Miracle-dz/UniFAR-FTUs huggingface dataset:
The Emergence of Hypergraphs in Complex System Analysis

Collaborative systems, as complex systems, consist of a large number of interacting components. Complex system analysis is a well-established field of research that has been applied to a wide range of domains, including social networks, biological and metabolic structures, transportation systems, and software systems. In this paper, we show that many complex systems can and should be modeled as hypergraphs. This is an emerging trend and we believe that it is a promising direction for future research. We aim to analyze the reasons behind this trend and elucidate the advantages of using hypergraphs in complex system analysis.
Semantic Segmentation-based Automatic Wood Cell Detection from Raster Images

A comprehensive examination of all of the wood’s elements is required in order to identify wood by its characteristics. There is no comprehensive solution to this important issue in structural wood research. For most tree species, the vessels of trees and their mutual location have a distinct personality. Semantic segmentation has become increasingly popular in a variety of scientific fields in recent years. Automated image annotation is a critical step in extracting functional information. This study focuses on the development of a method for automatic tree vessel detection from raster images using semantic segmentation techniques. We present three model architectures and compare them to find the optimum effectiveness of the segmentation. Segmentation involves extracting semantic content in the neural network encoder and then adding/replacing it in the decoder.
A Comparative Study of Copy-Move Forgery Detection Techniques

In the modern world, the popularity of image editing software has risen sharply, making it easy for non-experts to manipulate images. This poses significant challenges regarding image authenticity and integrity. Consequently, a lot of attention has been aroused by researchers working on this topic and many methods have been developed to discover image forgeries. Copy-move is a manipulation technique that involves copying a region in an image and pasting it in a different location on the same image, leading to misinterpretation. In this paper, a comparative study between conventional and deep learning-based methods is presented. More specifically a statistical-based method is compared against a transfer learning method. Various experiments have been conducted on six different publicly available datasets for different copy-move scenarios. The experiments revealed the superior performance of the deep learning approach, surpassing the statistical method in most cases. This study highlights the effectiveness of deep learning techniques in the field of copy-move forgery detection. Furthermore, the comparative analysis presented in this paper serves as a valuable reference for researchers to accomplish further advancements in image forensics.
Edge Computing Power Optimization Method in Container Cloud Environment

Due to slow CPU speed and small battery capacity, intelligent mobile devices cannot perform computing themselves, which causes many problems such as resource scheduling security technology. To reduce these problems, edge computing techniques are needed. However, moving some computing tasks from mobile devices to edge servers increases the additional energy consumption of transport and server computation. Considering the four factors affecting mobile devices and servers, as well as the energy consumption of data transmission, a particle swarm optimization algorithm based on hierarchical learning was proposed to solve the problems of computing speed, power consumption of downloaded data, and computing security of mobile devices. The proposed algorithm optimizes these four parameters for each mobile device to allocate computing resources more rationally, minimize the total energy consumption, and ensure security during scheduling. When modeling computing resources, the maximum energy consumption, computation cycle, storage, bandwidth, and delay constraints are also considered. Compared to the other algorithms in the container cloud, the proposed particle swarm optimization algorithm optimized by hierarchical learning can get the optimal solution of resource scheduling satisfying the constraints faster, with lower energy consumption and higher security.
Low-Cost Implementation of Adaptive Luminescence Intensity in Headlamp Based on Scenario Extraction

In this work, we try to achieve a low-cost and easy implementation of an adaptive headlamp. We propose this simple system implementation to avoid accidents caused by improper lighting and exposure to glare from passing vehicles. In this work, we have used a classification network trained using a Google teachable machine to classify the parts of the day and the luminous intensity during driving time based on the live capturing of san image. Further using this classification, the required luminous intensity for comfortable driving is decided. The pattern required in the headlamp to provide the same is decided and controlled by the proposed system. The work has also been compared with the results using the averaging pixels method for each frame for classification. Both approaches are implemented on Raspberry Pi4 model B+, Raspberry Pi Cam, and Raspberry Pi sense hat to visualize the results and their effectiveness.
A Study of the Unbalanced Image Dataset for Classification Problem

In the case of experimental data it is evident that the dataset obtained is unbalanced. The main reason for the same is the probability of the occurrence of a particular event in real life. As these events have very less probabilities hence the collection of such sample data is very difficult. Therefore, various approaches have been suggested to overcome this problem. This paper compares the two approaches for handling the unbalanced data. In the first approach, the minimum class with random selection was used to balance the data and in the second approach, the maximum class with random duplicate is used to balance the data. Based on the results of the sugarcane disease detection using densenet with SVM it was evident that incorporating the maximum data available is helping the machine learning model to more effectively.
An Empirical Framework for Malware Prediction Using Multi-Layer Perceptron

