Miracle-dz/UniFAR-FTUs huggingface dataset:
How well can a seasonal forecast system represent three hourly compound wind and precipitation extremes over Europe?

Extreme precipitation and winds can have a severe impact on society, particularly when they occur at the same place and time. In this study the Met Office’s Global Seasonal forecast system version 5 (GloSea5) model ensembles are evaluated against the reanalysis dataset ERA5, to find out how well they represent three hourly extreme precipitation, extreme wind and extreme co-occurring events over Europe. Although substantial differences in magnitude are found between precipitation and wind extremes between the datasets, the conditional probability of exceedance above the 99th percentile, which measures the co-occurrence between the two extremes, compares well spatially over Europe. However, significant differences in frequency are found around and over some areas of high topography. Generally GloSea5 underestimates this co-occurrence over sea. The model’s co-occurring events at individual locations investigated occur with very similar synoptic patterns to ERA5, indicating that the compound extremes are produced for the correct reasons.
The Construction of Language Laboratory Based on VOI Technology

VOI (Virtual Operating System Infrastructure), a cloud desktop technology, is popular in educational area recently, especially in colleges, because of its many advantages. It widely used in the laboratory which main equipment are computers, such as computer rooms and language laboratory. Language lab equipped computers acts as an important actor for language teaching in many colleges. Constructing a laboratory with high ratio of quality and value is the focus of many laboratory researchers. The study explores the requirement of teaching and learning for labs, surveys market and several other colleges, and designs how to construct NWAFU language labs based on VOI, compared with other prevalent technologies on market, from requirement, cost, deployment and management aspects. The study provides a reference for other type laboratory’s construction and gives some suggestions to a decision maker.
Formation of hydrophobic polymer coatings on the track-etched membrane surface

Methods of forming coatings on the hydrophilic poly(ethylene terephthalate) track-$$$etched membrane surface by means of plasma polymerization of organic compounds, magnetron sputtering and electron-beam dispersion of polymers in vacuum are considered. It is shown that the usage of compounds with low surface energy as pristine materials to coatings application allows to obtaining hydrophobic coatings on the membrane surface. The resulting composite membranes with hydrophilic and hydrophobic layers can be used in membrane distillation processes for desalination of seawater.
Research on Dynamic Reconfigurable Convolutional Neural Network Accelerator

The hardware implementation of convolutional neural network has the problem of resource limitation, which can be solved by the design of convolutional neural network accelerator based on FPGA dynamic reconstruction. The whole parallel strategy and architecture of CNN accelerator are designed, and the functional modules of CNN are designed based on pipeline. The dynamic reconstruction technology is used to redesign the convolutional neural network accelerator, and the dynamic reconstruction region and division are established; BPI flash is selected to store the configuration file, and the configuration file is read internally to dynamically configure the dynamic reconstruction area. Finally, for lenet. 5 handwriting recognition, compared with the corresponding static design, the use of slice LUTS, slice registers and DSP resources of the accelerator based on dynamic reconstruction design is reduced by 46%, 25% and 68% respectively. Compared with the implementation based on software platform. The system execution time is greatly reduced. However, due to the bandwidth limitation of the internal configuration port, the reconfiguration configuration time prolongs the execution time of the whole convolutional network.
Handwritten Digit Recognition Scheme Based on Noise Reduction and Trestle Self-Coding

In this paper, in order to improve the accuracy of handwritten number recognition, a convolutional neural network based on noise reduction and stack auto coding is formed by combining the denoising auto coding algorithm with stack auto coding algorithm. In this paper, the MNIST dataset is preprocessed first, then the noise reduction and stack auto coding network layer are constructed, and finally the stack auto coding algorithm based on the improved denoising auto coding is used to train and test the dataset. Experimental data show that the proposed method achieves low loss value and effectively improves the recognition accuracy in the MNIST test set.
A monocular vision positioning method based on topology pattern of artificial landmark

