Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic sign recognition based on deformable convolutions

A traffic sign recognition model based on deformable convolution is proposed to address the low accuracy of existing traffic sign recognition algorithms for small and occluded targets. Firstly, replace some convolutions in the backbone network with deformable convolutions to adapt to different shape feature maps during feature extraction and improve the network's sensitivity to small targets; Secondly, in order to reduce the loss of information in the process of upsampling, the original Nearest-neighbor interpolation upsampling module is replaced by the sub-pixel convolution module in the feature fusion network to reduce the loss of information due to upsampling. Finally, design experiments on the TT100K dataset and test the advantages of this model in real-world scenarios. The experimental results show that the average accuracy of this model (mAP@0.5) It has reached 92.54%, with a Recall rate of 87.42%, which is 2.18% and 1.58% higher than the benchmark network YOLOv5n, respectively. The frame processing speed has reached 96.06.
A novel Recommendation Algorithm Based on Knowledge Graph Convolution Networks and LDA

Recommender systems exist with the aim of helping users discover personalized and useful content and products in a sea of information. Recommender systems have long faced challenges such as data sparsity and cold start. In this study, we propose a recommendation model based on Knowledge Graph Convolutional Network and LDA (KGCN-LDA), which first constructs a collaborative knowledge graph, and deep mines the auxiliary entity information in the collaborative knowledge graph using graph convolutional network(GCN). In the knowledge graph, GCN is used to deep mine the auxiliary entity information in the collaborative knowledge graph, while LDA mines the potential features in the semantic information, and finally, the user and entry embeddings obtained from the two parts are concatenated to generate the final recommendation results. In order to evaluate the effectiveness of the proposed model, we conducted experiments on a public dataset, and the results show that the proposed method significantly outperforms several existing methods in terms of precision, recall and F1.
Application in Five-Phase Permanent Magnet Synchronous Motor of Smooth Second Order Integral Sliding Mode Control Technique

This work introduces a Vector Control (VC) approach based on an integral second order Sliding Mode Control (SMC) for achieving robust and precise control of a five-phase Permanent-Magnet Synchronous Motor (PMSM) supplied through a five-leg inverter. The VC strategy for the five control loops based five-phase PMSM is usually for the current and the rotor speed based on conventional PI controllers. Thus, the main goal of this study is to substitute PI controllers with proposed SMC controllers, with the aim of enhancing the control system performance in terms of tracking accuracy, robustness, uncertainties and external disturbances. In contrastoov to conventional VC-PI, the simulation results in the Matlab/Simulink environment reveal that VC-SMC can rapidly attain the desired reference speed without experiencing overshooting, all while maintaining a high degree of robustness and precise system control.
Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.
Comparative Study of Hybrid PM Assisted Synchronous Reluctance Machines with Parallel and Series Configurations

In this paper, the rotor topology of hybrid permanent magnet assisted synchronous reluctance machines (HPM-SynRMs) with parallel and series configurations is given and the analytical models are presented. Based on the analytical models, a method for calculating the dimensions of the hybrid permanent magnets is presented. The influence of three rotor parameters on the performances of the HPM-SynRM is discussed. Based on the initial scheme, the rotor topology is changed to meet the same demagnetization capability with a demagnetization rate of less than 3%. Then the two configurations of machines are compared and it is concluded that the HPM-SynRM with parallel configuration can significantly reduce the usage of NdFeB to a greater extent and has better development potential.
Improved Deadbeat Predictive Current Control for SPMSM based on Multi-Step Prediction

In this paper, a multi-step accurate deadbeat predictive current control (MADPCC) algorithm is proposed to improve the current loop’s dynamic performance of the surface-mounted permanent magnet synchronous motor (SPMSM). Under high speed and heavy load conditions, the motor’s back electromotive force is large and the actual output voltage is limited, which cannot provide sufficient current change rates. Therefore, the traditional single-step prediction algorithm is difficult to achieve the optimal transient process because the current cannot reach its reference value in one control cycle and the variation of the voltage vector’s angle is also neglected during the prediction process. To solve this problem, the proposed MADPCC method first establishes an accurate discrete model of the SPMSM, and estimates the control step length required to reach the given reference current through a simple algorithm. Then the single-step prediction is extended to multi-step prediction. Simulation results show that the dynamic performance of MADPCC is improved compared with the single-step DPCC method.
Characterization of aerosol optical depths and rainfall at two semiarid regions in India

