Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Study of Efficiency with Inductance Load and Relay Coil for Magnetic Field Coupling WPT

In magnetic field coupling wireless power transmission, the effects of inserting a parasitic coil between the transmitter and receiver coils and inductance loading on the wireless power transmission efficiency were investigated through measurements using a vector network analyzer. We achieved the significant improvement in wireless power transmission efficiency from 8 % to 44 % at the design frequency of 2 MHz by inserting the parasitic coil for relay between transmission coil and received coil on all of which were impedance loaded, while the improvement in efficiency was only a few per cent when either inductances were loaded or only relay coils were inserted. The measured and analyzed results of both efficiency and maximum efficiency were very similar variation for frequency, although the levels of efficiency of experimental results are less than analyzed one.
COVID-19 Impact on Mental Health Analysis based on Reddit Comments

As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians.
Comparison of Feature Extraction and Classification Techniques of PE Malware

Static and dynamic analysis are widely used in malware research. Although both approaches present considerable advantages according to their specific applications, they also present some challenges in terms of the time and resources constraints when extracting features. To alleviate those constraints, recent research works explore the use of techniques such as computer vision, in detecting malicious samples and their corresponding malware family. In this research, we aim to conduct a thorough comparison between a combination set of different feature extraction and classification techniques in order to identify the optimal approach that can be used in a real-time malware detection application portable executable (PE). Multiple Machine Learning (ML) and Deep Learning (DL) algorithms were used with image-based features and non image-based features. The results obtained show high performance when using static PE header features with K-Nearest Neighbor (KNN) and Bagged Trees algorithms for bi-level classification and Image-based Fast Fourier Transform (FFT) features with AlexNet for multi-level classification.
A crack model for the onset of blisters using finite surface thicknesses

In this paper, hydrogen-induced delamination of a bulk material with a finite thickness is investigated. Hydrogen implanted interface splitting is considered as the growth of the crack by forming blisters. The radius of a blister depends on the amount of the implanted hydrogen, crack surface energy, and annealing temperature. For a finite thickness of the superstrate, the evolution of the blisters is calculated adopting a smooth bell-shape function and applying the Rayleigh-Ritz method [K. K. Raju and E. V. Rao, J. Eng. Mech. 119, 626 (1993)]. The required minimum implanted gas Nmin is calculated accordingly. The calculated Nmin value is compared with an experimental result in literature.
A Novel Short-term Vehicle Location Prediction using Temporal Graph Neural Networks

Location prediction is essential for location-based applications such as route recommendation systems, resource allocation optimization in congested areas, congestion avoidance, traffic management, just to mention a few. Knowing approximate vehicle locations can enhance our ability to better manage and prepare the existing mobile edge computing resources in a Vehicular ad hoc network. In this paper, we focus on a graph neural network model to estimate vehicle locations in a timely manner. We first model the vehicle locations based on events that happen between different regions of an area and the car itself. Leveraging our event-based model, we introduce Temporal Location Prediction (TLP) to capture essential features from each node, edges, and neighboring nodes to achieve timely location prediction. Afterwards, instead of using GPS coordinates as input, we demonstrate a new data structure to feed Bidirectional LSTM (BiLSTM) and LSTM for vehicle location prediction in different time intervals. Thus, our main contribution is a network model that utilizes a Temporal Graph neural network for dynamic location prediction. We explain the advantages and disadvantages of each model and how we can improve them. Our experiments on a real dataset show that our model (TLP) outperforms LSTM and BiLSTM in short-term prediction, considering the same scenario and conditions.
A Lightweight Unsupervised Learning Architecture to Enhance User Behavior Anomaly Detection

In recent years, user behavior anomaly detection has been gaining attention in cybersecurity. A crucial challenge that has been discussed in the literature is that supervised models that use vast amounts of data for training do not apply to real scenarios for anomaly detection. Within this context, the requirement to gather datasets with labeled behavior anomalies has proven to be a significant limiting factor for evaluating different models. This paper presents WEAPON, an unsupervised learning-based architecture for user behavior anomaly detection that requires a small amount of data for building behavior profiles considering the individuality of each user. WEAPON implements the weak supervision-based behavior anomaly labeling approach using Snorkel. When compared to other approaches, WEAPON proved to be more efficient, surpassing the ROC curve of the second best model by 4.31%. Furthermore, WEAPON outperforms rule-based methods by finding anomalies that an expert would not anticipate.
Automatic Alignment of Human Generated Transcripts to Speech Signals

