Miracle-dz/UniFAR-FTUs huggingface dataset:
Multidisciplinary Virtual Environments for Kids with ADHD: A Pilot Study

This work addresses the incorporation of virtual environments as a support to the needs of elementary school students with ADHD. The incorporation of these environments under a multidisciplinary approach can help to strengthen children’s skills in a playful and motivating way allowing specialists to work on specific areas of ADHD. For this purpose, a model based on the algorithm for the identification and diagnosis of ADHD is presented from the computational point of view to obtain the design requirements of the proposed virtual environments. A pilot study involving 5 children identified with ADHD and learning disabilities is presented. It was a multidisciplinary team composed of a special education teacher who supports children with learning disabilities and ADHD and the group’s teacher in an elementary education institution. Four key ADHD skills (Instruction, Directionality, Memory and Attention) were identified and assessed through the use of the Maze3D virtual environment in which the user used the LeapMotion controller. The results obtained from the evaluation of skills showed an increase in the directionality and memory skills while the instruction and attention skills presented similar or lower values than the pre-test. We also conducted a user experience evaluation which indicated that users were most satisfied with the use of the application.
Review of the Evaluation Methods for Stress, Anxiety, and Depression Levels

Nowadays, everyone experiences stress and anxiety occasionally. The main difference is that stress is generally a response to a threat, and anxiety is a stress reaction. Stress, anxiety, and depression are recognized as global disorders. Therefore, it is essential to reduce it as much as possible. Clinical psychological tests have been introduced to quantify stress, anxiety, and depression levels. These lend support to specialists in treating patients suffering from one or all these disorders. Thus, this review aims to present some tests used nationally, in Romania, and internationally, to assess the patient's degree of impairment and to determine if one of the tests can differentiate these diseases better based on their symptoms.
Automatic Removal of Scalp EEG Artifacts Using an Interpretable Hybrid Deep Learning Method

The application of scalp electroencephalogram (EEG) in brain-computer interface (BCI) is inevitably hindered by the contamination of various physiological artifacts. Artifact elimination plays an important role in EEG signal analysis due to its weak power and low signal-to-noise ratio. Recent studies using deep learning (DL) methods have reported good performance for offline artifact elimination. However, existing methods still neglect two key defects in clinical applications: (1) offline processing may not fit inreal-time BCI application scenarios; and (2) direct and arbitrary usage of conventional neural network structures lacks physiological interpretability. To address these problems, we propose a hybrid method for EEG artifact elimination, combining a canonical correlation analysis (CCA) module–a blind source separation method with a multi-scaled convolutional neural network (CNN) module. Specifically, the CCA module transfers the recorded EEG signals simultaneously from the channel space to the source space and the CNN module detects and eliminates artifacts with explainable processing. Extensive experiments on semi-simulation, resting-state, and motor imagery EEG datasets were conducted and the results showed competitive performance and strong adaptability of the proposed method.
Understanding fly-ear inspired directional microphones

In this article, the equivalent two-degree-of-freedom (2-DOF) model for the hypersensitive ear of fly Ormia ocharacea is revisited. It is found that in addition to the mechanical coupling between the ears, the key to the remarkable directional hearing ability of the fly is the proper contributions of the rocking mode and bending mode of the ear structure. This can serve as the basis for the development of fly-ear inspired directional microphones. New insights are also provided to establish the connection between the mechanics of the fly ear and the prior biological experiments, which reveals that the fly ear is a nature-designed optimal structure that might have evolved to best perform its localization task at 5 kHz. Based on this understanding, a new design of the fly-ear inspired directional microphone is presented and a corresponding normalized continuum mechanics model is derived. Parametric studies are carried out to study the influence of the identified non-dimensional parameters on the microphone performance. Directional microphones are developed to verify the understanding and concept. This study provides a theoretical guidance to develop miniature bio-inspired directional microphones, and can impact many fronts that require miniature directional microphones.
Transferable Discriminative Learning for Medical Open-Set Domain Adaptation: Application to Pneumonia Classification

Previous pneumonia classification algorithms have succeeded in the clinic under closed and static environments. However, in the real world, the emergence of new categories (e.g., COVID-19) and changes in data distribution will cause the existing methods to lose their robustness. In this paper, we formalize this problem as medical open-set domain adaptation under open and dynamic environments. The critical challenge of this problem is to accurately detect the open class samples with subtle differences from the common class. To achieve that, we propose transferable discriminative learning that remarkably achieves robust pneumonia classification with distribution shift and open class emerging. First, we propose the transferable high-density clustering module to detect open class samples and obtain reliable common class samples by considering the density degree. Secondly, we present the transferable triplet loss to enlarge the semantic feature difference between common class and open class samples. Finally, we design the transferable scoring function to detect open class samples effectively. A series of empirical studies show that our algorithm remarkably outperforms state-of-the-art methods. This result demonstrates its potential as a clinical tool for medical open-set domain adaptation.
Feasibility Evaluation of Compact Flow Features for Real-time DDoS Attacks Classifications

