Miracle-dz/UniFAR-FTUs huggingface dataset:
Toolset Development for Modelling Sympathetic Phenomenon and its Detection by a Neural Network

One of the most important elements of any AC power supply system is the transformer. Each stakeholder, such as Distribution Network Operators (DNOs), Transmission System Operators (TSOs), and other generators, is paying close attention to investigating possible negative impacts that could disrupt the normal distribution of electricity. One of them is short-term transient processes that occur in the network when the transformer core is turned on. These transients can cause significant distortion on the power line, which in turn can cause nuisance tripping of protection devices. For a small and isolated electrical system, this is not usually considered a major problem. However, as the electrical grid grows, this becomes more important. This is a problem that machine learning can help with. However, the problem is that there is currently no significant data on the transformer excitation phenomenon.This article gives a brief explanation of transformer excitation phenomena in an introductory section. With this information in mind, we developed a SIMULINK model and a MATLAB script to describe and automatically run a new experiment design and generate data. Finally, we use the generated data as a training dataset to fit it into a supervised convolutional neural network. The final data set that we created and used for the purposes of this work, as well as the SIMULINK model and the MATLAB script for automatically generating the experiment design, are in the public domain.
Brain imaging and the difficulties of understanding cognitive development

Adolescent Brain Cognitive Development (ABCD) has been studied in ways never before possible because to breakthroughs in brain imaging technology. Indirectly, the accuracy and interpretability of findings affect our understanding of neurodevelopmental processes, highlighting the crucial necessity of dependable techniques. There are a wide variety of obstacles to overcome in this field, from the intricacies of researching developing brains to the subtle interplay of different cognitive processes. Overcoming these problems demands creative approaches and enhanced procedures that can capture the changing character of cognitive growth. The goal of this framework is to improve the accuracy and sensitivity of cognitive neuroscience studies, leading to a deeper comprehension of the complex mechanisms at play during brain development. In this study, there is a method called functional magnetic resonance-based tensor imaging (FMR-TI) that combines the strengths of both methods to provide more precise mapping of cognitive growth throughout time. As a bonus, the suggested method uses simulation analyses to verify its efficiency and resilience in capturing the nuances of cognitive development, making the results of the study more trustworthy. The proposed methodology can be used to a wide range of fields, such as educational neuroscience, clinical psychology, and developmental neuroscience. Understanding cognitive growth better has far-reaching effects on educational programs, diagnostic methods, and treatment plans. Moreover, this methodological improvement holds promise for unraveling the neurological foundations of neurodevelopmental diseases, helping to the development of focused therapies and tailored treatment strategies. The robustness of the processing, the flexibility of the paradigm, and the validity of the simulation are all taken into account during the investigation.
Multivalued attribute encoding and feature generating for zero-shot diagnosis of machine faults

This study addresses a machine fault diagnosis task in which the model is trained without any samples, specifically those involving the monitoring of sensor signals related to the target faults. Recognizing that attributes or semantic descriptions of the unseen faults might be available in advance, this study employs zero-shot learning (ZSL) techniques, which have their origins in the field of computer vision, to tackle the challenge of zero-sample fault diagnosis of mechanical systems. To enhance the representation of fault semantic information, a multivalued encoding approach is employed. This method allows for the expression of both the presence and absence of attributes, as well as the strength of these attributes. As a result, it provides a more comprehensive and nuanced understanding of machine fault characteristics. Building upon this attribute encoding approach, a zero-shot fault diagnosis model is established by utilizing an attribute conditional feature generating adversarial network. The results obtained unequivocally demonstrate that the proposed method exhibits competitive performance when compared to state-of-the-art zero-shot learning algorithms. This underscores the effectiveness and robustness of the proposed approach in the zero-shot fault diagnosis of mechanical systems.
Sentimental Analysis Using Deep Learning Models

Textual data is a rich source of relevant information; however, extracting meaningful insights from unstructured texts can pose challenges. This research focuses on the critical task of text classification, particularly in the context of commercial sectors. The study explores a range of deep learning methodologies to effectively categorize review texts, addressing the complexities inherent in this process. Among the existing methods, the research identifies and presents the most accurate model for classifying reviews into positive and negative sentiments. The central theme of this investigation is categorization, a subject with widespread applications across industries such as marketing, product management, social media analysis, food reviews, customer satisfaction evaluation, spam detection, and intent recognition. Using movie reviews as a case study, this article delivers a comprehensive overview of the approaches employed and highlights the optimal review classification accuracy achieved through deep learning techniques. The findings of this study contribute to advancing text classification methodologies and underscore its relevance in diverse commercial domains. Among using all the existing model, the model with best accuracy for classifying the text (sentimental analysis) is 0.9497.
RPL Routing Metrics for 5G Networks: Systematic Review in IIoT

