Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intrinsic Interference Mitigation Scheme for FBMC-QAM Systems

Quadrature amplitude modulation-based filter-bank multicarrier system (FBMC-QAM) is a promising new radio access technology for the upcoming 5G standalone standard (5G-NR SA). By way of highly localized prototype filters, FBMC-QAM system is able to achieve low out-of-band (OOB) radiation and high spectral efficiency without the cyclic prefix (CP), at the price of non-orthogonality between adjacent subcarriers, intrinsic interferences, and bit error rate (BER) degradation. As an attempt to mitigate these impairments, a precoding scheme is proposed in this paper, which aims to maximize signal-to-leakage-and-noise ratio (SLNR) while allowing pulse-shaping filters with superb spectral characteristics to be leveraged. To justify the efficacy, performance analysis is conducted for prototype filters such as square-root raised cosine (SRRC), isotropic orthogonal transform algorithm (IOTA), and Mirrabasi-Martin (same as suggested in the PHY-DYAS project) for both additive white Gaussian noise (AWGN) and time-dispersive channels. The computer simulation shows that SLNR-based FBMC-QAM system incurs low complexity overhead and is able to enhance BER performance effectively.
A Guide to IL‐1 family cytokines in adjuvanticity

Growing awareness of the multiplicity of roles for the IL‐1 family in immune regulation has prompted research exploring these cytokines in the context of vaccine‐induced immunity. While tightly regulated, cytokines of the IL‐1 family are normally released in response to cellular stress and in combination with other danger‐/damage‐associated molecular patterns (DAMPs), triggering potent local and systemic immune responses. In the context of infection or autoimmunity, engagement of IL‐1 family receptors links robust innate responses to adaptive immunity. Clinical and experimental evidence has revealed that many vaccine adjuvants induce the release of one or multiple IL‐1 family cytokines. The coordinated release of IL‐1 family members in response to adjuvant‐induced damage or cell death may be a determining factor in the transition from local inflammation to the induction of an adaptive response. Here, we analyse the effects of IL‐1 family cytokines on innate and adaptive immunity with a particular emphasis on activation of antigen‐presenting cells and induction of T cell‐mediated immunity, and we address in detail the contribution of these cytokines to the modes of action of vaccine adjuvants including those currently approved for human use.
Unknown-Length Handwritten Numeral String Recognition Using Cascade of PCA-SVMNet Classifiers

Automatic recognition of handwritten digit string with unknown length has many potential real applications. The most challenging step in this problem is how to efficiently segment connected and/or overlapped digits exhibited in the input image. Most existing numeral string segmentation approaches combine several segmentation hypotheses to handle various types of connected digits. This paper proposes a new handwritten digit string recognition without applying any explicit segmentation techniques. The proposed method uses a new cascade of hybrid principal component analysis network (PCANet) and support vector machine (SVM) classifier called PCA-SVMNet. PCANet is an emerging unsupervised simple deep neural network typically with only two convolutional layers. The proposed PCA-SVMNet model adds a new fully connected layer trained separately using SVM optimization method. Cascaded stages of PCA-SVMNet classifiers are constructed and trained to recognize various types of isolated and connected digits. Every PCA-SVMNet classifier is trained separately using combinations of real and synthetic touching digits. The first 1D-PCA-SVMNet stage is trained to recognize isolated handwritten digits $(0\ldots 9)$ while forwarding non-isolated digits to the next stages. Each of the following stages is designed to recognize a class of connected digits and forwards the higher class to its successor. Multiple stages can be added accordingly to classify more complex touching digits. The experimental results using NIST SD19 real dataset show that the cascade of PCA-SVMNet classifier efficiently recognizes unknown handwritten digit string without applying any sophisticated segmentation methods. The proposed method achieves state-of-the-art recognition accuracy compared to other segmentation-free techniques.
Close Sound Source Localization incorporating Semi-Supervised Variational Bayesian NMF

This paper proposes a method to integrate a sound source separation method based on Nonnegative Matrix Factorization approach that is able to cope with partially known acoustic characteristics, and a sound source localization method with a frequency bin selection utilizing the separated signals. The proposed method is effective in the case when the target sound source locates close to a noise source, and the noise characteristics are available owing to the sound sound separation suppress the effect of noise. Numerical simulations showed that the proposed system succeeded to localize two sources of 5° interval, which validates the approach.
Turbulence model of the cosmic structure

