Reviewerly/exHarmony huggingface dataset:
Research on aeromagnetic data error analysis and processing of multi-rotor UAV based on variational mode decomposition algorithm

Aeromagnetic survey of multi-rotor UAV is widely used in small and medium-sized mineral resources survey, engineering investigation, non-explosive detection and other fields due to its advantages of high safety, low cost and convenient operation. The original data of UAV aeromagnetic survey includes interference from external environment, interference from UAV platform and interference from airborne electromagnetic equipment. The interference is mainly shown as striping anomaly along the direction of the survey line in the aeromagnetic anomaly map, which affects the accuracy of aeromagnetic compensation and information detection of geological anomaly body, thus affecting the accuracy of interpretation of geological anomaly body. In this paper, the algorithm of Variational Modal Decomposition (VMD) is introduced to filter and preprocess the aeromagnetic data to obtain high quality aeromagnetic data. At the same time, the source and characteristics of the errors are predicted according to the interference size and spectrum characteristics of the Modal function errors, which provides a reference for the optimization of aeromagnetic system.
Using photonic reservoirs as preprocessors for deep neural networks

Artificial neural networks are very time consuming and energy intensive to train, especially when increasing the size of the neural network in an attempt to improve the performance. In this paper, we propose to preprocess the input data of a deep neural network using a reservoir, which has originally been introduced in the framework of reservoir computing. The key idea of this paper is to use such a reservoir to transform the input data into a state in a higher dimensional state-space, which allows the deep neural network to process the data with improved performance. We focus on photonic reservoirs because of their fast computation times and low-energy consumption. Based on numerical simulations of delay-based reservoirs using a semiconductor laser, we show that using such preprocessed data results in an improved performance of deep neural networks. Furthermore, we show that we do not need to carefully fine-tune the parameters of the preprocessing reservoir.
A Generalized Linear Mixed Model for Data Breaches and Its Application in Cyber Insurance

Data breach incidents result in severe financial loss and reputational damage, which raises the importance of using insurance to manage and mitigate cyber related risks. We analyze data breach chronology collected by Privacy Rights Clearinghouse (PRC) since 2001 and propose a Bayesian generalized linear mixed model for data breach incidents. Our model captures the dependency between frequency and severity of cyber losses and the behavior of cyber attacks on entities across time. Risk characteristics such as types of breach, types of organization, entity locations in chronology, as well as time trend effects are taken into consideration when investigating breach frequencies. Estimations of model parameters are presented under Bayesian framework using a combination of Gibbs sampler and Metropolis–Hastings algorithm. Predictions and implications of the proposed model in enterprise risk management and cyber insurance rate filing are discussed and illustrated. We find that it is feasible and effective to use our proposed NB-GLMM for analyzing the number of data breach incidents with uniquely identified risk factors. Our results show that both geological location and business type play significant roles in measuring cyber risks. The outcomes of our predictive analytics can be utilized by insurers to price their cyber insurance products, and by corporate information technology (IT) and data security officers to develop risk mitigation strategies according to company’s characteristics.
Deep Learning Anomaly Classification Using Multi-Attention Residual Blocks for Industrial Control Systems

This paper proposes a novel method monitoring network packets to classify anomalies in industrial control systems (ICSs). The proposed method combines different mechanisms. It is flow-based as it obtains new features through aggregating packets of the same flow. It then builds a deep neural network (DNN) with multi-attention blocks for spotting core features, and with residual blocks for avoiding the gradient vanishing problem. The DNN is trained with the Ranger (RAdam + Lookahead) optimizer to prevent the training from being stuck in local minima, and with the focal loss to address the data imbalance problem. The Electra Modbus dataset is used to evaluate the performance impacts of different mechanisms on the proposed method. The proposed method is compared with related methods in terms of the precision, recall, and F1-score to show its superiority.
Analysis of the Security and Reliability of Cryptocurrency Systems Using Knowledge Discovery and Machine Learning Methods

Cryptocurrency, often known as virtual or digital currency, is a safe platform and a key component of the blockchain that has recently attracted much interest. Utilizing blockchain technology, bitcoin transactions are recorded in blocks that provide detailed information on all financial transactions. Artificial intelligence (AI) has significant applicability in several industries because of the abundance and processing capacity of large data. One of the main issues is the absence of explanations for AI algorithms in the current decision-making standards. For instance, there is no deep-learning-based reasoning or control for the system's input or output processes. More particularly, the bias for adversarial attacks on the process interface and learning characterizes existing AI systems. This study suggests an AI-based trustworthy architecture that uses decentralized blockchain characteristics such as smart contracts and trust oracles. The decentralized consensuses of AI predictors are also decided by this system using AI, enabling secure cryptocurrency transactions, and utilizing the blockchain technology and transactional network analysis. By utilizing AI for a thorough examination of a network, this system's primary objective is to improve the performance of the bitcoin network in terms of transactions and security. In comparison to other state-of-the-art systems, the results demonstrate that the proposed system can achieve very accurate output.
Optimization of bilateral filter parameters using a whale optimization algorithm

