Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of the Information System of Retired Veteran Cadres Bureau Based on SpringBoot Framework

The bureau of retired veteran cadres is a department that serves and manages the work of retired veteran cadres. There are problems such as complex management processes and low efficiency in work execution. The application of Internet technology to the government services of the retired veteran cadres bureau can effectively solve these problems. In order to promote the information management of retired veteran cadres bureau and provide convenience for retired veteran cadres to carry out learning and education and participate in cultural activities through the Internet, this paper proposes to use the SpringBoot framework to customize the development of the retired veteran cadres bureau information system. The article adopts the concept of microservice architecture, uses SpringBoot to build the overall architecture of the system, and integrates Mybits, Redis and other technologies on the SpringBoot framework. This system effectively solves the problem of the difficulty of refined management of retired veteran cadres bureau information. At the same time, the system meets the requirements of multi-terminal access, front-end separation, multi-function, low coupling, high cohesion, and easy scalability.
Optimizing the parameters of spiking neural networks for mobile robot implementation

In this paper, a reward-based spike-timing-dependent plasticity method is used for the learning process of non-holonomic robots to acquire the task of target attraction. A specific fit function is developed to measure the effects of different dopamine multiplication coefficients on the training process of the spiking neural networks as well as determining the optimal operating frequencies for the network. Genetic Algorithms are used for both approaches. Several coefficients are chosen and the performance of the robot is detected based on the value of the developed fit function and the total training time. Moreover, different operational frequencies are associated with different neural regions to enhance the functionality of the network after the training phase is complete. The trained network is implemented on a mobile robot to evaluate robot performance.
SIMULTANEOUS DETERMINATION OF PARABENS IN PHARMACEUTICAL PREPARATIONS USING HIGH-PERFORMANCE THIN-LAYER CHROMATOGRAPHY AND IMAGE ANALYSIS

A study for quantitative evaluation of thin layer chromatographic plates by using, for the first time, the BioDit Thin Layer Chromatography (TLC) Scanner device in UV mode was developed for simultaneous determination of the parabens (methyl, ethyl, propyl, and butylparaben) in pharmaceutical preparations. ImageDecipher-TLC software and Sorbfil TLC Videodensitometer were used for image processing analysis. After their chromatographic separation, the quantification of the parabens was made by the conversion of RGB colored images of TLC plates (obtained by UV light scanning at λ = 254 nm) into grey scale. Experimental results showed a strongly linear correlation between area of grey spots and concentration of parabens covering a relatively large domain of concentration in all cases. The linearity of the analytical procedure was in range of 0.46–2.47 µg/spot for methylparaben, 0.50–2.99 µg/spot for ethylparaben, 0.54–3.24 µg/spot for propylparaben, and, respectively, 0.58–3.49 µg/spot for butylparaben, the regression determination coefficient being higher than 0.9977 in all cases. The recovery studies performed on two levels of concentration gave values higher than 96.32% in the case of ImageDecipher-TLC software for all studied parabens. The limits of detection and quantification varied in that case between 0.10–0.37 µg/spot and 0.20–0.44 µg/spot, respectively. The proposed method was found to have a good precision and accuracy and their applicability was assessed for commercial pharmaceutical syrup samples analysis.
Design of a Differential Coupled-Line Directional Coupler Using a Double-Side Coplanar Waveguide Structure With Common-Signal Suppression

In this work, a rigorous approach for the design of a differential coupled-line directional coupler (DCLDC) implemented in double-side coplanar-waveguide (CPW) technology is presented. Due to the double symmetry of the structure, it is possible to obtain the coupling level and port characteristic impedances of the coupler in a simple and efficient way. In addition, due to the broadside coupling of the involved differential CPW lines, a wide range of coupling levels can be covered. Design curves relating the geometrical parameters of the structure to the electrical parameters of the DCLDC are provided, and a 3-dB coupler with an impedance of $50~\Omega $ at the terminal ports has been designed, fabricated, and measured. Common-mode (CM) transmission to both through and coupled output ports has been reduced by adding a simple and compact CM filter at the input port. The good agreement between the results obtained with EM simulation, transmission line models, and measurements confirms the benefits of the proposed structure and the suitability of the design procedure.
Discovering Inconsistencies between Requested Permissions and Application Metadata by using Deep Learning

Android gives us opportunity to extract meaningful information from metadata. From the security point of view, the missing important information in metadata of an application could be a sign of suspicious application, which could be directed for extensive analysis. Especially the usage of dangerous permissions is expected to be explained in app descriptions. The permission-to-description fidelity problem in the literature aims to discover such inconsistencies between the usage of permissions and descriptions. This study proposes a new method based on natural language processing and recurrent neural networks. The effect of user reviews on finding such inconsistencies is also investigated in addition to application descriptions. The experimental results show that high precision is obtained by the proposed solution, and the proposed method could be used for triage of Android applications.
A Probabilistic Routing-Based Secure Approach for Opportunistic IoT Network Using Blockchain