Image-based positioning accuracy is usually affected by image rotation and/or distortion due to the changes of the sensor orientation. In this paper, the artificial landmark is composed of color block with different shapes, and its topology pattern is composed of the number of the block shapes, colors, shape and color of each block and topological relation among the blocks, which are robust for the artificial landmark rotation and/or tilting. The positioning is based on artificial landmark pattern matching and the corresponding relation between artificial landmark and its position. In our experiments, 196 different artificial landmarks are layered on the 3 × 3M plane uniformly. There are four blocks in an artificial landmark and each block has six different shapes and four different colors. The experiment results show that the attributions of the block were calculated accurately, the topology pattern construction and recognition method for the artificial landmark are feasible, reliable and can be used in monocular vision positioning.
Evolving Data Monitoring Algorithm based on Data Stream Clustering

Clustering is one of the most important methods in the data stream mining. The nature of evolving data streams puts forward the following requirements for clustering: discovery of clusters with arbitrary shape, determination of the optimal number of clusters, anomaly detection and processing the influence of time. In the communication process, timely detection of anomalies is very important for the coordination of various devices. In this paper, we propose an adaptive density-based approach for clustering in evolving data streams(SagsDStream), which automatically updates parameters in subsequent stages based on the offline clustering result, so that the parameters can adapt to the change of data stream. In the online mode, the judging rules are saved in Improved Time Cluster Feature (ITCF). In the offline model, we use the density of the data distribution and the cluster distance to set the parameters of clustering algorithm, which overcome the sensitivity of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) threshold setting. Experimental results show that SagsDStream has better processing efficiency and clustering effect when dealing with data streams.
Voltammetric Determination of TBHQ Individually and Mixed with BHT in Petroleum Products Using a Gold Disc Electrode

A new method is proposed for determination of TBHQ (tert-butylhydroquinone) individually and in a mixture with BHT (butylated hydroxytoluene) in mineral oil and biodiesel when using linear-sweep voltammetry in combination with a gold disc electrode. Both of these analytes gave rise to the respective anodic peak in the supporting electrolyte based on a mixture of dilute H2SO4 and isopropanol. The samples of mineral oil were extracted using ethanol, and the samples of biodiesel were analyzed without any special sample treatment or separation. The electroanalytical method developed has enabled the determination of antioxidants in model and real samples with satisfactory results and good prospects for practical analysis.
ECONOMICS AND ENVIRONMENTAL DEVELOPMENT: TESTING THE ENVIRONMENTAL KUZNETS CURVE HYPOTHESIS

The Environmental Kuznets Curve (EKC) shows the relationship between economic development, which is proxied in per capita income, and environmental quality. This study aims to view the existence of the EKC by arranging the distribution of 62 countries into four sub-samples based on per capita income from 1992 to 2017. By applying panel data, the model uses the Error Correction Mechanism (ECM) method to see the effect of economic growth on environmental quality (CO2 emission) in both the short and the long term. The results show that the EKCs on our four subsamples used are not conclusive, and are more likely to refer to the “N” pattern. This result confirms that the impetus of development at a higher level will lead to a degradation of environmental quality. Therefore, policy intervention becomes an important thing.
Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks

The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.
Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability.
Iterative Design for the Common Solution of Monotone Inclusions and Variational Inequalities

Some new forward–backward multi-choice iterative algorithms with superposition perturbations are presented in a real Hilbert space for approximating common solution of monotone inclusions and variational inequalities. Some new ideas of constructing iterative elements can be found and strong convergence theorems are proved under mild restrictions, which extend and complement some already existing work.
Side-channel Programming for Software Integrity Checking

Verifying software integrity for embedded systems, especially legacy and deployed systems, is very challenging. Ordinary integrity protection and veriﬁcation methods rely on sophisticated processors or security hardware, and cannot be applied to many embedded systems due to cost, energy consumption, and inability of update. Furthermore, embedded systems are often small computers on a single chip, making it more di ﬃ cult to verify integrity without invasive access to the hardware. In this work, we propose “side-channel programming”, a novel method to assist with non-intrusive software integrity checking by transforming code in a functionality-preserving manner while making it possible to verify the internal state of a running device via side-channels. To do so, we ﬁrst need to accurately proﬁle the side-channel emanations of an embedded device. Using new black-box side-channel proﬁling techniques, we show that it is possible to build accurate side-channel models of a PIC microcontroller with no prior knowledge of the detailed microcontroller architecture. It even allows us to uncover undocumented behavior of the microcontroller. Then we show how to “side-channel program” the target device in a way that we can verify its internal state from simply measuring the passive side-channel emanations.
DOES FDI AND CORRUPTION AFFECT ENVIRONMENTAL QUALITY IN TUNISIA?