The Kolmogorov approach to turbulence is applied to the Burgers turbulence in the stochastic adhesion model of large-scale structure formation. As the perturbative approach to this model is unreliable, here is proposed a new, non-perturbative approach, based on a suitable formulation of Kolmogorov’s scaling laws. This approach suggests that the power-law exponent of the matter density two-point correlation function is in the range 1–1.33, but it also suggests that the adhesion model neglects important aspects of the gravitational dynamics. The large-scale structure of the universe is produced by the gravitational clustering of an initially homogeneous matter distribution. This process can be described by the Newtonian equations of motion of the matter fluid, written in comoving coordinates and in terms of the peculiar velocity and gravitational fields [1]. These equations are nonlinear and, although they can be linearized to describe the early growth of small perturbations of the initially homogeneous distribution, the actual structure formation takes place when the nonlinearity plays a major rôle, at the aptly called nonlinear stage of gravitational clustering. The nonlinearity of fluid mechanics also plays a major rôle in the phenomenon of turbulence and this is the cause of the difficulty in treating this phenomenon (often referred to as the “unsolved problem of classical physics”). Since turbulence is a key actor in many astrophysical scenarios, it is tempting to apply methods and ideas of turbulence to the study of large-scale structure formation. An early attempt to apply Kolmogorov’s scaling laws to the origin of galaxies was made by Weizsäcker [2], but his ideas have been since mostly restricted to intragalactic turbulence and have played no role in the study of the formation of clusters and superclusters of galaxies or the large-scale distribution of the dark matter. However, a popular model of large-scale structure formation, namely, the adhesion model [3, 4], is essentially a model of pressure-less turbulence, namely, the type of turbulence that occurs in strongly compressible flows and is usually called Burgers turbulence. As shown in this letter, the Kolmogorov approach to turbulence can be applied to cosmic Burgers turbulence, employing a suitable formulation of the adhesion model.
Mispronunciation Detection Using Deep Convolutional Neural Network Features and Transfer Learning-Based Model for Arabic Phonemes

Computer-assisted language learning (CALL) systems provide an automated framework to identify mispronunciation and give useful feedback. Traditionally, handcrafted acoustic-phonetic features are used to detect mispronunciation. From this line of research, this paper investigates the use of the deep convolutional neural network for mispronunciation detection of Arabic phonemes. We propose two methods with different techniques, i.e., convolutional neural network features (CNN_Features)-based technique and a transfer learning-based technique to detect mispronunciation detection. In the first method, we use deep CNN features to detect mispronunciation. We also extract features from different layers of CNN (layer4 to layer7) to train k-nearest neighbor (KNN), support vector machine (SVM), and neural network (NN) classifiers. In the transfer learning-based method, we trained the CNN using transfer learning to detect mispronunciation. To evaluate the performance of the system, we compare the results of these methods with baseline handcrafted features-based method for 28 Arabic phonemes. In the baseline method, we use the same classifiers; KNN, SVM, and NN to detect mispronunciation. The experimental results show that handcrafted_features method, CNN_features, and transfer learning-based method achieve an accuracy of 82%, 91.7%, and 92.2%, respectively. The performance analysis shows that transfer learning-based method outperforms handcrafted_features and transfer CNN_features-based methods and achieve an accuracy of 92.2%. The proposed transfer learning-based method also outperforms the state-of-art techniques in term of accuracy.
Response Prediction of Stochastic Dynamics by Neural Network: Theory and Application on Railway Vehicle

Stochastic dynamics is a research topic for railway vehicles involving a wide range of randomness or uncertainty. However, the modeling and calculation of stochastic dynamic systems are often high-cost and low-efficiency. Neural network is an effective machine learning tool driven by data; this paper devotes to bridge the gap between neural networks and stochastic dynamics and to attain proper uses of this technique in railway vehicles. The mapping capability of neural networks for various stochastic suspension dynamics is validated by the proposed random repetition scheme. And this powerful computational tool is applied to predict the dynamic performance of high-speed trains in service instead of dynamics calculations; a typical case is analyzed to emphasize the advantage of the dynamic performance evaluation considering the coupling of various factors that it can enhance the security and reliability by attaining prognostic and health management and condition-based maintenance.
Formation-based 3D Mapping of Micro Aerial Vehicles

Micro Aerial Vehicles have brought tremendous interests to the research community, particularly in localization and mapping. While there are many commercially available sensors, such as Laser Range Finders (LRF) and RGBD cameras, that provide accurate 3D maps, they usually have significant power and payload requirements. This means, small flying robots are unable to handle such sensors. This study explores the possibility of collaborative mapping using formations from multiple simple cameras to obtain an accurate map similar to that of the LRF and RGBD cameras. By using multiple small robots and integrating them as one, we have created a platform for 3D reconstruction in which formations can be incorporated. Thus, the proposed method can be used with a low-cost system for surveying, disaster management, and surveillance in the future.
Advances in cellulose-based superabsorbent hydrogels

