Reviewerly/exHarmony huggingface dataset:
Multi-Agent Deep Reinforcement Learning Based Incentive Mechanism for Multi-Task Federated Edge Learning

Federated edge learning (FEL) is capable of training large-scale machine learning models without exposing the raw data of edge devices (EDs). Considering that the learning performance heavily depends on the active participation of EDs, it is essential to motivate the resource-limited EDs to contribute their efforts to learning tasks. In this paper, a learning-based multi-task FEL mechanism is proposed to design the economic incentive and participation contribution strategy jointly. Specifically, the incentive-based interaction between the edge servers and EDs is formulated as a multi-leader multi-follower Stackelberg game. Then, the theoretical analysis is provided to prove the existence and uniqueness of the Stackelberg equilibrium. To obtain the equilibrium solution under the incomplete information, a Markov decision process is formulated for the two-stage Stackelberg game. Considering the high dimensionality of the continuous action space, a multi-agent double actors deep deterministic policy gradient algorithm is employed to achieve the optimal training-ratio of EDs and the payment policies of edge servers. Numerical results validate the effectiveness and efficiency of our proposed incentive mechanism.
Analysing the geographical impact on short-term solar irradiance forecasting using univariate and multivariate time series models

Sustainable development is significantly hampered by air pollution brought on by greenhouse gas emissions. One of the primary contributors to air pollution is coal, which is used in thermal power plants. Substituting power plants from combustible sources to renewable energy sources can reduce air pollution to an extent. Solar energy is one of the most readily accessible renewable energy sources. High Global Horizontal Irradiance (GHI) is advantageous for deploying solar power plants, allowing maximum electricity production. However, the spatiotemporal factors affecting GHI are not analyzed, which can improve the forecasting accuracy. The proposed work analyzes the geographical impact of solar irradiation using statistical time-series models called ARIMA (Auto Regression Integrated Moving Average) and VAR (Vector Auto Regression) using four datasets from two different locations in India. The statistical time-series models can analyze the impact of the features and can show how the effect improves the performance of the multivariate model compared to the univariate model. The study aims to find the influence of the geographical factors of Global Horizontal Irradiance (GHI) based on the location chosen to build a solar power plant. The effectiveness of the univariate and multivariate models in forecasting solar irradiance is also discussed. The RMSE (root mean square error) analysis shows that the multivariate model VAR outperforms the univariate model in predicting solar irradiance. There is a 1–25% variance in the RMSE values between the ARIMA and VAR models at different locations and a 1–19% variance in MAE (Mean Absolute Error) value, while a 94–98% variance in MAPE (mean absolute percentage error) value. A deep analysis of factors that affect the precision of global horizontal irradiance (GHI) and the causal impact of variables is also analyzed using different variable impact tests. This variable selection is achieved by a feature selection algorithm called Maximum Relevance Minimum Redundancy (MRMR).
Scams, Cons, Frauds, and Deceptions

The following Thematic Issue presents new research on the nature and extent of a wide range of scams, cons, frauds, and deceptive activities, including sextortion, the use of ransomware, phishing, identity theft, Ponzi schemes, online shopping fraud, gift card scams, and health care fraud targeting elderly victims. Are Americans particularly vulnerable to these scams? And does the public actually admire individuals who get involved, in large part due to the positive portrayal of scammers, fraudsters, and con artists in both literature and cinema? The potential impact of literary and cinematic depictions of these scams on the public’s view of both offending and victimization are considered, along with the government’s ongoing attempts to censure a wide range of books and films that may influence the public’s view of morality–and more recently, the public’s view of our government– due to their content.
Super-resolution three-dimensional structured illumination profilometry for <i>in situ</i> measurement of femtosecond laser ablation morphology

Femtosecond laser ablation has found wide-ranging applications in the surface structuring of nanoelectronics and nanophotonics devices. Traditionally, the inspection of the fabricated three-dimensional (3D) morphology was performed using a scanning electron microscope or atomic force microscopy in an ex situ manner after processing was complete. To quickly monitor and efficiently optimize the quality of surface fabrication, we developed an in situ method to accurately reconstruct the 3D morphology of surface micro-structures. This method is based on a triangulation optical system that utilizes structured illumination. The approach offers a super-resolution capacity, making it a powerful and non-invasive tool for quick in situ monitoring of surface ablation structures.
When FrFT meets quadratic frequency modulation functions—A novel tool for nonstationary signals and time-varying systems

