Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning-based Autonomous Parking with Expert Demonstrations

As an important task in autonomous driving of intelligent vehicles, the autonomous parking technology has a broad market potential. The traditional parking algorithm based on trajectory planning often results in significant tracking errors due to the non-linearity of vehicle dynamics. While general reinforcement learning algorithms may require the assistance of domain experts due to the complexity of the parking scene. However, relying on expert demonstrations can also be problematic due to the high collection cost and potential for uneven quality of the demonstrations. To address these problems, we propose an end-to-end reinforcement learning-based parking method using expert demonstrations. Firstly, we add Gaussian noise to the collected expert demonstrations for data augmentation, which expands the diversity of samples and guides the agent to learn the expert policy quickly. Then we introduce the mixed priority sampling strategy, and employ it on the augmented expert demonstrations and experience obtained from exploration in the environment to jointly train the agent. We evaluate our method on the benchmark algorithm, Soft Actor-Critic, on the CARLA autonomous driving simulation platform with vertical and parallel parking environments. The experimental results indicate that the proposed algorithm can greatly improve the convergence rate and achieve a higher parking success rate.
Analysis and Configuration of IoT Sensors in Smart Structure by Employing QR Code Generator

The Internet of Things (IoT) has transformed many different industries, but its effects on building automation stand out in particular. Building IoT sensor networks are essential for maximizing energy use, boosting security, and raising tenant comfort. Large-scale smart structures, however, can make manual configuration of a large number of IoT devices time-consuming and error-prone. In this study, we introduce a novel architecture based on QR codes that is intended to speed up the autoconfiguration of IoT sensor networks in smart structures. The suggested framework intends to ensure resilience and security while streamlining the deployment, configuration, and maintenance of IoT devices. Through study and actual analysis, we assess the framework's performance and show its usefulness and potential for real-world applications. Overall, our suggestion offers an automatic and economical energy management system that offers a sustainable approach to energy conservation and reduced carbon emissions and can be used in both newly constructed smart structures and existing ones.
Fine-Tuning MultiFit for Enhanced Legal Sentence Basis Classification

Deep learning algorithms have shown promise in effectively classifying legal texts, surpassing traditional methods. However, existing approaches are primarily designed for English text and lack suitability for other languages, mainly Portuguese. This study addresses the challenge of classifying legal basis in first-degree sentences within Brazilian law by fine-tuning the multilingual MultiFit model using a novel basis dataset, comprehensively training the model for accurate legal basis classification.The bidirectional deep-learning MultiFit model has been subjected to rigorous fine-tuning, resulting in exceptional performance while maintaining consistently high quality. Results obtained highlight the model’s remarkable proficiency in precisely categorizing legal bases in first-degree sentences, achieving an accuracy rate of 80.0%, precision of 83.3%, recall of 80.7%, and an F1 score of 82.0%. These results demonstrate the model’s adaptability, versatility, and suitability for legal applications. In addition, it exhibits high precision, accuracy, and efficiency in classifying legal bases in first-degree sentences. Moreover, successfully fine-tuning pre-trained models for new tasks, leveraging extensive datasets, highlights their significant potential in enhancing performance in legal applications.
Low-Dose Tau PET Imaging Based on Swin Restormer with Diagonally Scaled Self-Attention

Though successful and efficient in local feature extraction, convolutional neural networks (CNNs) cannot capture long-range dependencies well due to its limited receptive field. Transformer-based network structures can exploit the long-range information through a self-attention mechanism and have recently demonstrated better performance than CNN in various computer vision tasks. However, self-attention is hard to be directly applied to 3D images due to the computational cost. In this work, we proposed a novel transformer architecture, Swin Restormer, which integrates two efficient transformers, namely Swin Transformer and Restormer, to improve low-dose tau PET image quality. The proposed Swin Restormer can efficiently perform self-attention in both spatial and channel dimensions. A diagonally scaled self-attention mechanism was also proposed to further improve the attention map calculation. Quantitative evaluations based on clinical 18F-MK-6240 tau imaging datasets show that the proposed transformer architecture can achieve better denoising performance than the UNet structure.
Artificial Intelligence Education - Self Guided Learning