According to the research trend, training the distributed denial of services (DDoS) attacks classifier using network flow features will yield higher classification performances and efficiency than the per-packet-based approach. Nonetheless, the existing flow-based classifier uses bloated features and offline flow extraction that is not suitable for real-time DDoS protection. This study investigates the feasibility of compact flow features that can be directly extracted using a programmable switch for real-time DDoS attack classification. The proposed method considers only four flow features: IP protocols, packet counter, total byte counter, and the delta time of a network flow. The evaluation results on the CICDDoS2019 dataset showed a comparable classification performance to the works that use bloated features (24 - 82 features). The best result was achieved by the decision tree and the random forest classifier showing ≥ 89.5% scores in accuracy, precision, recall, and F1 score. The proposed models can classify 10 out of 12 DDoS attacks correctly, failing only to discriminate between SSDP and UDP-based DDoS attacks. In addition, the trained classifier shows a better generalization ability by retaining similar performances on unseen 42.8 millions flow data while trained on ≤ 200 thousand flow data. At last, the proposed method is suitable for real-time application since it supports quick classification performance of up to 9.6 millions of flow inferring per second on the Decision Tree classifier.
Investigation of the Possibility of Damped Sinusoid Decomposition in a Turn of a Meander Microstrip Line

The paper presents a study of the possibility of decomposition of a damped sinusoid in a turn of a meander microstrip line. To do this, firstly, the properties of a damped sinusoid, which can be used to reduce the amplitude of the output signal, is analyzed. Then, simple theoretical foundations of the approach are proposed. The simulation demonstrated that the input signal can be attenuated for three signal frequencies f0=1,3, and 10 GHz. Several options are considered that minimize the amplitude of the output signal. First, we show the possibility of complete signal decomposition, as a result of which the amplitude was attenuated up to 3 times. Then, we consider the possibility of partial decomposition. In this case, the amplitude is decreased by superposition and mutual compensation of half-waves of a sine wave of different polarity. As a result, a decrease was of up to 3.05 times. finally, we present the possibility of reducing the length of the turn to 3.4 times while maintaining attenuation efficiency. The results obtained indicate that these approaches are promising and there is a need to search for the possibility of their practical implementation.
Improving Distribution Systems Maintenance Management, by Predicting Reliability Indices Using Fully Connected Neural Networks

Due to the increasing amount of industrial data worldwide, deep learning solutions have become extensively popular for preventive maintenance programs. Preventive maintenance reduces the costs of equipment failures, thus crafting detailed preventive maintenance programs are very effective. Neural networks are intelligent computing techniques inspired by biological neurons. Neural networks are one of the most common and practical machine learning algorithms that are utilized in many industrial applications. In this research, a fully connected neural network is developed to predict the systems reliability indices, i.e. the number of failures occurring in feeders and the amount of energy not served (ENS) in electricity distribution feeders. As a result, a data-driven improvement is provided for the preventive maintenance program.
Image Recognition Based on Enhanced-Conformer

Convolutional Neural Networks (CNNs) has always dominated visual recognition tasks, and it is difficult to link distant information in images due to the size limitation of each convolution filter. Vision Transformer (ViT) can capture features at a distance in an image, but lacks the details of local features. Conformer combines the advantages of both using Convolutional Neural Networks (CNNs) and Attention mechanisms in parallel, but it does not take into account the relationship between different samples. Therefore, we propose a new attention calculation method, Extra-Attention, which can effectively learn intra-sample and inter-sample relationships. In order to combine the advantages of CNNs and Attention, in our work, we proposed a new network Enhanced-Conformer (ENC) based on Conformer, in which the attention mechanism adopts a more efficient computational module Inside And Outside Transformer (IAOT), which contains three parallel Attention: Extra-Attention, Self-Attention, External-Attention. Enhanced-Conformer (ENC) can fuse local features, global features and external features at the same time. We conduct experiments on the commonly used image recognition datasets, the recognition accuracies reach 93.29%, 68.70% and 57.63% on Tiny- ImageNet, CIFAR-10 and CIFAR-100, respectively.
Azimuth and Elevation Scanning with Stacked Modulated Geodesic Luneburg Lenses