With expanding applications of the opportunistic Internet of Things (IoT), there has been ever-increasing enthusiasm in integrating blockchain with the IoT for enhancing privacy and security. However, due to high resource consumption by the blockchain, its implementation becomes challenging task for IoT networks. A traditional opportunistic IoT network sends multiple copies of messages for communicating information from source to destination, which gives rise to two risky situations: communication overhead and network security. Therefore, this paper proposes a probabilistic-based blockchain algorithm for opportunistic IoT network. The algorithm presents a secure routing protocol based on the delivery predictability of messages by integrating blockchain to ensure security and privacy. The simulation result shows that our proposed protocol outperforms some traditional routing schemes like epidemic routing and PRoPHET (Probabilistic Routing Protocol using History of Encounters and Transitivity) routing with more secure transmission.
Octa-functional POSS: An Epoxy Filler or Co-monomer?

Particle tethering with the matrix permits property manipulation depending on the respective architecture and chemistry. However, integration of highly reactive moieties into epoxy networks may detrimentally affect the reaction stoichiometry. In this study, octa glycidyl polyhedral oligomeric silsesquioxane (OG POSS) was incorporated into an epoxy matrix (a) by considering it as a co-monomer (requiring stoichiometric compensation) and (b) by considering it as a silica-based filler (simple addition without compensation). The simple addition of OG POSS resulted in a considerable glass transition temperature (Tg) decrease due to impaired stoichiometry, while the stoichiometric addition moderated this effect. These results, supported by the dielectric data, demonstrate that OG POSS is better considered as a co-monomer.
Distributed ADS-B system based on MPLS VPN

This paper introduces the ADS-B system. The distributed ADS-B system can increase the scope of monitoring and share data. Two distributed algorithms of ADS-B system are proposed, and their advantages and disadvantages are analyzed in detail. D-select algorithm is used to realize the online operation of the system. MPLS VPN technology is used to interconnect the ADS-B system, and the simulation experiment is used to test the results, the simulation experiment shows good results. The real ADS-B distributed system is built, all network parameters such as jitter, delay and packet loss are normal, and the functions of ADS-B distributed system are normal.
Super-resolution Reconstruction of Infrared Images of Internal Defective Metal Plates Based on Generative Adversarial Networks

Infrared thermal imaging non-destructive testing technology has made great progress and has been used in the field of defect detection. However, due to the internal noise of the infrared imaging equipment and the influence of the surrounding environment interference, the infrared images used for defect recognition have the disadvantages of low contrast, low resolution, and low signal-to-noise ratio. Our article first briefly introduces the basic principle of infrared thermal imaging detection technology and the development status at home and abroad, then we builds an infrared image acquisition system that uses long-pulsed thermal method to collect internal defects of a metal plate. In order to solve the problem of noises, our paper first adopts Gaussian blur and homomorphic filtering on the acquired infrared images. Then we use Laplacian operator to process the filtered images and obtained second-order differential images. Finally, we use GAN for super-resolution reconstruction of the filtered second-order differential images. The results show that the super-resolution reconstructed images have higher PSNR and SSIM. What’s more, it retains detailed information about defects in the original infrared images.
SSIM Motivated Quality Control for Versatile Video Coding

In this paper, we investigate the encoder control algorithm driven by the visual quality in Versatile Video Coding (VVC) standard. The proposed framework shifts the traditional bit rate-centered paradigm to the quality-centered paradigm, and adopts the repeatedly proven perceptual quality measure structural similarity index (SSIM) as the core in guiding the behaviors of encoder. In particular, a reduced-reference evaluation metric which could statistically establish the relationship between the SSIM and quantization parameter, is adopted for quality control. Extensive experiments verify the effectiveness of the proposed method, leading to precise quality control in the VVC Test Model (VTM-9.0).
Adverse Drug Reaction Detection Using Data Mining Techniques: A Review Article