This paper introduces a novel, completely automatic, audio-subtitle synchronization algorithm designed to increase the accessibility and comprehension of the hearing-impaired people over the video documents. The major contribution of the paper involves the anchor words matching strategy that can reliably put in correspondence the subtitle document generated by a human transcriber and the automatic speech recognition (ASR) textual file. In addition, we propose a subtitle positioning strategy that automatically determines the optimal location for each phrase on the user screen, with respect to the video semantic content. The experimental evaluation validates the proposed method with average accuracy scores superior to 90%.
Autonomous Robot Navigation in Crowd

This study presents a review of the literature that addresses the problem of autonomous navigation in crowded indoor environments. Navigation in this type of environment is a particularly challenging task because a robot must be able to navigate autonomously without endangering nearby people. We analyze a few selected studies in this field published in the last seven years. The analysis shows that a combination of different techniques is necessary for safe navigation in this type of environment. Therefore we follow a line of previous studies to seek new solutions, or investigate and improve existing solutions to address the aforementioned problem.
Correlation Functions of the Noise Modulation Function under the Influence of Stationary and Slow Multiplicative Noise

An approach for determining the correlation functions of the noise modulation function under the influence of modulating (or more often multiplicative) noise is described. Both stationary and slow noise is considered as influencing multiplicative noise. Correlation functions of noise modulation and fluctuations of the latter can be found by the four-dimensional characteristic function of phase and amplitude distortions. We have derived expressions for the abovementioned correlation functions affected by static multiplicative noise for the normal law of variation of amplitude and phase distortions in the case of their uncorrelation and cross-correlation. The correlation functions of noise modulation and noise modulation function fluctuations under the influence of slow modulating noise were determined for the case when the phase and distortion amplitude variations are independent. The expressions of the correlation functions of noise modulation and its fluctuations for correlated and normal distribution of phase and amplitude distortions under the influence of slow modulating noise are obtained.
Learning Analytics and Machine Learning

This paper integrate the state-of-the-art processes, practices, and challenges of applying learning analytics and data modeling methods in learning and teaching contexts. A large amount of research employed analytics techniques to support the learners and their learning environments to improve learning outcomes and performance. This study aims to analyze Learning Analysts in data science, especially how they can be used for better learning processes. This study uses a secondary research design to collect data from various online databases. These databases include Google Scholar and ProQuest. Only research published in 10 years is considered to ensure the recency of the extracted insights and information. The research outcome is presented as a taxonomy for learning analytics and the associated machine learning application in the education domain.
Towards Heart Rate Categorisation from Speech in Outdoor Running Conditions

The heart rate (HR) provides key information about the intensity of the cardiorespiratory workout, the level of exertion, and the overall heart condition. In sports, and especially running, tracking the HR and other metrics to monitor training progress and avoid injuries has been recently gaining momentum - a trend titled smart exercising. However, especially for beginners, it can be difficult to properly interpret a metric such as HR, which is why an expert categorisation can be beneficial. Furthermore, it can be uncomfortable to put on multiple wearable sensors or buy extra gadgets for measuring the HR during a running session. In order to tackle these issues, we propose a machine learning pipeline for the prediction of various HR categories based solely on speech samples recorded by a smartphone in outdoor running conditions. To this end, we first extract data representations utilising fine-tuned Transformers, pre-trained convolutional neural networks, and conventional, interpretable feature extraction methods. Afterwards, we apply synthetic feature augmentation on all feature sets to cope with potential class imbalance problems. Finally, we train and optimise various linear support vector machine (SVM) and feed forward neural network (FFNN) models on the obtained and augmented features. The results demonstrate the suitability of the proposed machine learning pipeline for automatic speech-based HR classification.
Speech Detection of Depression Based on Multi-mlp

