Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine Learning-Based Job Forecasting And Trend Analysis System To Predict Future Job Markets Using Historical Data

Over the last two decades, technological advancements have created more job markets and job opportunities than ever. With the ever-increasing demand, it has become vital for academic institutions and businesses to keep up with employment requirements. The problem is more severe for modern and rapidly evolving industries such as software development. This study implements a prediction system for job trends to enable job seekers, organizations, and academic institutions to understand and align their endeavors to match the market requirement. Employers can benefit from the system by using it to identify potential talent shortages and proactively address them. Policymakers can also use the system to understand the potential impacts of changes in the job market on the economy, enabling them to make informed decisions about supporting employment growth.The prediction is made possible by creating a rich dataset based on more than 522,180 job listings from the past 24 months in the software industry. The dataset is fed to a Bidirectional LSTM model to predict the future trends of the job market for various roles and technologies. Auto-aggressive prediction is implemented using the bi-directional LSTM model as this combination proves to produce the most accurate results after multiple quantitative analyses and evaluations. This study evaluated the proposed solution against known real-world data and it was concluded that the system can predict the job trend for at least the next 12 months with a relatively high accuracy of 95.71%.
Domain Adaptation Using DCGAN For Glaucoma Diagnosis

The second most prevalent eye condition is glaucoma to cause neurodegenerative sickness among eye conditions. Improper intraocular pressure in the human eye is the main reported reason for this disorder. The beginning stages of glaucoma do not exhibit any symptoms. Glaucoma patients show no symptoms and no signs of illness early in the disease, but vision loss occurs as the disease advances. Techniques for glaucoma auto-detection can be advanced using computational methods from machine learning in conjunction with digital image processing. Domain shifts are very common in fundus images as they are captured on different devices with different rules. The domain shift is a very pressing issue in glaucoma diagnosis. In this paper, we employ a Deep Convolutional Generative Adversarial Network (DCGAN) to generate source-like target images which are tested on a classification model, ResNet-50. The results show that we can successfully achieve good accuracy using domain adaptation.
Instagram vs TikTok: Which Engage Best for Consumer Brand Engagement for Social Commerce and Purchase Intention?

Restrictions from the government during the Covid-19 pandemic have made people spend more of their time online, browsing social media and encouraging them to shop online to fulfill their needs. The two social media with the most number of users are Instagram and TikTok. This popularity makes consumers interact more actively with other people on social media, which can increase product evaluation and make better purchasing decisions through product comments and reviews. This factor marks the beginning of the "social commerce" phenomenon. The purpose of this research is to compare the two social commerce, Instagram and TikTok, which platform engage best for consumer brand engagement and purchase intention, and how they relate to each other.This research uses a quantitative method with a SEM-based Partial Least Square (PLS) model using the Smart PLS 4 application. The result of this research are as follows, the selection of social media network platforms does not have a significant effect on consumer brand engagement and social commerce purchase intention. Instead, social commerce purchase intention is significantly influenced by the engagement between users. In turn, this consumer brand engagement is affected by the users’ habits when using social media.
The Influence of Video Advertising Content and Trending Video Advertising Usage on Hedonism and Customer Loyalty Factors

Various industries have used video as a medium for advertising; prior research indicates that video advertising influences raising sales and even the company's image. Trending video advertising is one method of using video for advertising (viral video ads). However, a study reports that in 2022, the number of people watching trending video advertising has decreased. This phenomenon serves as the study's backdrop. This study sought to determine how the factors of vividness, accuracy, and consistency in video advertising content affected trends in video advertising. and how the hedonism and customer loyalty factors are impacted by trends in video advertising. The study was carried out using a qualitative approach that combined partial lease square (SEM-PLS) and structural equation modeling (SEM) techniques. This study gathered data from as many as 476 respondents using the Snowball sampling method. Trending videos had no effect, the consistency factor had an impact on both the accuracy and trending video advertising factors, the hedonism factor had an impact on the customer loyalty factor, the vividness video content factor had an impact on the accuracy factor, and the vividness video content factor had no impact on trending videos.
Iris and Periocular Recognition using Shape Descriptors and Local Invariant Features