Adverse drug reactions (ADRs) are side effects that occur when taking the drug in natural doses. ADRs are a public health issue because they hospitalize millions of patients worldwide each year. Early detection of ADRs reduces economic costs and prevents fatality.Diagnosis of ADRs usually depended on voluntary reporting or medical information. But in recent years, the data sent by the user on social media has become a significant source for detecting ADR. Twitter is a social media where people use short messages as a way to communicate. Limit the number of words on Twitter allows users to use words purposefully and focused. The information provided by users about drugs and their adverse reactions on Twitter is an important resource for post-marketing drug monitoring.In recent years, machine learning and data mining methods have been considered in the field of data science for ADR detection. Important challenges in this area are divided into three parts: data pre-processing, extracting meaningful features, and selecting the best model for classification.The aim of this study is to study, review and challenge the methods of ADR diagnosis by data mining on social media, especially Twitter.
Feedback Error Learning Controller based on RMSprop and Salp Swarm Algorithm for Automatic Voltage Regulator System

The primary goal of the Automatic Voltage Regulator (AVR) is to control the terminal voltage at the desired level. The controller used in AVR must be capable of maintaining generator terminal voltage in various operating conditions and also withstand uncertainty. In this paper, Feedback Error Learning (FEL) controller has been proposed to control the AVR system. FEL structure consists of the classical controller (PD controller) and the intelligent controller (MLP neural network controller). This control strategy has been employed to control unknown and uncertain plant model. Salp Swarm Algorithm (SSA) has been used to obtain the initial weights, biases and the number of neurons of the MLP neural network. The training methods used in this research are Stochastic Gradient Descend (SGD) and Root Mean Square propagation (RMSprop) that in particular; these methods are used in deep learning. The robustness and effectiveness of the proposed method has been studied in different operating conditions. The results demonstrate that the proposed strategy outperforms other methods.
Common-Mode EMI Mathematical Modeling Based on Inductive Coupling Theory in a Power Module With Parallel-Connected SiC MOSFETs

Parallelization of silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) could significantly improve the current capability and power rating of power converters. However, the inductive coupling effect of parasitic inductance between the paralleled branches is a critical challenge for the mathematical modeling of electromagnetic interference (EMI) in a power module with parallel-connected SiC MOSFETs (PMPSM). In this article, first, an EMI source generation mathematical modeling method for PMPSM is proposed. The quantitative analysis of EMI interference source caused by inductive coupling of PMPSM is introduced. Second, a common mode (CM) interference propagation mathematical modeling method for PMPSM is proposed. Through the equivalent CM interference source and equivalent impedance model, the strong and weak inductive coupling theory of CM interference equivalent circuit based on parallel-connected SiC MOSFETs is proposed. Third the effect of different decoupling capacitance values on EMI source is compared and analyzed and the influence of changing the ratio of coupling inductance inside and outside the decoupling branch on EMI source of parallel-connected switches is analyzed. Finally, a self-made 1.2-kV 160-A PMPSM based on EMI optimized layout is proposed and tested via an experimental platform of synchronous buck converter, which can verify the feasibility and validity of the proposed power module and the theoretical analysis.
An Automatic Hierarchical Leaf Venation Segmentation Based on Region Growing

Leaf venation segmentation plays an important role in understanding the biological significance of leaves and the laws of plant growth. Although the hierarchical venation network can better reflect the characteristics of veins, most of the existing methods only extract the complete vein network without hierarchical segmentation. The few methods available are either semi-automatic that requires manual intervention, or for leaves with smooth edges that do not segment leaves effectively. Thus we proposed a novel venation hierarchical segmentation method based on region growing, which is suitable for segmenting venation of sweet cherry leaves with serrated edges. The performance of the proposed method was evaluated on 80 images of a sweet cherry leaf dataset. The average completeness on the primary vein reaches 0.8658 and on secondary veins reaches 0.6407. It proved the effectiveness of this method, which also had value in leaf phenotype extraction and leaf classification.
Performance Investigation of Robust Linearly Constrained Minimum Variance Beamforming for Direction of Arrival Mismatch

A robust linearly constrained minimum variance (LCMV) beamforming using directional and null constraints with a diagonal loading technique is discussed in this paper. The diagonal loading adds robustness against the direction of arrival mismatch and provides satisfactory output power and signal to interference plus noise ratio (SINR) in the mismatch region without broadening the beam. This paper first compares the proposed robust LCMV method with the conventional LCMV method in the presence of direction of arrival mismatch. Then its performance is compared with the LCMV using existed fixed diagonal loading method. The proposed robust method raises the output power by 1.88 dB and output SINR by 0.58 dB at 3 degree disparity angle compared to the LCMV with fixed diagonal loading technique. Simulations are performed in MATLAB to analyze the performance of the proposed method.
Impact of Minimum Hyperspherical Energy Regularization on Time-Frequency Domain Networks for Singing Voice Separation