Depression is one of the most prevalent mental illnesses on a global scale. In view of the inefficiency of current depression screening methods. This paper proposes a depression detection model based on a deep model hybrid architecture to assist doctors in diagnosing depression. 157 Chinese subjects were investigated in this study. It is worth noting that we propose a word reading experiment to make the subject’s emotional change rapidly. We extract the Low-level audio features to find out the different emotional change in the process of reading different parts of speech words. We use convolutional neural network to extract deep spectrum features and Multi-mlp to detect depression. The experimental results show that the accuracy rate of speech depression recognition reaches 82.70%, which can effectively assist doctors in diagnosing depression.
CVD19-Net: An Automated Deep Learning Model for COVID-19 Screening using Chest CT Images

COVID-19 is the most recent coronavirus-related disease that has been declared a pandemic by the World Health Organization (WHO), causing a global emergency that has resulted in a large number of deaths and is rapidly spreading around the world. It causes respiratory illness and is highly contagious, putting a strain on health and medical systems worldwide. With the help of various deep learning (DL) techniques, chest CT scans are considered an effective tool for diagnosing COVID-19 because it directly affects the lungs. In addition, the visual similarities between COVID-19 and pneumonia make identification even more challenging, as COVID-19 is also a virus. In this paper, we designed a unique lightweight DL model named CVD19-Net with fewer layers as an accurate diagnostic method for COVID-19. Different regularization techniques such as dropout layers, batch normalization layers and data augmentation are injected into the CVD19-Net model to improve classification accuracy and reduce overfitting. We considered three different publicly available datasets for our experiments. (1) Dataset 1: 2482 CT images were collected; (2) Dataset 2: 7544 CT images were collected; (3) Dataset 3: 3190 CT images were collected. The experimental results show that the proposed model achieves 98.59% accuracy on dataset 1, 98.21% on dataset 2, and 95.61% on dataset 3, which is better than the existing methods. The proposed model requires less training time and storage space, which makes it computationally efficient while maintaining a high level of accuracy, which can help clinicians quickly identify COVID-19 patients.
Determination of Neuron Activation States Facilitated by Artificial Intelligence

Environmental stimuli evoke gene expression changes in neurons to achieve synaptic reconfiguration and memory formation. During the neuronal activation process, immediate early genes (IEGs) and late response genes (LRGs) have been defined according to their temporal changes in gene expression. Recent single cell studies indicated that different subtypes of neurons may have distinct sets of IEGs and LRGs in response to neuronal activation. Currently, it remains unexplored whether the gene expression patterns provided by single cell studies are sufficient to distinguish neuronal subtypes together with their activation states. In this study, we adopt Artificial Neural Network (ANN) models to predict neuron cell subtypes and activation states simultaneously based on a single cell RNA-Seq dataset generated from mouse visual cortex in response to light stimulus exposure. Four kinds of ANN architectures were designed to compare the prediction efficacies using two kinds of inputs: the full list of genes or the markers selected for neuron cell subtypes, IEGs, and LRGs. We observed that, with single-cell RNA-Seq data, neuron cell subtypes and activation states can be predicted accurately by ANN models. Moreover, compared to feeding the entire list of genes to fully connected ANNs, using selected marker genes and partially connected ANNs achieves comparable prediction accuracy with substantially improved computational efficiency. Altogether, our results demonstrated that ANNs are powerful in predicting neuron activation states and cell subtypes and may serve as a useful data mining approach to explore single cell RNA-Seq profiles.
Design and Development of a Prototype Maquette for Smart Adaptive Street Lighting System

Smart technologies in street lighting systems create a healthy and safe environment for citizens around the world. The smart street lighting system can adapt to the movement of bicycles, cars and pedestrians. This system detects car traffic, bicycle movement, or pedestrians through sensors. When there is no movement in the street, the street lights get dimmer, while, when a moving object is detected, the closest lamp to the moving object lights up with 100% of its light. The main goal of the smart lighting system is to save the lighting system from staying on with maximum light all night, thus reducing energy loss when no activity is detected on the street. In this research, a prototype street maquette with five street lights is designed and implemented using IR sensors and LEDs, controlled by an Arduino Uno board, to provide a practical proof-of-concept (POC) for the whole notion of usefulness and practicality of adaptive street lighting. Furthermore, this prototype maquette may be used to conduct experiments. According to the study, the intelligent lighting system can save up to 80% of energy consumption when no car is on the streets.
Multi-Robot Distributed Coordination in IOT Systems: An ACP Framework