In recent years, there has been a noticeable surge in the utilization of artificial intelligence (AI) across various domains, including science and engineering. This growing demand has placed greater importance on AI education, with undergraduate students from diverse majors being introduced to introductory AI modules. However, it's worth noting that not all students come with a pre-existing background in problem-solving, which can result in some finding the content covered in AI courses to be both challenging and lacking in engagement. To address this challenge, we propose the implementation of AiEduSys (AI Education System). In AiEduSys, we aim to provide a solution by offering customized programming assignments and quizzes tailored to students' specific majors. Additionally, our course recommendations will be customized to align with their chosen fields of study. This approach is designed to enhance students' understanding of AI concepts and, in turn, stir up their interest in AI algorithms. We aspire to motivate them to explore how AI can be applied within their respective domains, extending beyond the confines of the AI module itself. In our user study, we created customized programming assignments and quizzes centered around the topic of the K-Nearest Neighbour (kNN) algorithm. Furthermore, we employed a hybrid model that combines Content-based filtering and Collaborative filtering to develop the AI course recommendation system. The results were promising, as we observed positive feedback from students. The majority of participants expressed significant interest in the custom-tailored programming assignments, personalized quizzes, and course recommendations. Most notably, these students demonstrated a heightened level of motivation to explore AI applications within their fields of study following their interaction with AiEduSys.
Research on the Effects of Grid-Forming Control on Islanding Detection Effectiveness of Distributed Energy Resources

With the proliferation of Distributed Energy Resources (DER), advanced inverter functions such as Grid-forming (GFM) have been developed. Aiming to maintain the frequency and voltage stability, such functions, however, may deteriorate the effectiveness of islanding prevention. This issue has not yet been thoroughly studied and the impact of GFM on active islanding detection schemes remains unclear. To fill the void, this paper evaluates the performance of the widely used Slip-Mode Frequency Shift (SMS) method operating together with the classic droop control. The relationship between main electrical quantities in a typical islanded operating system is firstly investigated, and its small-signal state space model with the droop and SMS control is derived. Based on this model, eigenvalues and sensitivity of key parameters are analyzed, and the effect of droop control on the detection effectiveness of SMS is investigated. In addition, the range of SMS coefficients to guarantee a successful islanding detection under the droop control is characterized. Finally, Matlab/Simulink simulation is performed to validate the above analysis. Results show that parameters dominating the effectiveness of islanding detection include the Q-v coefficient of droop control and the SMS coefficient. Additionally, the SMS coefficient should exceed 0.72 to guarantee an islanding prevention in the studied system.
Role of VANETs in Machine Learning and Deep Learning

Machine learning and Deep Learning with transportation opened new avenues of research by accessing to internet for agile conveyance, safety, efficiency, and comfort applications. However, the combination of Vehicular Adhoc Networks with machine learning and deep learning acts as an expansion of drivers' perceptions by providing road network predictions to avoid road fatalities, traffic congestion, and traffic jams. Thus, enhancing safe, comfortable, quality life, and efficient driving. This article summarized the role of the machine and deep learning algorithms in intelligent transportation according to the application domains. This paper presents applications, evaluation metrics, recognition rate, and limitations of machine learning and deep learning techniques with VANETs. In addition, it describes the different data sets and background of machine learning and deep learning models.
INTELLIGENT SEGMENTATION OF FRUIT IMAGES USING AN INTEGRATED THRESHOLDING AND ADAPTIVE K-MEANS METHOD (TSNKM)

Recent years, vision-based fruit grading system is gaining importance in fruit classification process. In developing the fruit grading system, image segmentation is required for analyzing the fruit objects automatically. Image segmentation is a process that divides a digital image into separate regions with the aim to obtain only the interest objects and remove the background. Currently, there are several segmentation techniques which have been used in object identification such as thresholding and clustering techniques. However, the conventional techniques have difficulties in segmenting fruit images which captured under natural illumination due to the existence of non-uniform illumination on the object surface. The presence of different illuminations influences the appearance of the interest objects and thus misleads the object analysis. Therefore, this research has produced an innovative segmentation algorithm for fruit images which is able to increase the segmentation accuracy. The developed algorithm is an integration of modified thresholding and adaptive K-means method. The integration of both methods is required to increase the segmentation accuracy for fruits images with different surface colour. The results showed that the innovative method is able to segment the fruits images with high accuracy value,
An Adaptive Model Predictive Control for Three-Level Flying Capacitor Boost Converter in Photovoltaic System

