Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPROVE ACCURACY IN FACE MASK DETECTION USING DEEP LEARNING

: Coronavirus illness 2019 has affected the glob seriously. One major protection technique for individuals is to wear masks publicly areas. what is more, several public service suppliers need customers to use the service provided that they wear masks properly. However, there square measure solely some analysis studies regarding mask detection supported image analysis. during this paper, we tend to propose mask, that may be a high-accuracy and economical mask detector. The projected mask may be a one-stage detector, that consists of a feature pyramid network to fuse high-level linguistics info with multiple feature maps, and a completely unique context attention module to target police work face masks. additionally, we tend to conjointly propose a completely unique cross-class object removal formula to reject predictions with low confidences and therefore the high intersection of union. Besides, we tend to conjointly explore the chance of implementing mask with a light-weighted neural network for embedded.
Concept-Aided Explanation of Graph Neural Networks

Despite its success in graph data, explaining the result of graph neural networks (GNNs) is challenging due to the complex structure. In this study, we develop Graph Concept Score Evaluator (GCSE) to evaluate the model's decision by introducing the notion of ‘concept’ that captures complex information of graphs in a human-interpretable fashion. Experimental results using a synthetic infection dataset show that our model can successfully evaluate the concepts of a given GNN-based model
CISH attenuates homeostatic cytokine signaling to promote lung-specific macrophage programming and function

Description The protein CISH fine-tunes homeostatic lung cytokine signaling to maintain alveolar macrophage identity and function. Calibrating macrophages for lung homeostasis Dysregulation of alveolar macrophages (AMs) in the lung results in the accumulation of surfactant, pulmonary alveolar proteinosis, and respiratory failure if left untreated. Shoger et al. found that CISH, a member of the suppressor of cytokine signaling (SOCS) family, was increased in abundance in response to the homeostatic cytokine GM-CSF. Loss of CISH led to increased GM-CSF signaling, the generation of enlarged, foamy AMs, the increased abundance and activity of the transcriptional regulator GATA2, and dysregulation of genes responsible for AM function. These findings suggest that the fine-tuning of cytokine signaling is required to regulate tissue-specific pathways that maintain macrophage identity and function. Tissue-specific cytokine stimuli orchestrate specialized homeostatic functions of resident macrophages. In the lung, steady-state signaling by the cytokine GM-CSF is critical for alveolar macrophage (AM) development and function. Here, we showed that CISH, a suppressor of cytokine signaling (SOCS) family member that is acutely induced by diverse cytokine stimuli in many tissues, was expressed constitutively in AMs in response to steady-state GM-CSF signaling. Cish deficiency led to the generation of foamy AMs and the accumulation of pulmonary surfactant. These phenotypic changes were associated with enhanced activation of STAT5, AKT, and ERK and increased expression of the gene encoding the transcription factor GATA2. RNA-seq analysis of Cish−/− AMs revealed a set of dysregulated immune and lipid-process modules, including the increased expression of genes enriched for GATA2-binding motifs. Last, Cish-deficient, bone marrow–derived macrophages showed increased Gata2 expression and accumulated more lipid upon incubation with bronchoalveolar lavage fluid compared with Cish-sufficient cells. Thus, CISH is part of a feedback loop that constrains homeostatic cytokine signaling and Gata2 expression to maintain AM identity and function.
Design A Novel of Path Planning Method for The Vascular Interventional Surgery Robot based on DWA Model

In this paper,The surgical navigation system solves the problem of fast and safe delivery of the catheter and guide wire to the target point. A new concept was proposed to help doctors provide better navigation visual feedback, so that doctors can perform surgical operations more effectively and safely. The surgical navigation system proposed in this article is designed based on the DWA(Dynamic window algorithm) model. First of all, we must first take the three-dimensional coordinates and the corresponding image coordinates to obtain the accurate location of the image coordinate collection. We use its own impixelinfo function in matlab to calculate the pixel coordinates of the marker point. We put the glass vessel in the experimental position, use matlab to collect the image of the glass vessel wall coordinates and set up the target point coordinates, then after collecting the coordinates of possible obstacles, the dynamic window algorithm will be implemented for obstacle avoidance planning and Design an evaluation function based on the planned possible path at the next moment, distinguish the pros and cons of the path. Finally, The experimental analysis and verification of surgical operations compared with or without navigation system was demonstrated.
A Denoising Method for Ship Radiated Noise Based on Adaptive Neuro-Fuzzy Interference System

