Reviewerly/exHarmony huggingface dataset:
Identification Method of Fault Hidden Danger of Transmission Line for Reclose by Mountain Fire Based on ARMA Model Fitting

The transmission line fault caused by mountain fire is a fault state between transient fault and permanent fault. For the reclosing after the transmission line fault caused by mountain fire, the randomness and nonlinearity of the transition impedance often lead to the reclosing failure, and even lead to the burning of the circuit breaker. If the potential fault of transmission line fault can be found as soon as possible, the circuit breaker can be controlled to trip to avoid damage. In this paper, a feature extraction method based on autoregressive moving average model(ARMA) fitting is proposed. The characteristic of fast and smooth signal of ARMA is used to select the appropriate order for the half cycle voltage signal of the transmission line to perform coefficient fitting,and to complete the feature extraction of the potential fault signal. The feature vectors are input into support vector machine (SVM) and K-nearest neighbor classifier (KNN) respectively for identification potential fault, and it is found that SVM has higher identification accuracy. Compared with other sequential signal identification algorithms with similar identification accuracy, the complexity of this algorithm is greatly reduced. The algorithm is verified based on the existing recording voltage wave of reclosing failure by mountain fire fault. The calculation results show that the recall rate, precision rate and F1 index are 98.5%, 98.4% and 98.4% respectively by combining the feature engineering composed of ARMA model fitting coefficient with support vector machine classifier.
Improved YOLOv5 Network with CBAM for Object Detection Vision Drone

Recently, drones have been applied and used in various tasks in many fields. Due to the development of camera technology and hardware, it is possible to detect objects using deep learning technology in real time. Most of the object sizes of videos and images taken by drones are very small objects. The dataset that collects these image data is the VisDrone dataset. As deep learning technology develops a lot, object detection accuracy is getting higher and higher. However, it is still hard to perform object detection in real-time and show high object detection accuracy. In this paper, we combine YOLOv5 and CBAM to increase object detection accuracy by focusing more on the features required for object detection. The model is trained using the VisDrone dataset, and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP}$</tex> value is measured at 22. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$56\text{mAP}$</tex> ,which is 2. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$45\text{mAP}$</tex> higher than the original YOLOv5.
Properties and Future of the Skew Kalman Filters

The Kalman filter (KF) and its variants are powerful numerical tools for estimating the unmeasured states of dynamical systems. However, traditional KFs assume Gaussian noise in measurements and processes, which may not always hold in practice. This paper reviews recent developments in non-Gaussian Kalman filters with non-zero skewness, which have received relatively little attention despite their potential benefits. This paper mainly focuses on skew Kalman filters (SKF), which replace the Gaussian assumption with the closed skew normal (CSN) distribution. This allows SKF to capture outliers in dynamical systems, resulting in improved performance and flexibility. Although there is limited literature on skew Kalman filters, tiiis study provides an overview of their potential and motivation for use in a wide range of scientific applications.
A Partitioned Poisson Multi-Bernoulli Filter

The Poisson multi-Bernoulli (PMB) filter offers an elegant Bayesian formulation for multitarget tracking (MTT) by approximating the complex Poisson multi-Bernoulli mixture (PMBM) density as a PMB density. This approximation allows for more efficient computation and simpler implementation while maintaining reasonable accuracy. However, approximating the posterior density involves a certain degree of tracking accuracy loss, and different approximation methods may result in different types of tracking accuracy losses. To address this problem, this article proposes a partitioned PMB (PPMB) filter that decomposes the MTT problem into smaller sub-problems, which can synthesize the advantages of different approximations to avoid tracking accuracy loss. Furthermore, to address the issue of tracking lost for close-spaced and undetected targets, an improved measurement-oriented multi-Bernoulli Poisson (IMOMB/P) filter is also proposed, which utilizes more accurate marginal association probabilities. Simulation results demonstrate that the proposed PPMB filter outperforms existing PMB filters, such as the measurement-oriented multi-Bernoulli Poisson (MOMB/P) and track-oriented multi-Bernoulli (TOMB) filters, thus showing promising application prospects.
Performance Analysis of Classical Data Transmission Over a Quantum Channel in the Presence of Atmospheric Turbulence

This letter presents the performance analysis of classical data transmission over a quantum channel in the presence of atmospheric turbulence. To transmit classical data, quantum coherent states are modulated over a log-normal turbulent channel for satellite-to-ground applications. The expression for the number of photons in the turbulent coherent state is approximated. Performance analysis is done in terms of error probability, channel capacity, and quantum bit error rate (QBER). The simulation results are presented using the Monte-Carlo simulation method. Numerical results show that the error performance improves with increasing the number of signal photons. However, for turbulence strength greater than 0.8, QBER increases more rapidly with a higher number of signal photons. Hence, a trade-off is essential between an achievable QBER and the number of signal photons.
Collaborative Learning-based Dual Network for Few-Shot Image Classification