This article designed an adaptive model predictive control method which used in three-level hybrid flying capacitor boost converter (HFCBC) in photovoltaic system. In this article, a model predictive control method with continuous control set (CCS-MPC) is used as the inner-loop to control the flying capacitor voltage and inductor current for the HFCBC. Besides, an adaptive outer-loop controller is proposed to provide an appropriate reference of inductor current and eliminate the steady state error and accelerate the response speed. Compared with the earlier control methods, the adaptive CCS-MPC has more advantages such as fast dynamic response, solving the problem of model mismatch effectively and additive system state variables constraints. The advantages and effectiveness of the control strategy is shown by simulation results.
High Quality Segmentation Using Deep Learning Centered Detection And Correction of Cardiac MR Motion Artefacts Throughout Reconstruction

Deep learning techniques have been effectively used for a variety of purposes to separate anatomical features in healthcare imaging. Yet, the caliber of the picture being split has a significant impact on this outcome. The large quantity of clinical photographs with significant picture artefacts caused by organ motion, patient mobility, and/or picture acquisition-related difficulties is a frequently ignored topic inside the medical imaging evaluation field. This study compares various methods for concurrently compensating for artefacts and segregating the heart cavity. We address the effects of picture image noise on cardiac MR segments. The technique is based on our most current joint artefact detecting and reconstructing approach, which creates high-quality MR pictures from k-space the use of a By requiring a data integrity term, the associated logistic regression effectively transforms the artefacts correction problem into an under-sampled image enhancement challenge. In this research, we suggest coupling this with a recognition networks in edge architecture. Our instruction enhances three distinct tasks: Segmentation process, artefact restoration, and identification of artifacts are the first two. Using ventricular MR k-space data that has been artificially damaged with untreated rebuilt pictures; we train the reconstructing networks to autonomously repair formation-related aberrations. We demonstrate a high level of classification performance and clearly good pixel density in the absence of manufactured motion artifacts to use a training dataset comprising 500 2D+time cine MR observations from of the UK Biobank set of data. In comparison to several image correcting structures, we demonstrate improved efficiency.
Evaluating Sense Amplifier Performance in SRAM Circuits

Examining sense amplifiers is vital to enhance circuit efficiency, optimize reads, minimize power usage, and elevate system functionality through precise signal detection and faster data retrieval. This paper analyses and compares the performance of existing sense amplifiers based on read power, read delay, and energy consumption per switching activity. To observe the influence of diverse operating conditions, the analysis is also performed at different temperatures, supply voltages, and all corners of the fabrication process. The results demonstrate that the current latch sense amplifier performs the fastest read operation even in the presence of PVT -variations, while consuming moderate read power, and energy compared to other designs. In contrast, the differential current-mirror sense amplifier consumes the highest power and energy while exhibiting the slowest read operation. The results also indicate that the sense amplifiers execute the fastest read operation at high supply voltage, SF corner, and low temperature. The read power and energy consumptions are at their minimum levels when operating under low supply voltage, SS corner conditions, and high temperature. Conversely, the designs reach their highest values at high supply voltage, FF corner conditions, and low temperature.
A Voltage Control Strategy of Differential Buck-Boost Inverter Based on Third Harmonic Injection

The development and utilization of renewable energy has become an important measure to alleviate the contradiction of energy supply and cope with climate change. New energy sources typically operate at low-voltage DC sources, requiring two power conversion stages to achieve boost and inverter outputs. The differential Buck-Boost inverter has gained popularity in academia due to its ability to accomplish both boost and inverter operations in a single stage. In this research, the relationship between DC voltage utilization and voltage stress is discussed. A new modulation scheme based on third harmonic injection is proposed. This improved modulation scheme enhances differential inverter's boost capability and mitigates voltage stress on capacitors and switching devices. Moreover, the proposed scheme facilitates precise control through simple closed-loop control. Analysis and simulation results confirm the advantages and effectiveness of this method.
Bicycle-Friendly Urban Planning using IoTConnected Traffic Signals in the Cloud

Optimizing signal timing for bicycle-friendly communities with the use of IoT technology is the primary objective of the proposed system, which aims to improve urban mobility and safety. Increasing urbanization and public interest in environmental protection have made it imperative to promote cycling as a healthy and environment friendly form of transportation. However, the efficient traffic signal management may help to solve the problems that occur when bicycles and vehicles share the roads. To better serve bicycles, this study presents a complete architecture that uses Internet of Things (IoT) sensors and data analytics to modify traffic light schedules. IoT sensors are placed strategically at major combinations to monitor traffic patterns in real-time, including the number and velocity of bicycles. This information is then used to optimize traffic light timing in a way that reduces delays for bicycles without negatively impacting the experience of other road users.
Machine Learning for Autism Spectrum Disorder Detection: A Systematic Survey