Ship radiated noise denoising is an important step before underwater target detection. To obtain better denoising effect, adaptive fuzzy inference system (ANFIS) is used to denoise ship radiated noise. Experimental results show that there is a nonlinear component in ship radiated noise signal, and ANFIS can deal with this nonlinear relationship. Firstly, the received signal is composed of the random noise with a certain signal to noise ratio (SNR) and the expected signal with a certain SNR, and then the random noise with a certain SNR is added to the mechanical noise to form the reference noise. Finally, the received signal and reference noise are used as the input of ANFIS to get the denoised signal. The experiment is carried out by Monte Carlo method, and the results are evaluated by SNR. The results show that the method has good denoising effect on ship radiated noise.
Structure-Behavior Coalescence Abstract State Machine for Metamodel-Based Language in Model-Driven Engineering

In model-driven engineering (MDE), the unified modeling language (UML) 2.0 metamodel solution includes a metamodel that defines the language concepts and a user model that defines how the language concepts are represented. In UML 2.0, an important usage of metamodel is to ensure model consistency between different diagrams in the user model. However, most existing UML metamodels lack an integrated semantic framework to project each diagram in the user model as a view of the metamodel. To overcome the shortcomings of the current UML 2.0 metamodel approaches, we developed structure-behavior coalescence abstract state machine (SBC-ASM) for metamodel-based language (MBL), which provides an integrated semantic framework that is able to integrate structural constructs with behavioral constructs. Using SBC-ASM MBL as the metamodel solution of UML 2.0, each diagram in the user model can be projected as a view of the SBC-ASM.
A Self-Powered and Area Efficient SSHI Rectifier for Piezoelectric Harvesters

This article presents an area efficient fully autonomous piezoelectric energy harvesting system to scavenge energy from periodic vibrations. Extraction rectifier utilized in the system is based on synchronized switch harvesting on inductor (SSHI) technique which enables system to outperform standard passive rectifiers. Compared to conventional SSHI circuits, enhanced SSHI (E-SSHI) system proposed in this paper uses a single low-profile external inductor in the range of $\mu \text{H}$ ’s to reduce overall system cost and volume, hence broadening application areas of such harvesting systems. Furthermore, E-SSHI does not include any negative voltage converter circuit and therefore, it offers area efficient AC/DC rectification. Detection of optimal voltage flipping times in E-SSHI technique is conducted autonomously without any external calibration. Energy transfer circuit provides control over how much energy is delivered from E-SSHI output to electronic load. The proposed system is fabricated in 180 nm CMOS process with 0.28 mm2 active area. It is tested using a commercial piezoelectric transducer MIDE V22BL with periodic excitation. Measured results reveal that E-SSHI circuit is capable of extracting up to 5.23 and 4.02 times more power compared with an ideal full-bridge rectifier at 0.87 V and 2.6 V piezoelectric open circuit voltage amplitudes (VOC, P), respectively. A maximum voltage flipping efficiency of 93% is observed at VOC,P = 3.6 V, owing to minimized losses on charge flipping path. Measured results are compared with state-of-the-art interface circuits. Comparison shows that E-SSHI design offers a huge step towards miniaturized harvesting systems thanks to its low-profile and fully autonomous design.
Origin and Mitigation of Extreme Performance Loss Rate Values Calculated using RdTools

RdTools is a python package developed to calculate Performance Loss Rate (PLR) of PV plants using time-series power data. The package does normalization, filtering of observed time-series data to avoid bias caused due to non representative data points resulting from different conditions. However, in the histogram of PLR, a significantly high spread is often observed. In this paper, one of the reasons for the occurrence of high PLR values in RdTools is explored and an improved filter that successfully removes the extreme PLR values in the histogram of degradation rates is proposed.
Hyperspectral Image Superresolution via Structure-Tensor-Based Image Matting

Hyperspectral (HS) imaging has achieved breakthroughs in many applications, such as remote sensing and object recognition. However, the spatial resolution of HS images is still insufficient due to the limitations of sensor technology and cost. In this article, we propose an HS image superresolution method that combines low-resolution (LR) HS images and high-resolution (HR) panchromatic (PAN) images. To exploit the spectral signatures in the LR-HS images while introducing details from the HR-PAN images during the image fusion procedure, an image matting model is used to fuse the original LR-HS images and the HR-PAN images. Specifically, to preserve the spectral components during the fusion procedure, two different alpha channels in the image matting model are generated based on the HS and PAN image structure tensors, which suppress spectral distortion and improve the quality of the reconstructed HR-HS image. Experimental results based on public datasets demonstrate the advantage of our proposed method in both preserving spectral information and enhancing HS image spatial resolution.
Research on Inversion of Soil Moisture in Karst Area Based on Full-Polarization SAR Data

