Miracle-dz/UniFAR-FTUs huggingface dataset:
GPT Based Malware: Unveiling Vulnerabilities and Creating a Way Forward in Digital Space

The rise and development of AI-based solutions like ChatGPT have significantly changed the functioning of many enterprises, organizations, and domains including cybersecurity. The public’s open access to ChatGPT and its resources does, however, present significant security challenges. This review study aims to shed light on the emergence of GPT-based malware. As traditional malware detection systems, have advanced to mitigate many sophisticated malware attacks, threat actors are now aiming to utilize GPT and other Large Language Models (LLMs) to create sophisticated tactics to infect systems with new malware. Furthermore, this study seeks to present a select number of methods that may be utilized to reduce the hazards posed by malware developed with ChatGPT and other LLM-based tools.
A Smart Elevator Design Using Intelligent Video Surveillance System

It takes a lot of time and work to manually monitor and react to unexpected occurrences that arise in a lift cab. We develop and proclaim a smart video surveillance system in this study. An identifying system for lift cars that allows data like occupancy level, human body behaviour, and the condition of the cab door to be estimated and assessed for security. According to this research, an intelligent video surveillance-based lift system should be designed to detect activity in a designated region and start capturing video only when motion is detected. A adjacent lift is similarly controlled by the system; however, it only operates when motion is detected. The system's camera records live footage of the scene, which OpenCV then processes to detect motion. The lift will activate and video recording will begin as soon as motion is detected by the device. The gadget will cease capturing video and turn off the lift if it detects no motion. The system can be used for a variety of purposes, including building, office, and home security systems. The project makes use of OpenCV, a well-known open-source computer vision library that provides a broad range of image processing and motion detection capabilities. The main objective of this project is to create a smart surveillance system that increases the area's overall security while minimizing storage requirements by only taking pictures when necessary and only running the elevator when motion is detected.
Evaluation of Effectiveness of MAC Systems Based on LSM for Protecting IoT Devices

Numerous active attacks targeting Internet of Things (IoT) devices exist. They exploit the latest vulnerabilities discovered in IoT devices. Therefore, Mandatory Access Control (MAC) systems based on Linux Security Modules (LSM), such as SELinux and AppArmor, are effective security features for IoT devices because they can mitigate the impact of attacks even if software vulnerabilities are discovered. However, they are not adopted by most IoT devices. The existing approaches are insufficient for investigating the causes of this problem.In this study, we comprehensively investigated what factors can affect the applicability of MAC systems based on LSM in IoT devices. We focused on how frequently cases can occur where they cannot be adopted, owing to each factor. To increase the comprehensiveness of the factors affecting the adoption of MAC systems in IoT devices, we investigated the kernel version, CPU architecture, and support for BusyBox in addition to the investigation of resources, which conducted in previous studies. We also conducted simulated experiments based on the attack method of Mirai to investigate whether MAC systems can protect against IoT malware. Finally, we discuss the impact of a combination of these factors on MAC system adoption.
Methods of Transfer Learning for Multiclass Hair Disease Categorization

In the field of dermatology, skin disorders, particularly hair-related conditions, present a significant challenge. Image-based automated categorization of hair problems has gained a significant research attention due to its potential to assist dermatologists in the process of early diagnosis and treatment planning. Transfer learning, a technique that utilizes pre-trained deep neural networks, has proven to be valuable in various computer vision applications. This study investigates the application of transfer learning for leveraging a multiclass classification of hair disorders by utilizing the three commonly used Convolutional Neural Network (CNN) architectures: AlexNet, VGG16, and ResNet50. This research study begins with the collection of a comprehensive dataset comprising high-resolution images of various hair disorders, including but not limited to alopecia areata, tinea capitis, and androgenetic alopecia. Categorizing the dataset into different groups based on the type and severity of each condition enables a thorough evaluation of the models. Transfer learning approach is employed for fine-tuning these neural network architectures by using the hair disease dataset. A hyperparameter tuning strategy is also adopted to optimize different parameters such as learning rates, batch sizes, and optimization methods to enhance the model performance. The results reveal that all three architectures, including ResNet50, achieve a 99% accuracy rate in classifying multiclass hair diseases. Such technology has the potential to assist dermatologists in their clinical practice by enabling rapid and precise disease detection, thereby improving patient outcomes and healthcare efficiency. Further research could explore the integration of these models into clinical workflows and their application in telemedicine for enabling remote diagnosis and consultation.
Logic-Based Motor-Battery Power Management Strategy for in-Wheel Motor Electric Vehicles