A neurodevelopmental illness called autism spectrum disorder (ASD), often referred to as autism causes issueswith social communication. These issues are evident in the way ASD youngsters talk, according to prior study. This shows that quantitative speech variables may be used to judge the severity of ASD.A common way research is one method of identifying ASD behavioral child's expression of signals. Using Machine Learning can obtain the data of children having the autism spectrum disorder. Through last years, applying magnetic resonance imaging (MRI) to detect common patterns type community is a set of stacked auto encoders trained in an unsupervised manner and multilayer perception trained in a supervised manner. Quantitative assessment was completed on 817 multi-site Autism Brain Imaging I Global Data Exchange I (ABIDE I) data cases, including 368 ASD patients and 449 control subjects, and received an accuracy of type 85% when assessed using a set of classifiers.
Industrial Cybersecurity Game-Scenarios Based on the MITRE ATT&CK Framework

In modern society, cybersecurity has become an increasingly crucial challenge. To prepare the next generation of professionals in this field, we propose interactive approach to teaching industrial cybersecurity concepts. However, it's important to recognize that people are often the weakest point in the security systems of these industries. Additionally, the lack of published cybersecurity training that focuses on Industrial Control Systems (ICS) is a significant challenge that needs to be addressed. There is currently a dearth of training materials available that are tailored specifically to the needs of ICS-based cybersecurity, which is a major issue. To address this problem, discovering new and innovative security training methods that specifically target ICS is necessary. Serious games have become increasingly popular among cybersecurity researchers in recent years as an innovative and engaging approach to educating users and enhancing their awareness and knowledge of cybersecurity-related issues. The purpose of this paper is to provide examples of industrial cybersecurity game scenarios based on the MITRE ATT&CK framework. Our target audience is students with minimal knowledge of industrial cybersecurity.
Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm

Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably.
Coupling Characteristics of Subsynchronous Oscillation Frequencies in Multi-Renewable Power Plants Integrated to Power System

The subsynchronous oscillation (SSO) frequently occurs in the renewable power plants, which is harmful to the stability of power system and the development of the renewable power. This paper studies the coupling characteristics of subsynchronous oscillation frequencies in multi-renewable power plants integrated to power system. Combined with the characteristics of PLL and Park’s transformer, the analysis of the mechanism on the oscillation frequencies coupling is carried out. Then, based on eigenvalue analysis and simulation analysis, a case that multi-wind farms integrated to power system is considered to verify the mechanism analysis. A conclusion can be made that the characteristics of PLL and Park’s transformer cause the oscillation frequencies coupling in the multi-renewable power plants integrated to power system. At the same time, the influence of the oscillation components derived by natural modes on the system should be considered.
Ensemble Learning Using Weighted Convolutional Neural Networks for Parkinson’s Disease

In recent years, there has been a growing interest on Computer-Aided-Diagnosis (CAD) systems based on Deep Learning to disentangle how neurodegenerative disorders such as Parkinson’s Disease (PD) behave. Although these models are quite accurate, they are often criticized for their lack of interpretability, high complexity and tendency to overfitting (as a result of the amount of input features –intensity voxels from brain scans– in comparison with the reduced number of input samples). Though there are some proposals that are trying to solve these limitations, such as data augmentation and/or interpretable models, it would be interesting, from the point of view of neuroimaging, to develop a CAD model able to 1) split the problem of analyzing the full brain scans and 2) combine the contribution of smaller Regions of Interest (ROIs). This could help us not only to reduce the complexity of the neural networks focused on the analysis of each ROI, but also as a way to quantify the contribution of each region to CAD decisions. In this context, we have proposed an ensemble learning methodology with penalization of non-reliable input sources focused on the analysis of 7 ROIs (including: accumbens, amygdala, anterior cingulate cortex, cingulate, striatum, substantia nigra and thalamus). This method is able to combine the resulting predictions obtained for each ROI by means of a Weighted Majority Voting decision schema using the scores derived from a two-sample Welch’s test. As shown by our results, this proposal achieves classification rates of 97.5% and is an easily adaptable solution for the analysis of other pathologies.