Present investigation has been carried out to understand the importance of the role of atmospheric aerosol on rainfall over two similar types of regions. One is in Western India and another in Southern India. MODerate resolution Imaging Spectroradiometer Terra derived aerosol optical depth (AOD) seasonal, inter- and intra-annual variations over Ahmedabad and Madurai were analyzed from 2001-2009. AOD values were minimum during winter seasons for both regions, for Madurai, the maximum was in monsoon seasons while for Ahmedabad it is in southwest monsoon. Annual variations show that the AOD was high in Madurai and the annual variations are 0.37 to 0.42 for Madurai and for Ahmedabad 0.33 to 0.42. Mann Kendal trend analysis results show that there is no significant trend in both studied areas. Pearson correlation analysis clearly elucidated interesting positive correlation between AOD and rainfall for Madurai and Ahmedabad. It shows that AOD plays an important role in the precipitation processes.
Sentiment Analysis on English and Greek Twitter Data Regarding Vaccinations

COVID-19 is one of the most discussed diseases worldwide. To control its spread, several vaccines have been developed and approved. However, low acceptance and worries about the efficacy of vaccines exist due to insufficient information, and social media influence. This study aims to classify people who are in favor or against vaccination, as well as people's preferences for three main types of available vaccines, Pfizer, Moderna and AstraZeneca. Twitter data were retrieved over seven months. This work proposes the adoption of an on-the-fly approach involving a sentiment analyzer that includes Machine Learning (ML). Moreover, this paper compares sentiment analysis techniques by applying the following Supervised ML algorithms: Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), XGBoost Classifier (XGB) and Support Vector Machines (SVM). The dataset was divided into two periods in 2022: summer, and autumn. The main contribution is the achievement of high accuracy and F1-Score, when combining these models. The best results were obtained when tweets were analyzed using TextBlob and DT. Regarding the English tweets, the top accuracy was 97.99%, while the top F1-Score was 97.98% in the summer period. With Greek tweets, the performance was better in the autumn period reaching 99.92% accuracy and F1-Score.
Application of Latent Dirichlet Allocation (LDA) to Identify Research Topics in Journal Publications

Undergraduate and applied graduate students often face the challenge of selecting an interesting and relevant topic for their thesis topic, culminating in a scientific research journal. The conventional approach to topic selection involves reviewing several journal articles, a time-consuming process. Data science techniques, specifically Latent Dirichlet Allocation (LDA) topic modeling, are used to speed this up. LDA is a versatile technique for identifying common topics across different types of documents. This research utilizes LDA to identify the most common issues in a collection of journal articles related to the thesis topic. Topics with the highest coherence scores will be prioritized. The selected topics are then grouped to provide students with a list of the newest and most relevant areas of study for their thesis topic. The study results show that "Data Science" has become the most used topic in the last three years. Some of the resulting Data Science topics: data mining, image processing, data clustering, and expert systems. Several Network and Security topics are developed, including network devices and malware protection. This research aims to simplify the topic selection process and enable students to choose topics that align with current research trends, thereby encouraging more efficient and relevant research results.
Joint Transmit and Receive Antenna Selection Based on Cat Swarm Optimization

In large-scale multiple antenna systems, the complexity of detection and signal processing will get unbearable high. Although antenna selection at transceiver may decrease the complexity of system, exhaustive searching for optimal combination of antennas will bring higher complexity as the number of antennas increases. In the paper, cat swarm optimization (CSO) is employed in the joint transmit and receive antenna selection. The capacity of MIMO systems is considered as the fitness function of the algorithm, which is used to calculate the fitness of each cat and output as the final result. Meanwhile, in the cat swarm algorithm, each cat is composed of position, velocity, flag and fitness. Simulated results shows that it can have lower computational complexity without much loss of performance.
AgroMart: A Decentralized Farmers' Market Using Blockchain