The management of motor and battery power in electric vehicles plays a pivotal role in maintaining the battery state of charge and extending the vehicle's driving range. This paper introduces a logic-based strategy for managing the power of in-wheel motor electric vehicles. First, a vehicle model is established in Simulink based on longitudinal dynamics. The model integrates in-wheel electric motors into the vehicle as the main powertrain configuration and a battery management system to monitor the state of a lithium-ion battery. Next, a power management strategy is developed considering the energy source state of charge and charging/discharging power limits to control the command distribution between motor command torque, friction brakes and regenerative braking. The strategy is tested under different driving cycles to track desired speed. Finally, the strategy is tested under three driving cycles, namely; FTP-75, HWFET, and Artemis cycles. The simulation results have demonstrated the impact of different drive cycles on the in-wheel motors' torque and the battery's current and they have shown the efficacy of the devised power management strategy.
Double Cavity Coupling Structure Design of a Capacitive Wireless Power Transfer System Under Seawater

This paper presents the design of a capacitive wireless power transfer system (WPT system) under seawater. This paper first reveals the different coupling capacitance and resistance between the coupled plate in seawater and fresh water. Then the double cavity coupling structure is proposed to eliminate the effects of the crossing capacitance and the self-capacitance of coupling plates under seawater and get higher transfer efficiency. Next, to reduce the effect of corrosion of insulation and keep zero phase angle (ZPA) input and constant-current (CC) output under seawater, a capacitance in series the coupling plates is introduced. Finally, the prototype of proposed CPT system under seawater with 100mm transfer distance is constructed then the efficiency of 70.1% and the output power of 200 W is gotten.
Credit Card Users Churn Prediction Using Ensemble Techniques

Credit card companies are continuously working to keep hold of their customers due to the tough competition in the financial industry. Churning takes place when a customer leaves one company to join some other company. It is a notable and complicated issue that many businesses, including credit card companies and telecommunications companies face globally, on a daily basis. Devastating a business are the potential effects of a high churn rate, as it not only diminishes income but also hinders company operations. Although a certain degree of turnover is inevitable, it is vital to manage it to avoid adverse consequences. Stakeholders are increasingly dedicating their focus and efforts towards grasping the factors that drive customer churn and predicting if loyal customers will continue to engage with their businesses. These efforts aim to mitigate the potential impact that the loss of valuable customers can have. Leveraging the strengths of multiple algorithms in order to enhance predictive performance for credit card user churn prediction, we present an ensemble method in this study. Utilizing a real-world credit card user dataset, we conducted experiments to determine the effectiveness of our proposed method and the results revealed that it surpassed the individual algorithms in terms of predictive accuracy, precision, and recall. Customer retention strategies can be optimized with insights about what leads to credit card user churn, and our approach yields just that. By reducing churn and creating greater customer satisfaction with this knowledge, credit card companies will improve their overall business performance. Valuable findings from this study can help them achieve these goals.
Model-based and Data-driven Calibration of the X-ray CT Image based on Proton Radiographies

In proton beam therapy the semi-empirical calibration of the X-ray CT image introduces inaccuracies into the treatment planning. The promise of proton imaging is to potentially eliminate these inaccuracies by (1) replacing the X-ray CT image with the proton CT image based on tomographic image reconstruction of many proton radiographies or (2) adjusting the semi-empirical calibration of the X-ray CT image based on optimization algorithms for few proton radiographies. Both (1) and (2) are based on the model of the proton radiography (in water equivalent thickness, WET) as a line integral of the proton CT image (in relative stopping power, RSP) along the estimation of the proton trajectory. However, this model (i.e., the forward-projection) is not perfectly consistent to the measurement (i.e., the proton radiography). Depending on the detector configurations, in clinical data these intrinsic inconsistencies can result in the same order of magnitude as the calibration inaccuracies that are intended to be eliminated. The purpose of this work is to overcome the intrinsic limitation of the model-based optimization with a novel data-driven approach relaying on supervised machine learning. The availability of the ground truth CT image in simulations is exploited. Realistic proton radiographies are simulated relying on pre-clinical data. Results demonstrate the potential capability of the data-driven approach. A tuning of the hyperparameters and the number of layers of the convolutional neural network is ongoing. Further investigations involving different data and different detector configurations are foreseen.
RBF Neural Network-Based Output Constrained Control of Servo System with LuGre Friction

In order to weaken the adverse effect of friction nonlinearity on electromechanical servo system with LuGre friction, an adaptive control strategy was proposed. The LuGre friction model of servo system was first given. By introducing the stabilizing function, using backstepping approach and selecting the Barrier Lyapunov function, and adopting a radial-basis-function (RBF) neural network to design adaptive law, a RBF neural network-based output constrained backstepping adaptive controller was designed, and its stability was analyzed. Compared with the single RBF neural network control in simulation results, the proposed control strategy shows better position and speed tracking performance.
Peer Learning in an Undergraduate Linear Algebra Course - A Social Network Analysis