Iris is a popular biometric modality that has been deployed in uncontrolled environment for various applications like the Aadhaar project in India, in airports, banks, health and educational institutes. However, occlusion of eyelids, eyelashes and illumination variations result in degradation of biometric system recognition. Thus, another biometric modality ‘periocular’ has been proposed in the paper in complementary to ‘iris’ modality. ‘Periocular’ refers to the region surrounding eye i.e. eyelids, eyelashes, eyebrows and skin texture. A periocular database consisting of 1000 images has been prepared. The paper proposes a feature-set consisting of shape descriptors: Local Binary Pattern (LBP) and Scale-Invariant Feature Transform (SIFT) along with orthogonal moments like Zernike, Krawtchouk, Tchebichef and Dual-Hahn. The feature-set is concatenated and fed into a K-NN classifier. Experiments are performed on publicly available database: IIITD Multi-spectral periocular and self-developed PEC periocular database. Results demonstrate that Dual-Hahn moments show recognition accuracy of 97.8% for IIITD database and Tchebichef moments show an accuracy of 92.7% for PEC periocular database. The proposed method achieves superior results when compared to other methods available in literature.
The Number Sense: How the Mind Creates Mathematics.

L'A. propose quelques observations concernant l'ouvrage de Stanislas Dehaene The number sense. How the mind creates mathematics (1997) qui explore tous les aspects de la relation entre les hommes et les nombres : la numerosite chez les autres animaux, la numerosite et le calcul simple chez les bebes, l'histoire de l'expression du nombre dans le langage, l'histoire de la notation du nombre, le circuit neuronal necessaire pour faire de l'arithmetique et du calcul, la localisation dans le cerveau, l'ordre mathematique de l'univers, etc . L'A. examine ici en particulier les questions portant sur la relation entre les nombres et le langage dans une perspective cognitive, puis explique ce que Dehaene entend par le sens du nombre en caracterisant les mathematiques comme une formalisation progressive de nos intuitions sur les ensembles, le nombre, l'espace, le temps et la logique
Deep Convolution Neural Network-Based Classification and Diagnosis of Heart Disease using ElectroCardioGram (ECG) Images

A cardiovascular disease, if identified correctly at an early stage, could reduce the critical consequences in patients , including fatality. One of the best diagnostic tool for detecting heart disease is through an ECG test. Models trained using signal data related to ECG is difficult to be implemented in an actual healthcare scenario. A CNN model is proposed which makes use of 12-lead ECG images to diagnose cardiac conditions such as myocardial infarction, abnormal heart beat, history of myocardial infarction and normal heartbeat. The ECG image can be taken by scanning the image using a smart phone. This would be very helpful in small healthcare centers where there are no experts for diagnosis. The proposed model was efficiently trained with an accuracy of 99% and cardiac condition was diagnosed using ECG images scanned using a mobile with a superior performance. The work also compares the performance of model with pretrained models as ResNet and EfficientNet-B0 for the same ECG image dataset.
Cerebral venous circulatory disturbance as an informative prognostic marker for neonatal hemorrhagic stroke

Neonatal hemorrhagic stroke (NHS) is a major problem of future generation’s health due to the high rate of death and cognitive disability of newborns after NHS. The incidence of NHS in neonates cannot be predicted by standard diagnostic methods. Therefore, the identification of prognostic markers of NHS is crucial. There is evidence that stress-related alterations of cerebral blood flow (CBF) may contribute to NHS. Here, we assessed the stroke-associated CBF abnormalities for high prognosis of NHS using a new model of NHS induced by sound stress in the pre- and post-stroke state. With this aim, we used interdisciplinary methods such as a histological assay of brain tissues, laser speckle contrast imaging and Doppler coherent tomography to monitor cerebral circulation. Our results suggest that the venous stasis with such symptoms as progressive relaxation of cerebral veins, decrease the velocity of blood flow in them are prognostic markers for a risk of NHS and are an informative platform for a future study of corrections of cerebral venous circulatory disturbance related to NHS.
Impulsive Stabilization of Unconstrained Multilayer Recurrent Neural Networks with Node-Based Time-varying Delays

This article discusses the exponential stabilization of node-dependent delayed multilayer neural networks (NDDMNNs) under impulsive control. To address different modeling requirements in complicated applications, node-based interlayer and intralayer parameters are presented to design the neural network model, indicating that The nodes constituting the network can have different structures. Meanwhile, the novel model considers the node-dependent time-varying delays, and this article develops the sparse matrix approach to translate the node-dependent delayed NDDMNNs model into an multiple delayed model, ensuring that the vector form of NDDMNNs can be constructed and studied by using existing technical approaches. Then, an analytical framework with super-Laplacian matrix and time-dependent Lyapunov function methods is proposed to derive exponential stabilization results. Finally, a numerical simulation example is given to verify the obtained results.
Investigation on the Vibration of a 3-Phase SRM Analysis