Traditional supply chains in the agriculture business are afflicted by several major concerns, such as many parties, difficulty in communication brought on by an extended supply chain timeline, a lack of confidence, and a centralized system. However, there is a solution to these problems: the decentralized system. Due to the intricate and interrelated nature of agricultural supply chains, it can be particularly challenging to keep track of product safety or quality issues. This is especially true for the core agricultural food supply networks that make up the daily diets of most people. One of the persistent problems that had been plaguing the mechanisms used for food supply chain traceability has been solved thanks to the development of blockchain technology. Blockchain has evolved as a prominent technology since it can provide secure traceability and control, immutability, and the creation of stakeholder confidence in a cost-effective information technology solution. The widespread implementation of blockchain technology in supply chains is being hampered by several problems, even though blockchain is having a significant impact in a great many other areas. In this paper, we propose a blockchain-based application with the purpose of directly connecting local farmers and proprietors of small-scale grocery shops with one another, eliminating the requirement for a third party to act as an intermediary.
Policy Model for Task Reallocation at Runtime in Edge Computing Infrastructures

In the field of edge computing, improved processing power of embedded systems and increasing connectivity between nodes are constantly creating new opportunities for innovation and novel applications. Due to the complex and rapidly changing environment in which these systems operate, there is a need for self-adaptive capabilities. One way to react to changes in operation is to reallocate tasks to different edge nodes. The highly networked, heterogeneous and multilayer nature of such systems makes it difficult to decide on a design-compliant task allocation during operation. This leads to a major challenge, as the relevant information for task allocation must be described so that the demands of the components can be automatically checked and fulfilled at runtime. This paper proposes a policy model to describe the demands of system components during design time and reuse this information to compute task allocations at runtime as part of a continuous development process. The policy model is tested and evaluated for function offloading in the context of autonomous micromobiles. Our results show that the description of component-demands as policies is suitable to collect the allocation-relevant information. On this basis, the task allocation can be recalculated at runtime while maintaining design conformity. Furthermore, the timing behavior of the recalculation is analyzed. With the design-compliant task reallocation, redesign loops and downtimes can be reduced, increasing the resilience and availability of the system. Such self-adaptive capabilities are of great importance for the development and maintenance of novel applications in the field of the Industrial Internet of Things.
Gestão internacional comparativa de recurso humanos e gestão de recursos humanos no Brasil: Uma análise frente aos modelos calculativos e colaborativos

The aim of this study was to analyze the adoption of calculative and collaborative practices dominating comparative international human resources management, according to the different profiles of the areas of Human Resources Management (HRM) of private organizations operating in Brazil. The method employed was a Survey, operated by means of an electronic questionnaire on HRM practices and organizational characteristics. A total of 326 respondents was obtained. Initially a cluster was conducted, in which respondents were clustered into four groups with different HRM profiles. The use of calculative and collaborative practices was compared in the four groups formed through the ANOVA (analysis of variance) collection of statistical models. The main findings showed that the strategic group was the one with the highest average of adoption of calculative and collaborative practices. The Communicative HRM group showed a higher propensity to collaborative practices and the Formalized HRM group would adopt calculative practices, although none of the groups showed an average of adoption than the Strategic HRM group. This suggests that it is necessary to learn how to deal with different aspects of the management of people in organizations operating in Brazil.
Dual Attention Network for Retinal Layer and Fluid Segmentation in OCT

Automatic segmentation of retinal optical coherence tomography (OCT) images has recently emerged as a significant research direction in medical machine learning. To address the task of retinal OCT image segmentation, we propose a method based on self-attention mechanism, which introduce a dual attention network to adaptively combine dependencies between local and global features for capturing rich contextual information. The spatial attention mechanism selectively aggregates weighted sums of features at each position, enabling selective integration of features. The channel attention mechanism selectively emphasizes interdependent channel features by considering the related factors among channels for weighted feature integration. We demonstrate promising segmentation performance on the publicly available Duke DME dataset, with an average Dice score of 0.824 and a Dice score of 0.53 for fluid segmentation, and the Dice scores of all seven retinal layers outperform other state-of-the-art techniques methods.
Improvement of the Precision of a Two-Degree-of-Freedom Manipulator Arm PID Controller

Manipulator arms play a crucial role in various fields and are used to perform difficult, dangerous, or repetitive tasks, thus making their control highly significant. Different control strategies have been proposed in the literature to achieve their objectives, with the most commonly used being the PID (Proportional-Integral-Derivative) controller, despite its inaccuracies resulting from the mathematical model. To improve the PID control, fractional control can be used. This paper proposes an improvement to the PID control of a two-degree-of-freedom manipulator arm by incorporating a fractional integration operator, namely Euler's Gamma function, in the controllers. The drawbacks observed with the PID controller have been eliminated by using the optimized PID controller $(\text{PID}_{G})$ with Euler's Gamma function
Torque Ripple Suppression of Permanent Magnet Synchronous Motor Based on Improved Duty Ratio Regulation Considering Speed Region