The fractional Fourier transform (FrFT) is a general form of the Fourier transform, which has provided a theoretical framework for linear frequency modulation signal processing. The quadratic frequency modulation (QFM) function is a generalization of the linear frequency modulation function, which balances the precision and complexity of nonstationary signals in practical applications. However, there is no framework for analyzing and processing QFM signals, which limits the applications of QFM signal model. The QFMFrFT, a generalization of the FrFT, is developed in this study to tackle this issue. The impact of the QFMFrFT on the time-frequency distribution explains its usefulness in sampling, denoising, and filter design of QFM signals. Two one-place statistical functions are defined for QFM-type stochastic signals, of which the input-output relationships of a linear time-varying system are discussed. Using these relations, a linear time-varying matched filter is designed. Two types of generalized convolution operators are designed associated with the QFMFrFT, which allow a multiplicative realization of a time-varying system. The QFM signal is demonstrated as a singular function of this type of linear time-varying system, which inspires a multi-carrier technique in spread-spectrum communications. Simulations verify the applications of the QFMFrFT in parameter estimation, matched filter design, and communications.
Towards evaluating the robustness of deep neural semantic segmentation networks with Feature-Guided Method

Although deep neural networks (DNNs) demonstrated its superior performance in computer vision, recent works proved that the vulnerability of deep neural task systems to carefully crafted human-imperceptible perturbations. We observe that the majority of adversarial attack methods for generating perturbations are based on rotation or modification of the input-diagnostic. Regardless of the characteristics completed by additional sources, such approaches yield comparable properties to enrich initial details. It motivates us to consider views about using various information apart from original inputs to deliver adversarial features. For such needs, we induce a simple yet adaptable adversarial attack strategy with a Feature-Guided Method (FGM) for crafting adversarial examples (AEs) in segmentation domain. FGM first induces the multi-source patterns that are apart from the original inputs. Then, producing feature diversities generated with the original data to deliver the perturbed components. Finally, FGM blends original inputs with created features in a defined norm constraint to form the adversarial examples. In such way, it preserves the original positive class-general characteristics and enriches the new positive class-specific diversities when performing adversarial attacks. Moreover, FGM employs the adaptive gradient-based strategy on such generated information, which lowers the risk of falling into the local optimum when searching for the decision boundary of the source and target models in latent space. We conduct detailed experiments to evaluate the performance of the proposed method compared to baselines on public segmentation models. The experimental results reveal better performance of FGMs in fooling source and target segmentation systems leading large margins over 5% on mIoU, mRec, and mAcc. We also deploy the adversarial training with proposed work and PGD on widely used models. Our approach improves the robust quality of adversarially trained models on FCN, PSPNet, and DeepLabv3 with various backbones by significant margins with over 13% improvement on mIoU and 12% on mRec, which indicates a better impact of deploying such mechanisms on robust deep neural segmentation models.
Highlighting bugs in software development codes using SDPET for enhancing security

The requirement for high-quality, inexpensive software that can be maintained is being driven by the rise in demand for automated online software systems. Early defect identification in SDLCs (Software Development Life Cycles) results in early adjustments and eventually on-time delivery of maintainable software, satisfying the client and fostering his trust in the development team. Many MLTs (Machine Learning Techniques) have been put out in the last ten years to increase SDP accuracy. Most of the suggested SDPs frameworks and models employ ANNs (Artificial Neural Networks), which are a popular MLTs. Software defect data are hampered by a number of problems, including duplication, correlation, feature relevance, and missing samples. However, because to the under/over fitting issues, most existing SDPs utilising ANNs have low accuracy. SDPET (Software Defect Predictions Ensemble Technique), an ensemble learning technique to produce accurate SDPs based on the AdaBoost algorithm, is proposed. The proposed schema's efficacy against RFs (Random Forests) and GBs(Gradient Boosts) for needed values through experiments. The experiment results verify that the suggested SDPET has good accuracy in training and better accuracy in test datasets when compared with other methods. The original obtained dataset was cleaned of unnecessary features, converted to csv, and is stored as dataset. csv.
Inpainting GAN-Based Image Blending with Adaptive Binary Line Mask

Image blending is a scheme for image composition to make the composite image looks as natural and realistic as possible. Image blending should ensure that the edges of the object look seamless and do not distort colors. Recently, numerous studies investigated image blending methods adopting deep learning-based image processing algorithms and contributed to generating natural blended images. Although the previous studies show remarkable performance in many cases, they suffer from quality drop when blending incompletely cropped object. This is because partial loss and unnecessary extra information on the cropped object image interferes with image blending. This paper proposes a new scheme that significantly reduce the unnatural edges and the color distortion. First, to detect and handle the incompletely cropped region, an adaptive binary line mask generation utilizing color difference checking algorithm (CDC) is proposed. The generated mask is exploited to improve image blending performance by isolating incompletely cropped image edges from image blending. Second, in order to perform inpainting the missing or masked area of the object image and image blending together, the inpainting generative adversarial model is adopted. Experimental results show that the blended images are not only more natural than those of the previous works but the color information is also well preserved.
A Method for Detecting Lightweight Optical Remote Sensing Images Using Improved Yolov5n