This contribution provides a brief overview of recent progress in cellulose-based superabsorbent hydrogels, fabrication approaches, materials and promising applications. First, different synthesis methods are introduced, including physical, as well as chemical cross-linking. Second, some of the cellulose series original materials were introduced in this work. In addition, some applications and future research in cellulose-based superabsorbent hydrogels are also discussed in this review.
Histone demethylase PHF8 accelerates the progression of colorectal cancer and can be regulated by miR-488 in vitro

Plant homeo domain finger protein 8 (PHF8), as an oncogene, has been highlighted in cancer development and progression. However, its clinical significance and underlying molecular mechanisms in colorectal cancer (CRC) remain to be fully elucidated. In the present study, the role of PHF8 in the progression of CRC was investigated. The mRNA and protein levels of PHF8 in tissues from patients with CRC and cell lines were detected using the reverse transcription-quantitative polymerase chain reaction and western blotting, respectively. Cell viability was analyzed using an MTT assay. The targeted genes were predicted using a bioinformatics algorithm and confirmed by a dual luciferase reporter assay. Cell migration was evaluated using a Transwell assay. The results demonstrated that the expression of PHF8 was significantly increased in tumor tissues from patients with CRC and was correlated with tumor-node-metastasis stage. In addition, it was found that overexpressed PHF8 was a predictor of poor overall survival rates in patients with CRC. PHF8 loss-of-function significantly inhibited proliferation and migration, and promoted apoptosis of CRC cells. In addition, bioinformatics methods demonstrated that PHF8 was a putative target of microRNA (miR)-488, and miR-488 was able to inhibit the expression of PHF8 in CRC cells. miR-488 loss-of-function showed increased proliferation and migration, and these effects were reversed by sh-PHF8 transfection in CRC cells. In vitro and in vivo experiments revealed that PHF8 accelerated cancer cell growth and migration, confirming the oncogenic role of PHF8 in human CRC. In conclusion, PHF8 and miR-488 may serve as CRC biomarkers for the prediction of clinical outcome and provide a target for the diagnosis and therapy of CRC.
DyLink2Vec: Effective Feature Representation for Link Prediction in Dynamic Networks

The temporal dynamics of a complex system such as a social network or a communication network can be studied by understanding the patterns of link appearance and disappearance over time. A critical task along this understanding is to predict the link state of the network at a future time given a collection of link states at earlier time points. In existing literature, this task is known as link prediction in dynamic networks. Solving this task is more difficult than its counterpart in static networks because an effective feature representation of node-pair instances for the case of dynamic network is hard to obtain. To overcome this problem, we propose a novel method for metric embedding of node-pair instances of a dynamic network. The proposed method models the metric embedding task as an optimal coding problem where the objective is to minimize the reconstruction error, and it solves this optimization task using a gradient descent method. We validate the effectiveness of the learned feature representation by utilizing it for link prediction in various real-life dynamic networks. Specifically, we show that our proposed link prediction model, which uses the extracted feature representation for the training instances, outperforms several existing methods that use well-known link prediction features.
Acoustic Methodologies for Classifying Gender and Emotions using Machine Learning Algorithms

The machine learning classifiers are found to be the most relevant classifiers regarding emotions and gender recognition using speech. In this research, emotions and gender are recognized with the help of two machine learning classifiers: Support Vector Machine and Naive Bayes which incorporates four speech features: shimmer, jitter, energy, and pitch for classification. The classifiers take these input sequences and correctly classify the gender as well as the emotion in the speech signal. Results shows, regarding classification accuracy, Support Vector Machine outperforms the Naive Bayes classifier by almost 10% in gender recognition and 35% in emotion recognition. The emotion and gender recognition techniques are applying in many telecommunication and security-based applications such as sentiment analysis, speech analysis, and other security purposes.
Robust Polarimetric SAR Imaging Method With Attributed Scattering Characterization

Conventional ideal point model-based synthetic aperture radar (SAR) imaging techniques may lead to scattering estimation inaccurate and object-level information loss because they ignore the frequency, angle, and polarimetric dependence of canonical scatterers. These issues are especially severe when SAR measurement is contaminated with impulsive noise. To tackle the problem, in this paper, we propose a robust polarimetric SAR imaging method with attributed scattering priors in the framework of compressive sensing. In this scheme, the overcomplete dictionary is formed by taking advantage of the physically relevant attributed scattering center (ASC) model, which is capable to represent the electromagnetic behaviors of man-made assemblies. By using the robust data-fitting model and employing the joint sparsity of multiple polarization measurements, the problem of estimating ASC attributes can be converted into robust group sparse recovery via <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> optimization. To solve this formulation efficiently, we develop an alternating direction method of multipliers framework via exploiting the proximity operator of complex <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> norm and <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> norm functions for complex-valued SAR images. Finally, the super-resolution SAR image is achieved via spectrum extrapolation based on the estimated ASC attributes. The proposed technique can enhance the SAR image features arising from the non-ideal scattering components of the target and gain robust performance to impulsive noise. The experimental results confirm the effectiveness and efficiency of the proposed algorithm.
Influence of Ceramic Laminate Veneer Thickness on Sorption and Solubility of Light-Cured Resin Cement