In today’s interconnected world, where sensitive data and financial transactions are exchanged and stored online, the increasing prevalence of sophisticated and evolving malware attacks poses a significant threat to the cybersecurity of computer systems and network infrastructures. Therefore, accurate malware classification is pivotal to effectively defend against these hostile attacks. In recent developments, malware variants use sophisticated packaging and obfuscation techniques to gain unauthorised access to valuable information. The urgent need for improved security has become imperative in light of these developments. In this paper, we conducted an empirical study, in particular, the impact of data balancing, feature selection and dimensionality reduction, and the application of proposed MLP (Multi-layer perception) for malware prediction are investigated. The proposed MLP-enabled prediction model is efficient and effective, due to its ability to learn complex patterns across different malware families, allowing them to make accurate predictions about new, unseen malware patterns. The experimental results assert that the prediction models trained on sample data using a single-layer MLP with the Adaptive Movement Estimation (ADAM) algorithm and using GA selected features have better predictive ability. The experimental results confirmed that the models trained using 51.44% of the features proposed at GA had significantly similar predictive abilities compared to all features.
Research on Visual Detection and Classification Technology for Surface Defects of Printed Circuit Boards

In recent years, intelligent defect detection technology based on computer vision technology is gradually replacing traditional detection methods and beginning to be applied in the industrial field. With the development of deep learning based object detection algorithms, the accuracy of detection models in object detection has improved. Applying deep learning based object detection algorithms to PCB defect detection will further improve the efficiency and accuracy of defect detection. This project proposes a PCB image defect detection technology based on deep learning, using PP-YOLOE as the core algorithm to achieve high-precision and fast detection of PCB defects.
Multi-sensor multiple extended objects tracking based on loopy sum-product algorithm

A remarkable feature of extended objects (EOs), compared to the traditional point target, is that an EO normally produces more than one measurement, resulting in challenges for finding the associations among objects and measurements. In this paper, we are interested in tracking multiple EOs with multiple sensors by resorting to the loopy sum-product algorithm (LSPA). The marginal posterior of an EO state is approximately by belief, which is obtained by executing the LSPA on a suitably devised factor graph. The existence probability and belief of each EO are explicitly estimated, where the belief is modeled by mixture gamma Gaussian inverse Wishart (GGIW) distribution, so as to jointly estimate the measurement rate (MR), centroid state and extension. Simulation experiments verify the performance of the proposed method.
A comparative phylotyping analysis of E. coli originating from avian colibacillosis based on the Clermont triplex scheme and gyrA gene sequencing

Avian colibacillosis (AC) is frequently reported from poultry farms in the west of Iran. The aim of the present survey was to evaluate the phylogeny of Escherichia coli isolated from AC through the Clermont triplex protocol and gyrA gene sequencing. One hundred and fifty fresh carcasses suspected of having died from colibacillosis were sampled. The specimens were examined for E. coli isolation and identification. The extracted DNA repertoire of the isolates was subjected to phylogenetic analysis using Clermont triplex PCR. A DNA fragment of 1.8 kbp of the gyrA gene was amplified from a representative of each phylogroup or subgroup and the final product was sequenced to depict a phylogenetic tree. In general, among 119 E. coli isolates, 31.93%, 6.72%, 34.45%, and 23.52% were clustered in phylogroups A, B1, B2, and D, respectively. The phylogenetic assignment of the isolates based on the phylogenetic tree was completely uniform with the results of the triplex method. The outcome represents the segregation of avian pathogenic E. coli isolates to various phylogroups in the region. Moreover, the precise cluster analysis of E. coli based on gyrA sequencing confirms this method as a robust candidate for phylotyping of the bacterium.
Driver Fatigue Detection Based on Nested Residual Attention Deep Ensemble Model

Electroencephalogram (EEG) signal is one of the most reliable standards for detecting driver fatigue states. In order to achieve driver fatigue detection, this paper constructs an EEG signal classifier based on a nested residual attention deep ensemble model. Firstly, design the efficient channel attention (ECA) - convolutional neural network (CNN) - long short-term memory (LSTM) network hybrid model (ECA-CNN-LSTM) based on the nested residual attention structure. Then, train the model based on multiple feature datasets, and select sub models from the training results through F1 score. Use the weighted voting method to achieve model ensemble, obtaining a fatigue detection ensemble model with stronger robustness and detection performance. The method proposed in this paper was validated using the SEED-VIG dataset and compared with various models. The experiment results showed that the method can effectively detect driving fatigue states, with an accuracy of 96.75%. Compared with other models, it has significant advantage.
DeepHC: Efficient Generating Tests with High Coverage for Deep Neural Networks