In order to select a reasonable controlling mode of rocky desertification and restrain the development of rocky desertification with local conditions, the soil moisture in Karst area was studied by using full-polarization Synthetic Aperture Radar (SAR) data in this paper. Based on the decisive role of rocky desertification degree, vegetation, slope and characteristic of the soil, different models were employed to invert the soil moisture in different rocky desertification degree areas according to their land use type and vegetation as follows: (1) The advanced integral equation model (AIEM) was employed in the intensive rocky desertification and serious rocky desertification areas. (2) The water-cloud model (WCM) coupled with AIEM was employed in the slight rocky desertification and moderate rocky desertification areas. (3) The Michigan microwave canopy scattering (MIMICS) model coupled with AIEM was employed in the non-rocky desertification and potential rocky desertification areas. As a result, the soil moisture was inverted successfully in the whole study area with the total inversion accuracy of 84.37% and RMSE of 2.10. This result demonstrates that the proposed method can invert the soil moisture accurately and provide a powerful technical support for inverting the soil moisture in the Karst area with large span, fluctuating landform and diverse vegetation.
Incorporating Structural Plasticity Approaches in Spiking Neural Networks for EEG Modelling

Structural Plasticity (SP) in the brain is a process that allows structural neuronal changes, in response to learning. Spiking Neural Networks (SNN) are an emerging form of artificial neural networks that use brain-inspired techniques to learn. However, the application of SP in SNNs, its impact on overall learning, and network behaviour is rarely explored. In the present study, we use an SNN with a single hidden layer, to apply SP in classifying Electroencephalography (EEG) signals of two publicly available datasets. We considered classification accuracy as the learning capability and applied metaheuristics to derive the optimised number of neurons for the hidden layer along with other hyperparameters of the network. The optimised structure was then compared with overgrown and undergrown structures to compare the accuracy, stability, and behaviour of the network properties. Networks with SP yielded ~94% and ~92% accuracies in classifying wrist positions and mental states(stressed vs relaxed) respectively. The same SNN developed for mental stress classification produced ~77% and ~73% accuracies in classifying arousal and valence states. Moreover, the networks with SP demonstrated superior performance stability during iterative random initiations. Interestingly, these networks had a smaller number of inactive neurons and a preference for lowered neuron firing thresholds. This research highlights the importance of systematically selecting the hidden layer neurons over arbitrary settings, particularly for SNNs using Spike Time Dependent Plasticity learning and provides potential findings that may lead to the development of SP learning algorithms for SNNs.
Location Optimization of Wind Plants Using DEA and Fuzzy Multi-Criteria Decision Making: A Case Study in Vietnam

The objective of the present paper is to develop a systematic and sustainability-focused evaluation framework to determine the best locations for wind plants in Vietnam. For the decision-making that influences wind plant site selection, while some criteria can be evaluated based on a numerical database, numerous qualitative criteria must be under experts’ judgments in the form of linguistic terms and can be represented through fuzzy numbers. In this direction, a two-phase framework of multi-criteria decision making (MCDM) methodologies using the merits of Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Weighted Aggregated Sum-Product Assessment (FWASPAS) is proposed for the first time. In the first phase using DEA, the most potential locations regarding their efficiency on various quantitative criteria are selected. These locations are further assessed in the second phase in terms of qualitative criteria viz. technical, economic, environmental, and socio-political factors. For this evaluation, FAHP was used for criteria weighting, and FWASPAS ranks the provinces. A comparison with the existing locations, sensitivity analysis of criteria, and comparative analysis of methods are performed to support the outcomes of the proposed work. The results illustrate that all methods reach common rankings, in which Binh Thuan province got the highest rank. This shows that the applied models are robust in nature. Implementation of the proposed approach can enable governments and stakeholders to save costs and make the most out of resources in the planning phase of wind power plants or any other renewable energy projects.
Sexual Harassment in Display Work: The Case of the Modeling Industry

This feminist analysis focuses on sexual harassment within a specific category of jobs known as display work, where primarily women’s bodies are commodified and sold to consumers, and often through the conduits of powerful male industry leaders. Using qualitative content analysis methods to analyze 88 subjective, first-person narratives of harassment from 70 models working within the fashion business, I describe how the commodification of bodies interacts with the particular features of the modeling industry—the premium placed on youth, ambiguous industry demands, and the presence of kingmakers—to produce an environment in which opportunities for sexual harassment can proliferate. All these factors impose extreme worker vulnerability costs on predominantly women and ultimately contribute to maintaining gender-based, hierarchical power differentials between men wielding authority within the industry and these models over time.
Frames associated with shift invariant spaces on positive half line