This study highlights the importance of Wireless Sensor Networks (WSNs) in the Industrial Internet of Things (IIoT). It explores the key role played by the RPL protocol in routing data in industrial environments. It examines the characteristics and limitations of WSNs, including limited processing capacity and data management, as well as the challenges associated with reliability and latency in the context of the Internet of Things (IoT), WSNs, and 5G. The paper presents a systematic review that establishes a starting point for further research to determine the most commonly used metrics in RPL-based protocols designed in IIoT applications with 5G. How have these metrics been addressed in the literature? What are the future trends in terms of measuring these metrics? This work will likely contribute to advancing the development of latency-reliability solutions in the RPL protocol. Focusing on EED, ETX, and EC metrics can improve the efficiency of 5G networks in IIoT by enabling better measurement and management of network traffic, which may contribute to quality of service and reduce response times.
Sea Ice Assimilation into a Coupled Ocean-Sea Ice Adjoint Model of the Arctic Ocean

High-resolution satellite sea ice concentrations (SIC), together with several ocean parameters, are assimilated into a regional Arctic coupled ocean-sea ice model covering the period 2000–2008 using the adjoint method. There is substantial improvement in the representation of the SIC spatial distribution, in particular with respect to the position of the ice edge and to the concentrations in the central parts of the Arctic Ocean during summer months. Seasonal cycles of total Arctic sea ice area show an overall improvement. During summer months values of sea ice extent become underestimated, however, it is shown that this metric is not suitable to characterize the quality of the sea ice simulation, as the root-mean-square difference to the data is reduced in nearly all months and years. Along with the SIC the sea ice thickness fields also become closer to observations, providing added-value by the assimilation. Very sparse data in the Arctic ocean, corresponding to a very small contribution to the cost function, prevent sizable improvements of assimilated ocean variables, with the exception of the sea surface temperature. The bias between simulated and observed SIC decreases mainly due to corrections to the surface atmospheric temperature, while contributions of other control variables remain small.
Investigation of Big Data for Human Emotion Detection using Deep Learning

The significance of this study relies upon inside the numerous fields it has the potential to impact positively; these fields consist of human-computer interaction, medicinal drug, advertising and marketing, and academia. The study intends to improve user experience, targeted actions, and make a contribution to the creation of emotionally wise structures by deciphering complex emotional states within extensive datasets. Data privacy issues, algorithmic biases, and the inaccessibility of deep mastering models represent merely some of the problems that the advised Human Emotion Detection technique seeks to solve. To correctly negotiate moral concerns and assure sturdy emotion detection in a huge variety of situations, Human Emotion Detection (HED) prioritizes a balance among model complexity and transparency. The counseled technique of Deep Learning-assisted Robust Human Emotion Detection (DL-RHED) makes use of sophisticated deep learning architectures, which includes attention mechanisms and recurrent neural networks, to hit upon nuances in expression over time. This technique seeks to offer an all-encompassing information of human emotions, permitting the version to dynamically adjust to new emotional displays. Validation of the proposed DL-RHED approach using simulation analysis is a fundamental a part of this research. The effectiveness and consistency of DL-RHED across distinct datasets are established inside the conceptual, which incorporates details about the design of the experiment, evaluation measures, and simulation consequences. These investigations provide real proof for the practicality and scalability of the proposed method.
Diagnóstico y propuesta de un sistema de innovación tecnológica cafetalera en Ecuador

The present investigation work describes the problemic situation of the Ecuadorian coffee sector, the main causes that have given origin to the central problem that constitutes it the drop productivity and faulty quality of the grain and the consequences that have been derived of this highlighting. The study includes a statistical analysis related with the problem of the coffee sector. Initially, a brief introduction is made of like one has come developing the coffee activity in Ecuador, for that which descriptive statistical techniques were used in order to presenting the nature of the data in variables related with the coffee surface, production units, national production, exports and imports of coffee and the organization of producing of coffee. As a result outstanding of the study he/she intends to settle down in Ecuador a National System of Coffee Technological innovation, like an effective contribution to the process of transformation of the Productive Womb, in the coffee agricultural sector.
Enhancing QoS in IoT through Big Data-Driven Networking and Computing Technologies

