Reviewerly/exHarmony huggingface dataset:
A framework to quantify the effectiveness of earthquake early warning in mitigating seismic risk

Earthquake early warning systems (EEWSs) aim to rapidly detect earthquakes and provide timely alerts, so that users can take protective actions prior to the onset of strong ground shaking. The promise and limitations of EEWSs have both been widely debated. On one hand, an operational EEWS could mitigate earthquake damage by triggering potentially cost- and life-saving actions. These range from automated system responses such as slowing down trains to the actions of individuals that receive the alerts and take protective measures. On the other hand, the effectiveness of an EEWS is conditional on the ability to issue warnings that are sufficiently accurate and timely to facilitate an appropriate action. The refinement of earthquake early warning (EEW) algorithms and the installation of denser and faster seismic networks have improved performance; however, the benefit in risk reduction that an EEWS could achieve remains unquantified. In this study, we leverage upon regional event-based probabilistic seismic risk assessment to devise a quantitative and fully customizable framework for evaluating the effectiveness of EEW in mitigating seismic risk. We demonstrate this framework using Switzerland as a testbed, for which we compute and contrast human loss exceedance curves with and without EEW.
A self-attention multi-scale convolutional neural network method for SAR image despeckling

ABSTRACTABSTRACTThe speckle noise found in synthetic aperture radar (SAR) images severely affects the efficiency of image interpretation, retrieval and other applications. Thus, effective methods for despeckling SAR image are required. The traditional methods for SAR image despeckling fail to balance in terms of the relationship between the intensity of speckle noise filtering and the retention of texture details. Deep learning based SAR image despeckling methods have been shown to have the potential to achieve this balance. Therefore, this study proposes a self-attention multi-scale convolution neural network (SAMSCNN) method for SAR image despeckling. The advantage of the SAMSCNN method is that it considers both multi-scale feature extraction and channel attention mechanisms for multi-scale fused features. In the SAMSCNN method, multi-scale features are extracted from SAR images through convolution layers with different depths. These are concatenated; then, and an attention mechanism is introduced to assign different weights to features of different scales, obtaining multi-scale fused features with weights. Finally, the despeckled SAR image is generated through global residual noise reduction and image structure fine-tuning. The despeckling experiments in this study involved a variety of scenes using simulated and real data. The performance of the proposed model was analysed using quantitative and qualitative evaluation methods and compared to probabilistic patch-based (PPB), SAR block-matching 3-D (SAR-BM3D) and SAR-CNN methods. The experimental results show that the method proposed in this paper improves the objective indexes and shows great advantages in visual effects compared to these classical methods. The method proposed in this study can provide key technical support for the practical application of SAR images.KEYWORDS: Convolutional neural networkattention mechanismmulti-scale featuresSAR image despeckling Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by the Projects Funded by the Central Government to Guide Local Scientific and Technological Development [22ZY1QA005], the National Natural Scientific Foundation of China [42201459], Young Doctoral Fund Project of Higher Education Institutions in Gansu Province [2022QB-058], Research project of Transportation Department of Gansu Province (2021-31) and Key R & D programs – Industrial [21YF11GA008].
GLADS: A global-local attention data selection model for multimodal multitask encrypted traffic classification of IoT

With the rapid development of the Internet of Things (IoT), numerous of IoT devices and different characteristics in IoT traffic patterns need traffic classification to enable many important applications. Deep-learning-based (DL-based) traffic methods have gained increasing attention due to their high accuracy and because manual feature extraction is not needed. Furthermore, seek a lightweight, multitask methods that supports a “performance-speed” trade-off. Thus, we proposed the 0.11 M global-local attention data selection (GLADS) model. The core of the GLADS model includes an “indicator” mechanism and a “local + global” framework. The “indicator” mechanism is a completely different method for handling multimodal input that allows the model to efficiently extract features from multimodal input with a single-modal-like approach. The “local + global” framework for the “performance-speed” trade-off includes a “local” part to obtain the features of each patch in the model input and a Global-Local Attention mechanism in the “global” part outputs the classification results under all possible lengths. Tests on the ISCX-VPN-2016, ISCX-Tor-2016, USTC-TFC-2016, and TON_IoT datasets show that GLADS achieves better performance than several state-of-the-art baselines, ranging from 2.42% to 7.76%. Furthermore, we also propose the “indicator,” which allows the model to simply cope with multimodal input. Based on global-local attention, we analyze the relation of the input section and model performance in detail.
Complementary networks for person re-identification