The model predictive control (MPC) with duty ratio regulation has been widely studied. However, most of the existing MPC regulates the duty ratio without considering the speed region. In this paper, the generation of torque ripple is analyzed quantitatively. Based on the cause of torque ripple, this paper performs a four quadrants correction on the duty ratio regulation. The equation for the torque variation during one inverter cycle is rewritten. The torque variation is calculated in a new perspective. The proposed method can further reduce torque ripple in the whole speed region. In specific, the effect of the proposed method in the low-speed zone is better than that in the high-speed zone. Finally, simulation results are provided to demonstrate the effectiveness of this method.
Virtual Reality for Training in Bolivian Military Units

This work seeks to determine if the use of virtual reality trainers can improve the learning of military students. For this purpose, the workload perceived by an individual during the military learning of route and area reconnaissance patrolling is evaluated, comparing lectures versus virtual reality. The simulator used was built in the Military Simulation Laboratory of the Military School of Engineering (EMI). This tool will allow military students to experience and face challenges like those presented in the boot camp without the need to travel to real training grounds, significantly reducing the logistical costs involved in traditional training and providing a safe training environment and controlled that will increase the quantity and quality of practice, resulting in better student performance and preparation. The results obtained present a correlation between lecture learning and virtual reality of 0.71, which suggests a close link between the two. This indicates that the trainer does not exclude the delivery of content but provides a valuable space for intensive learning. This shows that virtual reality can be a useful tool to improve knowledge acquisition. Este trabajo busca determinar si el uso de entrenadores de realidad virtual puede mejorar el aprendizaje de los estudiantes militares. Para ello, se evalúa la carga de trabajo percibida por un individuo durante el aprendizaje militar del patrullaje de reconocimiento de ruta y área, comparando conferencias versus realidad virtual. El simulador utilizado fue construido en el Laboratorio de Simulación Militar de la Escuela Militar de Ingeniería (EMI). Esta herramienta permitirá a los estudiantes militares experimentar y afrontar retos como los que se presentan en el campo de entrenamiento sin necesidad de desplazarse a campos de entrenamiento reales, reduciendo significativamente los costos logísticos que implica el entrenamiento tradicional y proporcionando un entorno de entrenamiento seguro y controlado que aumentará la cantidad y la calidad de la práctica, lo que resulta en un mejor desempeño y preparación de los estudiantes. Los resultados obtenidos presentan una correlación entre el aprendizaje presencial y la realidad virtual de 0.71, lo que sugiere una estrecha relación entre ambos. Esto indica que el formador no excluye la entrega de contenidos, sino que proporciona un espacio valioso para el aprendizaje intensivo. Esto demuestra que la realidad virtual puede ser una herramienta útil para mejorar la adquisición de conocimientos.
Enhancing Feature Extraction in DBNet: A Novel Approach with Squeeze-and-Excitation Block and CBAM Module

Accurately extracting feature information from street view images can improve the accuracy of digital detection. However, street view images vary in resolution, have complex backgrounds, and exhibit irregular arrangements of digits, posing significant challenges to object detection tasks. To better extract features from street view images, this paper proposes the CBAM-SE-DBNet model by incorporating two attention modules, namely the CBAM Module and the Squeeze-and-Excitation Block (SE Block), based on the DBNet architecture. The CBAM Module adaptively adjusts the channel and spatial attention of feature maps, aiding in the extraction of crucial information. On the other hand, the SE Block selectively enhances useful features by learning the importance weights for each channel. The combination of these two modules allows CBAM-SE-DBNet to effectively capture the key features of digits in street view images. The proposed approach follows a three-step process: Firstly, the CBAM Module is utilized to extract initial image features. Secondly, a ResNet network improved with SE Blocks is employed to further extract features and obtain deep features. Finally, the features are fed into the DBNet network to obtain the prediction results. The SVHN dataset is used as a practical example in this study, and comparative experiments are conducted against the original DBNet model and the Bidirectional LSTM and Deep Neural Network model. The experimental results demonstrate that the proposed model achieves better precision and score compared to other models. This research provides valuable insights for further advancements in street view digit detection and holds wide-ranging application prospects.