The Internet of Things (IoT) has emerged as a transformative paradigm, enabling seamless connectivity among a vast array of intelligent devices. However, the exponential growth of connected things has led to increased strain on communication networks and processing mechanisms, subsequently impacting the Quality of Service (QoS) experienced by end-users. In this paper, we explore the significance of IoT and Big Data and their intertwined relationship. We present a comprehensive analysis of IoT’s QoS, which comprises three critical dimensions - QoS object, QoS communication, and QoS computing. To mitigate the challenges posed by burgeoning IoT deployments, we propose leveraging Big Data techniques for the adaptive management of these QoS dimensions. By harnessing Big Data analytics, we aim to optimize the performance and efficiency of IoT networks and computing infrastructure. Our research highlights the vital role of data-driven strategies in sustaining reliable and high-performing IoT ecosystems, ultimately fostering seamless interconnectivity between intelligent devices and improving overall user experience.
Research on Blockchain Consensus Mechanism for Judicial Depository Applications

Aiming at the efficiency and security of the blockchain application's consensus mechanism in the judicial depository field, this paper proposes an algorithm that integrates the PoS consensus mechanism based on node credibility with the Bitcoin-NG consensus mechanism. The method improves the security of the consensus algorithm by calculating and rewarding the credibility of nodes based on the improved Cobb-Douglas production function model. Moreover, by enhancing the key block generation algorithm in the Bitcoin-NG consensus mechanism, the consensus time of the blockchain network can be reduced, and the efficiency of the consensus mechanism can be improved. The experimental comparison results show that this improvement scheme can improve the throughput and security of the blockchain system. Applied in the judicial field, this scheme can achieve a credible, secure and efficient judicial depository.
Chinese Named Entity Recognition Base on Multi-Metadata Embedding and Global Pointer

Named Entity Recognition (NER) aims to identify entities from unstructured text and is an important subtask in natural language processing and knowledge graph construction. Existing NER methods mostly use Conditional Random Fields (CRF) as the label decoder or employ pointer networks for entity recognition. However, when the number of labels is large, CRF-based methods incur high computational costs and fail to address the issue of nested entities. Pointer networks use two modules to identify the first and last entity, respectively, where each module can only focus on the information of either the first or the last entity, lacking the ability to consider global information about the entities. Furthermore, recent pointer network models have neglected two important aspects that are specific to the Chinese language: structure and pronunciation. These two aspects carry essential syntactic and semantic information, which are crucial for language comprehension. To address these challenges, the paper proposes a Chinese NER model called Multi-Embedding Global Pointer (MEGP) that incorporates radical and Pinyin features to enhance performance. This model combines these features with a global pointer network to improve Chinese NER. The model employs a simple feature enhancement approach by merging radical and pronunciation information into the vector representation of each character, followed by entity recognition using the global pointer method. Experimental results demonstrate that compared to existing mainstream models, this model achieves improved F1 scores on multiple public datasets such as Weibo, Resume, and People’s Daily.
Constructing a Cloud Computing Elastic Scaling Technology Based on Load Prediction

This article explores the development and implementation of technology for elastic scaling in cloud computing, based on load prediction. It first presents the benefits of cloud computing and highlights the significance of Load Prediction in this context. It subsequently analyses previous research, including elastic adjustment grounded on load prediction or monitoring data, and cost-based elastic adjustment. Additionally, this paper presents a comprehensive guide to the Long Short-Term Memory (LSTM) load prediction method, including problem modelings, algorithm descriptions, and experimental analyses. The accuracy of the prediction and its resource optimization impact have been confirmed through rigorous experimentation. Finally, the paper examines future research directions, which include improving Load Prediction models, enhancing resource allocation strategies, conducting benefit analysis, and comprehensively considering additional factors and constraints. Overall, the cloud computing elastic scaling technology based on Load Prediction presents a significant potential for practical applications and research contributions to resource optimizations, system efficiency improvements, and cost reductions.
A Novel Approach to Feature Encoding

Target based encoding is a feature encoding technique that replaces categorical values with the mean of the target variable for each category. This can be done for both classification and regression problems. In this paper, we propose a novel target encoding method that can be used for categorical as well as continuous data. We evaluate our novel approach, referred to as kappa (κ) encoding, in regression problems with a series of data types. The approach is robust and shows significant improvement over a series of Machine Learning Regressors. We demonstrate that our proposed method has the potential to improve the performance of Machine Learning models on a variety of regression tasks.
Detection and Tracking of Multiple Faces in Video using Modified KLT Algorithm