Recently, person re-identification (re-ID) has gained much attention for its extensive applications in public security. In order to mine more detailed information, some researches have attempted to extract local representations from important regions through human semantic parsing. However, some valuable visual cues beyond human body, such as backpacks and handbags, which can provide complementary information for recognition, may be misclassified as background noise. In addition, when meeting low-quality captured images or severe occlusions, the semantic regions generated by parsing model are not accurate enough, even resulting in disturbance. In this paper, we present complementary networks for person re-ID, which aims to extract more discriminative and robust local representations with complementary clues. Our model contains two branches and one of which aims to mine potential discriminative information in the background that is useful for identifying a person, as a complement for foreground semantics of human body parts. Another branch is designed to capture the salient regions in global scope so as to make up for the problem of poor discrimination of representations caused by the inaccurate semantic confidence maps of former branch. Experiments show that our model achieves competitive performance in many cases.
Source free unsupervised domain adaptation for electro-mechanical actuator fault diagnosis

A common necessity for prior unsupervised domain adaptation methods that can improve the domain adaptation in unlabeled target domain dataset is access to source domain dataset and target domain dataset simultaneously. However, data privacy makes it not always possible to access source domain dataset and target domain dataset in actual industrial equipment simultaneously, especially for aviation component like Electro-Mechanical Actuator (EMA) whose dataset are often not shareable due to the data copyright and confidentiality. To address this problem, this paper proposes a source free unsupervised domain adaptation framework for EMA fault diagnosis. The proposed framework is a combination of feature network and classifier. Firstly, source domain datasets are only applied to train a source model. Secondly, the well-trained source model is transferred to target domain and classifier is frozen based on source domain hypothesis. Thirdly, nearest centroid filtering is introduced to filter the reliable pseudo labels for unlabeled target domain dataset, and finally, supervised learning and pseudo label clustering are applied to fine-tune the transferred model. In comparison with several traditional unsupervised domain adaptation methods, case studies based on low- and high-frequency monitoring signals on EMA indicate the effectiveness of the proposed method.
Investigating the Role of Block Chain to Secure Identity in IoT for Industrial Automation

The information is accessible to everyone using a block chain-capable app. Each single set of information that comes has its own block. When a blocks is loaded with information and linked to the block before it, a historic path for data are generated. The openness and virtual moral purity of IoT technologies shield smart devices from cyber-attacks. The brick chain's ability to store data in events and validate these activities with node may be utilized to provide secure connection amongst Connected systems. Automation has advanced due to the Internet of Things (IOT). By using block chain, we can increase the safety and confidentiality. This paper's focus is on the architecture and functionality of blockchain-based IoT technologies for industrial automation. Along with the Blockchain's numerous characteristics, its benefits are being investigated. The use cases and blockchain appropriateness studies for secure industrial automation have also been performed. In the last section, We examine the security components of the blockchain for a comparative analysis in both Man-in-the-middle and denial-of-service attack scenarios.
Multidimensional Scaling of Varietal Data in Sedimentary Provenance Analysis

Abstract Varietal studies of sedimentary provenance use the properties of individual minerals or mineral groups. These are recorded as lists of numerical tables that can be difficult to interpret. Multidimensional Scaling (MDS) is a popular multivariate ordination technique for analyzing other types of provenance data based on, for example, detrital geochronology or petrography. Applying MDS to varietal data would allow them to be treated on an equal footing with those other provenance proxies. MDS requires a method to quantify the dissimilarity between two samples. This paper introduces three ways to do so. The first method (“treatment‐by‐row”) turns lists of (compositional) data tables into lists of vectors, using principal component analysis. These lists of vectors can then be treated as “distributional” data and subjected to MDS analysis using dissimilarity measures such as the Kolmogorov‐Smirnov statistic. The second method (“treatment‐by‐column”) turns lists of compositional data tables into multiple lists of vectors, each representing a single component of the varietal data. These multiple distributional data sets are subsequently subjected to Procrustes analysis or 3‐way MDS. The third method uses the Wasserstein‐2 distance to jointly compare the rows and columns of varietal data. This arguably makes the best use of the data but acts more like a “black box” than the other two methods. Applying the three methods to a detrital titanite data set from Colombia yields similar results. After converting varietal data to dissimilarity matrices, they can be combined with other types of provenance data, again using Procrustes analysis or 3‐way MDS.
Experimental application and accuracy assessment of 2D-DIC in meso-direct-shear test of sandy soil

