Reviewerly/exHarmony huggingface dataset:
A Magnetic Interference Compensation Method for Airborne Electronic Equipment without Current Sensors

With the rapid development of unmanned aerial vehicles (UAVs) technology, using UAVs for magnetic surveys is a booming branch. However, the magnetic interference generated by the UAV hinders the further application of UAV magnetic survey systems. In addition to the interference caused by the UAV maneuvering, the dynamic interference of airborne equipment has also been found and become one of the factors restricted by the detection accuracy of magnetic surveys. This paper proposes a multi-source two-channel linear time-invariant (MTLI) correlation model, considering the maneuvering magnetic interference and airborne equipment magnetic interference. The magnetic interference can be estimated and compensated by interference correlation without current sensors. Compared with the traditional aeromagnetic compensation process and other compensation methods considering the magnetic interference of airborne equipment, the proposed method can provide stable compensation effects in maneuvers and smooth flight, and the workflow is simple and fast. The actual flight experiment is conducted, and the results show that the two kinds of UAV interference fields are suppressed significantly with a root mean square error of 0.0062 and 0.0296 nT in smooth flight and maneuvering flight.
Machine learning-based blockchain technology for protection and privacy against intrusion attacks in intelligent transportation systems

Intelligent transportation system (ITS) is a rapidly growing field of technology that combines network connectivity, modern sensors, control system, and data processing technologies to improve our daily life. With the growing popularity of ITS, concerns about its security have garnered considerable attention. SQL injection, denial of service, and ransomware assaults are all prevalent types of threats in an ITS. In automation/transportation systems, privacy and trust are also significant challenges. Today, everyone requires a vehicle to move around. Together with this, data security is crucial in a computerization system since the vehicle&apos;s user data is transferred to the extra operator via the web through wireless devices and routes such as radio channels, optical fiber, and so on. Certainly, every device is linked to the internet and one another, constituting the Internet of Things (IoT). Even as the network transitions to wireless devices, numerous risks to autonomous cars/vehicles have developed into a serious issue for service providers and car owners. The bulk of these attacks is detectable and preventable using a variety of intrusion detection methods. The blockchain and, more broadly, peer-to-peer techniques may be critical in the development of decentralized and data-intensive applications that run on billions of devices while maintaining user privacy. To solve these problems, this chapter proposes a machine learning (ML)-based blockchain intrusion detection for protection and privacy against intrusion attacks in ITS. The blockchain was used for aiding information exchange in ITS. As a result of the immutable and decentralized nature of blockchain-enabled ITS systems, a variety of desirable qualities such as security, decentralization, transparency, automation, and immutability are expected to exist. The experimental analysis was performed on the UNSWB-NB15 dataset. The results obtained reached an accuracy that is more than 99%. The AUC, recall, Mathew Correlation Coefficient (MCC), and training time metrics were also used to evaluate the performance of the models.
A Blockchain Consensus Protocol Based on Quantum Attack Algorithm

The blockchain is a distributed storage system of digital assets. This decentralized, non-copyable technology stems from universal standard password algorithm and the consensus mechanism of the game theory. The development of quantum computing poses threat to traditional algorithms of blockchain encryption, including symmetric encryption and hash encryption. Focusing on the traditional blockchain consensus mechanism, this paper designs a new blockchain consensus mechanism, based on the stochasticity, irreversibility, and uncertainty of quantum measurement. In the proposed consensus mechanism, complex calculations and intractability mathematical problems are abandoned. In this way, a huge amount of computing resources is saved, less energy is consumed, the time delay is shortened, and the throughput is increased. The proposed quantum consensus mechanism can withstand 51% attacks.
Early Warning of Infectious Diseases in Hospitals Based on Multi-Self-Regression Deep Neural Network

Infectious diseases usually spread rapidly. This study aims to develop a model that can provide fine-grained early warnings of infectious diseases using real hospital data combined with disease transmission characteristics, weather, and other multi-source data.Based on daily data reported for infectious diseases collected from several large general hospitals in China between 2012 and 2020, seven common infectious diseases in medical institutions were screened and a multi self-regression deep (MSRD) neural network was constructed. Using a recurrent neural network as the basic structure, the model can effectively model the epidemiological trend of infectious diseases by considering the current influencing conditions while taking into account the historical development characteristics in time-series data. The fitting and prediction accuracy of the model were evaluated using mean absolute error (MAE) and root mean squared error.The proposed approach is significantly better than the existing infectious disease dynamics model, susceptible-exposed-infected-removed (SEIR), as it addresses the concerns of difficult-to-obtain quantitative data such as latent population, overfitting of long time series, and considering only a single series of the number of sick people without considering the epidemiological characteristics of infectious diseases. We also compare certain machine learning methods in this study. Experimental results demonstrate that the proposed approach achieves an MAE of 0.6928 and 1.3782 for hand, foot, and mouth disease and influenza, respectively.The MRSD-based infectious disease prediction model proposed in this paper can provide daily and instantaneous updates and accurate predictions for epidemic trends.
Application of Deep Learning on the Prognosis of Cutaneous Melanoma Based on Full Scan Pathology Images