This paper presents the determination of natural frequency of vibration for a 1 kW, 48 V, 3000 RPM, 3-phase Switched Reluctance Motor set-up and experimental validation of the same. Vibration, audible noise and torque ripple being some of the disadvantages that hold back the SRM from gaining absolute popularity hence, this study is significant. The deformed shape and the natural modes of vibration are obtained from FEM-based static-structural and modal-analysis simulations, respectively. Dominant natural modes have been identified by comparing the deformed shape of the stator under normal operation and the mode shapes for the different frequencies. Analysis of the accelerometer signals in the frequency domain leads to the experimental identification of the dominant frequency causing vibration. Static excitation test has also been done for diagnosing peak noise in the same frequency range as obtained from other experiments. The dominant natural mode is thus identified and is found to be remarkably close to FEM-simulation prediction.
Information Security and the Quality of Online Loan Applications: A Societal Analysis

Online loan is viewed as an alternative to banking but easier and provide direct connection between public and loan offerer. However, online security threats and scam are undermining the quality of online loan. This study aims to determine how the public views their privacy while using online loan applications, perceived risk, perceived security, and qualities on intention to apply online loan. In order to examine the intention, a quantitative survey method was adopted and survey questionnaire was sent to the public who had experienced and apply for online loan applications. 153 responses were received and analysed using IBM SPSS version 28 for demographic analysis and SmartPLS 4 for model and structural measurements. Results show that perceived security, service quality and system quality were not critical to the respondents when choosing online loan applications while perceived risk, information sharing, and privacy concern were critical. This study shows that general public believed that security and quality are part of the package when organization offered a product or service. Interestingly, while privacy, risk, and information are important, public felt that it is the duty of organization to take care of their interests. Future research should look into behavioural aspects of public risk, information sharing, and privacy concern to understand in-depth.
PreAxC: Error Distribution Prediction for Approximate Computing Quality Control using Graph Neural Networks

While Approximate Computing (AxC) is a promising technique to trade off accuracy for energy efficiency, one fundamental challenge is the lack of accurate and informative error models of AxC applications. In this work, we propose PreAxC, a novel error modeling and prediction flow for AxC designs. Instead of using simple error statistics as in existing work, we use error distribution for AxC circuit error analysis with input awareness. We propose graph neural network (GNN) based methods to predict the error distribution of AxC programs, which are represented as data flow graphs (DFGs). We propose two approaches: model-free and model-based, where the former directly predicts the error distribution histogram, and the latter models the distribution using Gaussian Mixture Model (GMM) and predicts the GMM parameters. Experiment results demonstrate that our approaches can outperform existing error statistics and can successfully predict the error distribution, especially the model-free approach, even for completely unseen graphs (representing new AxC programs) during training.
YARS-IDS: A Novel IDS for Multi-Class Classification

An Intrusion Detection System (IDS) is a defence system that provides safety and security against different threats and attacks, acting as a wall of defence against attackers. As internet usage increases, IDSs are becoming an essential part of day-to-day life. Various Machine Learning (ML) and Deep Learning (DL) based IDS are available, and the domain of IDS is still evolving and growing. Here this paper proposes two DL-based IDSs, first is a combination of LuNet and Bidirectional LSTM (Bi-LSTM) and other is a combination of Temporal Convolutional Network (TCN), CNN and Bi-LSTM. Such IDS must be fed with an efficient number of samples to keep them updated and accurate. The first model has been trained and tested against two benchmark datasets, NSL-KDD and UNSW-NB15. The second model has been trained and tested against the NSL-KDD dataset. To overcome the insufficient number of samples, the models have used a technique called Synthetic Minority Oversampling Technique (SMOTE). These models provided better experimental outcomes than traditional ML-based approaches and many DL approaches. They have better results in classification accuracy and, detection rate. The classification accuracy of the first model for UNSW-NB15 and NSL-KDD is 82.19% and 98.87% respectively. The classification accuracy of the second model for NSL-KDD is 98.8%.
Post-Buckling Analysis for Addressing Asymmetric Warping of Fan-Out Reconstitution Process

Fan-out reconstitution process involves numerous thermal cycling and significant wafer warping would inevitable occur and full scale simulations are thus usually required for properly optimizing design and process parameters. However, the present nonlinear finite element approach of predicting wafer asymmetric warpage could result in extra computational cost and alternative approach might be worth to explore for performing efficient simulations. Thus, this work proposes a buckling/post-buckling scheme as a potential alternative for studying wafer warping problem and applies the schemes in real packaging process for demonstrating the feasibility and potentials of the approach. The simulation shows that the errors of bifurcation temperature and warpage of the two analyses are 14% and 6%, respectively w.r. t. nonlinear simulation thus the deviations are within acceptable range. Subsequently, process emulator with buckling/post-buckling analysis is established to predict warpage in the large deformation region. And compare with the nonlinear analysis, the error of warpage is within 3%. In the future, more numerical models are discussed to analyze the sensitivity of parameters thus the deformation behavior of actual process can be described more realistically.
Moving Car Observation (MCO) for Road Surface Defect Identification Using GPS Video