This study employs Social Network Analysis (SNA) to explore peer learning behaviors among undergraduate Linear Algebra students. By examining the relational dynamics within the classroom, SNA unveils patterns of interaction, information flow, and collaboration among students. Our analysis identifies the prevalence and evolution of peer learning, and how it influences the students’ academic performance. It also unveils the attributes of the students who engage in peer helping and the formation of small communities through such interactions. The findings of the study can provide valuable insights for educators aiming to enhance peer learning and improve educational practices in Linear Algebra and other fundamental subjects, thereby contributing to curriculum development and educational reforms.
IoT Malware Detection Tool with CNN Classification for Small Devices

Since the number of linked devices continues to grow quickly, the Web of Everything (IoT) is being employed change today in more industries. More issues in regards to safety, stability, and supportability are brought on by an increase in smart devices, particularly as it relates to 5G technology. We are concentrating on the safety component of IoT systems in just this article because it is a relatively new field. For varied purposes, such as cost savings or a desire to forego the use of generation system, many Internet device manufacturers need not take security into account when designing their products. The opponent may use those potentially dangerous technologies to carry out several destructive assaults. As a result, we created a device capable of recognizing malicious activity from a particular IoT networks node. They are capable to offer detection methods for IoT utilizing a primary key that may be put inside the system using a multilayer brain networks for identification and surveillance. The accomplishment dispels any misconceptions about methods for deep training while demonstrating how that systems can be simply adapted and deployed to every system.
GEOGRAPHIC PROTEIN VARIATION AND SPECIATION IN THE PLETHODON DORSALIS COMPLEX

In an analysis of protein variation among geographic populations of the zig-zag sal- amander, Plethodon dorsalis, Larson and Highton found that the amount of genetic divergence is much greater than occurs within most other species of Plethodon. A re-examination of patterns of genetic variation in P. dorsalis indicates that the three most divergent groups should be recognized as different species. Plethodon dorsalis ranges from Illinois and Indiana south to northern and western Kentucky and western Tennessee. Plethodon ventralis sp. nov. ranges from south-central Kentucky, southwestern Virginia, and eastern Tennessee south to northwestern Georgia, northern Alabama, and northeastern Mississippi. Plethodon angusticlavius is found in the Ozark Mountains of Arkansas, Oklahoma, and Missouri. A transect through a contact zone between P dorsalis and P. ventralis in Lincoln County, Kentucky, indicates that there is a narrow hybrid zone <13 km in width between the two species. There is concordant variation in four diagnostic allozyme loci through the hybrid zone, and hybrid populations are genetically more variable than populations of P dorsalis and P ventralis on either side of the hybrid zone.
Low Dose Brain PET Imaging using Denoising Diffusion Probabilistic Models

Moderating the injected dose to reduce the potential health hazards has been always a desirable goal that can reduce acquisition time and can have a positive impact on the patient’s comfort and on the scanner’s throughput. Reducing the scanning time and injected dose have a direct impact on image quality and can lead to hampering of signal-to-noise ratio, increase quantitative bias and reduce the diagnostic value of PET imaging. A novel deep learning image-to-image translation method called Denoising Diffusion Probabilistic Models (DDPMs) was utilized in this work to improve the quality of low-dose (LD) PET images. A dataset including 41 18F-FDG PET images belonging to patients with cognitive symptoms of the possible neurodegenerative disease was reconstructed to generate the full dose (FD) images and a subset of PET data containing 5% of the total events was considered as LD images. Quantitative analysis includes established metrics such as the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), and root means square error (RMSE) were calculated. The result shows that the model can increase the SSIM and RMSE and improve overall image quality. The suggested DDPMs denoising model enables us to reduce the radiation dose to patients to only 5% and increase its comfort in the case of fast scanning.
Analysis of Fake News Detection using Graph Neural Network (GNN) and Deep Learning

The rise of fake news in social networks has become a significant concern, necessitating innovative strategies to combat misinformation. One such approach is the use of graph neural networks (GNNs) to trace and analyze false information dissemination. GNNs are specialized DL model capable of processing graph-structured data, making them ideal for capturing intricate dependencies and relationships in the spread of fake news. Proposed implementation starting with data preparation, where a carefully curated dataset is essential. The news stories are processed, tokenized, and transformed into a graph representation to better understand the relationships between words or entities. Graph construction enables the formulation of ML problems as graph classification tasks. Graph embedding, achieved through methods like word2vec or GNNs, plays a crucial role in representing each node as a low-dimensional vector, capturing its semantic significance within the network. This technique facilitates ML activities, such as node classification and link prediction. Combining graph embeddings and GNNs for classification allows for a more precise identification of true and false news, considering the structure and content of news propagation graphs. Finally, evaluating false news detection involves performance metrics such as accuracy. Proposed implementation obtained detection accuracy of 94%. Striking a balance between minimizing false positives and false negatives is crucial to combat misinformation effectively. By employing this approach, society can create a more trustworthy information environment, enabling individuals to make informed decisions and reducing the negative impact of fake news.
Detecting Depression Level Scale from Social Media Text and Images Using Machine Learning Algorithms