Introduction. The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with different prognoses and to explore the application value of deep learning in the prognosis of cutaneous melanoma patients. Methods. In deep learning, VGG-19 is selected as the network architecture and learning model for learning and classification. In machine learning, deep features are extracted through the VGG-19 network architecture, and the support vector machine (SVM) model is selected for learning and classification. Compare and explore the application value of deep learning and machine learning in predicting the prognosis of patients with cutaneous melanoma. Result. According to receiver operating characteristic (ROC) curves and area under the curve (AUC), the average accuracy of deep learning is higher than that of machine learning, and even the lowest accuracy is better than that of machine learning. Conclusion. As the number of learning increases, the accuracy of machine learning and deep learning will increase, but in the same number of cutaneous melanoma patient pathology maps, the accuracy of deep learning will be higher. This study provides new ideas and theories for computational pathology in predicting the prognosis of patients with cutaneous melanoma.
Robustness evaluation on different training state of a CNN model

Abstract Convolutional neural networks (CNNs) have proved to be successful in many applications such as image processing. However, even imperceptible perturbations applied to the images can make the neural network performance unreliable. To guarantee an accurate performance in safety critical fields, it is necessary to assess the robustness of CNN solutions before launching. Adversarial attack is a machine learning approach to generate perturbations on real samples to detect the vulnerability of CNN. In this paper, we will use an adversarial attack technique to evaluate a CNN at different training states. The model was trained to perform surgical tool classification task, which was applied to recognize surgical tool in Cholecystectomy to further analyze surgical process. The experiments demonstrate the relation between training states and robustness, i.e. the robustness improved at higher training states, especially for some particular classes. In future work, additional training with generated adversarial images may improve the robustness of the model.
An Evaluation of Blockchain Application Requirements and Their Satisfaction in Hyperledger Fabric

Blockchain applications may offer better fault-tolerance, integrity, traceability and transparency compared to centralized solutions. Despite these benefits, few businesses switch to blockchain-based applications. Industries worry that the current blockchain implementations do not meet their requirements, e.g., when it comes to scalability, throughput or latency. Hyperledger Fabric (HLF) is a permissioned blockchain infrastructure that aims to meet enterprise needs and provides a highly modular and well-conceived architecture. In this paper, we survey and analyse requirements of blockchain applications in respect to their underlying infrastructure by focusing mainly on performance and resilience characteristics. Subsequently, we discuss to what extent Fabric’s current design allows it to meet these requirements. We further evaluate the performance of Hyperledger Fabric 2.2 simulating different use case scenarios by comparing single with multi-ordering service performance and conducting an evaluation with mixed workloads.
Two-mode light states before and after delocalized single-photon addition

We studied the effect of delocalized single-photon addition (DPA ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{a}}_{1}^{\ifmmode\dagger\else\textdagger\fi{}}+{e}^{i\ensuremath{\varphi}}{\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{a}}_{2}^{\ifmmode\dagger\else\textdagger\fi{}}$) on two input modes containing four cases: two independent coherent states (CSs), two independent thermal states (TSs), two independent single-mode squeezed vacuums (SVs), and an entangled two-mode squeezed vacuum (TMSV). In essence, four types of non-Gaussian entangled light states are generated. We studied three different resources (including entanglement, discorrelation, and Wigner negativity) for each two-mode light state. The output states after DPA are entangled, with more parameters and complex structures, characterizing more Wigner negativity or even discorrelation. In contrast, the CSs case is the most tunable protocol, because its negativity under partial transposition, discorrelation, and Wigner logarithmic negativity are more sensitive to superposition phase $\ensuremath{\varphi}$ than those in TSs, SVs, and TMSV cases.
A Virtual Data Collection Model of Distributed PVs considering Spatio-Temporal Coupling and Affine Optimization Reference