The examination of the meso-mechanical properties of soils is fundamental to understand its macro-behaviour. This paper aims to evaluate the potential application of Digital Image Correlation (DIC), an optical image processing approach, in direct shear test (DST) for soils. Hence, a new shear box was designed to investigate the close behaviour of granular materials. The noise-floor and shear displacement accuracy were discussed to assess the reliability of DIC, besides the effectiveness of the new box was also examined. The distribution of the displacements, strains, and shear angle were measured under four different normal stresses. From the results, the immediate settlement, and the dilative behaviour of the sand during shearing were observed. Furthermore, the investigation revealed a strain concentration at the interface between the boxes, where a shear band was formed. The use of DIC opens up new ways in soil mechanics, overcoming some limitations of the conventional DST.
Assessing the potential implementation of earthquake early warning for schools in the Patras region, Greece

Earthquake early warning (EEW) is currently deemed a credible approach to seismic resilience enhancement in modern societies, especially if part of a more holistic earthquake mitigation strategy involving other risk reduction tools such as structural upgrading/retrofit. Yet, there remains a strong need to 1) assess the feasibility of EEW in various seismotectonic contexts, considering specific target applications/end users; and 2) develop next-generation decision-support systems relying on interpretable probabilistic impact-based estimates toward more risk-informed decision-making on EEW installation/alert triggering. These challenges are addressed in this paper, which showcases a series of recent significant EEW contributions by the authors. First, we present the results of a state-of-the-art feasibility study for EEW in schools performed across the Patras region of Greece, attempting to spatially combine traditional seismologically-driven EEW decision criteria (i.e., warning time) with proxy risk-oriented measures for earthquake impact (i.e., building fragility and the number of exposed school students). These results show that, under certain conditions, EEW could be effective for the schools in the considered case-study region. We then demonstrate an advanced end-user-centred approach for improved risk-informed decision-making on triggering EEW alerts. The proposed methodology integrates earthquake-engineering-related seismic performance assessment procedures and metrics with multi-criteria decision-making (MCDM) within an end-to-end probabilistic framework. The performance-based earthquake engineering component of such a framework facilitates the computation of various damage/loss estimates (e.g., repair cost, downtime, and casualties) by combining target-structure-specific models of seismic response, fragility, and vulnerability with real-time ground-shaking estimates. Additionally, the incorporated MCDM methodology enables explicit consideration of end-user preferences (importance) towards the estimated consequences in the context of alert issuance. The developed approach is demonstrated using an archetype school building for the case-study region, for which we specifically investigate the optimal decision (i.e., “trigger” or “don't trigger” an EEW alert) across a range of ground-motion intensity measures. We find that the best action for a given level of ground shaking can vary as a function of stakeholder preferences.
CNN-Fusion: An effective and lightweight phishing detection method based on multi-variant ConvNet

Phishing scams are increasing as the technical skills and costs of phishing attacks diminish, emphasizing the need for rapid, precise, and low-cost prevention measures. Based on a character-level convolutional neural network (CNN), we present CNN-Fusion, an effective and lightweight phishing URL detection method. Our basic idea is to deploy multiple variants of one-layer CNN with various-sized kernels in parallel to extract multi-level features. Observing that differences between phishing and benign URLs might exhibit a strong spatial correlation, we choose SpatialDropout1D, making the model more robust and preventing it from memorizing the training data. To further reduce the probability of errors that may cause by irrelevant or noisy features, we apply a max-over time pooling technique over the feature map to pick only the most important feature. Finally, the model is evaluated using five publicly available datasets containing 1.85 million phishing and benign URLs. Other than that, we assess the model against AI adversarial attacks, known as "Offensive AI." Compared to existing methods, experiments demonstrate that our approach enjoys advantages in 5 times less training time and much more in memory consumption, achieving an average accuracy above 99% on five different datasets as well as on AI-generated malicious attacks.
Group signatures and more from isogenies and lattices: generic, simple, and efficient

Abstract We construct an efficient dynamic group signature (or more generally an accountable ring signature) from isogeny and lattice assumptions. Our group signature is based on a simple generic construction that can be instantiated by cryptographically hard group actions such as the CSIDH group action or an MLWE-based group action. The signature is of size $$O(\log N)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mo>log</mml:mo> <mml:mi>N</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , where N is the number of users in the group. Our idea builds on the recent efficient OR-proof by Beullens, Katsumata, and Pintore (Asiacrypt’20), where we efficiently add a proof of valid ciphertext to their OR-proof and further show that the resulting non-interactive zero-knowledge proof system is online extractable . Our group signatures satisfy more ideal security properties compared to previously known constructions, while simultaneously having an attractive signature size. The signature size of our isogeny-based construction is an order of magnitude smaller than all previously known post-quantum group signatures (e.g., 6.6 KB for 64 members). In comparison, our lattice-based construction has a larger signature size (e.g., either 126 KB or 89 KB for 64 members depending on the satisfied security property). However, since the $$O(\cdot )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mo>·</mml:mo> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -notation hides a very small constant factor, it remains small even for very large group sizes, say $$2^{20}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>20</mml:mn> </mml:msup> </mml:math> .
Efficient quantum analytic nuclear gradients with double factorization