Millions of individuals worldwide struggle with depression, which is a serious mental health issue. The quality of life of those with depression can be greatly enhanced by early detection and management. The objectives of this study were:(i) Develop a machine learning and deep learning approach to identify depression levels from social media text and images. (ii) Use the Hamilton Rating Scale for Depression as the classification tool for detecting the extremity of depression. (iii) Collect data from social media platforms through web scraping and pre-process the data for feature extraction. (iv) Utilize a combination of text-based and image-based features to train and evaluate the model. This method integrates data from many sources and makes use of cutting-edge machine learning techniques in an effort to increase the precision and effectiveness of depression identification. The positive outcomes of our trials, which achieved high accuracy and F1-score, highlight the promise of our method for identifying depression levels from social media data. The proposed solution suggests that by using this approach, depression can be identified and treated earlier, leading to better mental health outcomes for both individuals and society as a whole.
Particle Discrimination for Scintillator Neutron Detectors Using Graph Signal Processing

Highly efficient neutron detectors based on a GS20 or Ce-doped Gd3Al2Ga3O12 (Ce:GAGG) scintillator have low neutron-gamma discrimination (NGD) ratios (the ratios of neutron and gamma efficiencies), if traditional pulse-height or pulse-shape analysis is applied for NGD. To solve this issue, graph signal processing methods have been proposed. The similarity of neutron, gamma, and background events is represented by the Laplacian, L, of the graph or high-dimensional manifold of events. Using a Laplacian filter or a network dynamics filter, the projected signals produce excellent NGD ratios (~107-109) and simultaneously high neutron detection efficiencies (50-103%, relative to a standard 3He tube) for GS20 and Ce:GAGG. Two graph digital filters also provide excellent neutron and ambient-background discrimination, and their computational times are reduced significantly compared with the graph-embedded nonnegative matrix factorization (GNMF) method developed previously. It has been found that the NGD capability of a neutron scintillator is well correlated with the graph topology, which is influenced by the nature of light transport in the scintillator. The proposed graph-filters can provide particle discrimination not only for single-channel neutron detectors, but also for neutron position-sensitive detectors and high-resolution imagers, and other radiation detectors.
Cone-Beam Reconstruction for Circular Scanning with Bilateral Truncation Based on the VFB Method

We describe a new procedure, based on the virtual fan-beam (VFB) method, to perform volume-of-interest (VOI) reconstruction from cone-beam data acquired with a circular trajectory and bilateral transverse truncation of the projections. Our simulations with the Forbild thorax phantom suggest that this novel method gives, in a VOI, results quite similar to those obtained by the Feldkamp, Davis, Kress algorithm with non-truncated data.
A Novel Modulation Method to Reduce Leakage Current and Balance Neutral-Point Voltage for Neutral-Point-Clamped Inverters

Due to the low device voltage stress and the small output harmonics in high-power applications, neutral-point-clamped(NPC) three-level inverters are widely used in grid-connected photovoltaic inverters. However, the traditional modulation strategy may lead to low-frequency oscillation of the neutral point voltage, increasing the output harmonic content. Moreover, the large leakage current can reduce the efficiency and life of the system. To address these issues, this paper presents the extended discontinuous phase opposition disposition PWM (EDPOD PWM) strategy. By using the hybrid H-P modulation which is based on carrier-based PWM modulation strategy (CBPWM) and extended modulation discontinuous PWM modulation strategy (EDPWM), the neutral point potential can be balanced in full power factor and modulation ratio ranges with low switching losses. By inversing the carrier of a specific phase, the small vector switching level generating large common mode voltage is eliminated. Then the amplitude of common-mode voltage can be limited to 1/6 bus voltage. The proposed strategy is verified by simulation. Compared with the original H-P modulation without optimized common-mode voltage, the proposed strategy can reduces the leakage current by 66%. Compared with the common mode voltage suppression strategy based on CBPWM and VSVPWM hybrid modulation, the leakage current suppression effect is more obvious, and the effective value is reduced by 33%. Finally, the feasibility and superiority of the proposed method are experimentally verified.