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DWFed: A statistical- heterogeneity-based dynamic weighted model aggregation algorithm for federated learning

Federated Learning is a distributed machine learning framework that aims to train a global shared model while keeping their data locally, and previous researches have empirically proven the ideal performance of federated learning methods. However, recent researches found the challenge of statistical heterogeneity caused by the non-independent and identically distributed (non-IID), which leads to a significant decline in the performance of federated learning because of the model divergence caused by non-IID data. This statistical heterogeneity is dramatically restricts the application of federated learning and has become one of the critical challenges in federated learning. In this paper, a dynamic weighted model aggregation algorithm based on statistical heterogeneity for federated learning called DWFed is proposed, in which the index of statistical heterogeneity is firstly quantitatively defined through derivation. Then the index is used to calculate the weights of each local model for aggregating federated model, which is to constrain the model divergence caused by non-IID data. Multiple experiments on public benchmark data set reveal the improvements in performance and robustness of the federated models in heterogeneous settings.
Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images

The primary screening by automated computational pathology algorithms of the presence or absence of adenocarcinoma in biopsy specimens (e.g., endoscopic biopsy, transbronchial lung biopsy, and needle biopsy) of possible primary organs (e.g., stomach, colon, lung, and breast) and radical lymph node dissection specimen is very useful and should be a powerful tool to assist surgical pathologists in routine histopathological diagnostic workflow. In this paper, we trained multi-organ deep learning models to classify adenocarcinoma in biopsy and radical lymph node dissection specimens whole slide images (WSIs). We evaluated the models on five independent test sets (stomach, colon, lung, breast, lymph nodes) to demonstrate the feasibility in multi-organ and lymph nodes specimens from different medical institutions, achieving receiver operating characteristic areas under the curves (ROC-AUCs) in the range of 0.91 -0.98.
X-RAY FLUORESCENT DETERMINATION OF TITANIUM, ZIRCONIUM, AND CHROMIUM IN TITANIUM-ZIRCONIUM SANDS OF THE BESHPAGIRSKOE DEPOSIT

Gaining current information about the chemical composition of samples from different areas of the deposit during geological exploration necessitates the use of methods of rapid analysis, most often X-ray fluorescence analysis in combination with a method of external standard and its modifications. The existing certified methods assume the construction of calibration characteristics using reference standards. We propose to use a rapid method of X-ray fluorescence analysis (XRF) in aversion of the method of fundamental parameters to determine non-ferrous metals in the samples of titanium-zirconium sands of the Beshpagirskoye deposit and specified conditions of analysis. It is shown that when using the method of fundamental parameters, the accuracy indicators are highly competitive with the indicators obtained by the certified method.
Sub-microscale speckle pattern creation on single carbon fibers for scanning electron microscope-digital image correlation (SEM-DIC) experiments

High performance carbon fibers are widely used as fiber reinforcements in composite material systems for aerospace, automotive, and defense applications. Longitudinal tensile failure of such composite systems is a result of clustering of single fiber tensile failures occurring at the microscale, on the order of a few microns to a few hundred microns. Since fiber tensile strength at the microscale has a first order effect on composite strength, it is important to characterize the strength of single fibers at microscale gage lengths which is extremely challenging. An experimental technique based on a combination of transverse loading of single fibers under SEM with DIC is a potential approach to access microscale gage lengths. The SEM-DIC technique requires creation of uniform, random, and contrastive sub-microscale speckle pattern on the curved fiber surface for accurate strain measurements. In this paper, we investigate the formation of such sub-microscale speckle patterns on individual sized IM7 carbon fibers of nominal diameter 5.2 µm via sputter coating. Various process conditions such as working pressure, sputtering current, and coating duration are investigated for pattern creation on fiber surface using a gold-palladium (Au-Pd) target. A nanocluster type sub-microscale pattern is obtained on the fiber surface for different coating conditions. Numerical translation experiments are performed using the obtained patterns to study image correlation and identify a suitable pattern for SEM-DIC experiments. The pattern obtained at a working pressure of 120–140 mTorr with 50 mA current for a duration of 10 min is found to have an average speckle size of 53 nm and good contrast for image correlation. Rigid body translation SEM experiments for drift/distortion correction using a sized IM7 carbon fiber coated with the best patterning conditions showed that Stereo-SEM-DIC is needed for accurately characterizing fiber strain fields due to its curved surface. The effect of sputter coating on fiber tensile strength and strain is investigated via single fiber tensile tests. Results showed that there is no significant difference in the mean tensile strength and failure strain between uncoated and coated fibers (average increment in fiber diameter of ∼221 nm due to coating) at 5% significance level. SEM images of failure surfaces for uncoated and coated fibers also confirmed a tensile failure of fibers as observed for polyacrylonitrile PAN-based fibers in literature.
Do ideas have shape? Idea registration as the continuous limit of artificial neural networks