The rapid development of distributed photovoltaic (DPV) made the shortage of data transmission channels and the difficulty of comprehensive coverage of measurement equipment becoming more significant. To enable a high-precision data collection of DPVs with fewer sensory devices and low computation costs, this paper proposes a virtual collection technology based on computational intelligence. To capture the spatial-temporal correlation of DPVs and find the optimal reference power station (RPS), a deep recurrent denoising autoencoder (D-RDAE)-based model is proposed in this paper. An affine artificial neural network (AANN) is constructed to tackle the uncertainty of solar radiation intensity and select RPSs. To address the high-dimensionality RPS selection, an improved honey badger algorithm (IHBA) with enhanced global search ability is proposed. The operation data of 33 DPVs in Nanjing, China, are used to train and verify the proposed method. The experimental results showed the effectiveness and superiority of the proposed method. Compared with DAE and RDAE, the deeply trained D-RDAE has the best capacity for finding the spatial-temporal correlation of DPVs. In addition, IHBA has the best global search ability compared with other 4 optimizers, and the AANN can better reduce the uncertainty of solar radiation intensity than robust and stochastic optimization techniques.
Blind Dual Watermarking Scheme Using Stucki Kernel and SPIHT for Image Self-Recovery

In this paper we propose a blind dual watermarking scheme using Set Partitioning in Hierarchical Trees (SPIHT) and Stucki Kernel halftone technique for the tamper detection and image self-recovery.The watermark consists of authentication bits for tampering area location and recovery bits for image restoration.We generate two recovery bits to ensure the high-quality recovery of the tampered image.The primary recovery bit is generated by the SPIHT encoding, and the secondary recovery bit is generated by the Stucki Kernel halftone technique.Then the authentication bit is generated based on the recovery bits.Before embedding the watermark, we shuffle the watermark bits through Arnold cat mapping and diagonal mapping to improve the security and quality of the restored image.LSB-based watermarking technique is used to embed the watermark into the original image to ensure the invisibility of the watermarked image.Experiments have been conducted on two datasets, BOW2 and USC-SIPI, and results show that the proposed scheme can achieve high restoration quality.Comparison with the existing works demonstrate the good performance and superiority of the proposed scheme.
A Blockchain Protocol for Authenticating Space Communications between Satellites Constellations

Blockchain has found many applications, apart from Bitcoin, in different fields and it has the potential to be very useful in the satellite communications and space industries. Decentralized and secure protocols for processing and manipulating space transactions of satellite swarms in the form of Space Digital Tokens (SDT) can be built using blockchain technology. Tokenizing space transactions using SDTs will open the door to different new blockchain-based solutions for the advancement of constellation-based satellite communications in the space industry. Developing blockchain solutions using smart contracts could be used in securely authenticating various P2P satellite communications and transactions within/between satellite swarms. To manage and secure these transactions, using the proposed SDT concept, this paper suggested a blockchain-based protocol called Proof of Space Transactions (PoST). This protocol was adopted to manage and authenticate the transactions of satellite constellations in a P2P connection. The PoST protocol was prototyped using the Ethereum blockchain and experimented with to evaluate its performance using four metrics: read latency, read throughput, transaction latency, and transaction throughput. The simulation results clarified the efficiency of the proposed PoST protocol in processing and verifying satellite transactions in a short time according to read and transaction latency results. Moreover, the security results showed that the proposed PoST protocol is secure and efficient in verifying satellite transactions according to true positive rate (TPR), true negative rate (TNR), and accuracy metrics. These findings may shape a real attempt to develop a new generation of Blockchain-based satellite constellation systems.
Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition

Three-dimensional convolutional network (3DCNN) is an essential field of motion recognition research. The research work of this paper optimizes the traditional three-dimensional convolution network, introduces the self-attention mechanism, and proposes a new network model to analyze and process complex human motion videos. In this study, the average frame skipping sampling and scaling and the one-hot encoding are used for data pre-processing to retain more features in the limited data. The experimental results show that this paper innovatively designs a lightweight three-dimensional convolutional network combined with an attention mechanism framework, and the number of parameters of the model is reduced by more than 90% to only about 1.7 million. This study compared the performance of different models in different classifications and found that the model proposed in this study performed well in complex human motion video classification. Its recognition rate increased by 1%–8% compared with the C3D model.
Secure communication over wireless sensor network using image steganography with generative adversarial networks