Efficient representations of the Hamiltonian, such as double factorization, drastically reduce the circuit depth or the number of repetitions in error corrected and noisy intermediate-scale quantum (NISQ) algorithms for chemistry. We report a Lagrangian-based approach for evaluating relaxed one- and two-particle reduced density matrices from double factorized Hamiltonians, unlocking efficiency improvements in computing the nuclear gradient and related derivative properties. We demonstrate the accuracy and feasibility of our Lagrangian-based approach to recover all off-diagonal density matrix elements in classically simulated examples with up to 327 quantum and 18 470 total atoms in QM/MM simulations with modest-sized quantum active spaces. We show this in the context of the variational quantum eigensolver in case studies, such as transition state optimization, ab initio molecular dynamics simulation, and energy minimization of large molecular systems.
Data Balancing Techniques for Predicting Student Dropout Using Machine Learning

Predicting student dropout is a challenging problem in the education sector. This is due to an imbalance in student dropout data, mainly because the number of registered students is always higher than the number of dropout students. Developing a model without taking the data imbalance issue into account may lead to an ungeneralized model. In this study, different data balancing techniques were applied to improve prediction accuracy in the minority class while maintaining a satisfactory overall classification performance. Random Over Sampling, Random Under Sampling, Synthetic Minority Over Sampling, SMOTE with Edited Nearest Neighbor and SMOTE with Tomek links were tested, along with three popular classification models: Logistic Regression, Random Forest, and Multi-Layer Perceptron. Publicly accessible datasets from Tanzania and India were used to evaluate the effectiveness of balancing techniques and prediction models. The results indicate that SMOTE with Edited Nearest Neighbor achieved the best classification performance on the 10-fold holdout sample. Furthermore, Logistic Regression correctly classified the largest number of dropout students (57348 for the Uwezo dataset and 13430 for the India dataset) using the confusion matrix as the evaluation matrix. The applications of these models allow for the precise prediction of at-risk students and the reduction of dropout rates.
Efficient Selection of Gaussian Kernel SVM Parameters for Imbalanced Data

For medical data mining, the development of a class prediction model has been widely used to deal with various kinds of data classification problems. Classification models especially for high-dimensional gene expression datasets have attracted many researchers in order to identify marker genes for distinguishing any type of cancer cells from their corresponding normal cells. However, skewed class distributions often occur in the medical datasets in which at least one of the classes has a relatively small number of observations. A classifier induced by such an imbalanced dataset typically has a high accuracy for the majority class and poor prediction for the minority class. In this study, we focus on an SVM classifier with a Gaussian radial basis kernel for a binary classification problem. In order to take advantage of an SVM and to achieve the best generalization ability for improving the classification performance, we will address two important problems: the class imbalance and parameter selection during SVM parameter optimization. First of all, we proposed a novel adjustment method called b-SVM, for adjusting the cutoff threshold of the SVM. Second, we proposed a fast and simple approach, called the Min-max gamma selection, to optimize the model parameters of SVMs without carrying out an extensive k-fold cross validation. An extensive comparison with a standard SVM and well-known existing methods are carried out to evaluate the performance of our proposed algorithms using simulated and real datasets. The experimental results show that our proposed algorithms outperform the over-sampling techniques and existing SVM-based solutions. This study also shows that the proposed Min-max gamma selection is at least 10 times faster than the cross-validation selection based on the average running time on six real datasets.
A 3D chaotic system with multi-transient behavior and its application in image encryption

Based on the special structure of the variable-boostable chaotic system VB19, that includes a smooth secondary flux-controlled memristor, a three-dimensional memristive chaotic system with only one equilibrium point is constructed. Numerical analysis reveals that the system has novel dynamical characteristics of controlling multiple types of attractors by a single parameter. At the same time, the existence of multiple transient behaviors and the coexistence of periodic and quasi-periodic attractors significantly increase the complexity of the system. Moreover, the system has a large range of high complexity phenomena, which is relatively rare in low-dimensional chaotic systems. A circuit is designed and implemented for this system in the physical circuit. Finally, this memristive system is applied to image encryption, whose excellent performance is demonstrated by key space, key sensitivity analysis, histogram analysis, and correlation coefficient analysis, which provides a reference for the further research of low dimensional memristive systems in the field of image encryption.