Abstract In this paper, we introduce the notion of Walsh shift-invariant space and present a unified approach to the study of shift-invariant systems to be frames in L2(ℝ+). We obtain a necessary condition and three sufficient conditions under which the Walsh shift-invariant systems constitute frames for L2(ℝ+). Furthermore, we discuss applications of our main results to obtain some known conclusions about the Gabor frames and wavelet frames on positive half line.
A Deep Learning-Based Benchmarking Framework for Lane Segmentation in the Complex and Dynamic Road Scenes

Automatic lane detection is a classical task in autonomous vehicles that traditional computer vision techniques can perform. However, such techniques lack reliability for achieving high accuracy while maintaining adequate time complexity in the context of real-time detection in complex and dynamic road scenes. Deep neural networks have proved their ability to achieve competing accuracy and time complexity while training them on manually labeled data. Yet, the unavailability of segmentation masks for host lanes in harsh road environments hinders fully supervised methods’ operability on such a problem. This work proposes integrating traditional computer vision techniques and deep learning methods to develop a reliable benchmarking framework for lane detection tasks in complex and dynamic road scenes. Firstly, an automatic segmentation algorithm based on a sequence of traditional computer vision techniques has been experimented. This algorithm precisely segments the semantic region of the host lane in the complex urban images of nuScenes dataset used in this framework; hence corresponding weak labels are generated. After that, the developed data is qualitatively evaluated to be used in training and benchmarking five state-of-the-art FCN-based architectures: SegNet, Modified SegNet, U-Net, ResUNet, and ResUNet++. The performance evaluation of the trained models is done visually and quantitatively by considering lane detection a binary semantic segmentation task. The output results show robust performance, especially ResUNet++, which outperforms all the other models while testing them in different complex road scenes with dynamic scenarios and various lighting conditions.
Exploiting Fixed Charge to Control Schottky Barrier Height in Si|Al2O3|MoOx – based Tunnel Diodes

The insertion of a tunneling atomic layer deposited (ALD) Al2O3 film between MoOx and p-type Si has been studied to investigate Schottky barrier height tunability with varied negative fixed charge density. This work seeks to increase the hole-selectivity of MoOx-based contacts through manipulation of interface fixed charge. Fixed charge density and interface trap state densities are determined as a function of alumina processing conditions in metal-oxide-semiconductor capacitors (MOSCAPs). Schottky barrier heights were determined from Mott-Schottky analysis of tunnel diodes. An alumina deposition temperature of 80°C and post-deposition annealing at 425°C yielded the highest magnitude of negative fixed charge density (-3.5×1012 q.cm−2). High deposition temperature and/or post-deposition annealing produced the lowest interface trap state density (1×1012 eV-1.cm−2). On p-type silicon, Schottky barrier height minimization was not clearly correlated with increasing fixed charge density. However, on n-type silicon, a significant increase in Schottky barrier height was evident (~0.8 eV to > 1 eV) and is attributed to the large negative fixed charge. The findings from this work indicate that Schottky barrier height in carrier selective contacts can be tuned by electrostatic engineering of the SiOx|Al2O3 interface.
Cold Test Design of the Open Resonant Cavity in a High-Order Mode Gyrotron

A design of the cold test for frequency, Q-factor and electric field distribution of high-order mode resonator of gyrotron with a computer control is presented. The designed results show that the difference between maximum and minimum of electric filed can clearly be distinguished, which can be applied to the measurement of the electric field pattern on crossed section.
Assessment of workplace-based learning: key findings from an ECE professional master programme in Portugal

ABSTRACT Workplace-based experiences are considered centrally relevant in the professional preparation of prospective teachers. In spite of this, few studies have focused on the assessment of workplace-based learning (WBL) in early childhood education (ECE). This study aims to examine pathways of professional learning of students attending an ECE professional Master programme in Portugal, based on an authentic and participatory approach to assessment. A cohort of 62 students participated in this exploratory study. Data were collected through an assessment grid organised in four domains (observation, planning, action and reflection) and 25 dimensions, in two moments: interim regulatory assessment and final assessment. Key findings point to statistically significant differences between the two moments, showing the progress of students at the level of competences in all domains and dimensions. Results allow to identify tendencies on students’ learning pathways in core domains of professionalism and represent an input for understanding the effectiveness of the ECE professional Master programme.
A New Microorganism Dataset for Image Segmentation and Classification Evaluation

Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. The EMDS-5 dataset has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. We select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. Image segmentation includes single-object image segmentation and multi-object image segmentation. We extract nine features from the images in EMDS-5 and use the Support Vector Machine classifier for testing. In terms of image classification, we select the VGG16 feature to test different classifiers. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval.