Reviewerly/exHarmony huggingface dataset:
Level of satisfaction of hybrid and online educational systems in university students during Post-Pandemic times

This study aimed to establish the predictor variables in the satisfaction of the educational service implemented during the post-pandemic, such as the intention and use of the system, the interaction with teachers, the methodology in teaching, and the relationship with students. The research is non-experimental, applied, and comparative. The study used 162 students from a private university in the Arequipa area of Peru as a sample. The methodology that was used had two well-defined phases: first, an exploratory factor analysis was performed, which aimed to condense the information in the original variables into smaller series; second, a confirmatory factor analysis was performed by modelling structural equations based on variances. It is concluded that the intention to use hybrid and online systems, interaction with teachers, and teaching methodology are predictors of satisfaction in university students in post-pandemic times; however, the relationship with students does not positively influence the satisfaction of the educational service. However, there are differences in the detailed analysis of the hybrid and online systems.
Constrained random maps: shortest paths assessed on the Web : The Internet as a computing medium

When dealing with routes in maps, the shortest path is the commonest addressed problem, but the topology of the map affects the paths’ behavior in many ways. A study was conducted on random maps, by Monte Carlo simulation, in which the weights are uniformly distributed, in order to assess the impact of these conditions on the shortest paths. We provide a freely accessible webpage, where the user can insert the number of nodes, specify whether it is a directed or undirected graph, and the range of the distribution, to observe the influence of each parameter. We consider several goals: we offer the procedure computation, based on the well-known Dijkstra’s algorithm; we combine the PHP language with a numerical language (Fortran), with the gnuplot graphing utility; and we stress the suggestion of the Internet as a computing medium. The simulation runs on the webpage, thus needing no software installation or special power or matching operating system. This Web application is an example for many other problems, and is implemented on a Linux platform. The study draws attention both to the use of the Web for scientific computing, and to the convenience of this use in scientific publications. We advocate web-based computing in general, as it can use the same programs as classical computing, the programs being always the technical difficulty. In our technological era, this still scarcely explored approach also promotes the transfer of knowledge from academia to industry.
How Leading Public Cloud Providers Deliver FaaS Environments: A Comparative Study

Research indicates that Function-as-a-Service (FaaS) tends to be the main model of next cloud computing generation. In view of this, interest has grown in finding out how the FaaS environment behaves in different conditions. In this work, aided by the Orama framework, an analysis of the performance of FaaS environments in the providers AWS, GCP, Azure and Alibaba in five different geographic regions is carried out: USA (East), USA (West), Europe, Asia, and Australia. The results show the existence of performance differences according to the region for the same provider, as well as differences in the average performance of applications between providers.
Towards Collaborative Privacy-preserving Machine Learning on Private Blockchain

Approaches combining machine learning and blockchain technologies usually propose transferring the majority of the machine learning process to smart contracts, without considering implications on how it can impact the runtime complexity and flexibility of the system. In this study, we propose a method to enable distributed machine learning using blockchain without disclosing sensitive training data, or data stored on blockchain. The proposed method separates model training, model deployment, and model usage stages, allowing increased privacy. To demonstrate the possibility to apply the proposed method in practise, we developed a prototype that employs smart contracts, local blockchain Oracles, and distributed application that allows user to interact with blockchain network via graphical user interface. The prototype system was deployed into the Hyperledger Fabric environment and experimentally tested combining multiple machine learning models into a Shapley-weighted ensemble. We have shown that our method can be implemented using existing blockchain technologies and can be used to deploy data and machine learning models. Future developments would consider the network-based ensemble as a teacher for transferring knowledge to achieve privacy-preserving machine learning.
Clustering the Behavior of Objective Measures in Associative Classifiers

Associative classifiers (ACs) have been widely used in several domains due to their inherent interpretability, with CBA being the most used algorithm in the family. They are built in steps, one of them aimed at ranking the rules, usually performed through objective measures (OMs). Aiming to improve its performance, works seek to modify its sorting step (i) using new measures and/or existing measures individually or (ii) aggregating existing measures to use them simultaneously. However, more than 60 OMs are available in the literature. Therefore, this work aims to group these OMs according to their performance when applied in the ACs sorting step. 15 groups were obtained, which are ranked according to their performance. This work contributes to the improvement and/or development of works, providing guidance for choosing the most appropriate OMs. An example of the impact of the groups obtained is also presented.
AutoNLP: A Framework for Automated Model Selection in Natural Language Processing

Although numerous open-source tools exist for machine learning with tabular data, there is a scarcity of comparable resources tailored specifically for NLP. The lack of transparency in the inner workings of existing AutoNLP tools is a significant obstacle in scientific research. AutoML tools are known for their ability to perform model selection with little human intervention, improving the accuracy and reliability of the results. However, conducting a model space search among pre-trained NLP models can be computationally infeasible, making it challenging to determine the optimal NLP model for a given dataset. This research aims to enhance the performance of NLP model selection. Our approach has resulted in higher accuracy than existing methods on the dataset that was created. In our future work, we plan to benchmark our algorithm against datasets created by other researchers to validate its effectiveness. Additionally, we intend to use the same system to perform model selection among popular large language Transformer models.
Enhancing Banking Operations with Microservices and Mobile Technology

This paper presents a novel architecture for enhancing the banking experience by combining microservices and a mobile application. The use of microservices provides scalability and flexibility in the development process, making it easier to add new features or modify existing ones. The results of the study shows that the proposed architecture is capable of handling high volume of transactions and requests while providing high quality of service. The mobile application provides a user-friendly interface for accessing financial information, and the use of microservices ensures efficient management of data and transactions. The architecture also has the potential to improve security through the use of security measures to protect sensitive data. As a future research direction, the proposed architecture can be evaluated in real-world settings and its security can be further tested. The field of technology in the banking sector is constantly evolving, and it is important to stay updated with new advancements that can potentially improve the proposed architecture.
Hierarchical Clustering and Calibration Functions

We propose a novel approach applicable to hieararchical clustering for determining optimal clusterings. Starting from a dendrogram (representing a maximal chain of clusters) an optimal clustering is determined such that two criteria, entropy and calibration, are optimized. Experimental work suggest that our approach produced quality clusterings as the values of their silhouette indicates.
Multi-stream CNN-SVM Hybrid Model for Gesture Recognition based on sEMG Signals

Gesture recognition has various applications in fields such as prosthetic control and human-machine interaction. In recent years, various gesture classification models based on surface electromyography (sEMG) signals have been proposed. Feature extraction of sEMG signals is discussed in this paper, and four models are used for recognizing gestures based on sEMG signals, which are convolutional neural networks (CNN), Multi-stream CNN, support vector machines (SVM) and Multi-stream CNN-SVM. The classification results show that Multi-stream CNN and SVM have better recognition performance than classical CNN. In addition, a Multi-stream CNN-SVM hybrid model is proposed. Compared with relevant research by other scholars, our proposed hybrid model performs better in gesture classification problems, with the highest classification accuracy of 97.7%.
Efficient Bicubic Interpolation Architecture for RGB Image Data Stream

Super-resolution, which is applied to produce sharp and high-resolution images, has become an important research direction in the field of image processing. Interpolation-based super-resolution algorithms are widely used for hardware implementation. In this paper, an efficient super-resolution hardware architecture with an improved bicubic interpolation is proposed. First, the original algorithm is optimized by changing the algorithm order, and a three-stage pipeline circuit is implemented. Then, the hardware architecture, including a 4-bit shift register, two sliding windows and a controller, is proposed and successfully implemented on Xilinx Zynq 7020 platform, achieving about $96\times$ speedup compared with Intel i5 with the same image quality. Compared with other related works, the proposed bicubic compute module achieves a higher image quality while consuming similar hardware resources, especially the number of DSPs and Adders decrease greatly, which are 19 and 29, respectively.
Optimizing HVDC Maintenance and Training through Virtual and Augmented Reality: A Methodology Proposal

The importance of High-Voltage Direct Current (HVDC) transmission systems is growing due to the rising trends of digitalization, decarbonization, and distributed generation in the energy industry. These systems provide an efficient means of transmitting large amounts of power over long distances, while also connecting renewable energy sources to the power grid and balancing three-phase grids. Virtual Reality (VR) and Augmented Reality (AR) technologies present a range of opportunities to address challenges in the energy industry. In this study, we propose a methodology to implement a VR and AR system for the energy electric company Eletronorte that facilitates visualization, navigation, and interaction with the arrangements and components of the HVDC system substations. The project involves developing modeling techniques to improve the representation of the components, updating preventive maintenance procedures, and creating a system monitoring environment using VR and AR, enabling engineering teams to interact with field teams and integrate real and virtual systems. VR simulations will provide a safe and controlled environment for training and testing, while AR simulations can be used to provide real-time guidance to technicians in the field. This approach can improve the efficiency and dependability of step-by-step activities, while also reducing the time and costs associated with system maintenance.
Synthetic Loss Function on SAR Target Detection Based on Deep Learning

Synthetic Aperture Radar (SAR) target detection techniques based on deep neural networks (DNNs) have been proved vulnerable to maliciously designed adversarial examples. Adversarial SAR images including deliberately generated perturbations are imperceptible to human observers while can deflect DNNs’ inference. Most of Attack algorithms against detectors in previous studies draw lessons from adversarial methods on automatic target recognition (ATR), through attacking class loss functions or the position loss functions. Former research did not fully consider the composition of the loss functions of each part in the detection process and their different influence on the detection results due to different weights. In this paper, we take loss functions of different components in the model and their weights into account during fooling SAR images detectors. Named Synthetic Loss Function (SLF), the methods first focus on deceiving the bounding box regression and classification loss function of the Region Proposal Network (RPN) and the Fast R-CNN network, then wrong bounding box locations and false classification labels will be obtained. Secondly, weight parameters are set for different loss functions to quantity their individual contribution on detection results. Through our experiments, a relatively optimal proportion is acquired. Third, considering the advantages of the gradient-oriented attack algorithm, we use the Projected Gradient Descent (PGD), an untargeted attack method, to iteratively solve the adversarial disturbance, and optimize the corresponding adversarial parameters and increase the imperceptibility of the generated disturbances. Experimental results show that SLF obtains satisfactory adversarial results in terms of SAR images detection and higher white-box attack performance than previous methods with lower computational consumption.
An Efficient High-definition Real-Time Hardware Design for Bilateral Filtering

Bilateral filtering is a simple nonlinear video denoising method. As an effective edge preserving or denoising method, bilateral filtering requires less amount of data as input and involves simpler operation than spatiotemporal filtering. In addition, it can preserve spatial details well and realize satisfactory visual quality. However, bilateral filtering involves complex processing and is data-dependent, which is not conducive to hardware implementation. To address these problems, in this study, we proposed a high-definition real-time hardware design method for bilateral filters, including parallelism, look-up tables, and other design methods. By using the proposed design system, the data dependence of bilateral filtering can be removed, and the throughput can be improved. Furthermore, we developed an efficient field programmable gate array implementation of an edge-preserving fast bilateral filter on a hardware-software co-design environment. Results demonstrated that the goal of 1080p @ 30 fps was reached, and the number of cycles was reduced to 96, thus, greatly improving the filtering process. Moreover, video filtering speed plays a crucial role in video streaming.
Breast Cancer Pathological Image Classification Based on Deep Learning

Breast cancer is a very serious disease, but the breast cancer cure rate is very high if it can be detected at early stage. Recently, various AI technologies are used for diagnosis and prediction of breast cancer. In this paper, we have investigated breast cancer for image classification and detection by different convolutional neural networks and parameters using BreaKHis dataset. We merge all different magnifying factor images and shuffle their orders to get more image data and construct trained network model to fit the specific zones of breast tumors. Among 12 experimental cases which classify 8 sub-types the highest test accuracy is 0.8635. And the experiments show that Neural Structured Learning paradigm has no effective to enhance test accuracy, the suitable enhancement methods and more effective data can enhance test accuracy, and image sizes cropped by data preprocess are important to enhance test accuracy rates.
A Modified GCANet Image Dehazing Algorithm for Road Scenes

Environmental perception algorithm is one of the key technologies for driverless car, haze weather may greatly reduce the accuracy of object detection, bringing danger for this case. In this paper, we propose a modified image dehazing algorithm based on an excellent dehazing algorithm, i.e., GCANet. The modified dehazing algorithm presents the multiscale feature enhancement strategy, which adds the residual structure into the decoder module to fuse and enhance multiscale features. It also presents the pixel-level haze component association strategy, which extracts the information of adjacent pixels to help the prediction on haze component. Experiments show that both the two strategies can enhance the dehazing performance by itself, and the fusion of them can obtain a better dehazing performance. And compared with the mainstream dehazing algorithms, the modified GCANet achieves the optimal dehazing effect under the same experimental condition. It can output a high-quality dehazing image which is beneficial to the subsequent tasks of driverless vision perception algorithm.
Deep Cross-Modal Video Retrieval Based on Early Fusion And Bit-Wise Semantic Transformer Hashing

Hashing has become a popular, effective solution for large-scale cross-modal retrieval. It can improve retrieval speed and accuracy, and quickly retrieve large amounts of data. However, existing methods have several issues: (1) First, the complementarity of global and local information has not been fully utilized, (2) Second, most of the methods ignore the fine-grained semantic alignment and only encoding the features into a hash overall, (3) Third, shallow architectures cannot take advantage of neural networks, and interactive structures reduce the efficiency of retrieval. Hence, in this paper, we propose a novel deep cross-modal video retrieval method for text-video retrieval. It combines global and local patterns, learns the alignment of fine-grained semantics, and is trained in an end-to-end manner in a double-tower structure. Specifically, this framework integrates early features and the outputs of subsequent recursive neural networks, and use the attention mechanism to capture the fine-grained semantic correlation. The semantics are encoded into the bit-wise hash code to achieve a high-quality result. The results of extensive experiments conducted on text-video retrieval demonstrate the superiority of our method.
Optimal Placement Method for Substation Surveillance Cameras Based on Dynamic Programming

The optimization of the position and orientation of surveillance cameras is commonly regarded as a discrete combinatorial optimization problem. Traditional placement methods aim to maximize the monitoring coverage of camera arrays or minimize the cost of installing cameras while employing approximation techniques to derive the approximate optimal solution for the placement optimization problem. However, these methods are premised on the assumption of equally significant grid cells, which overlooks the actual monitoring demands of the target area. In this paper, we initiate a risk assessment of the monitoring area of the substation and assess the importance of grid points in the area to identify security-sensitive areas. We then employ a dynamic programming approach to resolve the risk-quantified area and obtain the optimal placement scheme for surveillance cameras. The case study findings demonstrate that the proposed method can considerably enhance the monitoring efficiency of surveillance cameras in substations and achieve effective monitoring of key areas.
An Optimization Method for UAV Formation Transformation Based on KM Algorithm

Aiming at the UAV position matching and energy consumption optimization problems in UAV formation transformation, a UAV formation position matching model is established. A UAV formation transformation optimization method based on KM (Kuhn Munkras) algorithm is proposed to solve the position matching model, and the time complexity of the proposed method is analyzed. Finally, by simulating the UAV formation transformation based on random matching algorithm and the UAV formation transformation based on KM algorithm, it is shown that the UAV formation transformation method based on KM algorithm has better performance.
Ensemble Based AI Detector for Cyber Crimes

In today’s environment, the whole world is connected; due to which there is an increase in concerns about privacy, trust, and security. Phishing is a type of social engineering attack that affects several things such as a person or company, Internet of Things (IoT) devices, and networks. Although there are several methods used by researchers for the detection of phishing tactics to counteract phishing sites. The modern world of technology requires more advanced and robust methods i.e Machine Learning and Deep learning to deal with modern tactics. In this paper, we propose a voting ensemble learning-based phishing URL detection, where we used state-of-the-art methods i.e., Support Vector Machine (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) to detect the attack more accurately and compare their false positive and false negative rate.
A Generalized Method for Silent Live Face Detection

Facial recognition technology is widely used in the field of public safety and plays an important role. However, with this comes the increasingly serious problem of facial attacks. Existing facial recognition systems are susceptible to attacks from presentation methods such as printed photos, video replays, makeup, and 3D masks. Therefore, in-depth research on live face detection technology is necessary. Currently, there are two mainstream live face detection methods used in industry. One is based on user cooperation to detect expressions or actions for judgment, which has a long verification time and poor user experience, making it unsuitable for large-scale deployment and use. The other relies on additional hardware (such as passive infrared and depth cameras) for multimodal live face detection, which has higher accuracy but requires strict image acquisition conditions and higher equipment costs, making it difficult to promote. This paper proposes a neural network-based silent live face detection method, which only requires the RGB image of the face to be collected to obtain the recognition result directly, without requiring the user to cooperate with any behavioral actions or introduce additional multimodal devices. Our network (referred to as TudouNet) significantly improves both accuracy and generalization ability, while having low computational complexity. Therefore, it can be deployed on resource-and energy-constrained embedded systems, achieving real-time processing.
Intelligent Opening and Closing Braking Method for Wind Tunnel Air Source Valves based on CPS

Clarify the relationship between wind tunnel test and power resource guarantee, take advantage of the existing information system resources, network resource to construct the intelligent opening and closing for the wind tunnel air source valves, abstractly express the state space, action space and objective function using knowledge related to graph theory to design an intelligent opening and closing method for wind tunnel air source valves based on Cyber-Physical System.