Optical remote sensing image detection has wide-ranging applications in both military and civilian sectors. Addressing the specific challenge of false positives and missed detections in optical remote sensing image analysis due to object size variations, a lightweight remote sensing image detection method based on an improved YOLOv5n has been proposed. This technology allows for rapid and effective analysis of remote sensing images, real-time detection, and target localization, even in scenarios with limited computational resources in current machines/systems. To begin with, the YOLOv5n feature fusion network structure incorporates an adaptive spatial feature fusion mechanism to enhance the algorithm’s ability to fuse features of objects at different scales. Additionally, an SIoU loss function has been developed based on the original YOLOv5n positional loss function, redefining the vector angle between position frame regressions and the penalty index. This adjustment aids in improving the convergence speed of model training and enhancing detection performance. To validate the effectiveness of the proposed method, experimental comparisons were conducted using optical remote sensing image datasets. The experimental results on optical remote sensing images serve to demonstrate the efficiency of this advanced technology. The findings indicate that the average mean accuracy of the improved network model has increased from the original 81.6% to 84.9%. Moreover, the average detection speed and network complexity are significantly superior to those of the other three existing object detection algorithms.
Decision-Making for Multi-View Single Object Detection with Graph Convolutional Networks

Aggregating predicted outputs from multiple views helps boost multi-view single object detection performance. Decision-making strategies are flexible to perform this result-level aggregation. However, the relationship among multiple views is not exploited in aggregation. This study proposes a novel decision-making model with graph convolutional networks (DM-GCN) to address this issue by establishing a relationship among predicted outputs with graph convolutional networks. Through training, the proposed DM-GCN learns to make a correct decision by enhancing the contributions from informative views. DM-GCN is light, fast, and can be applied to any object detector with a negligible computational cost. Moreover, a real captured dataset named Yogurt10 with a new metric is proposed to investigate the performance of DM-GCN in the multi-view single object detection task. Experimental results show that DM-GCN achieves superior performance compared to classical decision-making strategies. A visual explanation is also provided to interpret how DM-GCN makes a correct decision.
Reliability Techniques in IoT Environments for the Healthcare Industry

The internet of things is a powerful combination of wireless devices, radio-frequency identification, and numerous sensors that offer the challenging but powerful potential of shaping current systems to make them smarter. IoT has many different areas of application and one such area is healthcare. The health sector is another promising area for IoT, and several businesses are exploring its application in this sector. In this study, it is proposed to collect healthcare data through the IoT and store it in the Interplanetary File System using Ethereum-based blockchain technology for data security. Blockchain technology is used in IoT to ensure the safety of collected data. Blockchain technology is used in IoT to ensure the safety of collected data. Smart contracts are used to automatically execute, control, and record events following the terms contained in them, enhancing the characteristics already present in the blockchain. IoT-based healthcare solutions are tested on many blockchain networks.
Revisão bibliográfica sobre o impacto da pandemia de covid-19 na saúde mental dos enfermeiros

The Covid-19 pandemic brought a high demand for services to health professionals, such as an increased workload; lack of knowledge about the disease and its implications; isolation from the family for fear of transmission, these variables had negative consequences for the mental health of these professionals who worked on the front lines of fighting the virus. This is a literature review bibliographic study, which was carried out through a literature review regarding the scientific production of national articles, on descriptors relevant to the mental health of nurses in the pandemic. In the present study, an increase in mental disorders was found among nurses, including anxiety; consumption of substances such as alcohol, drugs, tobacco and medication; suicidal thoughts; post-traumatic stress; phobias; panic attacks at work; insomnia and burnout. In this way, we can conclude that COVID-19 did not have its side effect only on patients, but caused harm to health professionals, as they suffered emotional exhaustion during the pandemic period.
SOCIAL-EMOTIONAL SKILLS IN STUDENTS’ LEARNING IN CAN THO UNIVERSITY, VIETNAM