We introduce a Gaussian Process (GP) generalization of ResNets (with unknown functions of the network replaced by GPs and identified via MAP estimation), which includes ResNets (trained with L2 regularization on weights and biases) as a particular case (when employing particular kernels). We show that ResNets (and their warping GP regression extension) converge, in the infinite depth limit, to a generalization of image registration variational algorithms. In this generalization, images are replaced by functions mapping input/output spaces to a space of unexpressed abstractions (ideas), and material points are replaced by data points. Whereas computational anatomy aligns images via warping of the material space, this generalization aligns ideas (or abstract shapes as in Plato's theory of forms) via the warping of the Reproducing Kernel Hilbert Space (RKHS) of functions mapping the input space to the output space. While the Hamiltonian interpretation of ResNets is not new, it was based on an Ansatz. We do not rely on this Ansatz and present the first rigorous proof of convergence of ResNets with trained weights and biases towards a Hamiltonian dynamics driven flow. Since our proof is constructive and based on discrete and continuous mechanics, it reveals several remarkable properties of ResNets and their GP generalization. ResNets regressors are kernel regressors with data-dependent warping kernels. Minimizers of L2 regularized ResNets satisfy a discrete least action principle implying the near preservation of the norm of weights and biases across layers. The trained weights of ResNets with scaled/strong L2 regularization can be identified by solving an autonomous Hamiltonian system. The trained ResNet parameters are unique up to (a function of) the initial momentum, and the initial momentum representation of those parameters is generally sparse. The kernel (nugget) regularization strategy provides a provably robust alternative to Dropout for ANNs. We introduce a functional generalization of GPs and show that pointwise GP/RKHS error estimates lead to probabilistic and deterministic generalization error estimates for ResNets. When performed with feature maps, the proposed analysis identifies the (EPDiff) mean fields limit of trained ResNet parameters as the number of data points goes to infinity. The search for good architectures can be reduced to that of good kernels, and we show that the composition of warping regression blocks with reduced equivariant multichannel kernels (introduced here) recovers and generalizes CNNs to arbitrary spaces and groups of transformations.
Lightweight image super-resolution with a feature-refined network

In recent years, deep Convolutional Neural Networks (CNNs) have achieved impressive successes on the Single Image Super-Resolution task (SISR). However, it remains difficult to apply these CNN-based SISR methods in embedded devices due to their high memory and computational requirements. To alleviate this issue, we focus on lightweight SISR methods. The observed similarity between the feature maps in CNNs serves as inspiration to explore the design of a cost-efficient module to obtain feature maps whose representation ability is roughly equivalent to that of a conventional convolutional layer. We thus propose a shadow module applying simple linear transformations with a lower cost to generate similar feature maps. Based on this module, we design a Feature-Refined Block (FRB) to learn more representative features. Besides, we propose a Global Dense Feature Fusion (GDFF) structure to construct a Feature-Refined Network (FRN) with such FRBs for lightweight SISR. Extensive experimental results demonstrate the superior performance of the proposed FRN in comparison with the state-of-the-art lightweight SISR methods, while consuming relatively low memory and computation resources.
Fast and accurate superpixel segmentation algorithm with a guidance image

Superpixels are generated by automatically grouping image pixels into lots and lots of compact segments. In computer vision, it is widely used as an effective way to reduce the number of image primitives for subsequent tasks and for recognizing objects’ contours due to its excellent boundary adhesion. The primary concerns of a superpixel generation algorithm are its efficiency and accuracy. In this document, we aim to propose a fast and precise superpixel segmentation algorithm with a guidance image. Specifically, we introduce a rolling filter that can remove details while well preserving the boundaries to obtain the guidance image. In the meantime, we introduce a robust edge confidence operator to accurately detect image boundaries. From this, we define a distance measurement with adaptive parameters for each image. In this way, we can adapt and accurately group pixels into regions according to the new distance measurement. Furthermore, we adopt a noniterative framework to generate superpixels in real time by processing all pixels once. The experimental results show that the proposed methodology achieves state-of-the-art performance on two sets of reference data while operating in real time.
Event detection with multi-order edge-aware graph convolution networks