This paper investigates the impact of foreign direct investment (FDI) and corruption on the environmental pollution in Tunisia over the period 19842014 by applying an autoregressive distributed lag model. Our results revealed the existence of Environmental Kuznets Curve in Tunisian case. The pollution haven hypothesis postulates that polluting industrial activity in developed countries is shifting to developing countries with less stringent environmental regulations. This hypothesis has been proved. Hence, this study advises to make more aware to the negative effect of corruption. Overall, to improve environmental quality, the findings suggest that Tunisia should promote energy efficiency with sustainable growth. Therefore, results show that Tunisia should encourage more FDI inflows particularly in technologyintensive and environment-friendly industries.
Mid-frontal Theta during Conflict in a Value-based Decision Task

Abstract Mid-frontal theta is a sensitive marker for cognitive conflict. However, most research focuses on cognitive control paradigms (e.g., the Flanker task). Here, we ask if mid-frontal theta is also sensitive to response conflicts within value-based decision-making. We recorded electroencephalography activity during a value-based binary decision task. In this task, participants collected rewards in a virtual two-dimensional world. In each trial, we presented two reward options that were either quick to collect but were smaller in value, or took longer to collect but were larger in value. The subjective value of each option was driven by the options' value and how quickly it could be reached. We used this task to investigate three types of potential conflicts: choice ambiguity, choice repetitions, and temporal delay. We manipulated choice ambiguity by varying the subjective values of the decision options. For choice repetition, we biased participants toward one option for two trials and then tested how that affected the subsequent decision. We manipulated temporal delay by making one option quick to collect and one longer to collect. The behavioral results showed the expected effects: Decision times were shorter for unambiguous choices, participants showed a tendency to repeat the previous choice and decision times were shorter for repetitions, and decision times were shorter for earlier available choices. Response-locked mid-frontal theta power was increased for choice switches as compared to choice repetitions, and for the later available as compared to the earlier available option, but we found no effect of ambiguity.
Using Deep Learning for Gait Abnormality Classiﬁcation

Gait has been the target of many recent research works. Many pathologies aﬀect the normal gait patterns and the analysis of gait indicators can be a useful way of assessing the diﬀerent gait disorders and their evolution, while also providing valuable information for rehabilitation. Current gait assessment is mostly either based on subjective analysis, that lacks precision and quantitative measurements, or done in dedicated laboratories with complex and expensive equipment setups, that can be impractical. However, recent studies have been made with the objective of obtaining precise gait analysis and classiﬁcation using simpler systems, composed of a variety of sensors. This work presents a method for classiﬁcation of abnormal gait types from videos of gait sequences. The proposed system is based on the use of gait representation images, like the Gait Energy Image (GEI) and a novel representation we call Skeleton Gait Energy Image (SGEI), that uses information of the skeleton, obtained with a pose machine. These representations are used as input to a VGG-19 convolutional neural network, that is used to perform classiﬁcation or to extract a feature vector that is then classiﬁed using machine learning methods such as Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). The system is trained and tested in a newly acquired gait dataset, the GAIT-IST dataset, that simulates four diﬀerent types of abnormal gait and is also tested with another dataset, to assess its generalization ability.
Random Forests for Spatially Dependent Data