Based on an overview of domestic and foreign research related to the research field of the topic; the research of the topic has determined the urgency of the topic, research objectives, research objects and scope, research hypotheses, research tasks, and methods. The research on the topic has systematized the theoretical basis for the concept and manifestation of social-emotional skills in student learning: generalizing the picture of the current situation of recognizing the importance of social-emotional skills in student learning, the level of assessment of the manifestation and educational measures of social-emotional skills in learning, and finding out the factors that affect social emotions in student learning such as from the external environment, at school, family and external social relationships. Factors from the internal environment are the self-awareness of each student. From the current situation of the problem, the study has proposed educational measures for 504 students from 8 schools and colleges in Can Tho University (CTU), Vietnam. Some measures were to educate social-emotional skills for students through soft skills topics, teach soft skills to students through extracurricular activities, and soft skills education for students through integrated teaching.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0015/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Replay Attack in TLS 1.3 0-RTT Handshake: Countermeasure Techniques

Transport Layer Security (TLS) is an Internet Engineering Task Force (IETF) standard protocol commonly used on the Internet nowadays. TLS 1.3 the latest version published in August 2018 introduced a new mode to perform the handshake in zero round-trip time (0-RTT) which led to a significant improvement in the protocol performance. 0-RTT handshake mode allows the client to establish the connection and send the application data in the first message (ClientHello) without waiting for the server to send a response. But it also opens a window for replay attacks. The attack occurs when a valid 0-RTT handshake message is captured and sent again to the server causing the server to perform the same process again according to the application data carried in the message. Several anti-replay protection techniques were introduced to prevent or mitigate the replay attack. This paper sheds light on replay attacks countermeasures for TLS 1.3 0-RTT handshake mode and the most recent proposed methods. The strength and limitations of these countermeasures are discussed. In addition, the paper deliberates on the challenges such countermeasures impose in a distributed environment, for instance, multiple servers in a cloud computing environment or a content delivery network (CDN).
Analysis of the Effect of Atmospheric Turbulence on the Performance of Free-space Quantum Signal Transmission

The effect of atmospheric turbulent environment on the performance of free-space quantum communication is analysed. Based on the existing mathematical model of atmospheric turbulent fluid motion, the three most influential physical factors, i.e. wind speed, temperature and humidity, are abstracted out and simulated to study the variation of quantum channel utilisation, quantum channel entanglement, channel capacity and fidelity in amplitude damped channels under the atmospheric turbulent environment parameters. After a comprehensive analysis of the simulation results, it can be preliminarily judged that an increase in all three physical factors will increase the intensity of atmospheric turbulence and reduce the performance of quantum communication, and the magnitude of the influence is: temperature > wind speed > humidity. Therefore, corresponding adjustment strategies should be adopted to improve the communication performance of quantum communication and ensure the reliability of communication.
With A Knowledge Adaption Mechanism: A Novel Horizontal Collaborative Fuzzy Clustering Method

Being one of the most important models for multisource data clustering, Horizontal Collaborative Fuzzy Clustering (HCFC) implements the clustering by fusing the knowledge obtained from external source datasets during the clustering on local source dataset. The external knowledge obtained from each given external source dataset is represented by a partition matrix for this dataset, revealing the data structure from the corresponding perspective. Meanwhile, the sizes of the partition matrixes for external source datasets and local source dataset are required to be equal. This prerequisite is not always guaranteed in real applications due to the different structures of external datasets. In such cases, the HCFC algorithm fails to work, resulting in a limited application scope. Aim at this defect, this paper presents a novel Knowledge Adaption algorithm based on Spectral clustering and Relative entropy (KASR) to deal with this issue. Based on the proposed KASR, a novel algorithm called Horizontal Collaborative Fuzzy Clustering with Knowledge Adaption mechanism (HCFCKA) is formulated, which can work well when the sizes of partition matrixes for external source datasets and local source dataset are different and thus extends the application scope of the HCFC algorithm. Experiments on synthetic dataset and UCI real-world datasets demonstrate that the proposed KASR outperforms the existing knowledge adaption methods and the proposed HCFCKA can achieve significantly better collaborative clustering performance compared with the state-of-art methods.
Enhanced Lightweight Network with CNN and Improved Transformer for Image Super-Resolution

In recent years, there have been significant advancements in deep learning-based lightweight image super-resolution (SR) reconstruction techniques. However, in practical applications, there are still challenges to be addressed. Many existing lightweight SR reconstruction algorithms simplify the model by reducing the number of model parameters or changing the combination of convolutions, which results in significantly decreased model performance and stability, leading to poor reconstruction results. To address this issue, we proposed a Cosine Self-Attention mechanism and deepens the network depth to improve the Swin Transformer, enhanced the model’s performance and stability. Experimental results show that the proposed approach achieves stronger and more stable reconstruction performance with lower parameter count than existing lightweight SR models, outperforming them in reconstruction results and model complexity. The PSNR/SSIM of our proposed method for ×2 and ×4 SR reconstruction on the Set5, Set14, BSD100, Urban100, and Manga109 datasets exceed those of most existing lightweight SR models.