In proposed research, A system for accurate face detection and continual monitoring that makes use of face key characteristics as essential elements of the face that are incorporated in modified KLT approach. A greater degree of data is provided by video frames than by a single image. Critical attribute extractors (points) are used to automate the detection and monitoring of faces in a wide range of applications, such as human behavior identification, automated surveillance and human computer interface. By dividing the monitoring approach into three sections, one can construct a straightforward facial detection and continual monitoring system. Face detection, facial feature identification and tracking of faces are the first three steps. The Viola Jones object detector is used to achieve the face detection and modified KLT method is used to accomplish the continual monitoring. According to the findings of the investigation, our method is more reliable and effective at evaluating video frames to account for different types of occlusions and pose variations. Visual Tracker Benchmark database is used as a standard database. Further results are compared with staff database which was created to verify the outcomes. Average accuracy of 95% is obtained for the proposed method.
Fault Diagnosis of Controllable Pitch Propeller as Few-Shot Classification with Mechanism Simulation Data Augmentation

The Controllable pitch propeller(CPP) can control the thrust direction and power through the adjustment of blade pitch using a hydraulic system, significantly improving the maneuverability of the vessel. Given the harsh working environment of the on-ship CPP hydraulic system, it is prone to failure and affects pitch control performance. To address the challenges of limited real-world fault condition data, we utilize a few-shot learning approach and enhance the model’s sensitivity to target data distribution by introducing a simulation data augmentation phase during training. A mechanical model of the CPP hydraulic system is developed, and monitoring data under fault conditions of key components is simulated. After training with open-source time-series classification tasks, the augmentation phase is used to obtain prior knowledge of the equipment, and the model is eventually adapted to the recorded data task. The recorded data is derived from an experimental setup comprising the primary components of the CPP, and it is collected from multiple sensors under both normal and fault conditions. The target fault diagnosis task is treated as a time-series classification problem, and a network architecture based on ResNet with Triplet loss is employed.
Wavelet-Based Analysis of Fractional Electrical Circuit Models Using Orthonormal Lucas Wavelets

This paper presents a scheme for analyzing electrical circuit models using the Riemann-Liouville fractional-order derivative, with an order range of 0 ≤ $a$ ≤ 1. The approach involves two steps: calculating the orthonormal Lucas wavelet integrative operational matrix, and applying this scheme to transform fractional-order circuit equations into a familiar system of algebraic equations. The method accurately solves fractional electrical circuits like LC and RC circuits. To assess its effectiveness, the proposed scheme is compared to the Haar wavelets fractional approximation scheme, with the results demonstrating its superiority.
Film-film Scratch Repair Based on Space-Time Combined with Edge-connect Network

Film film plays an important role in the field of film production and projection. However, due to scratches and damage during long-term use and processing, film film often appears discontinuity problems after screening, which affects the viewing experience. To solve this problem, this paper proposes a film film scratch repair method based on space-time combined with Edge-connect network. The method combines computer vision and deep learning techniques designed to automatically repair scratches in film movies to restore its continuity and ornamental value. Through experimental evaluation, we demonstrate that the film-movie scratch repair method based on space-time combined with the Edge-connect network has significant effects and potential. The restored film showed better continuity at screening. In addition, this method can more effectively avoid the destruction of the original film content, thus protecting and preserving the important film cultural heritage.
Meta-learning of Semantic Attention Prototype Correction Based on Bayesian Estimation

The mean visual prototype is a mainstream meta-learning method for few-shot learning. It is often combined with semantic information to compensate for the lack of training samples. As the number of samples increases, the semantic gain reduces or even becomes noise, leading to a bias in the semantic attention prototype. To address the problem of semantic bias, we propose a prototype correction strategy based on Bayesian estimation. The strategy corrects the original semantic attention prototype defined from the sampling perspective according to the overall information. The probability of the corrected semantic attention prototype is calculated from the probability of the visual prototype and the semantic attention. The semantic attention prototype's posteriori probability center is taken as final prototype. Experiments on Mini-ImageNet and Tiered-ImageNet exhibit our method outperforms existing methods using semantic information by 0.7%~8% in terms of classification accuracy.