Identification of road surface infrastructure defects is a very important requirement and requires fast and accurate information. The purpose of this study is to identify road surface defects using recording technology with GPS video. The data collection method was carried out by surveying road defects using GPS video with moving car observation. Furthermore, the image data from the video recording is compiled to determine the condition of the road surface damage in accordance with the coordinates of the road segment. The method of analyzing the types of road damage used the Pavement Condition Index (PCI) method, then a roadmap of road damage conditions was made. The research results using GPS video obtained that the percentage of road surface defects for each type of damage is good 10 %, fair 45%, light poor 35% and heavy poor 10%. The results of the identification of road surface defects with GPS video are generally in accordance with the conditions in the field. From the results of this study, it can be recommended that a road defect survey using GPS video can be used as an alternative survey method and has the advantage of being faster.
SINCHDrone Technology Demonstration UAV Hybrid Incorporating Power Regeneration Technologies & Weight Minimization

In this paper, an in-flight power regeneration and structural battery integration approach were employed to increase the endurance, range, and therefore mission capability of a VTOL (Vertical Takeoff and Landing) fixed-wing UAV (Unmanned Aerial Vehicle) by a multidisciplinary student team in California State Polytechnic University, Pomona. The average flight time for a UAV drone has a maximum flight time of 30 minutes, limiting the number of applications that can be used. Although military drones have flight time measured in hours, their cost, size, and complexity eliminate them from commercial use even after they may be retired from military use. Project BANSHEE’s overarching goal is to increase the range and endurance of a UAV in a compact and economical manner. This is to be achieved by the integration of power generation and storage equipment into the load-bearing structure of the airframe. Doing so will decrease the parasitic weight from external components and extend the battery life of the craft, allowing for longer flight time. Modifications to currently available drones such as the Storm4 are being made in order to perform autonomous flight plans and in the future: return to home base when battery is low, exchange depleted battery with new one while maintaining power through induction charging.
Classification of Potholes using Convolutional Neural Network Model: A Transfer Learning Approach using Inception ResnetV2

Rough and damaged roads give poor ride quality which leads to poor transport experience, high travel cost, physical loss to vehicles and passengers, and high vehicle maintenance cost. Therefore, to plan a safe and optimal road trip, prior information of road conditions is most important. Poor road conditions are also responsible for traffic accidents. There exist many road conditions and potholes detected techniques and these techniques can be categorized into two basic categories named as vision-based techniques and vibration-based techniques. In this paper vision-based technique is used for road image classification. For the classification, a transfer learning-based Inception ResnetV2 transfer learning-based Convolutional Neural Network model is used. The selected model is trained and tested on 3211 road images. These images were collected from the public data available on the internet and captured using the camera. Data is classified into three categories plane road, large pothole, and small pothole. The accuracy of the classification is calculated in terms of precision, recall, F1-score, support, and accuracy percentage. According to performed analysis, the Inception ResnetV2 transfer learning-based Convolutional Neural Network model attained maximum accuracy of 94.42 percent with a 0.933 precision value. The performance of the model during the classification process is evaluated using the training and testing loss. Further, the accuracy of the proposed model is compared with Convolution Neural Network and Support Vector Machine using the same dataset. This paper also provides a comparative analysis of the proposed model with other published work.
Towards Generating True Random Numbers using Magnetoresistive RAM

Generating random numbers is a critical task in any security-related scenario, e.g., generating secure cryptographic keys for communication. Pseudo Random Number Generators (PRNG) do fulfill the task of generating numbers, though they are fundamentally dependent on a random seed or an entropy source. True Random Number Generators (TRNG) use other sources of entropy to generate a truly random number, e.g., by measuring the radioactive decay of an atom or electrostatic interference. This paper proposes a novel way of generating truly random numbers by leveraging the nondeterministic write behavior of Magnetoresistive Random Access Memory (MRAM) that occurs, if an external magnetic field is applied during operation. Since the MRAM chips are manufactured with nanoscale impurities on silicon level, it acts as a Physically Unclonable Function (PUF) and can be employed in security critical applications. For that, a formal description of the algorithm and technical details are thoroughly described. To show the true randomness of the operation, we provide a statistical significance evaluation which is based on real-world experiments. The results show that the MRAM behaves significantly nondeterministic while also providing an entropy of over 0.99 among all tested boards.