Event detection, specifically, identifying the type of events in a piece of text, is a crucial task in information extraction. Previous event detection task research proved that a syntactic graph based on a dependency tree can be integrated into a graph convolutional neural network to better capture the context of a sentence. However, most of the existing studies rely only on first-order syntactic relations and usually ignore dependence label information. In this paper, we propose a multi-order edge-aware graph convolution network (MEA-GCN) based on multi-order grammar and typed dependent label information. We use the BERT representation and the multi-head attention update mechanism to generate a variety of multi-order syntactic graph representations; consequently, our model can automatically learn dependent information and improve the representation ability of the syntactic relations. The experimental results on the widely used ACE 2005 and TAC KBP 2015 show that our model achieves significant improvement over the competitive baseline methods.
A new perspective for understanding generalization gap of deep neural networks trained with large batch sizes

Deep neural networks (DNNs) are typically optimized using various forms of mini-batch gradient descent algorithm. A major motivation for mini-batch gradient descent is that with a suitably chosen batch size, available computing resources can be optimally utilized (including parallelization) for fast model training. However, many works report the progressive loss of model generalization when the training batch size is increased beyond some limits. This is a scenario commonly referred to as generalization gap. Although several works have proposed different methods for alleviating the generalization gap problem, a unanimous account for understanding generalization gap is still lacking in the literature. This is especially important given that recent works have observed that several proposed solutions for generalization gap problem such learning rate scaling and increased training budget do not indeed resolve it. As such, our main exposition in this paper is to investigate and provide new perspectives for the source of generalization loss for DNNs trained with a large batch size. Our analysis suggests that large training batch size results in increased near-rank loss of units’ activation (i.e. output) tensors, which consequently impacts model optimization and generalization. Extensive experiments are performed for validation on popular DNN models such as VGG-16, residual network (ResNet-56) and LeNet-5 using CIFAR-10, CIFAR-100, Fashion-MNIST and MNIST datasets.
Two-stage natural scene image classification with noise discovering and label-correlation mining

In natural scene classification, it is common that one natural scene image may belong to multiple categories concurrently; as a result, multi-label learning has become a research hotspot. Despite recent rapid developments in multi-label learning, the increasing amount of high-dimensional data poses great challenges—such as redundant features and high computational costs—to conventional multi-label learning models. Most contemporary strategies for dealing with this issue depend on forcing feature learning into multi-label models. Notably, however, these approaches seldom pay attention to the label correlation and propagation in the feature subspace. To address this issue, we introduce an alternative multi-label feature learning solution that incorporates both labeled and unlabeled information. Unlike existing multi-label learning models , which rely on clean and trustworthy training datasets, we argue that in semi-supervised learning scenarios, the unlabeled data can be easily corrupted by noise or outliers, which causes the model performance to degrade. We next extract the label correlation and propagate the label information to discover the noise or outliers. Subsequently, our model adaptively searches the optimal feature subspace to reduce the influence of redundant features for high-dimensional data. The effectiveness of the proposed model is demonstrated by experimental observations on artificial and real-world datasets.
Automatic prediction of rejected edits in Stack Overflow

The content quality of shared knowledge in Stack Overflow (SO) is crucial in supporting software developers with their programming problems. Thus, SO allows its users to suggest edits to improve the quality of a post (i.e., question and answer). However, existing research shows that many suggested edits in SO are rejected due to undesired contents/formats or violating edit guidelines. Such a scenario frustrates or demotivates users who would like to conduct good-quality edits. Therefore, our research focuses on assisting SO users by offering them suggestions on how to improve their editing of posts. First, we manually investigate 764 (382 questions + 382 answers) rejected edits by rollbacks and produce a catalog of 19 rejection reasons. Second, we extract 15 texts and user-based features to capture those rejection reasons. Third, we develop four machine learning models using those features. Our best-performing model can predict rejected edits with 69.1% precision, 71.2% recall, 70.1% F1-score, and 69.8% overall accuracy. Fourth, we introduce an online tool named EditEx that works with the SO edit system. EditEx can assist users while editing posts by suggesting the potential causes of rejections. We recruit 20 participants to assess the effectiveness of EditEx. Half of the participants (i.e., treatment group) use EditEx and another half (i.e., control group) use the SO standard edit system to edit posts. According to our experiment, EditEx can support SO standard edit system to prevent 49% of rejected edits, including the commonly rejected ones. However, it can prevent 12% rejections even in free-form regular edits. The treatment group finds the potential rejection reasons identified by EditEx influential. Furthermore, the median workload suggesting edits using EditEx is half compared to the SO edit system.