As deep neural networks (DNNs) are increasingly used in the software of aerospace, aviation, and other safety-critical fields, ensuring their reliability and safety becomes paramount. Software testing is still the most critical means to ensure software quality. In order to guarantee the adequacy and effectiveness of testing, aerospace and other industrial areas usually require very high test case coverage. Despite considerable advances in structured coverage criteria and coverage-guided adversarial examples generation for DNNs, there is a lack of in-depth research on how to efficiently generate test inputs with high coverage, which is essential for software testing. In this paper, we propose DeepHC, a general framework combining fuzzing and gradient-based algorithms to achieve high coverage towards 6 most widely used coverage criteria. To explore test inputs with high coverage, DeepHC adopts a novel evaluation strategy for selecting inputs that can activate neurons more efficiently. We evaluate the effectiveness of DeepHC on 2 popular datasets and 3 DNN models with different complexities. The experimental results show that DeepHC outperforms the state-of-the-art method in enhancing coverage.
A Non-Singular Terminal Sliding Mode Control of Buck-Boost Converters Based on Extended State Observer

This paper focuses on the impact of Buck-Boost converter on the output voltage due to uncertain factors such as output voltage reference changes, load mutations, and input voltage changes. A non-singular terminal sliding mode control method based on an extended state observer (ESO) is proposed. The voltage control loop adopts a non-singular terminal sliding mode control law with an improved sliding mode surface. At the same time, an extended state observer is designed to estimate the uncertainty of the Buck-Boost converter and feed it back to the sliding mode control law, which reduces system chattering and improves system robustness. The simulation results show that the control method proposed in this paper can effectively improve the robustness and response speed of the Buck-Boost converter system.
Fractional-Order Cyber-tolerant Frequency Controller for Renewables-integrated Power System

With the continual growth and increasing model complexities of smart-grid power systems, this work has attempted to design a fractional-order (FO), finite-time non-singular terminal sliding-mode controller (NS-TSMC) to achieve resilient frequency regulatory requirements against stealthy deception (FDI) cyber-attacks and unsteady load demand. The unknown stealthy FDI attacks are estimated employing a FO disturbance observer (FODO). The asymptotic convergence of the estimation error has been confirmed using the Mittag-Leffler stability method. Subsequently, NS-TSMC with estimated FDI attack/plant uncertainties is applied to retain the synchronism of power systems and increase grid resiliency. The exponential stability of the applied control system is studied and presented applying the Lyapunov method. Simulation results performed on Kuntur’s two-area four-machine and IEEE 39-bus New-England testbeds have demonstrated proficiency and the disturbance suppression ability of the applied robust cyber-tolerant frequency control framework against stealthy FDI-attack.
Fusion of Variational Modal Decomposition and ResNet Network for Intelligent Fault Diagnosis of the Bearing under Time-varying Speed Condition

Given that the bearing often works under time-varying speed condition, vibration signal is modulated by speed, making difficulty in extracting fault features, resulting in low fault diagnose accuracy by the traditional intelligent diagnostic methods. To address above challenge, this paper proposes a novel method for intelligent fault diagnosis of the bearing under time-varying speed condition, which combines the advantage of joint feature extraction of one-dimensional convolution and two-dimensional convolution, as well as the training advantage of ResNet network. Firstly, Variational modal decomposition (VMD) is used for decomposing the original signal into multiple intrinsic mode functions (IMFs). Secondly, one-dimensional convolution is used for reconstructing all IMFs into a two-dimensional feature map called VMD feature map. Finally, the abstract features in VMD feature map are extracted by ResNet network. The experimental result indicates that the proposed method can weaken the influence of speed modulation, and improve the quality of the bearing abstract features under time-varying speed condition, thus raising the recognition accuracy of fault diagnosis. Compared with current first-rate methods, the proposed method shows better fault diagnosis performance.
Development of Non Invasive Blood Glucose Monitoring