Abstract Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process (GP) distributed spatial random effect, are widely used for analyses of geospatial data. We consider the setting where the covariate effect is nonlinear. Random forests (RF) are popular for estimating nonlinear functions but applications of RF for spatial data have often ignored the spatial correlation. We show that this impacts the performance of RF adversely. We propose RF-GLS, a novel and well-principled extension of RF, for estimating nonlinear covariate effects in spatial mixed models where the spatial correlation is modeled using GP. RF-GLS extends RF in the same way generalized least squares (GLS) fundamentally extends ordinary least squares (OLS) to accommodate for dependence in linear models. RF becomes a special case of RF-GLS, and is substantially outperformed by RF-GLS for both estimation and prediction across extensive numerical experiments with spatially correlated data. RF-GLS can be used for functional estimation in other types of dependent data like time series. We prove consistency of RF-GLS for β-mixing dependent error processes that include the popular spatial Matérn GP. As a byproduct, we also establish, to our knowledge, the first consistency result for RF under dependence. We establish results of independent importance, including a general consistency result of GLS optimizers of data-driven function classes, and a uniform law of large number under β-mixing dependence with weaker assumptions. These new tools can be potentially useful for asymptotic analysis of other GLS-style estimators in nonparametric regression with dependent data.
RECENT ADVANCES IN ESTABLISHING A COMMON LANGUAGE FOR AIRCRAFT DESIGN WITH CPACS

Today’s aircraft design projects are characterized by interdisciplinary collaboration between a large amount of heterogeneous disciplines. Each discipline contributes with specialized knowledge and software tools which are integrated in automated simulation workﬂows. The utilization of a common data model signiﬁcantly reduces the number of possible interconnections between the modules and ensures a consistent source of information. This paper presents the Common Parametric Aircraft Conﬁg-uration Schema (CPACS) and discusses the progress of establishing it as a common language for aircraft, space-craft and rotorcraft design. With increased ﬂexibility of the aerodynamic performance maps, a reformulation of the wing component segments for deﬁning the wing internal structure, a new deﬁnition for engine nacelles and py-lons and the possibility to include individual tool-speciﬁc schemas, the current enhancements and the corresponding development process of the latest CPACS release are presented. The paper concludes by showing that publishing CPACS and its corresponding libraries under an open-source license has been the enabler for a wide adoption of the data format in the aeronautical research and industry.
Fusion network for blur discrimination

Abstract. Blurry image discrimination is a challenging and critical problem in computer vision. It is useful for image restoration, object recognition, and other image applications. In previous studies, researchers proposed a discrimination method based on hand-extracted features or deep learning. However, these methods are either pure data driven by deep learning or over-simplified assumptions on prior knowledge. As a result, a discrimination method is proposed for distinguishing sharp images and blurry images based on a fusion network. The proposed method can automatically discriminate and detect blur without performing image restoration or blur kernel function estimation. Actually, the blur and the noise are extracted by the improved VGG16 network and texture noise extraction algorithm, respectively. Then the fusion network integrates the advantages of deep learning and hand-extracted features, and achieves ultimate high-accuracy discrimination results. Rigorous experiments performed on own dataset and other popular datasets with a number of blurry images and sharp images, including RealBlur dataset, BSD-B dataset, and GoPro dataset. The results show that the proposed method outperforms with an accuracy of 98% on our own dataset and 94.8% on the other dataset, which satisfies the requirements of the image applications. Similarly, we have compared our method with state-of-the-art methods to show its robustness and generalization ability.
From Rocks to Walls: a Model-free Reinforcement Learning Approach to Dry Stacking with Irregular Rocks

In-situ resource utilization (ISRU) is a key aspect for an efficient human exploration of extraterrestrial environments. A cost-effective method for the construction of preliminary structures is dry stacking with locally found unprocessed rocks. This work focus on learning this task from scratch. Former approaches rely on previously acquired models of rocks, which may be hard to obtain in the context of a mission. In alternative, we propose a model-free, data driven approach. We formulate the problem as the task of selecting the position to place each rock on top of the currently built structure. The rocks are presented to the robot in sequence. The goal is to assemble a wall that approximates a target volume, given the 3D perception of the currently built structure, the next object and the target volume. An agent is developed to learn this task using reinforcement learning. The deep Q-networks (DQN) algorithm is used, where the Q-network outputs a value map corresponding to the expected return of placing the object in each position of a top-view depth image. The learned policy outperforms engineered heuristics, both in terms of stability of the structure and similarity with the target volume. Despite the simplification of the task, the policy learned with this approach could be applied to a realistic setting as the high level planner in an autonomous construction pipeline.