Here, we propose a modulated geodesic lens antenna array in E-band (56-62GHz), which has scanning abilities in both azimuth and elevation. The array consists of four lens antenna elements, and each lens antenna produces 13 independent beams that cover 110 degrees in the H-plane with scan losses below 1dB. The array is fed with a 1:4 power divider comprising three phase shifters to scan in the E-plane. The distance between lenses in the E-plane is 0.7 wavelengths at 60GHz. The E-plane coverage is 60 degrees with scan losses around −2.2dB. The maximum realized gain varies from 20.8 to 24.1dB across the frequency band.
Effect of doping SiO2 on high-frequency magnetic properties for W-type barium ferrite

The microstructure, static and dynamic magnetic and dielectric properties of BaCo1.3Zn0. 7Fe16O27 doped with small amounts of SiO2 have been investigated. The results show that the crystal structure and static magnetic properties have no obvious changes as SiO2 varies from 0 to 1 wt %. However, the complex permittivity and complex permeability could be significantly modified by SiO2 doping. Due to the doping, both real and imaginary permittivity are greatly increased. The increase has its origin in the formation of Fe2+, due to minor Si4+ ions entering the crystal lattice of barium ferrites. The complex permeability spectra can be decomposed into two components, which are associated with natural resonance and wall resonance. By doping with SiO2, the contribution of wall resonance to the static permeability increases, while the contribution of the natural resonance decreases. The overall static permeability is increased as a result.
Operator Partitioning and Parallel Scheduling Optimization for Deep Learning Compiler

TVM(tensor virtual machine) as a deep learning compiler which supports the conversion of machine learning models into TVM IR(intermediate representation) and to optimise the generation of high-performance machine code for various hardware platforms. While the traditional approach is to parallelise the cyclic transformations of operators, in this paper we partition the implementation of the operators in the deep learning compiler TVM with parallel scheduling to derive a faster running time solution for the operators. An optimisation algorithm for partitioning and parallel scheduling is designed for the deep learning compiler TVM, where operators such as two-dimensional convolutions are partitioned into multiple smaller implementations and several partitioned operators are run in parallel scheduling to derive the best operator partitioning and parallel scheduling decisions by means of performance estimation. To evaluate the effectiveness of the algorithm, multiple examples of the two-dimensional convolution operator, the average pooling operator, the maximum pooling operator, and the ReLU activation operator with different input sizes were tested on the CPU platform, and the performance of these operators was experimentally shown to be improved and the operators were run speedily.
Tensor Robust PCA Based on Transformed Tensor Singular Value Decomposition for Cancer Genomic Data

The mining and analysis of genomics data provides a new idea for exploring the pathogenesis of human disease. Since these data often have the features of small samples, high-dimensional, and high redundancy, the traditional matrix decomposition method cannot fully mine the spatial structure and multiple perspective information of cancer genomics data. Inspired by the recently proposed robust tensor completion method, a tensor robust PCA method (TTTD) was proposed based on U-product and transformed tensor singular value decomposition (t-SVD) to explore the integrated cancer genomics data in this paper. Specifically, the unitary transform matrix is employed to replace the discrete Fourier transform matrix in t-SVD, which contributes to recover a lower tubal rank tensor to a certain extent. Meanwhile, the $\mathrm{L}_{2,1}-$norm is employed to learn the sparse term, and the row sparse constraint generated by it can better detect the abnormal value of the real tensor. In addition, the alternating direction method of the multiplier algorithm is used to optimize the TTTD method. Experimental results on the three integrated cancer multi-omics datasets show that the TTTD method achieves the better performance.
Dynamic Modeling of a Soft Laparoscope: A Deep Neural Network Approach

Soft robotics is a research area with a diverse number of designs and shapes depending on the application. It is due to this variety that the modeling of soft robots is reduced to a few methods. However, a model based on neural networks simplifies and connects the necessary variables to perform tasks in real-time. This model relates the coordinates of the end effector in the Cartesian plane with the inputs of the soft actuator, which are the internal pressures of each chamber. In addition, a model based on neural networks considers the limitations of the system since the base data for learning is similar to real conditions data. With this approach and the piecewise constant curvature kinematic modeling, the position and orientation in the workspace of the soft laparoscope can be accurately identified.
A survey of foot drop stimulator control for stroke patients

This paper reports several approaches by using sensors to capture gait for a foot drop stimulators control. Foot drop stimulator has been shown to be a safe and effective technique and has been put into correct foot drop for several years. The aim of this paper is to bring together research in the file of foot drop stimulator control and shed light on the control principles of foot drop stimulators. This paper is reviewed starting with gait implications of foot drop and current treatment options, and then several different sensors used to control foot drop stimulator. Finally, the advantages and disadvantages of several sensors for a foot drop stimulators control are critically analyzed.
Vehicular Traffic Offloading Through Intelligent RSU Selection in VANET