Over the last several years, various researchers have been driven to the challenge of addressing security issues in Wireless Sensor Networks (WSN). Steganography is one of the most secure encryption techniques used for network security around the globe. Steganography is a secret communication technique used in wireless sensor networks where the aggregated data can be hidden as a message behind a cover image that appears normal across an untrusted network. Steganography using Generative Adversarial Networks (GAN) transforms the secret information to noise vector and feeds the noise vector to GAN to generate the image like containers or stegno image. Secret information is extracted from the stegno by updating noise vector with gradient descent operation. Though this method is secure against steganalysis attacks, the error introduced in transmission of images over lossy network introduces fluctuations in the image as result the data extracted has noises. Also anyone with the GAN decoder can access the secret information. There is no protection mechanism to ensure only a valid user is able to extract the secret information. This work proposed an improvised GAN Steganography addressing the two problems of security and reducing loss to secret information. The proposed approach is evaluated experimentally using Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index Metric (SSIM), resulting in improved image quality and security that exhibiting the method's significance.
Fast and Accurate Illumination Estimation Using LDR Panoramic Images for Realistic Rendering

A high dynamic range (HDR) image is commonly used to reveal stereo illumination, which is crucial for generating high-quality realistic rendering effects. Compared to the high-cost HDR imaging technique, low dynamic range (LDR) imaging provides a low-cost alternative and is preferable for interactive graphics applications. However, the limited LDR pixel bit depth significantly bothers accurate illumination estimation using LDR images. The conflict between the realism and promptness of illumination estimation for realistic rendering is yet to be resolved. In this paper, an efficient method that accurately infers illuminations of real-world scenes using LDR panoramic images is proposed. It estimates multiple lighting parameters, including locations, types and intensities of light sources. In our approach, a new algorithm that extracts illuminant characteristics during the exposure attenuation process is developed to locate light sources and outline their boundaries. To better predict realistic illuminations, a new deep learning model is designed to efficiently parse complex LDR panoramas and classify detected light sources. Finally, realistic illumination intensities are calculated by recovering the inverse camera response function and extending the dynamic range of pixel values based on previously estimated parameters of light sources. The reconstructed radiance map can be used to compute high-quality image-based lighting of virtual models. Experimental results demonstrate that the proposed method is capable of efficiently and accurately computing comprehensive illuminations using LDR images. Our method can be used to produce better realistic rendering results than existing approaches.
Observability of fidelity decay at the Lyapunov rate in few-qubit quantum simulations

In certain regimes, the fidelity of quantum states will decay at a rate set by the classical Lyapunov exponent. This serves both as one of the most important examples of the quantum-classical correspondence principle and as an accurate test for the presence of chaos. While detecting this phenomenon is one of the first useful calculations that noisy quantum computers without error correction can perform [G. Benenti et al., Phys. Rev. E 65, 066205 (2001)], a thorough study of the quantum sawtooth map reveals that observing the Lyapunov regime is just beyond the reach of present-day devices. We prove that there are three bounds on the ability of any device to observe the Lyapunov regime and give the first quantitatively accurate description of these bounds: (1) the Fermi golden rule decay rate must be larger than the Lyapunov rate, (2) the quantum dynamics must be diffusive rather than localized, and (3) the initial decay rate must be slow enough for Lyapunov decay to be observable. This last bound, which has not been recognized previously, places a limit on the maximum amount of noise that can be tolerated. The theory implies that an absolute minimum of 6 qubits is required. Recent experiments on IBM-Q and IonQ imply that some combination of a noise reduction by up to 100<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&amp;#x00D7;</mml:mo></mml:math> per gate and large increases in connectivity and gate parallelization are also necessary. Finally, scaling arguments are given that quantify the ability of future devices to observe the Lyapunov regime based on trade-offs between hardware architecture and performance.
Fraud Detection Using Large-scale Imbalance Dataset

In the context of machine learning, an imbalanced classification problem states to a dataset in which the classes are not evenly distributed. This problem commonly occurs when attempting to classify data in which the distribution of labels or classes is not uniform. Using resampling methods to accumulate samples or entries from the minority class or to drop those from the majority class can be considered the best solution to this problem. The focus of this study is to propose a framework pattern to handle any imbalance dataset for fraud detection. For this purpose, Undersampling (Random and NearMiss) and oversampling (Random, SMOTE, BorderLine SMOTE) were used as resampling techniques for the concentration of our experiments for balancing an evaluated dataset. For the first time, a large-scale unbalanced dataset collected from the Kaggle website was used to test both methods for detecting fraud in the Tunisian company for electricity and gas consumption. It was also evaluated with four machine learning classifiers: Logistic Regression (LR), Naïve Bayes (NB), Random Forest, and XGBoost. Standard evaluation metrics like precision, recall, F1-score, and accuracy have been used to assess the findings. The experimental results clearly revealed that the RF model provided the best performance and outperformed all other matched classifiers with attained a classification accuracy of 89% using NearMiss undersampling and 99% using Random oversampling.