This paper investigates the distributed coordination problem of multiple robots in the Internet of Things (IoT) system based on the ACP framework. Considering that the robots in the IoT system may perform some tasks such as handling, cutting, profiling, etc., which require the posture of the end-effectors to maintain unchanged, targeted rules and constraints are designed to regulate the behavior of robots. Furthermore, a neurodynamic controller is utilized to construct the artificial societies describing the behavior of a multi-robot distributed system. Corresponding computational experiments select strategies that are beneficial to task execution for the multi-robot system. Finally, the multi-robot system is promoted to complete the assigned tasks cooperatively by means of parallel execution.
A Deep Learning-Based Tool for Face Mask Detection and Body Temperature Measurement

Due to the COVID-19 pandemic outbreak, wearing a mask and ensuring normal body temperature in overcrowded areas such as workplaces have become obligatory. In this paper, a deep learning-based tool for automatic mask detection and temperature measurement at the entrance of workplaces was developed to save costs of manual supervision and reduce human contact for safety concerns. Using Python, image/video processing techniques related to face and object detection are used to process image input from a webcam. A deep learning algorithm called MobileNetV2 was used to build the face mask detector model. Moreover, a non-contact thermal sensor, the MLX90614, along with Arduino, was employed to measure body temperature. The mask detection and temperature measurements are displayed correctly on a Graphical User Interface (GUI). Besides, an additional function related to the Internet of Things (IoT) was implemented, which sends high-temperature alerts to smartphones. It has been verified that the model can achieve an accuracy of about 98%. The developed system experiences a limitation when other objects are used to cover the mouth and nose in that they may still be classified as masks. However, compared to the mask detection systems available commercially, it can provide correct detection results when using the hand to pretend to be wearing a mask.
Enhanced CT Image Generation by GAN for Improving Thyroid Anatomy Detection

Computed tomography (CT) is one of the most imaging methods widely used to locate lesions such as nodules, tumors, and cysts, and make primary diagnosis. For clearer imaging of anatomical or lesions, contrast-enhanced CT (CECT) scans are imaging with injecting a contrast agent into a patient during examination. But there are limits to iodine contrast injections so that CECT scans are not convenient like non-contrast enhanced CT (NECT). Recently, deep learning models bring impressive results in computer vision, including image translation. So, we would like to apply image translation methods to generate CECT images from the more accessible NECT images, and evaluate the effects of generated images on image detection tasks. In this study, we propose a method called cross-modal enhancement training strategy for thyroid anatomy detection, which employs CycleGAN to translate non-constrast enhanced CT images to enhanced CT style images with content reserved. The experiments are conducted on thyroid CT images with anatomy object annotation. The experimental results show that by adding translated images into the training dataset, the performance of thyroid anatomy detection can be effectively improved. We achieve the best mAP of 82.5% compared to 73.2% in the along non-contrast enhanced CT training.
Machine Learning Methods to Detect Obstructive Sleep Apnea under a Robust Testing Framework

The obstructive sleep apnea (OSA) detection problem has been addressed through different approaches to overcome the limitations of polysomnography, the official clinical gold standard. On this matter, the combination of heart rate variability with modern machine learning (ML) techniques has become quite popular in recent years. Indeed, the evolution in performance of the models reported in the literature has been promising; however, clinical conventions have been urging the scientific community to employ more robust testing frameworks oriented to clinical practice, since most of such models had not been tested with newly unseen data. Hence, the present work follows the guidelines found in the state of the art to build a set of ML-based models and evaluate their performance in more real-life scenarios. More specifically, three publicly available databases were employed to assess the generalization capability of five different classifiers, i.e., support vector machines, k-nearest neighbors, decision trees, and two random forest-based algorithms. Eventually, although the obtained results were different from those reported in the corresponding literature, the experiments conducted in the present work have revealed interesting findings on further insights that could improve the clinical value of the modern ML-based OSA classifiers.
Masers associated with two carbon stars: V778 cygni and EU Andromedae

Presentation de plusieurs observations des raies masers H 2 O a 22.2 GHz et OH a 1665-1667 MHz pour 2 etoiles de type C, V778 Cyg et EU Ard. Pour chacune d'elle l'emission de la raie H 2 O a varie d'un facteur 5 en quelques mois et suggere qu'on a affaire a des systemes binaires ou la composante secondaire serait une etoile de type M ayant une enveloppe epaisse