Vehicular networks' data-hungry applications increase the workload on current 4G-5G cellular networks, Consequently delivering poor services to the vehicular user. This problem can be overcome by sharing the workload with other wireless networks. Road-Side Units (RSU) are stationary wireless stations that can be effectively used for data offloading in a Vehicular ad-hoc network (VANET). A vehicle can offload the data directly through an RSU whenever it comes inside the range of RSU or through a V2V2I path when it does not lie inside the RSU coverage. However, in Urban areas or busy roads, the vehicle comes inside the range of multiple RSUs, where it needs to select an RSU intelligently to maintain the qualitative services. This paper proposes a Suitability rewarding-based RSU selection (SRB-RSU) method for selecting an RSU intelligently that provides qualitative services. In the SRB-RSU algorithm, Befit Factor and RSU's Linkage Factor is utilized for calculating the Suitability Factor (SF). Based on SF top ranked RSU is selected for offloading. The proposed SRB-RSU method is evaluated on two metrics packet delivery ratio (PDR) and throughput. The proposed approach outperforms by increasing the PDR by 8.592% and throughput by 9.10% compared to other benchmark approaches.
End-to-End Deep Learning for Stress Recognition Using Remote Photoplethysmography

With the development of remote Photoplethysmography, contactless measurement of physiological parameters is possible. Several works have investigated stress recognition based on heart rate variability features extracted from cameras. In this paper, we study the application of deep learning networks for end-to-end cognitive stress recognition using remote Photoplethysmography. We compared different network architectures in an experiment with 15 participants at rest and under cognitive stress. The experimental results show that the CNN-based networks can learn stress-related characteristics even from inter-beat-interval signals of short length. The deep network-based approaches outperformed the classical HRV feature-based methods.
Verifying Liveness and Real-Time of OS-Based Embedded Software

Embedded devices are fundamental to a huge variety of application areas, with a wide range of complexity and criticality. Therefore, they must satisfy a variety of (non-)functional requirements, and reliable strategies are necessary to guarantee that these requirements are met. As an additional complication, modern software systems are composed from various modules that interact and interfere at runtime. Operating Systems (OSes) are then used to manage the concurrency, but introduce additional complexity and runtime effects. Where testing is no longer sufficient to assess the software correctness, formal methods are becoming increasingly popular. Respecting the typical layering in embedded software, we propose a generic formal modeling scheme for OS-based application tasks and the formal verification of their liveness and real-time requirements. We use UPPAAL to model the software composition as the conjunction of application tasks and the OS, and for taking the interaction and interference of tasks through the OS into account. Despite of focusing on only two requirements in this paper, the modeling strategy is generic and extensible, meaning that additional requirements can be modeled and verified in a similar manner. An evaluation shows the general benefit of the approach as well as the impact of various factors on the verification complexity and scalability.
A short introduction to broad and variable iron lines around black holes

Accreting black holes often show iron line emission in their X-ray spectra. When this line emission is very broad or variable then it is likely to originate from close to the black hole. The theory and observations of such broad and variable iron lines are briefly reviewed here. In order for a clear broad line to be found, one or more of the following have to occur: high iron abundance, dense disk surface and minimal complex absorption. Several excellent examples are found from observations of Seyfert galaxies and Galactic Black Holes. In several cases there is strong evidence that the black hole is rapidly spinning. Further examples are expected as more long observations are made with XMM-Newton, Chandra and Suzaku. Intriguing instances of rapid variability of some narrow iron lines, both emission and absorption, have been reported. These may reflect variations in the irradiation or motion of physical structures on the accretion disk. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Dual Gradient Alignment for Unsupervised Domain Adaptation on Optic Disc and Cup Segmentation

Accurate segmentation of the optic disc and cup (OD/OC) in fundus images is crucial for the glaucoma diagnosis and treatment. However, the distribution discrepancies (domain shift) between source domain and target domain hinder the generalization of segmentation models in clinical applications. In this paper, a dual gradient alignment framework (DGDA) for unsupervised domain adaptation is proposed to achieve accurate OD/OC segmentation. Specifically, feature gradient alignment module is well-designed to learn the domain-invariant representation by aligning the feature gradients between source and target domains. Furthermore, task gradient alignment module introduces the meta-learning to learn the task-agnostic representation to simultaneously balance domain adaptation and OD/OC segmentation. Here, meta-learning imitates multi-task training by aligning the gradients between the segmentation and domain classification task. Extensive experiments are conducted on the two public fundus image datasets (Drishti-GS and RIM-ONE-r3) to evaluate the effectiveness of the proposed DGDA. Experimental results demonstrate the DGDA successfully achieves the unsupervised domain adaptive OD/OC segmentation with competitive performance compared with the state-of-the-art.