Objective: This study evaluated the influence of different thicknesses of ceramic laminate veneers on water sorption and solubility of light-cured resin cement. Materials and Methods: Twenty-four specimens of light-cured resin cement (8 mm diameter and 1 mm thickness) were divided into four groups according to ceramic thickness interposed during cure: without ceramic laminate veneer; 0.7 mm, 1.0 mm and 1.3 mm. All samples were subject to water sorption and solubility process. The mean water sorption and solubility values were compared by One-way ANOVA/Tukey’s test (α = 0.05). Pearson's correlation was performed to identify correlations between water sorption and solubility as well between ceramic thickness and water sorption and solubility. Results: Significant differences were found in the sorption values (P < 0.05) and solubility (P < 0.01) for the tested conditions. Water sorption and solubility in the cement light-cured under thicker ceramic were statistically higher than control group. Moreover, there was a positive correlation between veneer thickness and water sorption and solubility of the light-cured resin cement. Conclusion: The variation in the thickness of the ceramic laminate veneer can alter water sorption and solubility properties of the light-cured resin cement tested.
A Practitioner’s Approach to Assess the WCAG 2.0 Website Accessibility Challenges

Now-a-days many of the users are accessing a website through their mobile devices. The user with a low vision disability finds difficult in accessing the content of the site using a mobile. This point made the researcher to think of website evaluation and identified that verification is needed to determine how best a website is accessible even for a person with different disabilities. The present article has reviewed 43 literatures and studied how their research is carried out, what suggestions they made, and what architecture frameworks or methods they suggested to evaluate the accessibility of a website content. The article also studied how the existing literatures considered W3C guidelines like WCAG 2.0 while developing the website. The main objective of the research is concluded with identifying of similarities with the past and the present research activities. Finally, a comparative study was made and later analyzed to determine their outcomes and some barriers if any.
The Effect of an Authentic Acute Physical Education Session of Dance on Elementary Students' Selective Attention

There have been calls to test the potential benefits of different forms of physical activity (PA) to executive function, particularly in authentic settings. Hence, the purpose of this study was to investigate the effect of an acute dance session within an existing physical education class on students' selective attention. The study employed a pre/posttest quasi-experimental design with a comparison group in one Aotearoa, New Zealand, primary school. Participants were 192 students (comparison group = 104 students) in Years 5 and 6. The intervention group participated in a dance-based physical education lesson while the comparison group continued their regular classroom work. PA during the physical education lesson was monitored using accelerometers. Selective attention was assessed at pretest and after the comparison/physical education sessions with the d2 Test of Attention. 2 × 2 ANOVA results suggested a significant time effect for all three measures, no significant group effects for any measures, and significant time by group interactions for TN and CP but not for E%. The intervention group improved significantly more than the comparison group for TN and CP. This study's findings suggest that existing school opportunities focused on cognitively engaging PA, such as dance, can improve aspects of students' selective attention.
From apoplexy to stroke.

". it is a disease in which the functions of relation are suspended, while those of organic life continue. A fit of apoplexy, it has been often observed, resembles in many respects, profound sleep. There is the same insensibility to external impressions, the same unconsciousness of everything that is passing around; the action of the heart and respiration go on in both instances, but the individual is shut out from the world, sight, hearing, smell, touch and taste being abolished for the time"[l].
Safety Aura Visualization for Variable Impedance Actuated Robots

This paper presents an augmented reality-based method to increase human cognitive awareness of the danger/safety in the workspace of a variable impedance actuated (VIA) robot. Specifically, the "safety aura" around the robot was created using a safety/index defined for each point of the workspace. The safety-index is a composite metric incorporating the measurements of the distance from a point in the workspace to the VIA robot and the corresponding velocity during its motion. The safety aura is task-specific and is able to represent the safety levels in the workspace of the robot as it follows different trajectories. We developed a robot simulator which allows the visualization of the robot’s motion and assessment of the corresponding safety aura in 3D space. The generated safety aura was overlaid into mixed reality using Unity 3D platform together with Vuforia and web camera. Simulation results demonstrated the ability of the safety metric based on distance and velocity measurements to qualitatively assess the amount of danger. Our method has the potential to make the physical human robot interaction safer.