not require head movements and other user interactions. While, on the other hand, our method does not employ expensive devices, such as depth or thermal cameras, and it operates both in indoor and outdoor settings.
Performance Improvement of the Unbalanced Voltage Compensation in Islanded Microgrid Based on Small-Signal Analysis

A low-voltage microgrid is naturally three-phase unbalance due to the connection of unbalanced loads. Voltage unbalance compensation (VUC) strategies of inverters have been proposed to compensate the voltage unbalance on the point of common coupling. However, the small-signal analysis of these VUC strategies has not been investigated in a microgrid with multiple inverters, which is due to the difficulty of the state-space modeling for unbalanced microgrids. This lack of discussion may result in a poor performance of the microgrid when the parameters of VUCs are not well designed. To investigate the dynamic behavior of the unbalanced microgrid with VUC strategies, this article develops a detailed state-space model for an unbalanced microgrid using the dynamic phasor method. Then, based on the developed model, small-signal analysis is carried out to investigate the effect of the VUC control on the dynamic behavior of a microgrid. It is found that a poor design of the VUC controllers may result in the instability of the microgrid. Finally, an improved compensation method with better compensation performance is proposed. Experimental results are used to validate the effectiveness of the proposed method.
Analysis of distributed ADMM algorithm for consensus optimization in presence of error

ADMM is a popular algorithm for solving convex optimization problems. Applying this algorithm to distributed consensus optimization problem results in a fully distributed iterative solution which relies on processing at the nodes and communication between neighbors. Local computations usually suffer from different types of errors, due to e.g., observation or quantization noise, which can degrade the performance of the algorithm. In this work, we focus on analyzing the convergence behavior of distributed ADMM for consensus optimization in presence of additive node error. We specifically show that (a noisy) ADMM converges linearly under certain conditions and also examine the associated convergence point. Numerical results are provided which demonstrate the effectiveness of the presented analysis.
Secure-Anonymous User Authentication Scheme for e-Healthcare Application Using Wireless Medical Sensor Networks

Wireless medical sensor networks are becoming very popular in improving the quality of the healthcare systems. Since the data to be handled are very sensitive, the exchange of information over insecure wireless channels can cause serious exposures. The authentication schemes to be employed with sensor networks should be handled with care because of the limited resources and energy available with the sensor nodes. Therefore, this study aims at improving existing authentication schemes by providing protection for healthcare infrastructures against potential, well-known attacks while minimizing the overhead. An anonymous-based user authentication scheme is presented to improve the security features, computation, and communication overhead of the wireless sensor networks. The proposed scheme uses improved elliptic curve cryptography and is secure against password guessing attacks and smart card lost/stolen verifier attacks, and also preserves user anonymity. Through the formal and informal security analyses, it is shown that the proposed scheme is secure against possible known attacks, including the password guessing attacks and smart card lost/stolen verifier attacks, and that it also preserves the user anonymity property. In addition, for the formal security verification, the proposed scheme is simulated using the widely accepted automated validation of internet security protocols and applications (AVISPA) tool. The proposed scheme provides high security along with incurring low computational and communication costs.
Joint Retrieval of Aerosol Optical Depth and Surface Reflectance Over Land Using Geostationary Satellite Data

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: $R = 0.88$ , root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of $\pm 0.2\cdot {\mathrm {AOD}}_{\mathrm {AERONET}}\pm 0.05$ . A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land.
A Compact Dental Robotic System Using Soft Bracing Technique

A wide range of commonly performed dental procedures, from operative caries removal, crown preparation, filling, to Orthodontia, could potentially benefit from robotic assistance or enhancement. Despite the wide applicability, dental robots have received far less research attention in comparison with surgical robots in general, with the vast majority of state-of-the-art dental robot systems built around commercially available industrial robotic manipulators. In this letter, we propose a novel robotic manipulator system dedicated to dental applications. The proposed robot design utilizes tendon-sheath transmission, by which the electric-motor actuators could be placed away from the manipulator, resulting in substantially more compact size and lighter weight than industrial-arm-based state-of-the-art systems. The main contribution of this letter is introducing a soft-robotic bracing element, which could substantially improve manipulator performance including stiffness, force capability, and accuracy. The concept, design, and fabrication aspects of the soft bracer are presented in detail in this letter. Design and system integration of the entire dental robot system are also introduced, and the performance of the system is validated using a fabricated prototype, where the benefits and unique performances of using the soft bracer are highlighted from experimental results. With compact size, excellent tool interchangeability, and fully customized toward dental procedure specifications, the proposed dental robot system with soft bracer could potentially be used in wide applications from caries removal to crown treatments, offering a promising alternative with substantially smaller footprint and lower cost to current solutions.