The task of singing voice separation requires the model to maintain a trade-off between signal quality, interference introduced by music accompaniment and algorithmic artifacts. A time domain-based singing voice separation system offers a challenge in designing for low latency and in minimizing computational cost. To overcome this problem, we propose to use Gammatone auditory features for the Time-Frequency (T-F) mask-based singing voice separation task. Minimum Hyperspherical Energy (MHE) regularization in the time-domain network has recently produced the state-of-the-art result in singing voice separation (our baseline). In this work, we apply MHE to the T-F domain networks. The MHE regularized T-F domain network significantly improves the separation performance over the base-line. The MHE regularized Wasserstein Generative Adversarial Network (GAN) achieves 0.21 dB improvement in mean Signal-to-Distortion Ratio (SDR) over the baseline. Our best performing T-F domain un-regularized GAN provides an improvement of 0.75 dB and 0.63 dB in SDR over the baseline and the GANMHE, respectively. We experimentally show the failure of MHE regularized T-F domain networks with respect to their unregularized versions and have shown the need of designing a suitable adversarial objective function. We report that modifying the GAN-MHE’s objective function with reconstruction loss and adapting Wasserstein GAN, results in a 0.45 dB improvement in mean SDR over its un-regularized version.
An Improved Cumulant-based Modulation Classification Method for Underwater Acoustic Applications

Modulation classification of acoustic signals is a crucial topic in the fields of underwater signal processing. It is usually challenged by the marine noise, multipath effect and time-space varying characteristic of acoustic channels. This paper proposes a new cumulant-based modulation classification method for underwater acoustic scenario. We consider the influence of multipath fading and noise variance on the ratio of the fourth-order cumulant of the transmitted symbols to that of the received symbols as a joint coefficient, so that the signal modulation modes can be accurately classified without apriority about acoustic channels. Simultaneously, Cramer-Rao lower bound for the coefficient estimator is derived. Results illustrate that the proposed method is closer to Cramer-Rao lower bound than the existing ones in real underwater acoustic channels, and its classification performance significantly outperforms the references.
Spectral Features and Pitch Histogram for Automatic Singing Quality Evaluation with CRNN

Deep neural networks (DNNs) have been applied successfully to music information retrieval (MIR). In this paper, we design a convolutional recurrent neural network (CRNN) for automatic singing quality evaluation, and present a comparative study over various acoustic features as network input. We optimize the CRNN so that the machine-predicted scores are closer to the human-annotated scores. Furthermore, we augment spectral features with pitch histogram (a musically-motivated representation) as network input. The experiments show that our proposed CRNN framework can learn the underlying discerning properties of singing quality effectively. Moreover, explicit incorporation of pitch histogram further improves system performance, and reduces the system’s dependency on song content.
Fault Classification Method based on Fast K-Nearest Neighbor with Hybrid Feature Generation and K - Medoids Clustering

Fast and accurate classification of the process faults in industry is important for ensuring reliable operation. The traditional K-Nearest Neighbor (KNN) algorithm needs to calculate the distance between the sample to be classified and all the training samples obtained under normal operation condition (NOC). Large computational cost involves in this step when the number of the NOC samples is huge. Aiming at this, existing methods reduce the training samples by using the clustering algorithms, so as to reduce the computational cost of the KNN. However, the reduction of training samples usually leads to a decrease in the accuracy of fault classification. Misclassification directly affects the normal production and safety of the industrial processes. To this end, a fast KNN fault classification method based on hybrid feature generation and K-Medoids is proposed. Firstly, a hybrid feature generation method combining ReliefF algorithm and linear discriminant analysis algorithm is used to select and extract the sample features, thus enhance the separability inter-classes and improve the accuracy of fault classification. Then, K-Medoids clustering algorithm is used to select few representative training samples and reduce the computational complexity of KNN algorithm. Finally, the simulation is performed on the Tennessee- Eastman process, which verifies that the proposed algorithm is superior than other related four methods and only consumes far less running time than the basic KNN algorithm while retaining higher classification accuracy.
EEG Analysis for classification of Seizures and Non Seizures Patients

Epilepsy is a spontaneous or provoked synchronized cortical activity in cerebral neurons that leads to epileptic seizures which affect the person in psychological sensations. The classification of epileptic seizure is achieved using the electroencephalogram (EEG). The EEG is a continuous, irregular signal in which any section is never repeated exactly at any other time. The analysis of EEG can therefore not be exactly described by certain parameters (such as amplitude or frequency). Proposed analysis of EEG signal and classified seizures and nonseizures patients Depending on the level of alertness, a distinction is made between different patterns in the electroencephalogram, namely Alpha, Beta, Theta, and Delta waves. These parameters are trained with the SVM classifier to classify the seizures and nonseizures patients, achieved the accuracy of 98.5.