In order to calculate their insulin dosage and make sure their blood glucose level remains within the normal range, diabetics must monitor and measure their blood glucose levels on a frequent basis. It is meant to be possible to test blood glucose levels non-invasively. use a near-infrared (NIR) transmittance spectroscopy without requiring a blood sample, skin puncture, or discomfort. It uses circuits for a light source and a light detector that are placed on the finger. The blood glucose level in that finger area is determined by the detector’s attenuated reception of the infrared light. An IR light source and a receiver module are used in the blood glucose measuring procedure. The receiver measures the changes in light intensity brought on by variations in blood glucose levels when infrared light is delivered through a fingertip and interacts with the blood.A microcontroller NodeMCU receives data received from the receiving circuit and uses it to create mean values and graphs.It is a dependable and easy-to-use tool that can test blood glucose levels precisely without requiring repeated, uncomfortable finger pricks. Individuals can customise their own cutoff points and get notifications when their blood sugar levels deviate from the target range.Users must enter their height and weight into the system’s BMI calculation component in order for it to calculate their BMI.
Breast Cancer Histopathology Images Classification Based on Masked Autoencoders and Knowledge Distillation

Breast cancer ranks among the most prevalent malignancies affecting women. Precise differentiation between benign and malignant histopathological images holds pivotal significance for the timely diagnosis and effective treatment of breast cancer. Breast cancer histopathology images present challenges due to patient privacy concerns and the intricate process of collection. Moreover, the manual annotation of these images necessitates significant human and material resources, contributing to an insufficient dataset for network training. Concurrently, the escalating intricacy of breast cancer histopathology image classification tasks demands more extensive networks for efficient handling. However, this requirement escalates hardware resource consumption and obstructs the deployment of compact medical devices. Furthermore, the complexity inherent in breast cancer histopathology images impairs networks proficiency in extracting pivotal global and local features. In this context, this paper introduces a classification approach that harnesses the benefits of masked autoencoders and knowledge distillation, alongside the integration of a global spatial attention mechanism. This method is formulated to effectively address the aforementioned challenges and predicaments. Our proposed method can effectively extract features from breast cancer pathology images with stronger feature fusion capability.
Transformer Encoder for Efficient CAPTCHA Recognize

CAPTCHAs have become a key factor affecting the efficiency of Robotic Process Automation. In this study, we constructed a large-scale CAPTCHA dataset encompassing up to 60 diverse stylistic variations, offering robust data support for model resilience and adaptability. We designed a CAPTCHA image prediction reconstruction task, allowing for unsupervised feature encoding that implicitly models the image spatial domain features. This enables the acquisition of feature representations for various CAPTCHA styles in a common subspace. Additionally, character length prediction and character set prediction tasks were established to explicitly model text label features, fine-tuning network model parameters in alignment with recognition tasks. Through lightweight sequence modeling and text prediction modules, our design achieves high predictive accuracy while maintaining a controlled model size. Our proposed model attained a full-word accuracy rate of 92.47% on the multi-style CAPTCHA dataset, surpassing existing mainstream approaches.
Deep Learning Method for Weight-Based Drug Dosage Calculation for Paediatrics

Accurate amount of drug dosage is very important, especially for infants. Pediatricians find it challenging to determine drug dosages for medications. Weight-based drug dosages avoid medical complications. But, in pediatrics determining the weight of the infant is a difficult task. High or low doses of medications can lead to serious consequences. Eradicating medical errors is very essential to provide a healthier and safer medical environment, especially for the future citizens. This study offers a novel approach for calculating the accurate dosage of a drug based on the infant’s weight using deep learning algorithms. The integration of CNN Mobilenet V2 for image classification, AR Core to measure the infant’s height, and Brose low tape which complements AR Core and also determines the infant’s weight is used for estimating the precise amount of drug. Mobilenet V2 is known for its balance between accuracy and efficiency which is used here to identify and analyze the given visual information. Broselow tape known as pediatric emergency tape provides precise weight assessment results. Combining advanced technologies offers a promising development in the future which ensures safer and more accurate drug administration in clinical environments.
Adaptive Classifier to Address Concept Drift in Imbalanced Data Streams

Concept drift adaptation is essential for maintaining the accuracy of machine learning models when facing changing data distributions in streaming environments. Class imbalance, a common issue in real-world datasets, further compounds the challenge of concept drift detection and adaptation. This paper proposes a comprehensive approach that focuses on effectively identifying and handling both concept drift and class imbalance to ensure the reliability and performance of predictive models. The approach involves two stages. First, a concept drift detection mechanism is employed to monitor the changing data distribution among the historical context of class distributions and adapts its sensitivity to class-wise changes. This minimizes the risk of false alarms while capturing subtle shifts in minority class distributions. Second, for concept drift adaptation, it uses ensemble-based resampling technique that dynamically adjusts the balance of classes by oversampling the minority class instances in a controlled manner. The approach is evaluated on synthetic and real-world streaming datasets with varying degrees of class imbalance and concept drift. Experimental results determine the advantage of the proposed method in comparison to baseline techniques in terms of detection accuracy and adaptability to changing conditions, particularly for underrepresented classes.