With the vigorous development of image classification technology in the field of computer vision, Few-shot learning (FSL) has become a research hotspot for solving classification task model training with a small number of samples. FSL aims to achieve efficient identification and processing of new category samples with few annotations. Previous works focus on information extraction based on one single model for FSL, lacking the distinction of the differences between data samples. Therefore, we present a meta-learning-based dual model with knowledge clustering for few-shot image classification, trying to learn the correlation between dual models and capture the information embedded in the data samples. In addition, we introduce the center loss to cluster the same sort of samples and to maximize the similarity among the intraclass and the difference among the inter-class. We adopt multiple tasks based on Meta-learning during the training stage. For each task, the training of dual models divides into two phases, which depend on each other under the guidance of the center loss. At the first phase, the first model is trained with a soft label obtained by the predicted label of the second model. The second phase repeats the information exchange of the first phase. We find that the optimal predictions of the active model are close to the soft and actual labels. Extensive experimental results on three general benchmarks illustrate the effectiveness of our proposed methods on few-shot classification tasks.
Multi-Scale Feature Enhancement Network for Face Forgery Detection

Nowadays, synthesizing realistic fake face images and videos becomes easy benefiting from the advance in generation technology. With the popularity of face forgery, abuse of the technology occurs from time to time, which promotes the research on face forgery detection to be an emergency. To deal with the potential risks, we propose a face forgery detection method based on multi-scale feature enhancement. Specifically, we analyze the forgery traces from the perspective of texture and frequency domain, respectively. We find that forgery traces are hard to be perceived by human eyes but noticeable in shallow layers of CNNs and middle-frequency domain and high-frequency domain. Hence, to reserve more forgery information, we design a texture feature enhancement module and a frequency domain feature enhancement module, respectively. The experiments on FaceForensics++ dataset and Celeb-DF dataset show that our method exceeds most existing networks and methods, which proves that our method has strong classification ability.
SARAF: Searching for Adversarial Robust Activation Functions

Convolutional Neural Networks (CNNs) have received great attention in the computer vision domain. However, CNNs are vulnerable to adversarial attacks, which are manipulations of input data that are imperceptible to humans but can fool the network. Several studies tried to address this issue, which can be divided into two categories: (i) training the network with adversarial examples, and (ii) optimizing the network architecture and/or hyperparameters. Although adversarial training is a sufficient defense mechanism, they suffer from requiring a large volume of training samples to cover a wide perturbation bound. Tweaking network activation functions (AFs) has been shown to provide promising results where CNNs suffer from performance loss. However, optimizing network AFs for compensating the negative impacts of adversarial attacks has not been addressed in the literature. This paper proposes the idea of searching for AFs that are robust against adversarial attacks. To this aim, we leverage the Simulated Annealing (SA) algorithm with a fast convergence time. This proposed method is called SARAF. We demonstrate the consistent effectiveness of SARAF by achieving up to 16.92%, 18.3%, and 15.57% accuracy improvement against BIM, FGSM, and PGD adversarial attacks, respectively, over ResNet-18 with ReLU AFs (baseline) trained on CIFAR-10. Meanwhile, SARAF provides a significant search efficiency compared to random search as the optimization baseline.
Attention-Based Recursive Autoencoder For Sentence-Level Sentiment Classification

Sentiment analysis is a crucial task in the research of natural language handling. Traditional machine learning approaches frequently employ bag-of-word representations that do not capture complex linguistic phenomena. The recursive autoencoder (RAE) method can availably learn the vector space representation of phrases, which is superior to other sentiment prediction methods on commonly used data sets. However, during the learning process, extensive label data is often required to label each node. In addition, RAE uses greedy strategies to merge adjacent words, it is difficult to capture long-distance and deeper semantic information. We put forward a semi-supervised approach that combines the SenticNet lexicon to train the recursive autoencoder for calculating the sentiment orientation of each node, and incorporates an attention mechanism to capture the contextual relationship between the words in a sentence. Experiments prove that the model proposed in this paper outperforms RAE and other models.
Enhanced Visual Cryptography Using Quadratic Anisotropic Diffusion

Visual cryptography is a surfacing form of encryption that enables decryption through the use of human eyesight features. This paper covers the implementation of enhancing visual cryptography through quadratic anisotropic diffusion. The initial encryption algorithm takes RGB images and encrypts them into Black and White images. The proposed implementation is to take the output of the original algorithm, apply quadratic anisotropic diffusion to result in the output of a secret crypto image with added noise. The experimental results indicate that the implementation of quadratic anisotropic diffusion improved the overall encryption of the secret image without distorting or affecting the original image. The comparative analysis with related research work shows that the proposed algorithm achieves high level of image confidentiality.
Recognition of Unsafe Behavior of Electrical Workers based on Spatial-Temporal Visual Attention

It is helpful for accident prevention to recognize unsafe behavior of electrical workers through computer vision. Surveillance video contains a lot of temporal redundant information, which affects the accuracy of unsafe behavior detection. To this end, this paper proposes a visual attention-based keyframe extraction method that eliminates temporal redundancy, reconstructs surveillance video, and uses it for unsafe behavior recognition. First, the spatial attention model is used to extract a saliency map for each frame. Based on the pixel-level features of the saliency map, the temporal attention model establishes the temporal connection between all frames and obtains the importance degree of each frame. The importance degree of the frame is ranked from high to low. An optimization functional model is established that combines the similarity degree and importance degree between frames to select the corresponding keyframe, based on which the new video is reconstructed. Finally the new video is fed into a traditional behavior recognition algorithm to recognize unsafe behavior. The experimental results show that the keyframe extraction method based on spatial-temporal visual attention can effectively improve the accuracy of unsafe behavior recognition, which is increased from 80.75% of the traditional method to 86%.
Informed Deep Learning for Anomaly Detection in Cyber-Physical Systems

Cyber-physical systems (CPS) are key components of modern infrastructure, and detecting anomalies is a critical role in assuring the safety and reliability of these cyber-physical systems. Deep neural networks (DNNs) have demonstrated excellent performance in a variety of domains due to their capacity to discover and identify patterns in high-dimensional data. However, their effectiveness is limited in the case of anomaly detection for cyber-physical systems where data collection is difficult, it is not always possible to obtain data for anomalous states, and people do not trust AI in securing critical infrastructure components. To address these limitations, prior knowledge can be incorporated into the modeling process to help fill in missing data and offer an explainable interface to the neural network's inner workings. In this paper, we will state the benefits and drawbacks of informed deep learning, present possible data sources and integration options, and provide examples from the literature where informed learning outperforms traditional data-driven methods in anomaly detection for cyber-physical systems. We hope that this evaluation will attract interest in informed learning and encourage the use of prior knowledge to improve purely data-driven approaches for anomaly detection in cyber-physical systems.
SimpleFusion: 3D Object Detection by Fusing RGB Images and Point Clouds

Achieving robust 3D object detection by fusing images and point clouds remains challenging. In this paper, we propose a novel 3D object detector (SimpleFusion) that enables simple and efficient multi-sensor fusion. Our main motivation is to boost feature extraction from a single modality and fuse them into a unified space. Specifically, we build a new visual 3D object detector in the camera stream that leverages point cloud supervision for more accurate depth prediction; in the lidar stream, we introduce a robust 3D object detector that utilizes multi-view and multi-scale features to overcome the sparsity of point clouds. Finally, we propose a dynamic fusion module to focus on more confident features and achieve accurate 3D object detection based on dynamic weights. Our method has been evaluated on the nuScenes dataset, and the experimental results indicate that it outperforms other state-of-the-art methods by a significant margin.
Application of a Deep Generative Model for Diversified Video Subtitles Based on Generative Adversarial Networks

With the continuous development of artificial intelligence, cross-field applications in image recognition, natural language processing, and semantic recognition have gradually emerged. Video subtitle generation is one of the typical cross-field application scenarios. By recognizing image semantics and using natural language for generative content construction, users can often understand the content of images intuitively and quickly. In addition, when the contents are displayed in text form, it is convenient for textual retrieval or other text-based image applications. This paper proposes the application of generative adversarial networks in subtitle recognition. By focusing more on generation diversity, the degree of the differentiated description of generated content can be further improved, making image and video subtitles generation content prone to human habits. Specifically, the idea is to use the generative adversarial network to generate the subtitles of the images in the video and use diversified generative models to conduct difference optimization to obtain better generative performance. It is hoped that the research in this paper can help the application of diversified video subtitles generation.
Stereo Image Analysis by Octonion Fractional-Order Orthogonal Color Moments

Polar harmonic Fourier moments (PHFMs) are popular for image analysis due to their properties of lower computation complexity and minimal redundant description capability of images. However, the traditional PHFMs are unavailable for color stereo image analysis on the one hand, and on the other hand the polar harmonic polynomials with integer-order are not able to extract fine features. In this paper, a new category of moments named octonion fractional-order PHFMs (OFrPHFMs) are proposed using the fractional-order basis functions of PHFMs and octonion theory. The proposed moments can be viewed as a generalization of quaternion orthogonal moments. Furthermore, since the image moments formed by the octonion descriptor can treat the color stereo image integrally, it has a strong representation capability. More importantly, some numerical instability and calculation issues are discussed and a fast computational framework using matrix operation and block Gaussian numerical integration is developed to improve the accuracy and efficiency of the proposed OFrPHFMs. Finally, to demonstrate the validation of the proposed moments, the image experiments are conducted and the results show that the proposed OFrPHFMs have favorable performance in the field of color stereo image analysis.
Interpretation of Interstitial Lung Diseases from Magnetic Resonance Image using deep learning

Usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP) shared similar patterns in medical images but had different treatments and outcomes. This study presented an automatically distinguishable tool, named ConvT, using deep learning methods to help the clinicians differentiate NSIP from UIP based on MRI images. ConvT employed a CNN feature extractor and a cross-view transformer to classify the bio-medical patterns that indicate NSIP or UIP. The experiment in a dataset with over 417 MRI T2WI images showed ConvT had 97.62% accuracy and 0.9767 F1 scores in the classification task. The visualization of ConvT illustrated that the proposed model can correctly identify the specific patterns of both NSIP and UIP.
Performance Optimization for Noise Interference Privacy Protection in Federated Learning

The data security issue in federated learning is critical. While federated learning allows clients to collaboratively participate in the global model training without sharing private data, external eavesdroppers may intercept the model uploaded by the client to the server, revealing some sensitive information. Noise interference, i.e., adding noise to the client model before transmission, is an effective and efficient privacy-preserving method, but it degrades the learning performance of the system at the same time. In this paper, to address the challenge of system performance degradation caused by noise interference, we propose the FedNoise algorithm, which adopts two separate learning rates at the client and server respectively. By carefully tuning these learning rates, the global model can converge to the optimum. We provide theoretical proofs of the convergence of FedNoise for both strongly convex and non-convex loss functions and conduct simulations on real tasks. Numerical experimental results demonstrate that, under the same privacy protection level, FedNoise significantly outperforms the state-of-art scheme on datasets MNIST, Fashion-MNIST, and CIFAR10.
Intelligent Early Warning System for Power Operation Safety Based on Laser Point Cloud Sensing

Safety prevention, control and risk warning at power operation sites are influenced by the safety regulatory system. Combining new technologies such as LIDAR intelligent perception and machine vision, a laser point cloud perception-based intelligent early warning system for power operation safety is proposed, and the components and workflows of each section are described in detail. The system is described in detail in terms of its components and functional realisation. For the functional description in the system framework, field simulations and functional verification of the system are carried out. The results verify that the designed system is capable of real time supervision, dynamic target detection and intelligent early warning of power operation sites, which significantly improves the safety supervision level and efficiency of the site operation and has feasibility and application prospects.
Intelligent Tools and Whole-process Control System Based on Artificial Intelligence and Internet of Things Technology

In order to solve the problems of power grid construction, urban and rural distribution network upgrading and transformation, hydropower construction and other tasks involved in the safety work of the operation site with a huge number and variety, complex and cumbersome workload and low efficiency, more personal casualties on site, and inadequate safety supervision, this paper uses artificial intelligence technology, Internet of Things technology, sensor technology to develop intelligent safety tools and a whole-process control system. The system enables safety tools to intelligently perceive on-site operations and warn in real time, improve the safety factor of on-site operations, realize the information supervision of the whole process of safety tools management from procurement to scrapping, and improve management efficiency.
Balanced-DRL: A DQN-Based Job Allocation Algorithm in BaaS

Blockchain as a Service (BaaS) combines features of cloud computing and blockchain, making blockchain applications more convenient and promising. Although current BaaS platforms have been widely adopted by both industry and academia, concerns arise regarding their performance, especially in job allocation. Existing BaaS job allocation strategies are simple and do not guarantee load balancing due to the dynamic nature and complexity of BaaS job execution. In this paper, we propose a deep reinforcement learning-based algorithm, Balanced-DRL, to learn an optimized allocation strategy in BaaS based on analyzing the execution process of BaaS jobs and a set of job scale characteristics. Following extensive experiments with generated job request workloads, the results show that Balanced-DRL significantly improves BaaS performance, achieving a 5% to 8% increase in job throughput and a 5% to 20% decrease in job latency.